UNIVERSITÄT DER BUNDESWEHR MÜNCHEN Fakultät für

Transcription

UNIVERSITÄT DER BUNDESWEHR MÜNCHEN
Fakultät für Elektrotechnik und
Informationstechnik
Basic Timing Concepts for the Execution of Multiple Motor
Tasks: Coordination of periodic tapping with discrete tasks
Cong Khac Dung
Vollständiger Abdruck der von der Fakultät für Elektrotechnik und Informationstechnik
der Universität der Bundeswehr München zur Erlangung des akademischen Grades eines
Doktors der Ingenieurswissenschaften (Dr.-Ing.) genehmigten Dissertation.
Vorsitzender des Promotionsausschusses: Univ.-Prof. Dr.-Ing. Gerhard Bauch
1. Berichter:
Univ.-Prof. Dr.techn. Christian Kargel
2. Berichter:
Prof. Dr. –Ing.habil. Werner Wolf
Die Dissertation wurde am 06.12.2011 bei der Universität der Bundeswehr München
eingereicht und durch die Fakultät für Elektronik und Informationstechnik am
29.02.2012 angenommen. Die mündliche Prüfung fand am 20.08.2012 statt.
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Abstract................................................................................................................................... 6
Kurzfassung ............................................................................................................................ 8
1 Introduction ..................................................................................................................... 11
1.1 Multi-tasking: sharing a single execution unit or coordinating multiple units .................... 11
1.2 Motivation for this dissertation ........................................................................................ 12
1.3 Motor Coordination – Task Scheduling and Timing......................................................... 13
1.3.1 Single-Task (ST) Condition ............................................................................................ 13
1.3.2 Dual-Task (DT) condition .............................................................................................. 17
2 Literature review on finger tapping ................................................................................ 23
2.1 Overview ........................................................................................................................ 23
2.1.1 Literature on Clinical research ........................................................................................ 23
2.1.2 Psychological and physiological research........................................................................ 23
2.2 Literatur on clinical research ........................................................................................... 26
2.3 Literature on psychological and physiological research .................................................... 27
2.3.1 ST condition .................................................................................................................. 27
2.3.2 DT condition .................................................................................................................. 41
2.4 Summary ........................................................................................................................ 52
3 Literature review on eye blinks ....................................................................................... 55
3.1 Fluctuation of blink number during an interval (Greene 1986) ......................................... 56
3.2 Patterns of Blink Rate in Normal Subjects (Bentivoglio et al. 1997)................................. 57
3.3 Stochastic models for spontaneous blink(Hoshino 1996).................................................. 58
3.4 Model for audiomotor integration (Bangert et al. 2006) ................................................... 59
3.5 Effect of mental task on eye blink rate (Karson et al. 1981) ............................................. 63
3.6 Mapping cortical areas with functional MRI (Tsubota el al. 1999) ................................... 63
3.7 The neural representation of temporal information (Ivry & Spencer 2004) ....................... 68
3.8 How brain activity correlates with temporal complexity (Lewis et al. 2004) ..................... 73
3.9 The role of supplementary motor area in moving preparation (Jenskin et al. 2000) ........... 74
3.10 Investigation of the daily pattern of eye-blink rate (Barbato et al. 2000) ........................... 77
3.11 A brain Stem Reflex in Eye Blink (Evinger 1995) ........................................................... 79
3.12 The Blink Recovery Process in Patients with bell’s Palsy (VanderWerf et al. 2007) ......... 82
3.13 Summary ........................................................................................................................ 88
4 Literature review on saccades and timing processes ...................................................... 90
4.1 Visual saccade and memory processes (Claeys et al. 1999) .............................................. 90
4.2 Saccade with concurrent auditory task (Malmstrom, Reed, & Weber 1983)...................... 92
4.3 Saccadic eye movement and manual control system (Megaw & Armstrong (1973)........... 94
4.4 Cognitive load on saccadic eye movements (Stuyven et al. 2000) .................................... 98
4.5 Human head-eye coordination during tapping task (Herst, Epelboim, & Steinman 2001) 101
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4.6 Distributed neural systems underlying the timekeeping (Rao et al. 1997) ....................... 103
4.7 Gaze effects on movement activation patterns (Baker, Donoghue, & Sanes 1999) .......... 107
4.8 Models for saccade generation circuitry (Girard & Berthoz 2005) .................................. 110
4.8.1 Reticular formation saccadic burst generators ............................................................... 110
4.8.2 Superior colliculus ....................................................................................................... 111
4.8.3 Cerebellum .................................................................................................................. 111
4.8.4 Basal Ganglia ............................................................................................................... 111
4.8.5 Premotor cortical areas ................................................................................................. 111
4.9 Gaze and Hand Position Effects on Brain Activation (Bedard & Sanes 2009) ................. 112
4.10 Coordination of ocular and hand motor systems (Bekkering et al. 1994) ........................ 112
4.11 Theoordination of saccadic and manual movements (Binsted & Elliot 1999).................. 113
4.12 Eye Position Effects on neuronal Activity (Boussaoud, Jouffrais, & Bremmer 1998)...... 115
4.13 The main function of the cerebellum and basal ganglia (Dreher & Grafman 2002) ........ 116
4.14 Central Bottleneck of Information processing with fMRI (Dux et al. 2006) .................... 117
4.15 The organization of eye and limb movements during reaching (Fisk & Goodable 1985) . 118
4.16 Modality pairing effects on dual-task (Hazeltine & Ruthruff 2006) ................................ 120
4.17 Summary ...................................................................................................................... 121
5 Motor coordination framework: which gaps are addressed by this study ................... 123
5.1 The form of continuous movement trajectories .............................................................. 123
5.2 The movement order ..................................................................................................... 123
5.3 The effects of feedback ................................................................................................. 123
5.4 The effects of amplitude (force) .................................................................................... 124
5.5 The effects of multiple effectors .................................................................................... 124
5.6 The effects of attention ................................................................................................. 124
5.7 Single-task conditions ................................................................................................... 125
5.7.1 Basic considerations ..................................................................................................... 125
5.7.2 The effects of feedback ................................................................................................ 126
5.7.3 The effects of amplitude (force) .................................................................................... 126
5.7.4 The effects of multiple effectors ................................................................................... 127
5.7.5 The effects of attention ................................................................................................. 127
5.8 Dual-task conditions ..................................................................................................... 127
5.8.1 Basic considerations ..................................................................................................... 127
5.8.2 The effects of feedback ................................................................................................ 128
5.8.3 The effects of amplitude (force) .................................................................................... 129
5.8.4 The effects of attention ................................................................................................. 129
5.8.5 Eye-Hand movement coordination without common spatial target ................................. 129
5.8.6 Multiple effectors ......................................................................................................... 130
6 Methods – Experimental Concepts, Recorded signals .................................................. 132
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6.1 Materials and method.................................................................................................... 132
6.1.1 Subjects and experimental setup ................................................................................... 132
6.1.2 Experimental setup ....................................................................................................... 132
6.2 Procedures and experimental paradigms ........................................................................ 136
6.2.1 The Synchronization-Continuation paradigm ................................................................ 136
6.2.2 Single-task conditions .................................................................................................. 137
6.2.3 Dual-task conditions..................................................................................................... 140
6.3 Signal analysis .............................................................................................................. 141
6.3.1 Event detection ............................................................................................................ 142
6.3.2 The time course analysis of signals ............................................................................... 142
6.4 Phase Resetting Curve (PRC) ........................................................................................ 147
6.4.1 General idea ................................................................................................................. 147
6.4.2 Phase Resetting Curve construction .............................................................................. 147
6.5 Periodic-discrete process Interaction Categories ............................................................ 149
6.5.1 Classification based on discrete events.......................................................................... 149
6.5.2 Classification based on continuous trajectories of fingers .............................................. 151
6.6 Statistical data analysis ................................................................................................. 156
7 Results............................................................................................................................ 157
7.1 Single-Task (ST) condition ........................................................................................... 157
7.1.1 Periodic tapping ST ...................................................................................................... 157
7.1.2 Periodic tapping and spontaneous eye blinks ................................................................. 162
7.2 Dual-Task (DT) condition ............................................................................................. 169
7.2.1 Basic interaction patterns and their PRCs ...................................................................... 170
7.2.2 Dominant tapping behaviour and discrete-tap timing characteristics in normal tapping .. 174
7.2.3 Time course analysis of the tapping process .................................................................. 179
7.2.4 Effects of physiological parameters .............................................................................. 188
7.3 Hand-Foot condition ..................................................................................................... 192
7.4 OM-DT condition ......................................................................................................... 198
8 Discussion ...................................................................................................................... 202
8.1 ST condition ................................................................................................................. 202
8.1.1 Timing task .................................................................................................................. 202
8.1.2 Timing task and spontaneous blinking .......................................................................... 202
8.2 DT condition ................................................................................................................ 204
8.2.1 BM .............................................................................................................................. 204
8.2.2 OM-SM condition ........................................................................................................ 209
8.2.3 Multiple effectors ......................................................................................................... 211
9 Summary and outlook ................................................................................................... 213
9.1 Summary ...................................................................................................................... 213
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9.2 Outlook ........................................................................................................................ 215
9.2.1 Effect of force, attention and external feedback ............................................................. 215
9.2.2 Mental task instead of motor task ................................................................................. 216
9.2.3 Checking memory limit of time interval........................................................................ 216
9.2.4 Audiomotor overlearned in musical trained people........................................................ 216
References ........................................................................................................................... 218
10 Appendix........................................................................................................................ 239
10.1 Classification of the interactions based on discrete events .............................................. 239
10.2 Software ....................................................................................................................... 242
10.3 Showdata ...................................................................................................................... 244
10.3.1
Variable .................................................................................................................. 244
10.3.2
Menu ...................................................................................................................... 246
10.3.3
Control buttons........................................................................................................ 254
10.4 AnalyzeTapResults ....................................................................................................... 254
10.4.1
Variable .................................................................................................................. 254
10.4.2
Menu: ..................................................................................................................... 257
10.5 Interaction classification in DT-experiment ................................................................... 270
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Abstract
Making fast decision such as football scoring or musical rhythm modulation requires timing
accuracy. Wing & Kristofferson (1973b) reported in the negative correlation between successive
intertap intervals of repetitive discrete motor responses based on the assumed two independent
processes (timekeeper and motor delay). Helmut & Ivry (1996) reported the better performance of
the repetitive tapping task when individuals tapped with two hands in comparison to single-handed
tapping. A pilot experiment replicated this topic but with repetitive mental task and different
peripheral motor implementations.
Numerous daily activities require timing and performing more than one task simultaneously.
Multi-tasking necessitates motor coordination. A challenging behavioral requirement especially in
multitasking is to maintain both spatial and temporal accuracy of all motor actions given in response
to an emergency, where possible resource bottlenecks may become apparent. Laboratory
investigations on this topic often use dual-task experiments, e.g. bimanual tapping (BM, i.e. hitting a
key or a surface by a finger tip) with different instructions for the right and left hand, respectively.
A conventional experimental setup for tapping data measurement consists only of the ground
contact sensors like micro switches for the motor action observation; the evaluation of the discrete
events provided by these switches is quite simple, but also the amount of obtained information is
limited. A novel experimental design for tapping experiments with high-resolution recording of the
complete time course of the continuous finger movements was approached and the required
evaluation procedures for the biomechanical and EMG data is described. The latter are based on
sophisticated maximum-likelihood-techniques, which again is an example of progress in research
through advanced bio-signal processing.
The finger tapping task as designed by Stevens (1886) was used. The experimental paradigm
consists of synchronization and continuation phase. Tapping included normal tapping, contact-free
tapping, and isometric tapping for both single-task (ST) and dual-task (DT) conditions. Furthermore
voice tapping and mental tapping in combination with normal tapping in ST condition were
approached. ST covers the control experiment as reference and experiments studying timing of
periodic movement. Finger positions and ground contact forces was recorded. The DT was
performed on different limbs. The coordination of periodic right hand tapping with single stimulus
evoked discrete left hand taps was investigated to check for task interactions and a possible
relationship between “phase resetting” (tapping literature, e.g. J. Yamanishi, Kawato, & Suzuki, 1979)
and “phase entrainment” (tremor literature, e.g. R.J. Elble, Higgins, & Hughes 1994).
In ST only the results of voice tapping consistently confirmed the proposed model of Wing &
Kristofferson (1973). The correlation biased to zero or even positive in isometric tapping and
sometime in contact-free and normal tapping. The bimanual advantage in repetitive tapping
performance was observed in isometric and in combined mental-normal tapping whereas
disadvantage was observed in normal tapping and sometimes in contact-free tapping. The results
proved that the different motor implementations leading to different motor delay and different
feedback in closed-loop control contributed differentially to the correlation function in successive
intertap intervals. The integration of central commands already occurs at high level in brain in case of
combined mental/normal tapping. Additional to correction process in timing based on feedback a
second correction process based on asynchrony between both fingers exists and caused the absence
of bimanual advantage sometime in contact-free tapping and more effective in normal tapping.
Four different types of coordination schemes were observed in DT tapping behavior: Marginal
Tapping Interaction (MTI), Periodic Tap Retardation (PTR), Periodic Tap Hastening (PTH) and Discrete
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Tap Entrainment (DTE); MTI and PTR correspond to the phase resetting effect as described earlier in
tapping for the coordination of periodic tapping with single discrete taps. (DTE) reflected the impact
of the periodic tapping on the discrete tap and PTH of the discrete tap on the periodic tapping both
leading to a synchronized execution of the two concurrent tapping tasks are the observed novel
aspects. All subjects showed a dominant tapping behavior but also other non-dominant forms of the
four reported coordination schemes in some trials. Even MTI presents marginal interaction,
continuous trajectory revealed hidden mutual interaction such as mutual modification of tap
duration and slope duration, tap embedding, varied force or amplitude, tap delay and tap cancelling
although rhythm is stable. The results reflect possible constraints of the sensorimotor system in
handling two competing tasks. From the point of view of the theory of oscillations the dominant
effect of the discrete movement is considered as a quasi-elastic force attracting the periodic
movement as a system to the position of equilibriums and proportional to the displacement of
system from these equilibriums or repelling it away from equilibriums. These two equilibriums are
reflected in PRC (Phase Resetting Curve). Against this repulsive force damping force and restoring
force are provided when oscillation grows too large or becomes too small respectively. This point of
view was verified by parameters such as specific instruction on task, required force, multiple
effectors, Feedback in closed-loop control, normal vs. contact-free movement.
To determine whether interaction is specific for hand-hand interaction only, extending of limb
homology (upper and lower) and limb laterality was applied for discrete response, i. e foot and handfoot combinations was done. In all conditions the same tapping behaviors were observed. The results
proved that the mechanism responsible for the observed interaction effect is not effector specific.
The location of the shared motor control for upper and lower limbs within the higher brain levels is
suggested.
The interaction of periodic self-paced finger tapping with concurrently executed saccades also
was addressed. Because the both movements share some known common neural control pathways.
Resource bottlenecks may become apparent in maintaining both spatial and temporal accuracy of
concurrent motor actions. Instead of the discrete left hand response, the participants now executed
a single saccadic eye movement to a fixed visual target in parallel to continuous periodic tapping of
the dominant hand. We expected these reactive saccades to act as a strong perturbation event to
the continuous tapping, but the experimental data did not reveal a considerable interference in this
specific oculo-manual (OM) DT experiment. I.e. sharing neural pathways reported in many
experiments for eye and hand movements does not always cause DT costs.
Not only the mutual cross-talk between voluntary movements but also between spontaneous
eye blinks and continuous, self-paced unimanual and bimanual tapping was studied. Both types of
motor activities were analyzed with regard to their time-structure in synchronization-continuation
tapping tasks which involved different task instructions, namely "standard" finger tapping, "strong"
tapping requiring more forceful finger movements, and "impulse-like" tapping where upwarddownward finger movements had to be very fast. In a further control condition, tapping was omitted
altogether. The manual tapping revealed a prominent entrainment on the blink behavior. Bimanual
tapping was more effective than unimanual tapping, “strong” and “impulse-like” tapping showed the
largest effects. Conversely, no significant effects of the eye blinks on the timing of periodic tapping
across all experiments were found. The functional control structures of finger and eye blinking
movement might contains some intersections.
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Keywords: Spontaneous Endogenous Blinks, Unimanual and Bimanual Finger Tapping, Index
Finger Tapping, Saccade, Dual Task, Interference, motor system, biosignal processing, motor
interaction, interlimb coordination, reaction time.
Kurzfassung
Schnelle bzw. spontane Entscheidungen zu treffen wie der Torschuss im Fußball oder wie die
plötzliche Rhythmusveränderung während einer musikalischen Ausführung erfordern eine hohe
zeitliche Genauigkeit bei der Ausführung. Unter der Annahme von zwei unabhängigen Prozessen
(Zeitgeber und motorische Verzögerung) berichteten Wing & Kristofferson (1973) eine negative
Korrelation zwischen sukzessiven Intervallen bei repetitiven diskreten motorischen Aktionen, was die
Interpretation einer permanenten Korrektur des zeitlichen Ablaufs zulässt. Helmut & Ivry (1996)
berichteten weiterhin, dass eine zweihändige Ausführung im Tapping-Experiment mit einer
geringeren Varianz der Folgeintervalle ausgeführt wird als eine als einhändige. Dieses Thema wurde
in einem Pilotexperiment in dieser Arbeit wieder aufgegriffen, wobei ein sog. mentales Tapping und
unterschiedliche Implementierungen der motorischen Aktion (z.B. Fuß) eingeschlossen wurden.
Auch zahlreiche tägliche Aktivitäten erfordern Koordination des zeitlichen Ablaufs und die
gleichzeitige Durchführung von mehr als einer Aufgabe, also das sog. Multitasking. Motorische
Koordination ist offensichtlich notwendig für Multitasking. Eine spezielle Herausforderung beim
Multitasking ist, die räumliche und zeitliche Genauigkeit der motorischen Bewegungskoordination im
Falle einer Notfallsituation einzuhalten; dabei werden oft Engpässe bei der Verfügbarkeit
notwendiger Ressourcen erkennbar. Viele Laboruntersuchungen verwenden „dual-task“
Experimente, z.B. bimanuelles Tapping (d.h. Aufschlagen eines Fingers auf eine Oberfläche oder einer
Taste) mit unterschiedlichen Instruktionen für den linken und den rechten Finger.
Der konventionelle experimentelle Aufbau für die Datenaufnahme von FingertappingExperimenten besteht nur aus einem digitalen Berührungssensor wie z.B. einem Mikroschalter, der
die motorische Aktivität registriert. Die Evaluierung dieser vom Mikroschalter erzeugten diskreten
Ereignisse ist einfach, aber die gewonnen Informationen sind beschränkt. In dieser Arbeit wird ein
neues experimentelles Design mit hochauflösender Aufnahme der kontinuierlichen Fingerbewegung
eingesetzt und die dafür benötigte Prozedur zur Evaluierung von biomechanischen Daten
beschrieben. Diese Evaluierung basiert auf einer aufwendigen „Maximum-Likelihood“ Technik, und
ist ein Beispiel für den Fortschritt in der Biosignalverarbeitung.
Grundsätzlich wurde das experimentelle Design des Tappings von Stevens (1886) verwendet. Das
experimentelle Paradigma besteht aus der Synchronisation- und der Fortführung-Phase. Die
experimentellen Bedingungen beinhalten normales, kontaktfreies und isometrisches Fingertapping
für „single-task“ (ST) und „dual-task“ (DT) Aufgaben. Außerdem wurden sie auf sprachliches und
mentales Tapping in Kombination mit normalem Fingertapping für ST erweitert. ST deckt die
Kontrollversuche als Referenzen für DT als auch unabhängige Experimente zur Untersuchung der
Zeitsteuerung ab. DT wurde mit verschiedenen Extremitäten (Finger, Fuß) ausgeführt.
Taskinteraktion und die Relation zwischen „Phase Resetting“ (Yamanishi, M. Kawato, & R. Suzuki,
1979) und „Phase Entrainment“ (tremor literature, e.g. R.J. Elble, C. Higgins & L. Hughes, 1994)
wurden durch die Koordination zwischen dominanten Finger und nicht-dominanten Finger
untersucht, wobei dem dominanten Finger das periodische Tapping und dem nicht-dominanten die
diskrete durch Stimulus ausgelöste Tap-Bewegung zugewiesen sind.
Bei den ST Experimenten bestätigten nur die Daten mit sprachlichem Tapping konsistent das
Kristofferson & Wing Modell (1973b). Die Korrelationsfunktion der sukzessiven Intervalle hat sich zu
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Null oder sogar in den positiven Bereich bei den isometrischen und manchmal auch bei den
kontaktfreien und normalen Konditionen geneigt. Der bekannte bimanuelle Vorteil beim repetitiven
Fingertapping-Experiment wurde bei der isometrischen Bedingung und in der „normal-mental“
Kombination gefunden. Dagegen war beim normalen Tapping und teilweise auch im kontaktfreien
Tapping dieser bimanuelle Vorteil nicht zu sehen. Das Resultat lässt darauf schließen, dass die
unterschiedlichen motorischen Implementierungen des Tappings zu unterschiedlichen motorischen
Verzögerungen führen sowie auch unterschiedliche sensorische Informationen (z.B. taktile
Empfindung an der Fingerkuppe) über die ausgeführte motorische Aktion liefern, was im
geschlossenen sensomotorischen Regelkreis sich auswirkt und zu unterschiedlichen
Korrelationsfunktionen der sukzessiven Tapping-Intervalle beitragen kann. Eine Integration auf der
höchsten Ebene der motorischen Kontrolle lässt sich aus Ergebnissen der „normal-mental“
Kombination zu vermuten. Die schlechtere Leistung in normalen und kontakt-freien Ausführungen
kann dahingehend interpretiert werden, dass bei dem Kontroll- bzw. Korrekturprozess des Tappings
zusätzlich zu der zwischen afferentem Feedback und interner (also mentaler) Ablaufprädiktion der
Tapping-Bewegungen ermittelten Differenz eine weitere afferente Korrekturkomponente existiert,
die auf dem Synchronisationsfehler zwischen beiden Fingeranschlägen basiert. Dieser Prozess scheint
den Genauigkeitsvorteil des bimanuellen Tappings manchmal beim kontaktfreien und meist beim
normalen Tapping zu beeinträchtigen.
Aus DT-Daten haben sich vier verschiedene Typen des Koordinationsverhaltens, nämlich
„marginale Tapping-Interaktion“ (MTI), „Retardierung des periodischen Tappings“ (PTR),
„Beschleunigung des periodischen Tappings“ (PTH) und „Synchronisation des diskreten Taps“ (DTE)
klassifizieren lassen. MTI und PTR entsprechen dem „Phase Resetting“ Effekt, der schon früher für die
Koordination von periodischen und diskreten Fingerbewegungen in Tapping-Experimenten
beschrieben wurde. DTE reflektiert die Einwirkung von periodischer Bewegung auf die diskrete
Bewegung, während PTH die umgekehrte Wirkrichtung darstellt. Diese gegenseitigen Einflüsse, die
zur Bewegungssynchronisation der zwei konkurrierenden Tapping-Bewegungen führen, ist ein neuer
beobachteter Aspekt. Die periodischen Fingeranschläge sind oft früher als die diskreten aufgetreten,
wenn die beiden zur gleichzeitigen Ausführung geplant sind. Dies lässt sich dadurch erklären, dass die
periodische Muskelaktivierung durch eine Verkopplung mit der diskreten Muskelaktivierung
unbewusst vergrößert wird, was zur schnelleren Abwärtsbewegung führt. Alle Versuchspersonen
zeigten ein dominantes Verhalten, aber auch die anderen nicht-dominanten der vier oben
angegebenen Interaktionstypen traten manchmal auf. Obwohl nur eine geringe Störung der TappingIntervalle beim MIT-Verhalten gezeigt wurde, ist dort im kontinuierlichen Bewegungsverlauf eine
gegenseitige Interaktion wie die Modifikation der Dauer der Tapping-Bewegung zu sehen. Obwohl
das periodische Tapping-Timing stabil war, wurden Phänomene wie erhöhte auf den Kraftsensor
ausgeübte Kraft der periodischen Tapping-Bewegungen bei Synchronisation, Einbetten vom
diskreten Tapping-Bewegungen in die periodischen Tapping-Bewegungen, Verzögerung der diskreten
Tapping-Bewegungen, und Rücknahme von periodischen Tapping-Bewegungen gefunden. Diese
Befunde lassen auf eine mögliche Beschränkung des sensomotorischen Systems bei der Behandlung
zweier konkurrierender Aufgaben schließen. Im Blickwinkel der Oszillationstheorie kann der
dominante Effekt der diskreten Tapping-Bewegung als die quasi-elastische Kraft betrachtet werden,
die die periodische Tapping-Bewegung zum oder weg vom Gleichgewichtszustand anzieht bzw.
abstößt. Dieser Effekt ist von der Auslenkung des Systems von den beiden Gleichgewichtszuständen
abhängig. Diese zwei Gleichgewichtszustände sind in PRC (Phase Resetting Curve) reflektiert. Der
Anziehungskraft bzw. Abstoßungskraft der diskreten Tapping-Bewegungen wirken eine Rückstellkraft
bzw. Dämpfungskraft den periodischen Tapping-Bewegungen entgegen, wenn die Amplituden der
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Oszillation zu viel wachsen beziehungsweise zu klein werden. Diese Ansicht ist durch Modifikation
der Parameter wie z.B. spezifische Instruktionen für die diskrete Tapping-Bewegung,
Kraftanforderung, und Modifikation der sensorischen Information (Feedback) verifiziert.
Um zu prüfen, ob die gefundenen Interaktionen nur für das manuelle Tapping spezifisch sind,
wurden auch Hand-Fuß-Kombinationen für die diskreten Tapping-Bewegungen getestet; damit
konnten auch Aspekte wie „homologe versus nicht-homologe (d.h. obere vs. untere) Gliedmaßen“
und „seitenspezifisch (links vs. rechts)“ betrachtet werden. In allen Konditionen wurden die gleiche
Koordinationsverhalten gefunden. Der Befund hat gezeigt, dass die für die beobachteten
Interaktionen verantwortlichen Mechanismen nicht Gliedmaßen spezifisch sind. Eine für die
Steuerung der oberen und unteren Körpergliedmaßen verantwortliche Gehirnstruktur ist als Ort für
diese motorische Interaktion zu vermuten.
In diesem Zusammenhang wurde auch die Koordination zwischen repetitiven manuellen TappingBewegungen und schnellen Augenbewegungen (Sakkaden) in DT Experimenten untersucht. Da die
beiden Bewegungen bekannterweise einen gemeinsamen neuronalen Kontrollpfad teilen, würden
Engpässe bei den Ressourcen sich auf die räumliche und zeitliche Genauigkeit der konkurrierenden
motorischen Aktionen auswirken. Statt des diskreten Fingertaps haben die Versuchspersonen eine
reaktive Sakkade zwischen zwei horizontal fixierten visuellen Zielen während des mit der dominanten
Hand ausgeführten repetitiven Tappings durchgeführt. Die Erwartung, dass auch die Sakkade eine
starke Störung auf das periodische Tapping haben wird, wurde durch die experimentellen Daten
widerlegt, da keine Interferenz gefunden wurde. D. h. gemeinsam benutzte Gehirnstrukturen
verursachen nicht immer die „dual-task“ Kosten.
Darüber hinaus wurden nicht nur das Übersprechen bei willkürlichen Bewegungen, sondern auch
zwischen dem spontanen Lidschlag der Augen und unimanuellen sowie bimanuellen periodischen
Fingertapping untersucht. Die Rhythmusstrukturen dieser beiden Typen der motorischen Aktionen
wurden bei verschiedenen Anforderungen an Kraft und Bewegungsart des periodischen
Fingertappings im Tapping-Experiment analysiert. Außer dem normalen Tapping wurden auch
impulsartiges Tapping (schnelle Aufwärtsbewegung und Abwärtsbewegung ohne Anforderung an
Kraft) und starkes Tapping (große Aufschlagkraft) untersucht. Auch der spontane Lidschlag ohne
Tapping wurde als Kontrollversuch getestet. Das Fingertapping hat dabei ein auffälliges Entrainment
auf den Lidschlag gezeigt, wobei die bimanuelle Bewegungssynchronisation mehr Effekt als die
unimanuelle hat. Impulsartiges und starkes Tapping haben die größten Auswirkungen. Dagegen
wurden keine signifikanten Effekte des Lidschlags auf das Fingertapping in den Experimenten
gefunden. Eine funktionelle Überlappung der neuronalen motorischen Kontrollstrukturen, die sowohl
für die rhythmische Fingerbewegung als auch für den spontanen Lidschlag verantwortlich sind,
können daher postuliert werden.
10
1 Introduction
1.1 Multi-tasking: sharing a single execution unit or coordinating
multiple units
In computer science, multi-tasking and parallel processing is a well-known research topic.
Nowadays, most of operating systems (like UNIX, WINDOWS, etc.) allow to safely and efficiently run
several independent tasks at the same time without any noticeable delays, thus the user can work on
different programs at the same time. But these programs are not handled exactly at the same time
by the computer – in fact, the user programs as sub-processes were interleaved under the regime of
the operating system and had access to the central processing unit (CPU) in successive turns until the
processes are finished. . Realizing this virtual multi-tasking by rapidly switching between different
processes provides the illusion of parallel executions. Priority schemes and response analysis were
performed to optimize this CPU sharing. Truly parallel processing, however, is possible if more
processor cores are integrated in one CPU (the actual trend in PC technology), or several CPUs are
plugged into one computer (workstation technology), or the task load is distributed over multiple
tightly connected computers (distributed processing), - all these advanced solutions allow the
simultaneous use of more than one processor core or CPU to execute multiple computational
threads. New ideas in the computer science could be motivated if the knowledge about how these
issues are done in the human brain would have been available.
In basic research, The Dutch physicist Christian Huygens (1629-1695) discovered the
synchronisation of two pendulum clocks when they are mounted near of each other on the same
support. His fortuitous observation that the clocks attained synchronisation after some time initiated
an entire sub-branch of mathematics: the theory of coupled oscillators. (The term “synchronisation”
delineates the tendency of two repetitive (periodic) processes to progress in a certain phase
relationship.) Two centuries later, this phenomenon of synchronisation was investigated
systematically, mainly by engineers, physicists and mathematicians. Synchronisation has been
studied in very different systems such as electronic devices (Ramírez-Ávila et al. 2003; Guisset,
Deneubourg, & Ramírez-Ávila 2002; Fortuna, Frasca, & Rizzo 2003), chemical systems (Wang, Kiss, &
Hudson 2000; Kiss, Zhai, & Hudson 2002a, b; Shabunin et al. 2003), biological systems (Bonabeau,
Theraulaz, & Deneubourg 1998; Delgado & Sole 2000; Glass 2001), and ecological systems
(Weatherhead & Yezerinac 1998; Blasius & Stone 2000).
In living subjects, cyclic processes are governing important behavioural processes like e.g. walking,
and therefore, were/are addressed by large body of research. A specific focus guides studies
investigating the mirror neuron system by using fMRI (functional magnetic resonance imaging) to
examine if certain voxels in the brain are shared between action observation and execution (Gazzola
& Keysers 2009). Not only cycles, waves, and frequencies have been the targets of the physical
scientists, engineers and mathematicians, but also the variety of biological rhythms and their
cooperation in a single body or in a group of individuals were investigated, and the findings have
impressed the biologists. Mutual synchronization occurs in many populations of biological oscillators.
Synchronisation in living organism can be observed in, e.g., the bees’ respiration (Moritz & Southwick
1992), human female menstrual cycles (McClintock 1971), the clapping of humans in theatres
(Maródi, D’Ovidio, & Vicsek 2002; Neda, Nikitin, & Vicsek 2003), and the flashing among fireflies
(Strogatz & Stewart 1993; Moiseff & Copeland 2000). In most of the theoretical work of mutual
synchronization, the smooth (global) interactions were studied. The episodic or pulse-like
interactions are a specific case such as coordination in finger tapping (a tap is a hit of a surface by a
11
finger tip); they can be observed, when a discrete movement is executed while performing periodic
movements concurrently.
Finger tapping is a motor process and has been widely used in many motor control investigations.
The Finger Tapping Test (FTT), originally developed as part of the Halstead Reitan Battery (HRB 1) of
neuropsychological tests, is a simple measure of motor speed and motor control timing and is used in
neuropsychology as a sensitive test for brain damage such as laterality of neuropsychological
functions (e.g. Chaves et al. 1983; Friedman, Polson, & Dafoe 1988), and performance at cognitive
levels (Dodrill 1978). The goal of the finger tapping experiments also was to study a certain definite
control variable by analysing the structure of discrete time events (like the movement onsets, the
maximum value of speed, etc.) during the tapping process.
Perceptual timing related to tapping was examined (Aschersleben & Prinz 1995; Wing and
Kristofferson 1973b; Swinnen 2002; Repp 2001). An oscillatory neural network controlling the
coordinated finger movements was assumed (Yamanishi, Kawato, & Suzuki 1980). The resetting of
the phase of circadian clocks by light was recognized by Bünning in the 1970s. Johnson (1999) listed a
number of circadian researchers in the 1950s developing these ideas further and began to map the
daily patterns of light responsiveness. Phase-Resetting-Curves (PRC) and Phase-Transition-Curves
(PTC) – as described in Sect.2.1.2.3 and 2.3.2.1 later - were proposed as useful descriptions to
present the phase changes of a circadian rhythm as a function of the circadian phase subjected to
stimuli. Stimuli can be light pulse, temperature pulses, or pulses of drugs or chemicals. PRC plots the
phase shift versus old phase (initial phase) whereas PTC plots a “new phase” versus “initial phase”.
The phase resetting experiment was applied to investigate the coordination of discrete and rhythmic
movements (Yoshino et al. 2002, De Rugy & Sternad 2003).
1.2 Motivation for this dissertation
Human life consists of many periodic processes, most of them running in the background. Neural
networks in the brain and spinal cord control rhythmic behaviours such as breathing, running and
chewing. And other motor activities can be performed concurrently, like speaking, reading, or
grasping. Moreover, many of human everyday motor activities require interlimb coordination: using
a mobile phone during walking, driving a car, playing tennis, performing piano; thus timing of several
movements in parallel is demanded. A fascinating example of such multi-tasking is the one-man band
- this musical tradition demonstrates the amazing capability of humans to execute several parallel
actions with reliable spatio-temporal accuracy, based on a high degree of motor coordination
between different effectors.
The coordinative process is so naturally governed by the central nervous system that many of our
daily multi-tasking activities seem to be effortless and easy. However, while musicians are trained to
perform more than one task, normal individuals dealing with some dual-tasking or multi-tasking are
usually troubled. Can a perfect efficiency, i.e. without any loss of speed and accuracy compared to its
performance in isolation, be expected, when at least two independent tasks are executed
simultaneously? This should be possible only if the multiple tasks do not share any capacity-limited
information-processing system. So, how can this multi-task processing be resolved in the brain? And
is that anything like currently done in operating systems of computer operating systems? Did
computer scientists ‘reinvent’ the wheel, or can they learn something from multiple task processing
strategies the brain employs? Is there a “central bottleneck” with the first come first served basis or
round-robin scheduling? Or are both serial processing and parallel processing ongoing in the brain?
1
http://www.minddisorders.com/Flu-Inv/Halstead-Reitan-Battery.html
12
Do dual-task (DT) interference and management overhead exist due to a “central executive” that
manages the whole thing?
1.3 Motor Coordination – Task Scheduling and Timing
1.3.1 Single-Task (ST) Condition
The single-task condition is the simplest case for motor control, since all resources of the system
are available for the currently active process without restrictions. Therefore, this case usually serves
as the reference for DT experiments, since comparison between results from ST and DT reveals the
load introduced by DT. At the first glance, it looks like that the ST case is the normal case in everyday
human behaviour. But analysing this behaviour in more detail will reveal that most of the single
actions are performed on the background of some other motor activity; i.e. the DT condition is the
very usual one. Within this work, the term ST also includes all those cases, where a background
action is active but at an unconscious level. In the following, different kinds of those ST conditions are
addressed.
1.3.1.1 Coordination of voluntary limb movement on the background of involuntary
(tremor) movement
Figure 1-1: Cognitive control of single-task on one hand involving internal timing for fast stimulus-evoked
reaction and prominent tremor-at-rest (Staude et al. 1995). (Muscle images taken from Thomas et al. 1990)
Tremors are understood to be generated by internal oscillators and feedback loops, respectively,
thus they represent an unconscious movement. Mutual interference between tremors and voluntary
movements on the same limb are reported: discrete movements superimposed upon a periodic
rhythmic movement such as tremor are supposed to be affected by phase entrainment (e.g. Elble,
Higgins, & Hughes, 1994; Staude et al. 1995); the so-called “entrainment effect” describes the
observation that the discrete reaction (when planed in the movement plane of the tremor) is
“waiting” until the tremor moves the limb in the direction of the required discrete movement. This
behaviour can be interpreted within the framework of the minimum energy model of Bernstein
(1967). Fig. 1-1 shows a functional scheme for this case. . Because this tremor condition represents a
pathological condition, it was not included in this study. Instead, the basic DT cases with a voluntary
periodic background movement (described below) will regard this condition.
13
1.3.1.2 Timing in periodic finger tapping
Note: The literature is using the term “periodic” for a continuously executed tapping, even if the term
does not really fit to the process: due to the stochastic variation of the intertap-intervals (i.e. the time
between two taps), this tapping is not really periodic but repetitive. Nevertheless, this work uses
“periodic” to be compatible with literature .
Figure 1-2: A) Two mechanisms for representing temporal information (Ivry 1996): Clock-counter models
postulate a pacemaker that produces output to a counter. Longer intervals are represented by an increased
number of pacemaker output pulses that accumulate in the counter. Interval-based models assume that
different intervals are represented by distinct counters, each corresponding to a specific duration. B) Schematic
of two process mechanism for timing of repetitive discrete motor response (Wing & Kristofferson 1973b).
(Taken and modified from Ivry 1996 and from Wing & Kristofferson 1973b)
For periodic movements by a single limb, the existence of an internal timing system which handles
the temporal information is suggested. It has been hypothesized that the cerebellum and the basal
ganglia operate as specialized modules for timing (Ivry 1993, 1996). Models of human tapping to a
periodic external auditory source (pace) have been developed in synchronization experiments
requiring performers to tap synchronously with this reference stream and in the absence of this
stream after a synchronisation phase with pacing. The basic functional concept is shown in Fig. 1-2
(Ivry 1996).
The most basic model in information processing theory is the Wing and Kristofferson model
(1973b); it consists of the timekeeper and the motor implementation (response) (Fig. 1-2B), and both
are subject to random fluctuations. The important role of sensory information in temporal control
was considered (Drewing & Aschersleben 2003). A nonlinear model was applied for error correction
in psychological processes (Pressing 1998). The variable of interest is the timing difference between
the tap (response) and the nearest reference point in the reference stream (trigger).
14
Figure 1-3: A) Multi timer model. Each timer is generated for each hand (Ivry & Richardson 2002). B) central
timing model of synchronous two-handed rhythm production (Vorberg & Hambuch 1984). (Modified from Ivry
& Richardson 2002 and from Vorberg & Hambuch 1984)
Controversial hypotheses
Dual output tapping like bimanual tapping is mainly considered in this work, because it addresses
motor coordination, and coordination means more than one motor action. For this case, a coupling
of effector-specific timing structures (Helmuth & Ivry 1996 (Fig. 1-3A)) is contrasted with the
proposal of one integrated timing structure that controls both movements (Vorberg & Hambuch
1984 (Fig. 1-3B); cf., Schmidt 1980). From experimental evidence in bimanual tapping, Ivry (Ivry 1996;
Ivry & Richardson 2002) favored the hypothesis that separate timing mechanisms become
functionally coupled when timing of multiple effectors is centrally controlled (Fig. 1-3A).
Figure 1-4: A) The dynamics is decomposed into end-effector x and neural units  . The latter are bilaterally
coupled via the function I and force (F) the end-effectors x. The state of x, in turn, is mapped to the  -level via
the feedback function G (Peper, Beek, & Daffertshofer 2000). B) Hypothetical timer intervals (Tn) and motor
delays (Ln, Rn) and their relation to the observable inter-response intervals of the left-hand (I n ) and righthand (Jn) response sequences. A rhythm consisting of three notes (1, 2, 3) is assumed to be produced
repeatedly (Vorberg & Hambuch, 1984). (Modified from Peper, Beek, & Daffertshofer 2000 and from Vorberg
& Hambuch, 1984)
Peper, Beek, & Daffertshofer (2000) tested the influence of amplitude on timing pattern stability
(as predicted by this hypothesis) in a bimanual 1:1 frequency coordination task without imposing
any amplitude constraints and proposed a dynamical model for rhythmic interlimb coordination
composed of the neural and effector level, respectively (Fig. 1-4A). Vorberg & Hanbuch (1984)
proposed that the same central timing commands control both motor subsystems for bimanual
tapping. An important feature of their model is the symmetry in the dependence of the left-hand and
the right–hand Interresponse interval (IRI) sequences. These dependence relations are described in
15
terms of covariance; i.e. the covariance between any pair of left-hand IRIs equals that between the
corresponding common timer intervals, but is independent of the properties of the motor delays (Fig.
1-4B).
Dissertation objective
Temporal assimilations and interferences during desynchronized bimanual movements (Deutsch
1983; Jagacinski et al. 1988) and bimanual advantage (Helmuth & Ivry 1996) were reported.
Experimental support for model applied to error correction is good but the variable of interest yet is
based on the timing difference between the tap and the nearest reference point in the reference
stream. This study also addresses the timing difference between the both fingers’ taps in the case of
simple bimanual synchronous tapping. The assumption of a common timing system which controls
both hands might be violated (Vorberg & Hambuch 1984) when a performer tries to compensate for
the asynchrony between the hands by triggering the early hand only after some delay. The crux of
the Wing and Kristofferson model (1973b) is that the presence of two independent fluctuation
sources (timekeeper and motor implementation) causes the autocorrelations between -0.5 and 0 for
adjacent intervals of lag one depending on the relative contribution between two source variances.
Modification of timer variance and motor delay might be obtained by different experimental
conditions such as contact-free, isometric and voice tapping (Fig. 1-2B). The mental task such as
counting together with unimanual tapping although does not require a second motor command. If
the tapping performance is changed, the question whether the common timer is improved by more
attention due to the mental task or a second trigger command for counting is integrated with motor
command for tapping has to be considered.
1.3.1.3 Timing in spontaneous blinking and finger tapping
Figure 1-5: Cognitive control of single-task on one hand involving internal timing for rhythmic movement and
spontaneous eye blinks.
Next item in the framework of motor coordination is the case where two independent motor
actions are performed, with one of them being unconsciously timed; a good example is the eye blinks
which occur at a spontaneous rate. Usually, individuals do not pay attention to eye blinks, since their
execution does not require much planning or even not any cognitive control (Fig. 1-5). There is a
distributed brain network active in blinking (Frederico et al. 1998; Mazziotta et al. 1998; Tsubota et
al. 1999). When a human performs a task requiring visual vigilance, the spontaneous blinking rate is
16
reduced (Bauer et al. 1985). Therefore, the study of blink rate is broadly estimated. Also, the
oculomotor system interacts constantly with blinking. Mutual interaction takes place between
blinking and eye movements. Smooth movements of eye suppress blinking whereas blinks tend to
follow saccade eye movements and always accompany combined rapid head and eye movements
(Evinger et al. 1984). Various external factors influenced blink behaviour (Ponder & Kennedy 1927).
Psychological and perceptual factor (fatigue, attention, stress, cognitive or emotional states)
dramatically change the blink rate. Therefore, completion for an arithmetic solution as a cognitive
task can be marked by spontaneous blniks (e.g., Evinger 1995). It was also found that blinking often
accompanies other tasks with some regularity, for instance, at “physical gaps” or at “punctuationmarks” during reading, or at the onset of redirecting the gaze when sequentially looking at multiple
objects (Arthur 1945). Eye blinks can also be related to selective attention, and they can serve as an
indicator to disclose deception as well (Fukuda 2001).
This study addresses the central spontaneous blink control process which paces the blinking and
investigates blink behaviour during voluntary repetitive finger tapping to examine issues of motor
and perceptual timing as well as other effects such as task concurrency, laterality of
neuropsychological functions, etc. Various control mechanisms of tapping were proposed like, e.g., a
single central pacemaker, which successively provides relevant intervals and triggers motor
commands each time an interval has elapsed (Wing 2002; Ivry & Spencer 2004).
1.3.2 Dual-Task (DT) condition
As mentioned before, the ST situation is not dominant in human acting behavior rather dual-task
(DT) situations are more common. Certainly, conducting two independent tasks concurrently means
sharing the resources, which implicitly leads to DT interference. The onset times of the two stimuli
evoking task execution can be equal (simultaneous start) and different (asynchronous start). Both
situations have been widely studied in terms of ideomotor compatibility (i.e. one of the two motor
actions is an involuntary one like tremor in patients) (Staude et al. 1995) and of the psychological
refractory effect (i.e. there is a dead time after finishing an action before the next can be started)
(Greenwald 2003; cf. Lien, Proctor, & Allen 2002). As well, DT interference was investigated in
different tandems of tasks (Pashler 1984; Pashler, Carrier, & Hoffman 1993; Brass et al. 2000). A
broad range of sensorimotor and/or mental tasks ranging from simple task combinations to
complicated task pairs have been used and DT costs, i.e. signs of decreased performance were
evaluated. In simpler cases (e.g. Welford 1952), latencies and mutual dependencies of two tasks
were often analysed as a function of a varied SOA (Stimulus Onset Asynchrony), mostly in speeded
choice reaction experiments (e.g. Pashler 1984; Pashler & Johnston 1989).
Interference in DT should be minor, if both tasks are guided by exogenous timing trains, since a
timing deviation become directly obvious by the perceived asynchrony between stimulus and
response. A more sensitive method is to bind one task with an internal cue and observe its timing
changes due to the exogenously triggered execution of the other task (open loop condition).
17
Controversial hypotheses
Favouring the serial organization of sensorimotor transformation stages of perception, cognition
and action within a single-channel (Welford 1952; 1967), the so-called perception-cognition-actionloop (Gottlieb 2007), the central bottleneck model expressing a limited capacity at the central
response selection stage (Pashler 1984; Pashler & Johnson 1989) was accounted for DT costs,
whereas other research results which point at the limitations of central resources emphasized the
necessity of the strategic allocation and sharing of these resources in DT paradigms (Logan & Gordon
2001; Navon & Miller 2002). (For a more extended review of such models, see Byrne & Anderson
2001, Hazeltine et al. 2006). These dominant models are based on generic nature of central
processing being independent of stimuli and response modalities. Therefore, it was referred to as the
generic central bottleneck model (Hazeltine & Ruthruff 2006). On the other side, acknowledging
parallel flow of the central (cognitive) processing, the Executive Process Interactive Control - the EPIC
model of multiple task performance (Meyer & Kieras 1997) - has associated all bottlenecks with
peripheral processes or with explicit scheduling decisions. Irrespective the vast amount of data
accumulated and the extensive discussions, the question on serial versus parallel cognitive processes
involved in DTs is still being debated staying without consensus achieved (Hazeltine et al. 2006).
1.3.2.1 Coordination of two voluntary movements at a single upper limb with different
spatial targets
Figure 1-6: Cognitive control in dual tasking at a single joint. The concept shows two different internal timing
units for fast discrete and slow rhythmic hand movements, respectively.
Execution of two different motor tasks at the same elbow joint revealed the interdependence of
time characteristics. Subjects performed fast, discrete elbow flexion movements and simultaneously
produced rhythmical oscillations about initial and final visual targets embedded on a horizontal
surface (Adamovich, Levin, & Feldman 1994) (Fig. 1-6). When movements of the left and right index
fingers varied in spatial and motor congruence, dominated spatial incongruence on performance was
clear (Obhi & Goodale 2005). To approach spatial congruence and motor congruence pairs of letters
were presented on computer screen cueing the required movement directions and by altering hand
orientation, respectively
18
1.3.2.2 Coordination of two voluntary movements at different upper limbs with common
spatial target
Figure 1-7: Cognitive control of dual tasking but now executed by different limbs. Like in Fig. 1-6,
again the concept assumes two different internal timings for fast discrete and slow rhythmic
movements respectively.
The issue of motor coordination in dual-task (DT) and multi-task (MT) situations early attracted
research attention in motor control (Bernstein 1967; Greenwald 1972; Klapp 1979), and much work
was done since then to investigate the interlimb coordination and its timing (Fig. 1-7) (e.g. Yamanishi,
Kawato, & Suzuki, 1980; Kelso 1984; Latash et al. 2002; Semjen & Summers, 2002; Wei, Wertman, &
Sternad 2003, …). One specific instance is the existence of bimanual interference when two manual
tasks are conducted simultaneously (e.g. Swinnen & Wenderoth 2004).
The following fact is of particular interest for the present study which investigates interaction
effects within motor MT: due to the open loop condition, a self-paced tapping is highly sensitive to
interferences with other ongoing processes; e.g., the timing accuracy of periodic tapping with the
dominant hand suffers from the concurrent execution of another (discrete) motor task with the nondominant hand (Yoshino et al. 2002).
The interesting aspects such as the perturbation of the continuous process and the anti-phase
coordination between hands with respect to the agonist and antagonist muscle activity, or, more
generally, the phase locking phenomenon (i.e. periodic movements of two limbs show a strict
temporal relationship) are still obscured; how endpoint trajectories are shaped due to these action
elements during the joint action time. Analysis based on MEG waveform and PRCs maybe not reveal
all facts and mechanisms if the finally observable trajectories are dismissed as it was done by all the
“switch” studies which evaluates the times of switch closures (or openings) as the only characteristic
measures for the limb movement (i.e. discretisation to a single bit). These investigations require
more complete biomechanical and physiological observations like the continuous recording of, e.g.,
position of the finger (Semjen & Summers 2002), in order to detail the interaction of the fast discrete
movements with the repetitive tapping. This will allow for combining the dynamical system approach
with the information processing aspects of movement timing. But the analysis of these complicated
and somehow noisy signals requires highly sophisticated biosignal processing for their evaluation.
19
Thus, other objectives of this dissertation are to extend the “switch”-study of Yoshino et al. (2002)
appropriately and to clarify in more detail the strict temporal relationship and the mutual interaction
between both movements. The trajectory formations would explain the kind of optimality principles
used by the CNS (Central Nerve System) to satisfy the constraint of periodic stability with respect to
the endogenous timing cue. Therefore, inspecting e.g. the finger tip position data in normal tapping
leads to an extended analysis of the tapping movements which reflect the motor process in more
detail. In turn, this supports efforts to establish a model for a common abstraction of the DT
interference.
1.3.2.3 Coordination of voluntary movements on the upper limbs with different spatial
variables
In everyday behaviour, there are many cases when the hands perform discrete and/or repetitive
movements while the eyes are directed elsewhere. The nature of the coordination between eyes and
hands has been studied for visually guided manual actions (for review, see Jeannerod 1988; Binsted
& Elliot 1999; Crawford et al. 2003), with the general finding that ocular and manual reaction times
mostly co-vary. When directed to the same target, both movements tend to start almost
simultaneously (Fisk & Goodale 1985); also, temporal characteristics of saccades are influenced by
arm kinetics (Snyder 2000; Lunenburger, Kutz, & Hoffmann 2000; Snyder et al. 2002; van Donkelaar,
Siu, & Walterschied. 2004). Moreover, Fox et al. (1985) revealed that the execution of saccades and
finger movements activates overlapping cortical areas: both of them recruit the supplementary
motor area and the cerebellum. Neural activity in the (saccade related) superior colliculus and in the
(limb movement related) posterior parietal cortex is modulated by limb (Stuphorn, Hoffmann, &
Miller 1999) and eye positions (Snyder 2000). In an imaging study, Bedard, Thangavel, & Sanes (2008)
observed modulation of (visually-cued) finger tapping related brain activity for different static gaze
directions. This and more recent results (Bedard & Sanes 2009) further extend findings on brain areas
with combinatorial effects of static gaze direction and finger movements.
Figure 1-8: Cognitive control in a DT situation involving two different internal timings for fast horizontal
discrete eye and slow vertical rhythmic hand movements, respectively. The concept shows two different
internal timing units for the discrete eye movement and the rhythmic hand movements, respectively.
20
The above mentioned interaction of two movements related to the two tasks in the DT situation
can be effector-specific (i.e. more peripheral) or task-specific (more central); this ambiguity cannot
be decided by DT experiments employing two hands (i.e. two homological elements). Therefore, an
alternative DT paradigm combining such seemingly unrelated movements as manual (periodic
tapping) and eye movements (goal-directed saccade) is of specific interest in this context. If using a
saccade scheme with a primary stimulus evoked saccade starting from the fixation point and directed
to a target together with a second self-paced saccade back to the fixation point, this couple of
saccades form a period running in concurrence with periodic tapping. The DT required cognitive
control involving two different internal timings (Fig. 1-8).
1.3.2.4 Coordination of voluntary movements on the upper and lower limbs with
common spatial variables
Figure 1-9: Cognitive control of dual-task on hand and foot involving two different internal timings for fast
discrete and slow rhythmic movements.
There is a huge number of investigations concerning bimanual tapping, however, little is known
about foot tapping or combined hand-foot tapping (Fig. 1-9); hand-foot coordination seems to be
quite different from bimanual coordination: e.g., it is shown that in a hand-foot coupled motion the
foot’s influence on the hand is greater than that of the hand on the foot (Chipman 2004).
21
Figure 1-10: literality of limb control. Different coordination patterns between limbs on the same side and
between different sides due to the delay are assumed.
From the structural point of view, the interesting point is the different kind of possible
interactions; there are 2 tasks and 4 limbs, which results in 49 possible effector combinations. A main
aspect is the laterality of limb control shown in Fig. 1-10, which let assume a different coordination
(interaction) pattern between limbs on the same side and between crossed limb pairs due to the
delay.
From behavioral point of view, the purposes of the hands and of the feet are quite different: to
grasp or manipulate things and to move the hands together with the body to another place,
respectively. Therefore, it cannot be assumed that an interaction scheme found in the hands by DT
experiments can be replicated in the feet. In particular, homology and laterality of the observed
limbs are important aspects. Maybe, interaction between hand and foot both on the same side is
different from the cross situation when each of the two tasks in DT is exclusively dedicated to one
side, because for the cross situation the pathways via the Corpus Callosum are additionally engaged.
Also, the different peripheral delays due to the different length of the spinal pathways can influence
the interaction scheme.
Thus, combined hand and foot responses are investigated in the same way as in Section 1.3.2.2 to
check whether previously demonstrated bimanual DT interaction can be replicated in the case of
hand and foot responses.
22
2 Literature review on finger tapping
Note: This literature review reflects related work of other authors. To achieve a compressed but clear
description of this work, often original phrases were taken from the original papers without
specifically labeling them, because mostly they are optimal with respect to information density.
2.1 Overview
As already mentioned, finger Tapping (FT) represents a very simple task but FT behavior reflects
the whole sensorimotor chain, thus it allows checking the integrity of the participating mechanisms.
FT studies comprised different forms of movement (contact vs. contact-free tapping) and different
stimulation (auditory vs. visual), and different coordination forms (in-phase vs. anti-phase). This
chapter should give a rough overview about the different approaches and aspects before entering
into details in Sect. 2.2 and 2.3.
2.1.1 Literature on Clinical research
Obviously, the Finger Tapping Test (FTT) should reflect brain disorders, since it engages the whole
Perception-Cognition-Action (PCA) loop; if any mechanism shows pathological behavior, it should be
reproduced in the FTT up to a certain extent. Thus, the functioning of the central sulcus where the
motor strip is most important is reflected directly in the FTT (Mitrushina, Boone & D'Elia 1999). Tests
of motor performance can be used as reliable indicators of the integrity of brain functions by which
performance of both motor level (speed, coordination) and cognitive level (alertness, attention) are
examined (Dodrill 1978). The impacts of both neurological and psychological pathology on motor
speed have been shown by numerous studies. Shaw et al. (1987) investigated the effects of lithium
carbonate in bipolar patients. Heaton, Nelson & Thompson (1985) assessed neuropsychological
functioning in patients who had relapsing-remitting and chronic progressive multiple sclerosis. FTT
was impaired by Alzheimer’s disease (Wefel, Hoyt & Massama 1999), schizophrenia (Flashman et al.
1996) and traumatic brain injury (Gelmacher & Hill 1997).
2.1.2 Psychological and physiological research
Information-processing theory analyzing discrete events and dynamic systems theory concerning
continuous movement are two main theoretical approaches. Periodic tapping encompasses
continuous movement and discrete events generated on a hard surface required more explicit
temporal control than do continuous movement without contact (Delignières, Lemoine, & Torre
2004; Zelaznik, Spencer, & Ivry 2002) and may involve different brain circuits (Spencer, Ivry, &
Zelaznik 2005; Spencer et al. 2003). Balasubramaniam, Wing, & Daffertshofer (2004) observed that
paced finger movements are more asymmetric than unpaced ones.
2.1.2.1 Sensorimotor synchronization
Coordination of perception and action was involved during temporal coordination of a motor
rhythm with an external rhythm in music (Repp 2005). The notion sensorimotor synchronization
(SMS) was approached for this referential behavior. Pollok et al. (2005) investigated brain area
associated with a unimanual auditory paced finger tapping task and demonstrated a neural
oscillatory network. SMS apparently requires attention and intention (Repp 2005). Without error
correction, the variability inherent in any periodic motor activity would accumulate in progress. In a
study of sensorimotor synchronization, Repp (2000) instructed participants to tap their finger in
23
synchrony with an excerpt of piano music. He employed a perturbation method called “pulse
change” to lengthen or to shorten a single interval. He showed that not only stimulus changes that
are explicitly detected lead to error correction in synchronized tapping, i.e. pulse changes in stimulus
timing that were well below the explicit detection threshold led to effective adjustments in the
timing of the motor response. Repp (2001) investigated the finger tap synchronization with an
auditory sequence by which a small sudden tempo change in the sequence was approached. He
concluded that a rapid internal phase correction and a slow internal period correction of the tapping
period occurred. Period correction due to tempo changes of stimulation was found to be strongly
dependent on intention, attention, and awareness (Repp & Keller 2004). Repp (2006) reported the
distractor effects of an auditory sequence on the timing of self-paced finger tapping. Schmidt RC &
O’Brien B (1997) reported from investigations of unintended between-person coordination that
dynamical organizing principles are involved in natural interpersonal synchronization. Richardson,
Marsh, & Schmidt (2005) studied the visual and verbal interaction of coactors. The results showed
that verbal interaction alone was not sufficient for unintentional coordination to occur. Peters (1985)
claimed that attention is an important factor in the interaction between superordinate and
subordinate control mechanisms in a variety of bimanual tapping tasks. Miyake (2002) reported
anticipatory timing control with attention and without attention.
Most SMS studies found anticipation tendency or negative mean asynchrony (NMA) that taps
tend to precede sequence tones. Different nerve transmission times from the finger to the brain and
from the ear to the brain were suggested (Paillard 1949; Fraisse 1980). Aschersleben (2002;
Aschersleben, Gehrke, & Prinz 2001) proposed that consciously perceived synchrony is achieved by
accumulating evidence at different rates from different sensory channels. Enhanced auditory
feedback on taps decreased the NMA (Aschersleben & Prinz 1995, 1997) whereas reduced tactile
feedback through anesthesia increased NMA (Aschersleben, Gehrke, & Prinz 2001).
2.1.2.2 Timing in repetitive tapping
Schulze (1978), Keele et al. (1989) interested in the nature of human timing mechanism for
perceptual judgments about short temporal intervals. Schulz (1978) is in favor of an internal
timekeeper which is synchronized with the presented pattern. Keele et al. (1989) supports the
interval-based judgments (i.e. the timer records the intervals produced by finger hits) rather than the
beat-based judgments (i.e. the stimuli (tones) establish internal beats which continue and serve as
reference points for the perception of subsequent events). Wing & Kristofferson (1973a, b; Wing
1980) proposed a two-tier model that partitions the ITI variance into two independent central
timekeeper and peripheral motor implementation. This model predicts a negative correlation of
successive ITIs. The variability of this timekeeper increased with the metronome period (Semjen,
Schulze, & Vorberg 2000). Helmut & Ivry (1996) reported a reduced timing variability during
bimanual movements. The model provides the independence of motor variance on the period (Wing
& Kristofferson 1973a). Different control signals to each effectors’s movement In a repetitive
bimanual tapping task are averaged and provide timing advantage (Drewing et al. 2004). Various
feedback components (tactile kinaesthetic, and auditory) are linearly integrated to form one central
representation (Mates & Aschersleben 2000). Rhythmic tapping performance of patients with focal
lesions in the cerebellum was impaired when tapping with an effector ipsilateral to the lesion in
comparison with contralateral to the lesion (Ivry, Keele, & Diener 1988). Callosotomy patients
exhibited strong temporal coupling in the bimanual condition and within-hand temporal variability
was reduced in the bimanual condition compared to the unimanual conditions. Ivry RB & Hazeltine
24
(1999) suggested that motor commands from the two hemispheres are integrated subcortically.
Motivated by the previous results (Helmuth & Ivry 1996; Ivry & Hazeltine 1999; Ivry & Richardson
2002) proposed the multiple timer model assuming that separate signals are generated for each
effector. The intended anti-phase coordination in human hand movements became unstable and
changed to in-phase coordination as the tempo of the pacing sequence is increased (Haken, Kelso, &
Bunz 1985), Haken developed a theoretical model using concepts central to the interdisciplinary field
of synergetics and nonlinear oscillator theory. Beek, Peper, & Daffertshofer (2002) outlined a more
elaborate system of coupled oscillators comprising additional two coupled limit cycle oscillators at
the neural level, which are coupled to the oscillator representing the end-effectors. Yamanishi,
Kawato, & Suzuki (1980) reported that in-phase and anti-phase coordination are the preferred ones
to which other phase relationships tend to revert.
2.1.2.3 Phase Response Curve (PRC) and Phase Transition Curve (PTC)
Hastings & Sweeney (1958) presented the first form of phase response curve plotting the
relationship between the intensity of a single 3 hour light perturbation and the number of hours by
which the phase is shifted. Burchard (1958) and DeCoursey (1960) reported PRCs of rodents in their
Ph.D. theses. Pittendrigh & Bruce (1959) published PRCs of fruit flies, and DeCoursey (1959) of flying
squirrels. Winfree (1980) defined two types of PRCs (type 0 and type 1) depending on the strength of
the stimulus influence: Type 1 shows a small phase shift whereas Type 0 a large one. The average
slope of the PTC curve is referred by these indexes.
Yoshino et al. (2002) applied the phase resetting experiment to investigate the disturbance of the
series of left finger taps in response to impulsive auditory cues on the right periodic tapping
movement. The results showed type-0 and type-1 reset in Winfree’s definition. De Rugy & Sternad
(2003) investigated the combination of discrete and rhythmic movements in a single-joint (elbow)
rotation. The initiation of discrete movement was performed either in a reaction time (i.e. stimulus
enforced) or in a self-pace fashion. The results showed that its synchronization with the rhythmic
task was more pronounced in the self-initiated discrete movement than in reaction time fashion.
Yamanishi, Kawato, & Suzuki (1980) psychophysically studied the properties of the human finger
tapping using PTCs based on its average slope; they identified type-0 and type-1 assuming that an
oscillatory neural network controls the finger.
2.1.2.4 Human motor control and timing (Thomas et al. 1990)
Neurons and other specialized cells are components of nervous system. The peripheral nervous
system and the central nervous system are roughly separated.The peripheral nervous system is
composed of sensory neurons and the neuronal pathways. The later is the transfer medium to the
central nervous system. Spinal cord (with the neurons located there) and the brain make up the
central nervous system. Neurons generate and conduct impulse between and within the two
systems.
25
Figure 2-1: a) the circuit carries impulses produced by a reflex action: receptor cell, sensory neuron,
interneuron at spinal level, and motor neuron (according to Thomas et al 1990). b) Convergence of long and
short latency reflexes at spinal level is believed to ensure necessary feedback during ongoing movements. (Left
image taken from Thomas et al. 1990)
Neurons are organized in circuits and networks. The reflex arc is the simplest one designed to
administer unconscious automatic actions. These automatic actions are determined in nature to keep
in a protective way the body in homeostasis. For instance, in the knee jerk, a stimulus is detected by
a receptor cell detects a stimulus and synapses with a sensory neuron. The sensory neuron transfers
the impulses to the spinal cord and therefore is synapsed with an interneuron. A motor neuron
which is synapsed with the interneuron carries the impulse out to a motor unit in a muscle (Fig. 2-1a)
and a contraction occurs. The spinal rhythm generators are controlled by several parts of the brain. A
sequence of neuronal circuits comprised in the spinal cord mediates basic movement rhythms from
the command center. Afferent signals from the peripheral receptors can modify the rhythmic
movement providing information about how the movements are proceeding. The neural basis of
internal timing mechanisms is often investigated by exploiting the ability of humans to accurately
maintain temporal information. The required interval developed during pacing is represented in an
internal mechanism on which timing is assumed to be based on (Wing & Kristofferson 1973b; Fig. 21b).
2.2 Literatur on clinical research
The Halstead-Reitan battery is a fixed set of eight tests for evaluating a wide range of nervous
system and brain function2. The tests consists of the Finger Tapping Test (FTT) and other tests such
as the Category Test, the Speech Perception Test, the Tactual Performance Test, and the Seashore
Rhythm Test. The Klove Grooved Pegboard, the Reitan Aphasia Screening Test, the sub-battery of
perceptual tests, the various Wechsler Intelligence Scales, the Trail Making Test, and other test are
additional included. A procedure described as an “extended Halstead-Reitan battery” has recently
appeared that includes the original tests plus several additional ones. Participants have to tap as
2
http://www.minddisorders.com/Flu-Inv/Halstead-Reitan-Battery.html
26
quickly as possible with their extended index finger and hand palm down on a lever. They have to
keep hand and arm stationary. The lever is attached to a counting device. Five trials are approached
and each lasts ten seconds. There is a short pause between trials and 2-min rests after every third
trial. Motor speed was measured and the damage of particular areas of the brain is determined.
Four groups of 25 subjects consisting of control person, right-hemisphere damage, lefthemisphere damage, and bilateral damage were studied under the Tapping and Tactual Performance
tests (Dodrill 1978). Measures of performance included those of each hand taken separately as well
as their sum. The relative performances of each hand on each task were simultaneously considered.
The results revealed high level of statistical significant difference between the control and braindamaged groups. Shaw et al. (1987) assessed weekly over a 5-week period 22 outpatients with
affective disorders in remission. Placebo was given and lithium was reinstated during this 5-week
period. They used the finger tapping test and the Buschke selective reminding protocol for motor
speed and memory assessment respectively. To ensure remitted status mood assessment was
approached by clinical interview, a subjective state questionnaire, the Hamilton Rating Scale for
Depression, the Longitudinal Rating of Manic States Scale. Atomic spectrophotometry was used to
assess plasma lithium from blood samples. They reported that lithium had significant detrimental
effect on memory and motor speed. Geldmacher & Hill (1997) studied 20 patients after severe
traumatic brain injury (TBI) and 21 healthy controls. They reported that TBI subjects were slower on
finger tapping. Qualitative and quantitative factors contributed to impaired visuospatial ability
following TBI were concluded. Wefel, Hoyt, & Massama (1999) reported that Patients with
Alzheimer's Disease (AD) reporting depressive symptomatology (AD-Dep) exhibited an unexpected
pattern of greater right hand advantage on the Finger Tapping Test after investigation of the
differences in cognitive functioning between 37 AD-Dep and a control group of 98 nondepressed
participants with AD. Flashman et al. (1996) examined the relationship between soft signs and
neuropsychological performance in patients with schizophrenia and reported that patients with
neurological soft signs demonstrated significantly poorer performance on finger tapping and other
test such as the Purdue Pegboard task and part B of the Trail Making Test. They concluded that soft
signs are a manifestation of a localizable behavioral deficit of the systems involving in motor speed,
coordination, and sequencing and are not indicative of global cognitive impairment. The specific
deficit in motor abilities is consistent with the types of neurological soft signs that are minor (‘soft’)
neurological abnormalities in sensory and motor performance identified by clinical examination and
suggests involvement of frontal/subcortical circuitry in schizophrenia.
2.3 Literature on psychological and physiological research
2.3.1 ST condition
2.3.1.1 Timekeeper and motor delay (Wing & Kristofferson 1973b)
Wing & Kristofferson (1973b) studied the correlation of successive intertap intervals in a simple
periodic tapping task. They used a combination of paced and unpaced tapping of a telegraph key and
instructed participants to depress the key periodically to close the contact in synchrony with the
sequence of 10-msec-duration 2000Hz auditory pulses and continue tapping for further 32
responses. The pulses were separated by intervals of 180 through 350 msec. The last 30 intervals of
the unpaced phase were analysed. Estimates of the correlation lag 1 between adjacent intervals
were based on the ratio of the covariance lag 0 and lag 1 of these intervals. The results showed that
the correlation between successive intervals of lag one lied within the range (0, -1/2) for all subjects.
27
Figure 2-2: Schematic of two-process mechanism for the timing of repetitive discrete motor responses
according to Wing and Kristofferson model (1973b). (Modified from Wing and Kristofferson model 1973b)
They proposed a two-stage timekeeper model of timing in repetitive motor behavior. The model
comprises of a central timekeeper process on the controlling level and the peripheral
implementation system on the executive level. These two processes are independent of each other.
The central timer emits a motor command for the finger to tap to the peripheral implementation
system and the motor response is generated. The motor variance causes the delay observation of the
tap. The second source of variation in Timekeeper is also accounted in the absence of external
pacing. The intercommand interval C n separate the motor commands in time interval C n. The time
duration between the beginning of the implementation of the first tap and the beginning of the
implementation of the second tap defines the timing interval I of the response. The intercommand
interval fluctuates around its mean. The time duration between the central timer commands to the
observed tap defines the motor delay D (Fig. 2-2. Cn and D as uncorrelated stochastic processes are
n
n
in the following relation:
In= Cn+ (Dn+1- Dn)
(1)
And statistically an longer (than the mean intertap interval) intertap interval is usually followed by an
shorter ( than the mean intertap interval) one and vice versa and vice versa.Cn are Dn are assume to
be independent and identical distributed with a normal distribution N(µ,θ) and N(ɳ,ϕ) respectively.
The uncorrelation between Cn and Dn leads to Var(In) = θ+2ϕ and Cov(In, In+1) = -ϕ. Depending on the
relative contribution of Cn and Dn autocorrelations between -0.5 and 0 for successive intertap
intervals and autocorrelation of 0 for lags higher than 1 are implied.
2.3.1.2 Timing in repetitive tapping
2.3.1.2.1 Discrete timing
Schulz (1978) wanted to find out by which mechanism human discriminated a regular sequence of
beats from an irregular one.
28
Figure 2-3: Three different types of displacement that have to be detected. (Taken from Schulz 1978)
Figure 2-4: Ordinal predictions of the three theories with respect to the three different types of displacement.
(Taken from Schulz 1978)
Figure 2-5: Detectability of displacement in different conditions for five subjects. The data in one row are from
one subject. (Taken from Schulz 1978)
Five participants heard a random sequence of standard and comparison patterns and then had to
decide whether the sequence was regular or not. Two tones of different frequency (1600 and 122
Hz) were presented in alternation. The standard stimuli were regularly spaced with an interval of 300
ms and three types of displacements (Fig. 2-3) were introduced for testing the three theories:
successive interval discrimination (SID), comparison with an internal rhythm (CIR), and comparison
with internal interval (CII). The SID would be the comparison of the durations of successive intervals,
29
the CIR is the comparison of the actual input with the actively generated rhythmic sequence and the
CII is the comparison of an internal reference interval with the incoming intervals. The idea is that the
SID incorporates a mechanism that is only sensitive to local displacement in a rhythmic pattern
whereas the other two are more sensitive to global changes of a pattern. The arguments were based
on the probability of detecting a difference of the two temporal intervals of different length in
different displacement conditions. The order of the detection probabilities was predicted. Fig. 2-4
shows the ordinal predictions of the three theories in the three types of displacement.
Figure 2-6: Sensitivity index calculated from combined data. Three beats until the beginning of the
displacement. The size of the bars corresponds to one unit of the standard deviation. (Taken from Schulz 1978)
Figure 2-7: As Fig. 2-6 but under the condition of five beats until displacement. (Taken from Schulz 1978)
Participants were trained to direct their attention to the critical changes by a clearly perceptible
delay of 30 ms before the main experiment. Fig. 2-5 shows the values of discrimination index d’ of
signal detectability theory of all subjects from the hit and false alarm rates. Fig. 2-6, 2-7 shows the
results of analysis using the deviations of Gourevich & Galanter (1967). The results showed a
30
consistency with the theory that the subject establishes an internal time keeper that is synchronized
with the input and against which the input is evaluated. However the theory that the subject builds
up a referent interval with which the intervals in the stimulus are compared has not been ruled out
for some subjects.
Keele et al. (1989) was concerned with possible mechanism underlying the perception and
production of short intervals. Two experiments were conducted. Slightly increments and decrements
(10 ms, 15 ms or 25 ms) were used to the base-time interval (300 ms). One control condition for
constant intertone intervals and three conditions for interval change were approached. The difficulty
of task is predicted to be differentially presented in these conditions according to different
hypotheses. For instance for the hypothesis that a memory trace representing the perceived average
of the first two standard intervals is stored and used to compare to the subsequent intervals. This
hypothesis differs from the number of comparison intervals in different conditions. Let represent the
sequence as t t t t t t when the intertone intervals were equal. In condition 1 the sequence was
represented as t t t + t + t + t +, in condition 2 t t t + t t t and in condition 3 t t t + t – t t. In experiment
1 17 subjects listened to the total of 270 series of seven tones. The subjects judges whether the
intervals between the tones were all equal. The ANOVA showed significant difference between all
conditions. In experiment 2 the first four successive tones were separated by base-time interval.
After a pause of either 540 ms or 660 ms four more tones were presented. 10 Subjects should judges
whether or not all the intervals in the second part of a series were equal in length to those in the first
part. In condition 1 the following three intervals were all longer than the standard by 25 ms. In
condition 2 the first interval after the pause was incremented. In condition 3 the first following the
pause was incremented by 25 ms and the second was decremented.
They found in both experiments that judgments the easiest when all intervals following the
standards were incremented. The condition 2 was easier than the condition 3. The distinction
between beat theory and the memory-interval theory rides on the difference in the outcome of
condition 2 and condition 3. Both experiments in this study are consistent in suggesting that
judgements of temporal equivalence are based not on synchrony of events with internal beats, but
on a memory for interval durations. If a time interval is recycled from end to beginning, then in
essence it can mimic a beat mechanism. If a neural loop from cortex to cerebellum and back to
cortex is responsible for timing function (Ivry & Keele 1989), then internal beats might continually be
produced cycling through the loop.
2.3.1.2.2 Discrete and continuous timing
In a synchronization-continuation task Delignières, Lemoine, & Torre (2004) instructed
participants to synchronize their taps in the tapping task and synchronize the left reversal point of
the oscillation (maximal pronation) of a joystick with the 10 signals of a metronome. The frequency
of the metronome was 1.25Hz. After 10 signals participants continued the task regularly following
the initial tempo. The continuation phase was pursued up to the recording of 700 successive time
intervals. They applied spectral analysis on series of produced time intervals and the doublelogarithmic plot of average power spectra.
31
Figure 2-8: Averaged power spectra (in log-log coordinates), for the tapping task (left) and the oscillatory task
(right). (Taken from Delignières et al. 2004)
They found that the results in tapping were consistent with a discrete, event based timing model.
For the tapping task the slope of the average power spectra in the low frequency was close to -1, and
confirmed the hypothesis of Gilden, Thornton, & Mallon (1995) concerning the presence of 1/f noise
in the series of intervals produced by the internal clock. Fig. 2-8 shows the average power spectra.
The positive slopes in the high frequency region confirmed the presence of a differenced noise in the
series as postulated by Wing-Kristofferson model. In the oscillation condition the negative slopes in
high frequency region suggesting a single white noise error term in the series. Thus, the spectra
suggested a continuous dynamic mechanism based on the regulation of effector stiffness.
2.3.1.2.3 Implicit and explicit timing (Zelaznik, Spencer, & Ivry 2002)
Zelaznik, Spencer, & Ivry (2002) used tapping, circle drawing and duration discrimination tasks in
four experiments to explore the hypothesis that temporal processes may be represented and
controlled explicitly or implicitly. Twenty five participants performed the repetitive tapping task and
drawing task in experiment 1. In tapping task they tapped the index finger of dominant hand on the
table coincident with the high-pitch tone of the alternating high/low-pitch tone (interstimulus
interval=500 ms). In drawing task they were required to complete one circle during the interval
between the low and high-pitch tone and then to pause during subsequent 500 ms interval between
the two tones. Participants were guided with four circles positioned at 0 o , 90 o , 180 o and 270 o
along the imagined circumference of the target circle. The guide at 90 o served as the location of the
on-time marker. Instructions emphasized that the main goal was to be on time. Both tasks were
performed with the continuation procedure routinely used for studying timing.
The results showed a significant correlation between temporal variability on tapping and
intermittent circle drawing tasks in contrast with the findings of Robertson et al. (1999), i.e. the
fundamental difference between drawing circles in a continuous or intermittent manner existed.
They proposed that participants used an internal timing process due to the inserted pause to directly
control the required interval between movement circles and to orchestrate movement initiation. The
pause made the task similar to tapping by transforming the cycles from continuous to discrete, i.e.
explicit timing control of timing is used under such discrete conditions. The interonset times in the
circle task provide the requisite representation for specific events.
A continuous circle drawing condition was included in experiment 2 to compare the patterns of
correlations among the three tasks. They expected that a positive correlation between the measures
32
of temporal variability on the tapping and intermittent circle drawing tasks and conversely a low
correlation between tapping and continuous circle drawing would be obtained. A second change in
experiment 2 is the reduced cycle duration to 800 ms. Twenty five subjects took part in this
experiment.
Again, temporal variability in the tapping and intermittent circle drawing tasks was significantly
correlated whereas the obtained positive correlations between continuous circle drawing and
tapping tasks and between continuous circle drawing and intermittent circle task were not
significant. Total variability on intermittent drawing task was more highly correlated with the pause
phase than with the movement phase. Pause phase during this task was correlated with tapping
variability whereas movement phase was not. In general, the correlations for the spatial and
temporal measures were positive but low. Together, the results is consistent with the hypothesis
that explicit temporal control is related to timing the onset of each cycle, and it is the pause phase in
which such control is most required and factors contributing to temporal and spatial variability are to
some extent dissociable.
The relationship between the tapping and auditory duration discrimination task was addressed in
experiment 3. The finding that performance on an auditory duration discrimination task was
correlated with timing precision in tapping (Keele et al. 1985) was replicated. The positive correlation
between all three tasks (tapping, intermittent circle drawing, and duration discrimination) was
expected. Thirty five subjects were tested in this experiment.
Results: significant correlation on timing variability between the tapping task and both the
intermittent circle drawing task and the duration discrimination task was found. Correlation on
performance between continuous circle drawing and tapping task was lower and not significant. No
correlation was found between the continuous drawing task and the discrimination task whereas the
correlation for total variability on the intermittent drawing task and the discrimination task was high.
The correlation between the duration discrimination and temporal variability of the pause phase of
discrete drawing was significant. In summary the tapping, intermittent drawing, and duration
discrimination tasks draw on a common timing process, i.e. an explicit representation of temporal
information is required for these three tasks. Tapping was tested at 1000 ms, 800 ms, and 400 ms.
The cycle duration of the intermittent task was either 1,000 ms or 800 ms and the continuous circle
drawing task was performed with target intervals ranging from 400 to 800 ms in Experiment 1-3.
The correlation pattern varied as a function of the intervals was addressed in experiment 4. Thirty
participants were tested with two versions of the duration discrimination tasks (400 ms and 800 ms).
The tapping task with an 800-ms target interval and an intermittent drawing task with an overall
duration of 800 ms, partitioned into a 400-ms pause and a 400-ms movement phase were used.
The results showed that 800-ms tapping were more strongly correlated with the 800-ms version
of the perception task than the 400-ms version. The reverse pattern was found for the correlations
between duration discrimination and the pause phase of the intermittent circle drawing task. As
expected the spatial measures were not related to duration discrimination performance.
2.3.1.3 Neural network underlying finger movements ( Pollok et al. 2005)
Pollok et al. (2005) examined cerebromuscular and cerebrocerebral coupling in a unimanual
auditory paced finger tapping task for 5 min. Continuous brain activity was recorded with a 122channel whole-head neuromagnetometer and surface EMG of the first dorsal interosseus was
measured. Ten participants performed alternating brisk finger flexions and extensions of their right
and left index finger in synchronization with a regular auditory pacing signal. Interstimulus interval
33
was 800 ms and ingrained in white noise. As control condition, neuromagnetic activity at rest was
measured for 5 min.
The results showed that task execution was served by an oscillatory network which consists of
cerebellum, primary sensorimotor cortex, primary auditory cortex ipsilateral to the taping hand, ,
lateral as well as mesial premotor areas, the posterior parietal cortex and thalamus contralateral.
An asymmetric motor control in right-handers was indicated by a clear activity in the primary
sensorimotor cortex ipsilateral to the right hand. Significant Cerebrocerebral coupling was observed
at 8–12 Hz and also the significant execution 8–12 Hz oscillations in a large scale network of simple
motor tasks.
2.3.1.4 Movement trajectories in timing control ( Balasubramaniam, Wing, & Daffertshofer
2004)
Balasubramaniam, Wing, & Daffertshofer (2004) addressed the assistance of movement
trajectories on timing accuracy in an experiment involving synchronization or syncopation with an
external auditory metronome. The degree of asymmetry in the flexion and extension movement
times was positive correlated. The hard ground contact might cause these substantial differences.
Huys et al. (2008) demonstrated the requirement of a time keeper during discrete movements but
not during fast rhythmic movements. To accomplish varying behavioral functions such as speed
constraints different timing control mechanisms presumably are employed via differential
recruitment of neural subsystems.
2.3.1.5 The effects of attention
Repp & Keller (2004) investigated the adaptation to tempo changes for their hypothesis that
sensorimotor synchronization rests on phase correction and period correction. A two-process
correction model was applied to obtain separate parameter estimates for these two correction
processes. Phase correction was largely automatic and period correction required conscious
awareness and attention. They asked peoples to tap their finger in synchrony with an auditory
sequence. Ten participants tap with right index finger on an electronic percussion held on their lap.
Participant started tapping with the third tone of a sequence. There were single task and dual task
condition for two cases (adaptive and nonadaptive). The first session of the adaptive single-task
condition contains three tasks: 1) synchronization and adaption to tempo change, 2) keeping tapping
to the last sequence after it was ceased, 3) reporting the change by pressing one of three keys
labelled with “slowed down”, “no change”, and “speed up”, respectively. Participants were informed
about the change location in a sequence but were urged not to guess but to report their perceptual
experience as truth as possible. Except for the added mental arithmetic task session 2 was identical.
A random series of eight digits (1 or 2) were added to this task. The appearance of each digit was
synchronized with the sequence tones. Subjects had to remember the calculated sum and should
focus on this task but without sacrificing accuracy in tapping. In session 3 and 4 (single-task and dualtask, respectively) for non-adaptive case participants were asked not to adapt to any tempo change.
The assessment of awareness about tempo changes through perceptual judgments was performed.
Various attentions was realized by single-task and dual-task conditions. Instruction was given for
adaptation or not to the tempo change.
The result showed that their intention was manipulated through instruction. Their attentional
resources were varied by the mental task. A linear shift with perturbation magnitude of the next tap
occurred by a small phase perturbation in an otherwise isochronous sequence and is called phase
34
correction. The adjustment of period based on perceived discrepancies between its duration and the
tone interonset interval (IOI) in the sequence is called period correction and the adjustment based on
asynchrony between tap and tone is called phase correction. Period correction was strongly
dependent on all three variables (intention, attention, awareness), whereas phase correction only on
intention.
Repp (2006) investigated the distraction of an auditory sequence on the timing of self-pace finger
tapping. Two interleaved isochronous sequences of digital piano tones T and D were provided. The T
sequence had a fixed tempo and a fixed pitch that was always lower than that of the D sequence. The
T had an IOI (inter-onset interval) of 500 ms and D of either 450 or 550 ms. The D sequence started
inphase with the 7th T tone. Eight musical trained participants tapped in the first experiment with the
third T tone with the index finger of the preferred hand and should ignore the D sequence.
The results showed that the tap timing was modulated by the D sequence. A synchronizationcontinuation paradigm and two conditions (with or without auditory feedback) were used in the
second experiment. Different tempi for D sequences were approached during continuation tapping.
Participants first synchronized with the T sequence and continued their self-paced tapping after the
D sequence was stopped. Similar equipment and procedure similar to those in Experiment 1 were
used.
Tapping variability and of tempo deviation were increased. The negative correlation between
successive inter-tap intervals was eliminated and tapping period approached the period of D
sequence when they were closed to each other. These effects were independent of auditory
feedback.
Schmidt RC & O’Brien B (1997) studied the coordination between unintended persons by analysis
the cross-spectral of the movements of ten pairs of participants. They performed a simple pendulum
oscillation with comfortable rate in the same length and different length condition. Visual
information about each other’s movement in the first trial was available but not in the second trial
and no goal to coordinate for participants. The results showed an interpersonal synchrony and
particularly a higher coherence and a more dense distribution of relative phase angles near 0 o and
180 o and a greater tendency to entrain in the second trial half.
Richardson, Marsch, & Schmidt (2005) wanted to discover how the rhythmic limb movements of
coactors unintentionally are constraint by the visual and verbal action in two experiments. The first
experiment investigated the effect of conversational interaction on unintentional synchrony. Thirty
six participants were required to verbally identify as many differences as they could between two
cartoon pictures (puzzle task) and to swing a pendulum with their own self-selected tempo at the
same time. Three conditions were conducted: visual (looking at the picture positioned on each
other’s pendulum), visual-verbal (as in verbal + conversing for puzzle task), and verbal (conversing
and face away).
Higher coordination degree for visually coupled conditions was revealed by cross spectral analysis.
Verbal interaction was not found and did not enhance in combination with visual action. The absence
of influence of verbal action still even existed in the second experiment by longer trials and different
swinging times at begin.
Peters (1985) conducted two experiments for his postulation that a superordinate control
mechanism initiates action in subordinate control mechanisms, which in turn set the movement
trajectories in the two hands. The first experiment consists of three part experiment (1a, 1b, 1c). 20
left-handers and 12 right-handers were involved in 1a, 44 left-handers and 28 right-handers in 1b, 28
left-handers and 28 right-handers in 1c. Participants tapped on telegraph keys in 1a and on levers
35
mounted on microswitches in 1b and 1c. All subjects began by starting with single-hand
performances, three 10-sec trials each for each hand, alternating hands from trial to trial and
alternating the beginning hand from subject to subject. There are two task conditions: fast (F)/slow
(S) and 2:1 Rhythm for dual-task condition. Tapping as quickly as possible with one hand and keeping
a slow and regular beat of own choice with other hand were the combination for the first task
condition. There were four dual tasks: RS/LF; RF/LS; LS/RF; LF/RS. Performing two taps with one hand
against one with other hand with self-selected pace were the combination (L2:R1; R2:L1) for the
second task condition. Slow hand beginning and fast hand joining or fast hand beginning and slow
hand joining is the combination hand/task which is counterbalanced to reduced order effects. In
experiment 2 five musically sophisticated subjects were required to tap out the second of rhythms
with one hand while tapping slowly and regularly (beat) or as fast as possible with other hand.
Hand/task combination are RR/LB; RR/LF; LR/RB; LR/RF; LR; RR; LB; RB; LF; RF (RR/LH = right
rhythm/left beat, RR/LF = right rhythm/left fast, LR = single left rhythm, LB = single left beat, LF =
single left fast).
Both left- and right-handers performed the slow/fast dual task better when they commenced the
task with the fast rather than with the slow hand. For the right-handers performance asymmetry was
found, the right fast hand was superior in fast/slow task. In rhythm task for the right-handers the fast
right hand was significantly less variable and greater variability in the left hand was found. Focus of
attention on the nonpreferred hand was the interpretation for the performance asymmetries in
right-hands such that the right rhythm/left fast task was inferior to that of the left rhythm /right fast.
Movement intent is translated as immediately into action (superordinate) for the right hand and
hence the right hand has to vie for attention under right rhythm/left fast condition.
Miyake (2002) investigated the anticipation mechanism of timing control in sensorimotor
synchronization by using synchronization finger tapping between tap onset and periodic tone onset.
Four participants were instructed to adjust tap onsets to 110 stimulus onsets as precisely as possible
by pressing a button. Seven different ISI (interstimulus interval) (450, 1200, 2400, 3600, 4800, 6000,
and 7200 ms) were used. To clarify the relation between temporal integration (maximum capacity 3 s
window) and the emergence of consciousness selective attention was approached. Reading short
composition was used to control selective attention.
Mates (1994a, b) reported that isochronous sequences with ISIs up to 1800 ms resulted in
anticipatory responses and ISIs longer than 2400 ms in reactive responses. Miyake (2002) reported
that with attention and without attention stable anticipatory response was observed when ISI were
between 450 ms and 1200 ms. With attention the alternation between anticipatory and reactive
responses was typically in the sequences with ISIs between 2400 ms and 4800 ms and a stable
reactive response with ISIs beyond 6000 ms. However anticipatory responses disappeared in the
sequences with ISIs beyond 2400 ms and stable reactive response became dominant. The goal of this
study is to show that sensorimotor synchronization composed of two different dynamics (dualanticipation mechanism). One necessitates selective attention and it has long-range temporal
interaction, the other does not depend on the attention and its interaction range is short.
2.3.1.6 The effects of sensory information
To study the prediction of the temporal structure of events, Aschersleben & Prinz (1995)
instructed subjects to synchronize their finger taps with an isochronous sequence of signals (e. g
clicks). They confirmed the effect that the tap precedes the clicks described already more than 100
years ago (Dunlap 1910; Johnson 1898). Error between sensory feedback representation of the tap
36
and their central representation is suggested to be anticipated, i.e. in order to be perceived as being
in synchrony the establishment of the synchrony between tap and pacing signal has to be present at
the central representation level and the anticipated action effect determines the timing of an action.
Negative asynchrony arises due to differences in peripheral and central processing times, i.e.
differences in the nerve conduction time between click and tap and their corresponding central
representations (Fraisse 1980; Paillard 1949).
She continued to examine the "negative asynchrony". The subjects were instructed to use
different effectors for tapping (foot vs. hand) in experiment 1 and 2. Extrinsic auditory feedback was
added to the intrinsic tactile/kinaesthetic feedback in experiment 2. They also controlled whether
the results observed were due to purely sensory factors within the auditory modality in experiment
3. They argued that the tactile/kinaesthetic feedback representing the tap and the auditory guiding
signal represents the clicks are differently coded. These two central codes are different in code
generation due to different processing times and hence the asynchrony would be different..
In the first experiment 14 participants tapped with two effectors (index finger or big toe)
simultaneously in synchrony with 26 pace signals in two sessions. The first session contained three
subconditions horizontal (two hands or two feet), vertical (right hand and right foot or left hand and
left foot), and diagonal (left hand and right foot or right hand and left foot) and two tasks in each
subcondition differed in the pair of effectors involved. In the second session only one of the four
effectors was performed and hence the single condition contained four tasks.
As expected, a negative asynchrony was observed throughout. Foot showed larger absolute value
of the asynchrony. No effect of the effectors’ body side (left vs. right) or of the number of limbs
involved (single vs. coupled) and no difference in the size of the asynchrony between the three
coupling conditions were observed.
16 new participants tapped with the right index finger in the first session and with the right big
toe in the second one for the second experiment. Each session was subdivided into two blocks and
auditory feedback was provided in the second block. Asynchrony was observed as in experiment 1
and depended on effector. Asynchrony was smaller when additional auditory feedback was
presented.
In the experiment 3 subjects had to judge pairs of tones and were asked whether the signals had
been simultaneous or not. The SOA (Stimulus Onset Asynchrony) between the two signals, the 400Hz tone and the 2000-Hz tone, was varied in 20 steps ranging from -127 ms to +127 ms (±127, ±95,
±71, ±53, ±35, ±23, ±15, ±10, ±5, ±2). As in experiment 2, the interval between the 400-Hz signals was
800 ms.
The results showed that subjects were able to perceive very precisely whether or not the stimuli
used in experiment 2 appear simultaneously. There is no tendency to prefer one over the other
sequence of tones. Based on their results they suggested that tap-click synchronization took place at
the central level where theses two sensory codes were superimposed in time. To confirm again this
model the impact of peripheral nerve block and the effect of feedback delay were examined
(Aschersleben & Prinz 1997) (Aschersleben, Gehrke, & Prinz 2001).
The effect of feedback delay was examined in two experiments. Four delays (0. 30. 50, and 70 ms)
were applied in ascending order for the first experiment. In the first experiment 10 women and 10
men started to tap within the first 3 of the sequence of the pacing signals separated by 800 ms and
then tapped along with the signal as precisely as possible. In the second experiment 8 women and 4
men participated. The procedure was identical for both experiments. The set of four balanced orders
was used for the four delay conditions in the second experiment to prevent subjects from aware of
37
the delays. Both experiments revealed the same results. She found a linear relationship between the
size of delay and the asynchrony between the tap and click.
The impact of peripheral nerve block was realized by the elimination of tactile feedback (complete
anaesthesia of the moving index finger). As the first task nine participants were required to tap as
fast as possible with the index finger on a metal plate. Standard tapping, isometric tapping, and
contact-free tapping were performed for other three tasks. These three tasks required subjects to
tap in synchrony with an isochronous click sequence (400 Hz, interstimulus interval 800 ms). The
control conditions without peripheral nerve block were performed in the first session and tapping
tasks were tested under conditions with peripheral nerve block in the second session. The results
showed that timing was affected by anesthesia. Asynchrony was increased in standard and isometric
tapping under conditions with nerve block. Both amplitudes and forces (standard vs. isometric
tapping) showed no significant difference in the asynchronies.
Semjen, Schulze, & Vorberg (2000) extended and tested the Wing & Kristofferson model proposed
by Voberg & Wing (1994). They sketched a two-level model to synchronic tapping with a linear
feedback mechanism that corrects for the phase errors. A fixed proportion of the last synchronization
errors are subtracted from the timekeeper intervals. The next-to-the-last synchronization errors are
also taken into account (second-order correction). Twelve right-handed subjects tapped periodically
in synchrony with the clicks of the metronome and then without the metronome. Different rates
were used (period = 200, 240, 280, 320, 400, 480, 560, or 640 ms). The results showed that IRIs
(Interresponse intervals) tended to be shorter for the long interval range. The amount of anticipation
increased with target duration. Means and lag 0 to 3 serial auto-covariances of 30 interresponse
times (IRI) or asynchronies per series were calculated using the estimators given by Vorberg & Wing
(1996).These statistics were then averaged over the 16 trials for the combination (tempo * task
(synchronization vs. continuation) * subject). They concluded that as the prescribed interval
increased the first-order correction was more focused whereas the contribution of second order
correction was decreased.
2.3.1.7 Bimanual advantage in tapping (Helmut & Ivry 1996)
The reduced within-hand variability in studies of intertap intervals was reported when
participants tap with both hands, as opposed to single-handed tapping (Helmut & Ivry 1996;
Drewing, Hennings, & Aschersleben 2002, Drewing et al. 2004; Drewing & Aschersleben 2003).
Helmut & Ivry (1996) addressed performance on unimanual and bimanual tapping tasks in normal
individuals by comparing the within-hand variability and found that temporal variability was
consistently reduced during bimanual movements.
Participants typed the “ENTER” key on a standard keyboard in synchronization to a series of 12
50-ms tones separated by 400 ms and continued to maintain 32 target intervals. Three conditions
were conducted: right hand only, left hand only, and both hands at once. Furthermore,
nonhomologous actions and Between-Participants actions were performed. Flexion and extension of
the index finger for one limb and for other limb the forearm flexion and extension again were
involved in nonhomologous actions. Participants were tested in pairs for Between-Participants
actions. There were three conditions. Two conditions involved unimanual tapping. For one condition,
the participant on the right tapped alone. For the second unimanual condition, the participant on the
left tapped alone. The participant who was not tapping simply rested. In the third condition, the two
participants were asked to tap with the right index finger as in the unimanual condition and they
were instructed to watch the movements of their own and their partner’s finger.
38
The bimanual reduction was also found with nonhomologous movements involving the finger and
forearm. The bimanual advantage was found only when two movements were produced by a single
individual. They proposed separate timers for each hand whose outputs are then averaged.
2.3.1.7.1 The effects of central commands
The tapping performance of a person deafferented below the neck was compared with those of
age-matched controls (Drewing et al 2004). The deafferentation was due to a complete large sensory
fiber peripheral neuronopathy having an acute onset at age 19. The deafferented person lost all
cutaneous tactile and kinaesthetic sensibility below the neck. The participants tapped with their
index fingers either with the dominant hand or with both hands simultaneously on metal sensory
plates fixed to a wooden board. The same two-phase paradigm was approached (12 pace signals, 45
taps in synchronization phase). The results from controls still confirm bimanual advantage. The
deafferented person showed an even more pronounced bimanual advantage than controls. They
supposed the hypothesis that the integration of different central control signals relating to each
effector’s movements provides profits for bimanual timing.
Ivry, Keele, & Diener (1988) tested seven patients with focal lesions in the cerebellum on a
repetitive tapping task. Participants pressed with the impaired and unimpaired index fingers on a
microswitch mounted on a wooden block to a series of 12 tones presented at regular intervals of 550
ms and continued 31 taps at the same rate when the tone ended. They found that all patients have
more variable tapping with their impaired hand and suggested a dissociation of separable neural
systems responsible for rhythmic movements.
Figure 2-9: Multiple Timer Model. Timer1 and Timer 2 are generated for each hand during bimanual tapping
and assumed to be normally distributed. Independent signals are combined at the output gate and issued to
both hands simultaneously. (Taken from Ivry & Richardson 2002)
To account for the improved performance during multieffector tapping, Ivry & Richardson (2002)
proposed the multiple timer models rested on three assumptions. First separate signals are
generated for each effector with its independent temporal representations of the desired target
interval. These signals correspond to the clock signals in the Wing-Kristofferson model. Second there
exists a central gating process that provides the link between central control commands and the
motor periphery (Vorberg & Wing 1996) through which the two timing signals are averaged (Fig. 2-9).
Third the gating process not only initiates the responses of each effector but also triggers the next
timing cycle ensuring the temporal coupling despite the fact that they are associated with
independent timing elements.
39
2.3.1.7.2 The effects of sensory information
In this model sensory information of motor consequences are fed back to the level of timekeepers
in order to compensate for mismatches between the actual and the desired moments of the
execution of motor responses (Beek, Peper, & Daffertshofer 2000).
Drewing, Hennings, & Aschersleben (2002) tested the hypothesis that enhancement of sensory
reafferences might support the bimanual advantage. Additional to replication of the bimanual
advantage in tapping with two fingers of the same hand compared with single finger tapping they
reduced tactile-kinaesthetic reafferences stemming from the additional hand during bimanual
tapping and eliminated asynchrony by means of a firm mechanical coupling of the index fingers of
both hands. Reduction of reafferences was realized by three conditions: the left hand rested, tapped
contact-free or on a solid surface. The right hand always tapped on the solid surface. The
experimental protocol consists of synchronization phase and continuation phase. 12 pacing signal
were provided in synchronization phase and participants had to maintain the target ITI for further 45
taps.
The results demonstrated that the bimanual advantage decreased when tactile reafferences from
the left hand taps were omitted and the bimanual advantage replicated for the condition of
mechanical coupling.
Drewing & Aschersleben (2003) observed the within-hand variability of intertap intervals
(experiment 1) and examined the influence of additional sensory reafferences by adding and
removing auditory feedback to each tap (experiment 2). Participant synchronized their tapping with
their index finger(s) in synchrony with 12 pacing tones separated by 400 ms and continued to
maintain tapping with this target interval without the tones for 45 taps. Left hand only, right hand
only, and both hand were tapped synchronously. Tones were presented monaurally to the ear
ipsilateral to the tap in experiment 2.
Figure 2-10: Raw sketch of a reformulation of wing-Kristofferson model. The timer system provides the time
points for the goals of the action instead of those for triggering motor commands. The motor system plans
trajectories in accordance with the prescribed action goals. Action goals are communicated in terms of sensory
reafferences. An intertap interval Ij results from a timer interval Cj and the previous and the following errors of
the motor system, Mj-1 and Mj. (Taken from Drewing & Aschersleben 2003).
They found a reduction of timer variance when auditory feedback was added and the bimanual
advantage decreased when this feedback was removed. They assumed that the increase in sensory
reafferences in bimanual tapping is at least partly benefited. A reformulation of the WingKristofferson model (Fig. 2-10) was proposed wherein action goals were provided by the timer in
terms of sensory reafferences.
40
2.3.1.8 Error correction
In the context of synchronisation in tapping of a single hand with an external stimulus (Mates
1994a, b; Repp 2005) took in to account some correction errors. Thaut, Miller & Schauer (1998)
reported that a small step change of ISI (interstimulus interval) leaded to a rapid adaptation of the
tapping period but slow adaptation of the relative phase of the taps whereas a larger step change
leaded to initial period overshoot followed by rapid adaptation of both period and phase, To
replicate the results Repp (2001) instructed eight participants in one experiment to depress a white
key with the index finger of the preferred hand in synchrony with the sequences each of 22 tones.
The base IOI (inter onset interval) was 500 ms, each sequence contained a single step change in one
of possible positions ranging from 7th to the 16th tone in the sequence. All IOIs were longer or shorter
than the baseline IOI by a fixed amount. Participants were required to press one of three keys to
report whether there was a deceleration, acceleration or no change in tempo.
Repp (2001) separated in two error-corrective-mechanisms phase correction and period
correction which derived from two threshold hypotheses. To explain the results Repp (2001) used
the dual-process model of internal correction (Mates 1994b) with additional assumption that period
correction depends on conscious awareness of tempo change whereas phase correction does not.
The phase error correction in synchronization is limited by the temporal order discrimination
threshold which constrains perception of the synchronization error. The period error correction is
limited by an interval (or tempo) discrimination threshold that constrains the perception of changes
in stimulus interval duration (or tempo) and/or a mismatch between timekeeper and stimulus
periods. To strengthen this assumption there are three differences in the second experiment and
detection responses were required. 1) Participants were required to continue tapping for a while
after each sequence of tones was ceased. 2) The sequences terminated at various distances after a
step change. 3) Isochronous sequences with IOI durations corresponding to those following step
changes were included. On the whole he reported the success of the study.
2.3.2 DT condition
Discrete movements superimposed upon a periodic rhythmic movement are supposed to be
affected by phase entrainment (e.g. Elble, Higgins, & Hughes, 1994; Staude et al. 1995); i.e. the
timing of the discrete movement shows some dependence on the simultaneous execution of the
periodic movement. Beside the onset of the go-stimulus the onset of the single discrete motor
response is also affecteded by the ongoing periodic movement in the background. The onset
probability of the discrete response was modulated across the period of the rhythmic movement.
The initiation of the discrete movement is more likely when the periodic movement is in the same
direction with it and less probable in opposite direction. When subjects made fast, discrete elbow
flexion movements about initial and final visual targets and simultaneously produced rhythmical
oscillations only about the initial or the final target, an interdependence of time characteristics of
these two motor tasks was clearly observable (Adamovich, Levin, & Feldman 1994). The starting at
the same moment of the discrete agonist burst and the rhythmical burst caused the most likely onset
time and resulted in a smooth conjugation. The initiation of the discrete movement reset the phase
of the rhythmical movements.
An experimental paradigm requiring the execution of a discrete movement whilst performing a
periodic movement was reported by Yamanishi, Kawato, & Suzuki (1979), Yoshino et al. (2002),
Heuer & Klein (2005): A periodic tapping on one hand and a discrete response as reaction on other
hand were performed in a bimanual-task where the first one was in response to an external trigger
41
event. Observed data were analyzed under the concept of phase resetting according to limit cycle
oscillators (Winfree 1980) with the issue how is the impact of the execution of the discrete
movement on the rhythm of the periodic movement. Weak perturbations (Type 1) showed small
phase shifts of the ongoing rhythmic tapping movement and strong perturbations (Type 0) causes
large phase shifts similar to a restart of the current cycle were identified. Some subjects were able to
continue the self-paced tapping rhythm nearly without any phase shift whereas other subjects
typically showed the restart of their periodic tapping process by the discrete response and their
periodic tapping continues regularly as before the perturbation. Yoshino et al (2002) tried to clarify
the control mechanism of the internal neural clock assumed to exist (Wing & Kristofferson 1973b;
Ivry 1996). They continued the work of Yamanishi, Kawato, & Suzuki (1980) using multichannel
magnetoencephalography (MEG) to measure the neural activity during tapping. Based on the
temporal structure of MEG waveforms in response to the perturbation and the PRCs of the brain
activity they proposed a hypothetical block diagram of neural system that controls periodic right
index finger tapping and concurrent discrete left index finger tapping.
Figure 2-11: Definition of parameters and the evaluation method of a Phase Transition Curve (PTC). (Modified
from Yamanishi, Kawato, & Suzuki1979)
Figure 2-12: Two types of PTC. (a) Type The i-th cophase θi denotes the elapsed time of the i-th reference event
from the end of perturbation normalized by τ (Fig. 2-11)1; (b) Type 0. The abscissa is the old phase before
perturbation and the ordinate shows the new phase. (Modified from Yamanishi, Kawato, & Suzuki 1979)
42
2.3.2.1 Interaction in tapping (Yamanishi, Kawato, & Suzuki 1979)
Yamanishi, Kawato, & Suzuki (1979) studied the functional interaction between the neural
oscillator which is assumed to control finger tapping and the neural networks which control the same
tasks. Participants were instructed to perform a response task to the visual signal in combination
with the periodic tapping task. A disturbance of the periodic finger tapping caused by this response
task was studied by PTC which is explained by Fig. 2-11 and Fig.2-12. An arbitrary reference event
such as maximum of a physical quantity X projected by the internal state of the biological oscillator is
defined. Phase Ф is defined as t/τ where τ is the oscillation period and t is the actual time.
Perturbation of duration T is applied to the free running oscillator from the phase Ф – T/ τ to Ф. The
phase Ф when perturbation ends is called as the “old phase”. The ith delay бi and the ith new phase Ф’i
are defined as
бi = Ф + θi - i (mod1),
(2)
’
Ф i = Ф - бi(mod1)
Where the ith cophase θi denotes the elapsed time of the ith reference event from the end of
perturbation normalized by τ (Fig. 2-11).
The ith delay бi indicates how much the maximum of X are delayed (б i>0) or advanced (бi<0) by
perturbation. бi and Фi’ have their limit б and Ф’ after a long period because the period returns to its
stable state. Ф’ = Ф -  implies the new phase transited from the old phase by perturbation. If б i is
dependent on the actual phase Ф, then бi = f(Ф). Thus, бi(Ф) and Ф’(Ф) are called PRC and PTC,
respectively. Because both Ф and Ф’ are cyclic (i.e. mod 1), the PTC Ф’ (Ф) is biperiodic and Ф’ can
change only by an integral number while Ф changes from 0 to 1. The obtained PTCs are limited to the
two types. The one is the curve with an average slope of 1 (Type 1) and the other of zero (Type 0)
(Fig. 2-12).
Table 2.1: 10 experimental conditions for one session in the experiment 2. (Taken from Yamanishi, Kawato, &
Suzuki 1979)
In the experiments of Yamanishi, Kawato, & Suzuki (1979), tapping was performed on a key in
synchrony with the pacing signal and subjects had to learn the tapping interval to reproduce it 30
times without the pace signal after pace cessation. There were two experiments and three tasks
(voicing the “s” sound, pushing the key, discrimination of figures) for the response task. Four
participants had to perform three tasks in the first experiment and nine subjects had only the first
task in the second experiment. The pacing signal with a 1000 ms interval was used for the first
experiment. Parameters such as force pushing the key, tapping intervals and the choice of tapping
43
hand were varied in the second experiment. For the second experiment, a tapping interval was
chosen out of 1000, 700, 400, and 200 ms with a 20 g motor load. A motor load was chosen out of
200 and 800 g for the 1000 and 400 ms tapping intervals. Table 2.1 shows the conditions for one
session.
The PTC from the first experiment showed that interaction with key pushing was the largest and
with pattern recognition was the smallest. The second experiment showed that subject’s experience,
learning had effect on PTC. PTC tends to a Type 0 shape for short tapping intervals. Motor load and
alternation of tapping hand did not affect PTC. Although the perturbation caused the phase shifts,
the stability of the following tapping interval evoked the hypothesis that a neural network produce
stable periodic outputs controlling the finger and depend upon the strength of the interaction with
other network controlling key-pushing task.
Figure 2-13: The block diagram of the neural network which controls the finger tapping and the network which
controls the key-pushing response. Thin arrows indicate information flows within a neural network. Bold
arrows indicate information flows between both networks. Experimental design is bimanually. (Modified from
Yamanishi, Kawato, & Suzuki 1979)
Fig. 2-13 illustrates subsystems and information flows between them. They suggested that the
interaction between the tapping network and the key-pushing network is rather central as
interaction 2. Interaction 3 does not exist because the motor load had no effect. Functional
separation between the motor center of tapping and the motor center of the response has been
achieved for experienced subjects; i.e. the interaction 2 can be decreased step by step by learning.
2.3.2.2 Coupled oscillators
Yamanishi, Kawato, & Suzuki (1980) continued to investigate the assumption that for coordinated
finger movements the finger tapping by the left hand is controlled by one oscillatory neural network
and the finger tapping by the right hand by another oscillatory neural network. Subjects were
requested to tap their right hand or left hand on the key with one of 10 various phase differences in
synchrony with two pacing signals (1000 ms interval) with a constant phase difference presented for
the left and right hand, respectively, and learn these intervals. The phase differences were chosen
out of 10 steps such as 0, 100, 200 … 900 ms. After training, the pace signals were presented 10
times only, and subjects were asked to continue the tapping without pacing signals. The analysis was
based on systematic error and standard deviation of phase differences. Phase transition curves were
measured from previous tapping experiment and used to analyze the dynamical behavior of the
system model (Yamanishi, Kawato, & Suzuki 1979).
44
The results showed the performance of both hands finger tapping is better at the phase
difference 0.0 and 0.5 than that at other phase differences. They propose two coupled neural
oscillators as a model for the coordinated finger tapping and reported that prediction by the model is
in good agreement with the results of the experiments.
Another approach in the dynamical system approach evolving in the field of movement science
emphasizes on pattern formation and self-organization from synergetic, the interdisciplinary
approach to complex behaviour in physical, chemical and biological systems formulated by Haken
(Haken 1987, Haken et al. 1996). The qualitative changes in behaviour of the systems that are
composed of many individual parts mutually interacting in a nonlinear fashion are focused by
synergetic. For these qualitative changes of interest and hence for the pattern over which it is
defined, the collective variables or so-called order parameters can be mathematical identified.
The slower continuous change of variables, the so-called control parameters, associated with the
functional components of the system can lead to abrupt changes in the order parameters and affect
the stability properties of them. Points at which the rate of change is zero are called fixed points.
With appropriately chosen dynamical variables characterizing the state of the dynamical system, the
initial values uniquely specify the future evolution of the system in form of differential equations.
Once the fixed points have been reached, the state of the system no longer changes. The negative or
positive slope of the rate of change as it passes through the fixed point indicates that this is a fixedpoint attractor or a repeller respectively.
In a study of bimanual rhythmic movements (Kelso 1984) subjects were instructed to cycle the
hands at the wrist in the horizontal plane in an asymmetrical mode, i.e. one in which flexion
(extension) of one wrist was accompanied by extension (flexion) of the other. Subjects grasped a
handle with each hand. Potentiometers were mounted over the respective axis of motion. The
instruction to increase rate of cycling either in response to a verbal cue at 15-s intervals or by a
metronome with an interpulse interval that could be adjusted in 100 ms increments every 15 s. The
frequency of the metronome ranged from 1 to 5Hz. Resistive load was also applied in another
experiment by clamping the vertical rods leading to the potentiometers.
At a critical frequency, the antiphase pattern was abruptly abandoned and changed to the inphase pattern, i.e. homologous muscle groups were simultaneously activated. Only the in-phase
pattern could be stably performed when the frequency of the metronome was continue to be
increased. The hands move freely or are subject to resistive loading did not affect this critical
frequency.
Haken, Kelso, & Bunz (1985) identified the relative phase φ between the oscillating fingers as
order parameter due to its abrupt change and the frequency of the metronome as the control
parameter and adopted basic ideas from synergetic. The motion of the hands is assumed more or
less harmonic and takes the form
X1 = r1cos(ωt +φ1),
(3)
X2 = r2cos(ωt + φ2), (4)
Where ω is the basic frequency of the hand movement, r 1 , r 2 are amplitudes, φ1, φ2 time dependent
quantities. φ = φ2- φ1 is then the relative phase. A potential function V was specified such that the
differentiation (apostrophe expression) with respect to time of φ is
φ' = ӘV/Әφ,
(5)
The dynamic has to be 2  -periodic, the assumed symmetry between the both fingers leads to
the same transformation for φ = - φ The system is in equilibrium when the time-derivative of φ is
zero, i.e. the (local) minimum of V(φ) is a stable state or an attractor point, a (local) maximum of V(φ)
45
is an unstable state or a repeller point. Generally, when V’(φ)!= 0 is in an unstable state, the system
will be attracted towards (local) minimum. The following form of V(φ) was chosen to explain Kelso’s
experiment results:
V(φ) = - a cosφ - b cos2φ
(6)
Figure 2-14: The potential V for varying values of b/a given in each diagram in the upper right corner. The black
ball represents the stability of the antiphase coordination. (Modified from Haken, Kelso, & Bunz 1985)
Fig. 2-14 shows the shape of the potential layout changes as a function of b/a. As can be seen the
gradual change of the ratio b/a results in loss of stability of the antiphase coordination where φ = 
followed by a sudden transition of the system (black ball) to the stable state φ = 0. b/a functions as
the control parameter and hence describes the change in movement frequency.
Haken, Kelso, & Bunz (1985) derived the following complex system composed of the mechanical
motions of the hand generated with respect to the restoring and damping forces in f 1 and f 2 and the
coupling I 12 and I 21 between them.
X1’’ + f1 ( x1, x1’) = I12 ( x1, x1’, x2, x2’),
X2’’+ f2 ( x2, x2’) = I21 ( x2, x2’, x1, x1’),
(7)
(8)
2
Iij = ( xi’- xj’)( A + B(xi- xj) ), i = 1, 2; j = 1, 2,
(9)
The parameters a, b in equations (6) are related to the parameters A, B in equation (9) and to the
real amplitudes of the component oscillators. The so-called damping terms contained in f 1 and f 2
account for the injection of energy into the system or for the loss of energy. The component
oscillators will be self-sustaining and their long-term behaviour will be periodically stable with a
specific combination of these damping terms. For example with a Rayleigh term (of form βx ’3), the
amplitude of the oscillations decreases with frequency, and, with a Van de Pol term (of form γ x 2 x’)
the peak velocity increases with increasing frequency.
Aramaki et al. (2006) replicated the spontaneous transition from less stable antiphase patterns to
the more stable inphase pattern in bimanual finger tapping but conducted event-related functional
magnetic resonance imaging to depict the region of the brain in which cross talk occurs. They found
the interaction between the signals controlling each hand were prominent during the phase
transition.
46
2.3.2.3 Phase resetting on the simple limit-cycle oscillator (Yoshino et al. (2002; Winfree
1980 and other)
To understand how the rhythm of human periodic finger tapping is controlled by the existingassumed internal neural clock, Yoshino et al. (2002) instructed subjects to tap the left finger in
response to impulsive auditory cues. Periodic tapping was performed on the right hand. The
impulsive auditory signal as cues was presented randomly within the tapping cycle of the periodic
right hand at various phases. The paced and unpaced paradigm was used. Eight participants were
instructed to synchronize their right index finger tapping to 30 auditory pulses separated by 600 ms
and continue tapping at this rate after pace cessation. In response to an impulsive cue given
randomly (6.5 to 10 s) at various phases within the tapping cycle, participants executed single left
finger taps as rapidly as possible. They measured simultaneously the tapping movement and the
corresponding muscle activities with electromyography, responses of the neural activities with
magnetoencephalography (MEG). For the right index finger tapping response and for the left
sensorimotor cortex MEG response PRCs were established.
Figure 2-15: Left: PRC of MEG in response to single left index finger tap stimulations recorded from the left
sensorimotor cortex for a type-1 subject (subject 1). Magnitude of MEG response was gray-level-coded as in
the left panel of figure. Right: PRC of corresponding right index finger tapping. The abscissa is stimulation phase
Фstim; the ordinate is time (in s) with respect to right index finger tap just prior to left index finger tap. The left
index finger tap stimulation was applied along the line connecting (Фstim, time) = (0, 0) and (Фstim, time) = (1, N)
indicated by dashed line. Control tapping period was N ~ 0.6 s. Occurrences of right index finger taps is plotted
by the points, and their mean onset time within each set (1 ~ 10 divided according to the Фstim) is indicated by
an asterisk with an SD bar. Dotted lines above the dashed left tap stimulation line represent occurrences of
right index finger taps in the control case. (Taken from Yoshino et al. 2002)
47
Figure 2-16: Left: PRC of MEG in response to single left index finger taps recorded from left sensorimotor cortex
for a type-0 subject (subject 7). Right: PRC of corresponding right index finger tapping. For details, see Fig. 2-15
legend. (Taken from Yoshino et al. 2002)
Figure 2-17: Left: PRC of MEG in response to single left index finger taps recorded from left sensorimotor cortex
(subject 6). Right: PRC of corresponding right index finger tapping. For details, see Fig. 2-15 legend. This subject
showed a resetting pattern with little phase shift for Фstim < 0:5 and an obvious type-0 reset for Фstim > 0:5; the
latter led to the type-0 classification. (Taken from Yoshino et al. 2002)
48
Figure 2-18: Phase portraits of the limit-cycle oscillator model (Eq. 10). The phase of the state point on limit
cycle γ is defined as Ф = θ/2π [0, 1], where θ is counterclockwise angle from the positive X-axis. Impulsive
stimulation with intensity A translates a state point horizontally from C to D. Since limit cycle γ is stable, the
perturbed state point returns to γ. Thus, the phase is reset from Фstim to Фstim - Δ. a low stimulus intensity A =
0.5 and type-1 reset; b large stimulus intensity A = 1.5 and type-0 reset. The actual state point corresponding to
the stimulus onset is denoted as the stimulus phase Фstim. (Taken from Yoshino et al. 2002)
MEG data in response to stimulations were divided into ten sets according to Фstim. Set 1 consists
of data for Фstim Є [0, 0.1], set 2 for Фstim Є [0.1, 0.2]... set 10 for Фstim Є [0,9, 1.0], Each set includes
about 60 tap responses. Type-0 reset according to Winfree’s definition was shown in four out of
eight subjects, and the others showed type-1 reset (Fig. 2-15, 2-16, 2-17). They speculated a neural
pathway by which the left index finger tap system affects the periodic right index finger and results in
the phase reset. The following simple limit-cycle oscillator model in polar coordinates (r, θ) was
proposed
r . = Kr(1 – r2),
(10)
’
θ =ω
Where ω>0 represents angular velocity and K is a positive constant. A unit circle of the stable limit
cycle γ is formed at the origin O (Fig. 2-18) with natural period N = 2π/ω. Any trajectory deviated
from origin O moves asymptotically to γ as t -> ∞. The system’s state is defined by phase Ф = θ/2π Є
[0, 1] and the reference event of model occurs at Ф = 0, when the state point crosses the positive Xaxis.
A state point on γ will be pushed horizontally away (C->D) by the intensity (A) of the stimulus from
γ to γ’. The perturbed trajectory will return back to γ. The perturbed state point D behaves identically
to the point C’ on γ where γ intersects the line OD. The phase reset (delay) Δ can be written as a
function of Фstim as
Δ (Фstim) = Фstim - (1/2π)tan-1 (sin2π Фstim /(A + cos2πФstim))
(11)
Winfree (1980) started with the observation that most systems whose state vary in only one way
and have the same last state and first state can fall into either category of ring device that its rate of
advance through its cycle is conditioned by an external influence (intensity parameter I). The
instantaneous rate of change of phase is jointly determined by its instantaneous state (phase Ф) and
by the external influence parameter I.
Ф' = V(Ф, I);
49
’
Figure 2-19: (a) the angular velocity (vertically) as a function of phase (horizontally), using model Ф = 1 +
cos2πФ, with I = 0. The circle (left side) depicts by the length of the curved arrow the angular velocity at each
phase. (b) As in (a) but I = 1/2. (c) As in (a) but I = 1.5 past the bifurcation at I = 1. An attractor-repellor pair has
opened up from phase Ф = 1/2, inverting the angular velocity within that arc. Note that Winfree’s concept uses
a clockwise progressing of the system phase, whereas in Yoshino et al (2002) the usual behaviour is
counterclockwise progression. (Modified from Winfree 1980)
In standard environment with I =I0, the cycle is calibrated to define Ф’ = 1 (Fig. 2-19a). In any other
environment I  I0, the angular velocity of the ring device generally varies throughout its cycle (Fig. 219b). Thus, the ring device runs faster or slower, depending on its current phase (Ф(t)) as long as
exposed to I  I0. With sufficiently large I, V may even become negative during part of the cycle (Fig.
2-19c), then the phase “sticks” at the attracting stagnation point Фa but it will not start the next cycle
if there is no exogenous support to pass the repelling stagnation point Фr, e. g. by changing back I to
I0 for a while.
Investigating the collective behaviour of limit-cycle oscillators, Winfree discovered that collective
synchronization is a threshold phenomenon. By exceeding the critical coupling strength, some
oscillator transit to a common frequency (Ariaratnam & Strogatz 2000). Many other authors refined
the model with applications. Kuramoto’s model (1984) displays locked, partially locked, or incoherent
states, depending on chosen parameters. Ariaratnam & Strogatz (2000) discovered novel hybrid
states corresponding to various mixtures of locking, incoherence, and oscillator death (a cessation of
oscillation caused by excessively strong coupling).
2.3.2.4 Effect of periodic movement on discrete movement
Staude, Cong-Khac, & Wolf (2006) conducted experiments with subjects performing rapid finger
abduction movements during a secondary rhythmic adduction-adduction movement of the same
finger. Movements were either voluntarily produced by the subject or passively imposed by a torque
motor. The results confirmed the ability of one movement to constrain or even impede the execution
of the other due to gating process. Wachter et al. (2008) employed a dual-task condition as used by
Yoshino et al. (2002) and reported that one of four tapping behavior DTE (discrete tap entrainment)
is a directed effect of the periodic on the discrete process.
2.3.2.5 The effects of force (Loseby, Piek, & Barrett 2001)
Loseby, Piek, & Barrett (2001) investigated force requirement as variable which interact with
control variable (frequency) to produce a combined influence on stability of antiphase bimanual
finger tapping. Beginning with the right finger 10 right handed participants tapped their finger in an
50
alternating pattern. They should increase force on the right finger in response to a visual stimulus
and should maintain the required tapping speed. Three tapping rates were used. The phase relation
was affected by an increase of force at higher tapping rates (200 ms, 400 ms) but not at slower
tapping rate (600 ms). The results support the suggestion that force combined with the rate of
tapping to shift the critical point at which antiphase tapping becomes unstable. Force as a control
variable should be investigated.
Figure 2-20: The dynamics is decomposed into end-effectors X and neural units ξ. The latter are bilaterally
coupled via the function I and force (F) the end-effectors X. The state of X, in turn, is mapped to the ξ-level via
the feedback function G. (Modified from Beek, Peper, & Daffertshofer 2002)
2.3.2.6 The effects of sensory information (Beek, Peper, & Daffertshofer 2002)
Beek, Peper, & Daffertshofer (2002) tried to remedy the shortcomings of HKB-model (mentioned
in 2.2.1.3), namely the amplitude-frequency relation and the unclear correlation structure between
successive intervals, by proposing a more encompassing model. In the model there are two coupled
oscillators at the neural level. These oscillators are in turn coupled to a linearly damped oscillator
representing the corresponding end-effector (Fig. 2-20). The strengths of the original model in
describing the stability-related aspects of interlimb coordination were preserved while the effector
level has an effect on the neural level through a feedback function, and fluctuating forces are
included to account for phenomena such as critical fluctuations of the correlations between
successive intervals.
ξj" + ω2j ξj – Nj(ξj, ξj‘) = Gj(Xj) + Ijk(ξj, ξj‘, ξj, ξj")
{+ Гj(ξ)(t)}
(12)
’’
2
vXj + Ωj Xj + µjX’j = Fj(ξj)
( )
’
{+ Гj X (t)}
The indices j = 1, 2 and k = 1, 2 distinguish between the two coupled neural oscillators (left and
right), Ij is the coupling, Xj are the limb oscillations and ξj the neural oscillations; Fj(ξj) is driving force
of ξj on Xj; Gj (Xj) is feedback function and Гj(ξ)(t) fluctuating forces. The nonlinearities N generate selfsustained limit cycles for ξj.
2.3.2.7 The effects of attention (De Rugy & Sternad 2003)
De Rugy & Sternad (2003) performed a DT study with the focus on the effect of instruction. A
vertical wooden handle was affixed to the end of the forearm support and six subjects had to grasp
with their arm. They were instructed to oscillate (2Hz) their arm between two visible targets in
synchrony with an auditory metronome for 5s, one full cycle per beat. They had to continue
oscillating for between 2 and 5 s at the same frequency after metronome cessation.
51
Figure 2-21: Representative trial segments as an illustration of the three movements conditions. (Taken from
Rugy & Sternad 2003)
Three movement conditions in 4 different target arrangements were approached (Fig. 2-21): (1) a
shift in the midpoint of the oscillation (MID), (2) a change in the amplitude of the oscillation (AMP),
(3) both a shift in the midpoint and a change of the amplitude of the oscillation (MID+AMP). These
tree task conditions were performed under two timing instructions. The first, termed ‘‘reaction time’’
(RT) emphasized both the reaction time and the speed of the change, i.e. subjects had to react as fast
as possible but to maintain the required target hit. In the second instruction, termed “self-paced”
(SP) subjects could react whenever they felt most comfortable after the trigger signal but were
required to perform the change as fast as possible.
The results showed the similar tendency of synchronization between discrete and rhythmic
movements in all three tasks and instruction conditions but the synchronization was most
pronounced in the self-paced discrete movement.
2.4 Summary
Tapping performance was evaluated as reliable indicators of the integrity of brain functions in
clinical research. The Tapping Test discriminated between the control and brain-damaged groups at
high level of statistical significance (Dodrill 1978). Motor performance was declined when lithium was
introduced (Shaw et al. 1987). Patients with traumatic brain injury are slower on finger tapping
(Geldmacher & Hill 1997). Patients with Alzheimer's disease showed a greater right hand advantage
on FTT (Wefel, Hoyt, & Massama 1999). Patients with neurological soft signs demonstrated
significantly poorer motor speed and motor coordination in FTT (Flashman et al. 1996).
Information-processing theory and dynamic systems theory are approached in psychological and
physiological research. Periodic tapping encompasses continuous movement with and without
discrete events may involve different brain circuits (Delignières, Lemoine, & Torre 2004; Zelaznik,
Spencer, & Ivry 2002; Spencer, Ivry, & Zelaznik 2005; Spencer et al. 2003; Huys et al. 2008). The
lateral regions of the cerebellum are critical for an accurate internal timing function (Ivry, Keele, &
Diener (1988). For the production of an isochronous sequence of taps in the continuation phase after
the pacing stimulus is ceased, a time keeper generates motor commands in an interval C n without
any feedback or correction mechanism (Wing & Kristofferson 1973a, b, Wing 1980). The observable
taps are produced after a certain motor delay Dn (Fig.2-10). Both Cn and Dn are subject to random
fluctuations and assumed to be independent from each other. This open-loop model predicts
autocorrelations between -0.5 and 0 for successive intertap intervals, depending on the relative
contribution of the component variances. The model provides the indepence of Dn on the period
(Wing & Kristofferson 1973a). The regularity of C n is not based upon the comparison of successive
ones just as Schulze (1978) favored the internal timekeeper from experiments on the discrimination
of temporal intervals. Keele et al. (1989) supported the interval theory that the internal timer records
52
the intervals and this stored interval is reproduced and used in comparison.
Sensory input below the conscious detection threshold is still of use in controlling the timing
of motor control (Repp 2000). An auditory distractor sequence attracts the rhythmic movement
regardless of whether or not the taps generated auditory feedback (Repp 2006). Several experiments
showed the importance role of sensory feedback in timing control (Aschersleben & Prinz 1995, 1997;
Aschersleben, Gehrke, & Prinz 2001). Negative asynchrony let explained by different nerve
transmission times of the tap onsets and the audio signals (Paillard 1949; Fraisse 1980). Synchrony of
movements with sequence of events is established at the level of central representations
(Aschersleben 2002). Tactile, kinaesthetic, and auditory feedback are linearly integrated to form one
central representation (Mates & Aschersleben 2000). The internal timekeeper augmented by a firstorder feedback accounts well for the stochastic aspects of synchronisation performance (Semjen,
Schulze, & Vorberg 2000). If the sensory information is fed back to the level of the timekeeper, then
not only a compensation for the mismatches between the previous and the actual desired motor
responses for the individual finger but also between the two fingers in bimanual condition. Several
studies reported the bimanual advantage (Helmut & Ivry 1996; Drewing, Hennings, & Aschersleben
2002; Drewing & Aschersleben 2003; Drewing et al. 2004) in bimanual tapping in comparison to
unimanual tapping. On the one side, this advantage profits from the integration of different central
control signals related to each effector (Drewing et al. 2004; Ivry & Keller 1989; Ivry & Richardson
2002; Ivry & Hazeltine 1999). The sensory information of the other movements (Drewing, Hennings,
& Aschersleben 2002; Aschersleben & Prinz, 1995) and auditory feedback (Drewing & Aschersleben
2003) also contribute to the advantage. Mental tapping such as counting without voice together with
normal tapping on one hand do not require motor command for the second effector. Can C n be
improved by this additional mental task? The dynamical principles are even involved in natural
interpersonal synchrony (Schmidt & O’Brien 1997; Richardson, Marsh, & Schmidt 2005). Are these
dynamical principles also involved in individual without physical integration of different central
control signals? The discrete feature of timing behavior, the motor delay and the mismatches
between two hands are modified in isometric condition and in contact-free condition. The second
source of variance (Dn) is then modified or excluded in isometric tapping. The first source of variance
(Cn) is improved by sensory reafferences in tapping with contact and particularly in voice tapping but
impaired in isometric tapping and contact-free condition.
A system of coupled oscillators is outlined, which comprises two coupled limit cycle oscillators at
the neural level coupled with the ones representing the end-effectors (Beek, Peper, & Daffertshofer
2002). The initiation of voluntary movement was time-locked to the tremor cycle in patients with
moderate to severe essential tremor (Elble, Higgins, & Hughes 1994; Staude et al. 1995). One
movement constrains the execution of the other (Staude, Cong-Khac, & Wolf 2006, Wachter et al.
2008). The discrete movement affects the rhythm of the periodic movement (Yamanishi, Kawato, &
Suzuki 1979; Yoshino et al. 2002; Heuer & Klein 2005; Adamovich, Levin, & Feldman 1994). Winfree
(1980) approached the viewpoint of “phase resetting” according to definition of general limit cycle
oscillators. Phase Resetting Curve and Phase Response Curve provide crucial insights in many
researches (Burchard 1958; DeCoursey 1959; DeCoursey 1959; Pittendrigh & Bruce 1959). Phase
shifts of the ongoing rhythmic tapping movement were found. Haken emphasized timing structures
and self-organization mechanisms as described in synergetic (Haken 1987; Haken et al. 1996). The
phase of the luminescence was shifted by a manipulation of the dark and light periods (Hastings &
Sweeney 1958). Only two stable phase states (inphase and antiphase) between the hands are
present (Yamanishi, Kawato, & Suzuki 1980) and one attractor state migrates to the other at a critical
cycling frequency (Kelso 1984; Aramaki et al. 2006). The performance of bimanual tapping is better
53
at the phase difference 0.0 and 0.5 than that at other phase differences (Yamanishi, Kawato, &
Suzuki 1980). Movement force and rate interact to influence the outcome of the tapping pattern and
force as a control parameter is needed to investigate in further research (Loesby, Piek, &
Barrett2001).
High velocity movements towards the target provide perceptual information relevant to accuracy
in synchronization (Balasubramaniam, Wing, & Daffertshofer 2004). The postulated event-based
timing control could be restricted to a limited conditions characterized by the discrete events
(Delignières, Lemoine, & Torre 2004) and a continuous, dynamic timing mechanism is suggested from
the results of an oscillatory motion of the hand. Zelaznik, Spencer, & Ivry (2002) also supported the
hypothesis that there is an important distinction between the control processes associated with
timing tasks involving discrete and continuous events. This distinction is clearly presented in normal
tapping in comparison to isometric tapping and contact-free tapping. In ISI between 2400 and 4800
ms attention is needed for anticipatory responses but in ISIs up to 1200 ms attention is not needed
(Mates 1994a, b, Miyake 2002). Only intentional period correction seems to require attention,
whereas phase correction has an automatic component that cannot be suppressed (Repp & Keller
2004). Phase correction does not depend on awareness of tempo change (Repp 2001). Subconscious
mechanisms of action regulation (phase correction) and conscious processes (period correction) are
involved in perceptual judgment and action planning (Repp 2005). Continuous, dynamic timing
mechanism, event-based timing control, and trajectory contribution together with the role of
sensory feedback in timing control, the two stable phase states between the hands might still exist
but may be not expected to be 0 and 0.5. An asymmetric motor control in right-handers is suggested
due to additional oscillatory activity in the primary sensorimotor cortex ipsilateral to the tapping
hand (Pollok et al. 2005). The performance in dual task suffers when attention was focused on the
nonpreferred hand (Peters 1985). A specific focus on the constraints between the two movement
elements varied the synchronization (De Rugy & Sternad 2003). The role exchange between two
hands or the required focus on specific hand would also change the two stable states.
54
3 Literature review on eye blinks
description of this work, often original phrases were taken from the original papers without specially labeling
them, because mostly they are optimal with respect to information density.
The physiological basis of blinking is simple: two antagonistic muscles, the levator palpebrae
superioris (LPS) and orbicularis oculi (OO) muscles, participate in eyelid movements during blinking
(Evinger, Manning, & Sibony 1991; Esteban, Traba, & Prieto 2004); turning off the otherwise tonically
active LPS together with bursting OO activity causes a rapid lowering of the upper eyelid. The
opposite process with OO silence and LPS activity elevates the eyelid back to the upper position
(Esteban & Salinero 1979; Evinger 1995). (Tsubota et al. 1999). A distributed network built up by the
primary motor cortex, the visual cortex, the cingulate motor cortex, the posterior parietal cortex, the
dorsolateral prefontal cortex, the central thalamus and the cerebellum, which participate in
spontaneous as well as in voluntary and reflexive blinking, is active . Protection against corneal drying
mainly is guaranteed by the functional role of spontaneous blinking (Evinger et al. 2002) which is
avoided by an appropriate tear film distribution over its surface (Evinger 1995; VanderWerf et al.
2007). Spontaneous blinking rates reported so far differ: e.g., from 12/min by King & Michels (1957)
up to 24/min by Collins et al. (1989). All these reported blink rates, which are much higher than
required to keep the cornea moist, indicates the involvement of various processes different from
motor control in the blink mechanism,
Moisture is dispersed evenly across the surface of the eyeball and the eye surface of any debris is
cleared by the movement of the upper lid over the surface of the eye 3. An eye blink conditioning test
that can identify alcohol-exposed children who do not have distinctive fatal alcohol syndrome
features has been developed (Sandra W. Jacobson, of Wayne State University School of Medicine
(http://www.eurekalert.org/pub_releases/2008-02/ace-ebm012808.php). Pavlov I (1849 –1936, he
was a physiologist, psychologist, and physician) demonstrated a form of associative learning
procedure which involves presentations of a neutral stimulus along with a stimulus of some
significance. The neutral stimulus does not result in an overt behavioral response and is called
“conditioned stimulus” whereas the significant stimulus necessarily evokes an innate, often reflexive,
response and is called “unconditioned stimulus”. Eye blink conditioning (EBC) as a form of classical
conditioning has been used broadly for studying neural structures and the underlying memory and
learning mechanisms.
These mentioned factors mostly reflect cognitive (cortical) aspects, i.e., the central stage of
blinking control. Doughty (2001) supported the idea of blinks being controlled by a central
pacemaker residing in the basal ganglia. Providing further support for this view, Freudenthaler et al.
(2003) observed various blink patterns during video display terminal usage and presumed that at
least the frequency of homogenous blink patterns can be based on an endogenous pacemaker,
whereas heterogeneous patterns could also originate from a central pacemaker but being modulated
by internal as well as external factors. More solid ground to the central or endogenous control
explanation came from dopamine hypothesis of blink control assuming that spontaneous blinking
rate is a neurobiological measure of dopaminergic activity (Dreisbach et al. 2005; Taylor et al. 1999;
cf., van der Post et al. 2004). This supposed link between dopaminergic activity and spontaneous
blinking was confirmed in studies of different psychiatric and neurologic patients who suffered from
3
http://www.ehow.com/how-does_5245342_human-eye-blink_.html
55
consequences of the altered dopaminergic activity such as in Parkinson disease, schizophrenia,
depression, etc. (Karson 1983; Stevens & Livermore 1978; MacLean et al. 1985).
Besides these central factors in spontaneous blink control, peripheral factors exist as well: such as
damage of ocular surface (Tsubota et al. 1996), ocular anaesthesia (Collins et al. 1989; Nakamori et
al. 1997; Naase, Doughty, & Button 2005), presentation of sensory stimuli to eye surface (Nakamori
et al. 1997) and pharmacological substance effects (Dudinski, Finnin, & Reed 1983). Thus, a
controversy about dominance of central vs. peripheral factors in blink control evolved, which
stimulated a hypothesis on a basic central control being potentially also modulated by the peripheral
factors. If exogenous blink stimuli are eliminated (e.g., as a result of anesthesia or in constant
environmental conditions), then the central control should become dominant. Furthermore, if a
central blink generator with a stationary pace rate is responsible then the generator can determine
IBIs, which, however, can show some random fluctuations (Ponder & Kennedy 1927; Naase, Doughty,
& Button 2005).
3.1 Fluctuation of blink number during an interval (Greene 1986)
Greene (1986) recorded blinking rate of nine subjects (males, ages 18-22) over a 30-s time frame
during five different intervals each. During the observation period, the subject was engaged in a
question-and-answer type discussion with the interviewer in the format of a political poll.
Figure 3-1: A comparison of theory and experiment for nine subjects. Sampling time window is 30 s intervals.
Average blink rate for the group is 13.16 blinks per interval. Theoretical curve is the Poisson density with mean
of 13. Standard deviation of the theoretical density is ±3.61. Average interblink delay time is 2.28 + 1.35 s. A
continuous curve has been faired through the discrete density function for display purposes. The chi-squared
goodness of fit test yields significance at the 0.97 level for 15 data classes (13 degrees of freedom) and at the
0.94 significance for 12 classes (10 degrees of freedom). (Taken from Green 1986)
The blinking rate distribution was taken during active conversation (Fig. 3-1). Greene found that
the number of blinks per time interval and the delay time between blinks can fluctuate considerably
about the mean values and suggested that it indicates the need for the Poisson and exponential
distributions to describe the phenomenon. He assumed that the blinks are independent and the total
number of blinks over a given time period can be modeled using the Poisson probability density
56
function. He used the chi-squared test to compare the Gaussian and Poisson statistics with the
experimentally obtained, and confirmed the highly significant agreement.
3.2 Patterns of Blink Rate in Normal Subjects (Bentivoglio et al.
1997)
Bentivoglio et al. (1997) measured the normal blink rate (BR) variations in relation to behavioral
tasks of 150 healthy volunteers (70 males and 80 females; aged 35.9 f 17.9 years, range 5-87 years).
Figure 3-2: Relative frequency of blink rate values at rest (A), during conversation (B), and during reading (C)
are fitted with a log-normal distribution. (Taken from Bentivoglio et al. 1997)
57
Figure 3-3: Blink rate values in seven defined age groups during the three behavioural tasks considered.
Between-task differences are significant in each age group with the exception of conversation vs. rest at ages 514, 3544, and >65. (Taken from Bentivoglio et al. 1997)
Three videotape segments were recorded for 2 min 30 s in the following order: a) free
conversation b) reading aloud a passage that required mental and visual concentration c) quiet rest
with eye open. ANOVA and student’s tests were used for analysis. The data measured in the three
conditions (rest, reading, conversation) had similar distribution curves. Fig. 3-2 shows the best fit for
these distributions represented by a log-normal curve. Fig. 3-3 shows blink rate values in seven
defined age groups.
They found that 67% of the blink rate pattern conversation>rest>reading, 22.7% of
rest>conversation>reading and 8.0% conversation>reading>rest. The data measured in the three
experimental conditions had similar distribution curves. It was concluded that a log-normal curve was
the best curve fit for the data (Fig. 3-2), representing 70% of the measured values at rest and during
conversation and 80% of those measured while reading, and the remaining values being best fitted
with a normal curve. The idea that blink rate is modulated primarily by central mechanisms and
cognitive tasks and that local ocular conditions are of limited relevance is supported.
3.3 Stochastic models for spontaneous blink(Hoshino 1996)
Hoshino (1996) proposed one-dimensional stochastic diffusion models for spontaneous eye blink
and analyzed the blink burst during low vigilance. The models presumed the interblink interval
distribution to be first-passage-time probability densities of the Ornstein-Uhlenbeck process.
58
+
Figure 3-4: Interblink histograms and Ornstein-Uhlenbeck-first-passage-time densities (Taken from Hoshino
1996)
To estimate the parameters, he optically measured the behavior of upper eyelids with infrared
LED and CdS when the vigilance of the 6 subjects was high and low, and transformed into point series
of interblink intervals by peak-picking technique. Fig. 3-4 shows two examples of interblink
histograms fitted with the Ornstein-Uhlenbeck first passage-time probability distribution function.
The upper diagram represents the data during high vigilance and the lower during low vigilance. It is
assumed that the spontaneous interblink intervals are generated in accordance with a renewal
process formed by the first passage times Ts of a potential X of a virtual blink generator to a
threshold potential S. The blink potential is a one-dimensional diffusion process, the value of which is
reset to the resting potential X0 at the moment corresponding to the time of the previous response
generation. A good agreement with the model of the interblink distribution was confirmed.
3.4 Model for audiomotor integration (Bangert et al. 2006)
Bangert et al. (2006) performed an eye blink conditioning procedure to study the coupling of the
auditory and motor domains in non-musicians (NM) and professional pianists (PP). In the
conditioning procedure, a short airpuff against the cornea is used as the unconditioned stimulus (US),
and the eye blink represents the unconditioned reaction; a tone preceding the airpuff is used as the
conditioned stimulus (CS), and eye blink to the tone serves as the conditioned reaction. The
fundamental frequencies of the five different tones (c', d', e', f', g') were 1046.5 Hz, 1174.7 Hz,
1318.5 Hz, 1396.9 Hz, and 1568.0 Hz. The CS-US latencies were varied (200 ms, 400 ms, 800 ms, and
1000 ms). Pianists acquired an implicit knowledge of the organization of key-pitch associations on a
piano keyboard and may be considered as a conditioned reflex in itself with the tone as the
unconditioned stimulus. . As the unconditioned reaction the sensation of the tone, as the
conditioned stimulus the visual and tactile features of the keyboard, as the conditioned reaction the
mental image of the tone were considered. As a model for overlearned audiomotor integration
served 17 pianists, 14 non-musicians were instructed to respond to auditory stimuli (piano tones)
during the training session. Subjects performed keystrokes on a silent piano during a subsequent
testing session. About 70 random stimuli out of five tones used during auditory condition were
presented in the training session (session 0) to evaluate the eye blink baseline. In session of auditory
conditioning (session 1) 100 presentations of the randomized tones (ISI (Interstimulus Interval): 4000
 1500 ms) were used and a visual distractor stimulus with an ISI of 1300 ms was delivered. German
words for red, yellow, blue, white were colored by corresponding color and presented randomly
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(ISI=1300 ms) in a randomized sequence. Subjects had to count every colored word during a session,
but, depending on the background of the screen (dark grey or black), to attend either to the actual
color, or the semantic content of the words, respectively. This attention shift was prompted every 60
seconds. Session 2 was silent tapping requiring subjects to press the piano key without sound about
every 4-5 s.
Figure 3-5: Example of group-averaged eye blink signal for the Auditory Conditioning session (left) and the
Silent Tapping session (right). Example of a group-averaged time series of normalized event-related eye blink
signal for the Auditory Conditioning session (left) and the Silent Tapping session (right)(n = 9). Non-musicians
(NM) are depicted in the upper and professional pianists (PP) in the lower panel. The red curve is the response
to the target tone and the yellow curve is the response to nontarget tones. t = 0 (dashed line) refers to tone
(CS) onset or keystroke, respectively. The dotted line marks the onset of the airpuff (US, at t = 400 ms), but
note that only during the Auditory Conditioning the US was present. Peaks with a distance of more than
standard deviation from baseline (SD curves not shown) have been labelled T (twitches) and B (blinks),
followed by a number indicating the latency from event onset (e.g. "1" = 100 ms), or the relative latency from
US onset (subscript US for unified nomenclature despite varying CS-US latencies). NB: (1) In the average,
n(nontargets) = 4*n(target) applies. (2) In the PP Silent tapping condition, peaks received the labels T0 and Tus0
because the majority of the individual spikes coincided with the reference time (compare Fig. 3-6), although
the peak of the averaged time series appears at 100 ms offset. (Taken from Bangert et al. 2006)
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Figure 3-6: Peri-Stimulus Time Histograms. PSTHs of eye blinks (light green and light blue) and eyelid twitches
(dark green and dark blue) during the three experimental sessions (n = 5). Baseline session 0: left column;
Auditory Conditioning session 1: centre column; Silent Tapping session 2: right column. The histogram bins
were 40 ms wide; bars are stacked. The only condition with the US (airpuff) actually present have been
highlighted in green (Please note that the y-axis [events per bin] in these green panels have been scaled down
by a factor of 10 for display reasons, as the aversive stimulus generates a highly time-locked response in 100%
of the presentations, thus creating much higher event counts in the respective time bin). The nontarget
presentations (± 1, ± 2, ± 3, ± 4) have not been collapsed to one histogram, but have been ordered in four
different rows in the graph with respect to their perceptual 'distance' to the target, i.e. frequency distance in
the auditory session, and spatial distance on the piano keyboard in the motor session, respectively. The
category "± 1" designates the neighbouring key on the keyboard (to the left and right, respectively). The
maximum distance to the target within the 5-tone-space is ± 4. Non-musicians (NM) are depicted in the upper
5 rows and professional pianists (PP) in the lower five rows. t = 0 (dotted line) refers to tone (CS) onset or
keystroke, respectively. The dashed line marks the onset of the airpuff (US, at t = 200 ms). Note that only
during the Auditory Conditioning and only for the target tone the US was present (Green panels). Please note
the presence of the two twitch-related peaks during session 2 in the Pianist group. (Taken from Bangert et al.
2006)
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Figure 3-7: Overall Excitability (ratio of the number of trials containing a positive response to the total number
of trials). Excitability of eye blink events in the two experimental conditions. Red: NM group; Yellow: PP group.
(Taken from Bangert et al. 2006)
Figure 3-8: Sensitivity. Eye blink Sensitivity d' for the two groups in sessions 1 and 2. d' was high in the
conditioning session due to the presence of the US. In session 2, d' drops to a small value indicate no specificity
for the key related to the target tone. In any part of the experiment, no sensitivity difference between the
groups is observed. NB: The graph shows eye blink sensitivity only. Twitches, however, display an equally low d'
in both sessions 1 and 2. Red: NM group; Yellow: PP group. Inset: Correlation of d' with the US-CS delay in
session 1. A positive correlation is present in the non-musician group (upper panel, r = 0.8, p < 0.05) but not in
the musician group (lower panel, r = 0.4, p = n.s.). In session 2 (not shown), no positive correlation is found in
either group. (Taken from Bangert et al. 2006)
The results of the subject group with CS-US interval 400 ms are shown in Fig. 3-5 as an example
and entire dataset of the 200 ms group in Fig. 3-6 (the panels for the other CS-US delays are similar).
Ratio of the number of trials with positive response to the total number of trials was calculated and
shown in Fig. 3-7. The hit rates and the alarm rate are shown in Fig. 3-8. The silent tapping session
revealed a higher total likelihood of blinking in the pianist group, i.e. after classical conditioning to a
sensory stimulus involuntary reflex responses are elicited by a voluntary motor action, which through
long-term training is arbitrarily associated with the conditioning stimulus.
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3.5 Effect of mental task on eye blink rate (Karson et al. 1981)
Karson et al. (1981) studied how the spontaneous eye blink rate in 36 normal subjects are
affected by several mental tasks. Blink rates were measured in the following order: 1) casual
conversation with the examiner 2) silence 3) silence with simultaneous gum chewing 4) proverb
interpretation 5) memorization of paragraph 6) blink suppression 7) speeded blinking, and 8)
conversation with the examiner. 41 subjects were asked to read a handed card listing the proverb
and 4 lettered choices silently but say the letter of the correct interpretation. For the remainder of
the proverb task, the examiner read 5 proverbs aloud and asked the subject for an oral interpretation
of each. The examiner read a paragraph aloud, and the subject recounted it verbatim as best he
could. Subjects were asked to stop blinking as long as possible for suppression and to blink as fast as
possible for speeding.
Figure 3-9: Mean blink rate for each task. (Taken from Karson et al. 1981)
The mean blink rates measured during various tasks are given in Fig. 3-9. One-way ANOVA was
used for the overall comparison of the means. The results showed that tasks requiring speech and
listening to a paragraph to be memorized are associated with an increased whereas Reading with a
reduced blink rate.
3.6 Mapping cortical areas with functional MRI (Tsubota el al.
1999)
To examine the complicated process associated with vision-related functions Tsubota el al. (1999)
used Functional magnetic resonance imaging (fMRI) mapping cortical areas that control eye blink to
detect changes caused by focal variations in blood oxygenation.
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Figure 3-10: A conventional T1-weighted sagittal head image showing the orientation of the imaging plane and
the planned number of fMRI images (16 in the present case). Each image/slice was 7 mm thick and the slice
identification number is used to identify the corresponding axial fMRI image shown in Fig. 3-11. (Taken from
Tsubota et al. 1999)
Fig. 3-10 shows the conventional T1-weighted imaging of the head. Eight volunteers took part in
their study. Two of them were dry eye patients. (Eyes closed)- (blink or blink inhibition) as a two step
sequence in three cycles was contained in the experimental scheme. Participants lay supine on the
scanner bed and were asked to keep the eyes closed for 1 min (control phase) followed by blinking
for another minute (experimental phase), then the ‘same eye closed 1 min and blink 1 min’ repeated
once or more times. The investigator dictated the blink rate to the subjects. In inhibition phase
participants were asked not to blink or at most one blink within the 1-min interval if the inhibition
could not be sustained.
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Figure 3-11: A full series of fMRI images showing brain areas activated by normal blinking in the same volunteer
as in Fig. 3-10 (baseline : eyes closed). This series starts at top left (i.e., image No. 1), ending at bottom right
(i.e., image No. 16). The colorcoded P-map scale is shown on the right margin (in 10−n). Both the anterior
portion of the visual cortex (images Nos 5 and 6) and the orbitofrontal cortex (images Nos 15 and 16) showed
activation. (Taken from Tsubota et al. 1999)
65
Figure 3-12: Brain activation as a function of eye blinking of the same volunteer in Fig. 3-10 (baseline: eyes
closed and experimental: normal blinking). Top left image and bottom graph: orbitofrontal area activation (redyellow areas in image and  in graph); and top right and bottom graph: anterior visual cortex activation (redblue areas at bottom of image and  in the graph). The periodicity corresponding to the control and
experimental phases can be seen clearly in the orbitofrontal cortex although less obvious in the visual cortex.
(Taken from Tsubota et al. 1999)
Figure 3-13: Activation of orbitofrontal areas by normal blinking (right image and  in graph) and blinking
inhibition (left image and  in graph), both of which were compared with the baseline control of eyes closed.
(Taken from Tsubota et al. 1999)
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Table 3.1: Brain activation as a result of blinking inhibition with no light perception. (Taken from Tsubota et al.
1999)
Figure 3-14: fMRI images of the anterior portion of the visual cortex in a dry eye volunteer (Table 3.1, ID No. 8).
(Top): with (left) and without (right) blinking inhibition. Yellow rectangles are cursors showing corresponding
areas of interest in the visual cortex of the two images. (Bottom): After topical anaesthesia: with (left) and
without (right) blinking inhibition. Notice the visual cortex activation (left images: the hyperintense regions)
during blink inhibition (see also table 3.1). (Taken from Tsubota et al. 1999)
The three separate areas of activation (left, right, and central) contained in the orbitofrontal
cortex are shown in Fig. 3-11. An example of orbitofrontal and anterior visual cortex activation of the
same volunteer is shown in Fig. 3-12. The activity of central area is reduced while the activity of the
bilateral orbitofrontal areas is increased. Visual cortex activation was much larger with voluntary
blink inhibition (Fig. 3-14). Areas in the orbitofrontal cortex and in some cases, the primary visual
cortex and the visual cortex including the anterior portion of the visual cortex were activated by
normal blinking in all subjects. The visual vortex was strongly activated in dry eye patients when blink
was inhibited. Especially the blink rate appeared to be controlled in the orbitofrontal cortex. The
results strongly indicate that the orbitofrontal cortex is the primary site of blink control especially the
blink rate.
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3.7 The neural representation of temporal information (Ivry &
Spencer 2004)
Ivry & Spencer (2004) summarized recent investigation of temporal processing in a review. They
concluded that cerebellum is engaged during tasks requiring the precise representation of temporal
information such as sequence learning, rhythmic tapping, duration discrimination, phoneme
perception, and attentional anticipation.
Figure 3-15: Hypothesized gating operation of the basal ganglia as part of a decision making process. (a)
Potentiated cortical representations provide input to the basal ganglia. The output from the basal ganglia
reflects selected representations that have reached threshold. (From Gazzaniga, Ivry, & Mangun (2002), art
work by F Forney.) (b) The functional consequences of this gating process will depend on input–output circuitry
(Alexander & Crutcher 1990). For example, the motor loop will trigger overt movements, whereas the
prefrontal loop involves the updating of working memory. (Taken from Ivry & Spencer 2004)
Figure 3-16: Gating of activated representations through threshold adjustment. The green line represents the
activation signal that serves as an input to the basal ganglia. Drop-lines indicate time of gating for a particular
threshold setting (“DAant”,”Normal”. “DAag”) (a) Dopamine agonists lower the threshold, leading to the gating
operation being invoked with less activation. Dopamine antagonists raise the threshold. This mechanism can be
applied to understand the effects of dopamine depletion in Parkinson’s disease (PD) or the effects of
dopamine-based reinforcement. For the latter, reinforcement signals serve to lower thresholds, leading to
increased probability of an input reaching threshold in the future. (b) Tendency of PD patients to speed up
during unpaced finger tapping could result from short-term modulation of elevated thresholds. After each
output, the system resets and a new activation signal accrues for the next response. The gaps indicate that the
input to the gating mechanism might not be immediate, but builds up near the target time, reflecting activation
in upstream systems that determine onset time (e.g. cerebellum). Assuming that variation in the activation
function is random, gating will tend to occur earlier as the threshold is reduced over cycles. (Modified from Ivry
& Spencer 2004)
Imaging studies provided sufficient arguments and lesion studies stronger test. Increased
temporal variability is associated with lesions of the cerebellum. The recruiting of subregions within
the cerebellar cortex for timing was assumed. The basal ganglia operate as a threshold mechanism
and are an integral part of decision processes (Fig. 3-15). In the basal ganglia gated activations
reaching threshold are implemented. Threshold settings are modulated by dopamine which inputs to
the striatum (Fig. 3-16). The likelihood of reinforced actions to be implemented is increased by the
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lowered threshold even if the input patterns are unchanged. Dopamine agonists would lower
thresholds and dopamine antagonists would raise it.
Figure 3-17: Mean produced interval durations for group data as a function of ordinal position in each
condition averaged across the two slightly different duration ratios (integer and non-integer), and synchronise
and continue phases. Square and triangle symbols show the target intervals for the two rhythm conditions with
slightly differing ratios ((a) 321 and 858 ms (in ratio 1:2.7) or (b) 282 and 936 ms (1:3.3)). Diamond symbols
indicate the target interval for the isochronous condition. Error bars indicate the within group standard
deviation for producing each interval. (Taken from Lewis et al. 2004)
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Figure 3-18: Measures for accuracy for group data averaged across integer and non-integer, for both
synchronize and continue phases of the task. Error bars indicate the within group standard error of the mean
for each condition. (Taken from Lewis et al. 2004)
Figure 3-19: The mean coefficient of variation (CV) for group data averaged across integer and non-integer, for
both synchronize and continue phases of the task. Error bars indicate the within group standard error of the
mean for each condition. (Taken from Lewis et al. 2004)
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Figure 3-20: Functional activity in response to parametric modelling of initiate (red) and synchronise (blue)
phases with areas of overlap shown in green. Data was thresholded at P < 0.01. The slices shown were taken at
sagittal: −5, 10, 26, 34 mm; axial: 40, 50, 60, 70 mm; coronal: −33, −10, 7, 37 mm. The figure is in radiological
convention such that the L side corresponds to R and vice versa, the white dividing lines show the location of
the anterior commissure in some views, letters refer to specific structures: (A) SMA; (B) preSMA; (C) dPMC; (D)
DLPFC/dPMC; (E)DLPFC. (Taken from Lewis et al. 2004)
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Table 3.2: MNI coordinates for the highest local maxima of BOLD activity found in each functional area
associated with the synchronize > continue (A); continue> synchronize (B) contrasts. (Taken from Lewis et al.
2004)
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Table 3.2 (continued)
Columns show the coordinates in millimetres from the anterior commissure, the z-score value of each local
max, laterality, and an anatomical description of the point’s location on the SPM canonical brain. SFG: superior
frontal gyrus, SFS: superior frontal sulcus, MFG: middle frontal gyrus, MFS: middle frontal sulcus, IFG: inferior
frontal gyrus, IFS: inferior frontal sulcus, STG: superior temporal gyrus, TTG: transverse temporal gyrus, IIPCS:
inferior portion of inferior precentral sulcus, SSPCS: superior portion of superior precentral sulcus, ISPCS:
inferior portion of superior precentral sulcus, VVPCS ventral portion of ventral precentral sulcus, SOG: superior
occipital gyrus, MOG: middle occipital gyrus, IOG: inferior occipital gyrus. (Taken from Lewis et al. 2004)
3.8 How brain activity correlates with temporal complexity (Lewis
et al. 2004)
Lewis et al. (2004) searched for activity correlating with temporal complexity using fMRI
(functional magnetic resonance imaging). Images were thresholded using clusters determined by Zscore and cluster significance. Ten participants were involved in a production of temporal rhythms by
tapping with the right index finger on a force sensor. The task was divided into three phases: (1)
movement selection and initiation for an overlearned tapping, (2) synchronization of finger tapping
with an external auditory cue, and (3) continued tapping in absence of the auditory pacer. They
initially tapped in time with a sequence of auditory cues to identify it and then 18 s for
synchronization. They had to maintain their tapping accurately for 18 s after the cues were ceased.
The stimulus set comprised one isochronous pattern of repeating 500 ms and three set of multiinterval rhythms. In two further control conditions participants were required to maintain fixation
and to press a button in response to tones heard at two unpredictable intervals.
Fig. 3-17 shows the mean intervals in each rhythm produced by each subject during fMRI session.
Mean performance of target intervals was significantly better for isochronous sequences than for the
three rhythmic conditions (Fig. 3-18). The coefficient of variation (CV) for each interval was lower for
isochronous sequences than for the three rhythmic conditions (Fig. 3-19). Fig. 3-20 shows the
clusters of significant fMRI activity rendered onto the MNI (Montreal Neurological Institute)
canonical brain. The results showed the greater activation of bilateral Supplementary Motor Cortex
(SMA) and basal ganglia in continuation tapping than in synchronization tapping (Fig. 3-20 and table
3.2). Temporal complexity task revealed activity in bilateral supplementary and pre-supplementary
motor cortex (SMA and preSMA) during initiation phase, rostral dorsal premotor cortex (PMC), basal
ganglia, and dorsolateral prefrontal cortex (DLPFC), among other areas. Right primary motor cortex,
right DLPFC, ventral PMC and more caudal dorsal, and bilateral SMA showed correlated activity
during synchronization phase but not during continuation phase.
They suggested that during initiation phase selection of timing parameters is the reason for the
preSMA and rostral dorsal PMC activities while temporal error monitoring or correction for
centromedial prefrontal cortex during both initiation and synchronization phase. Further adjustment
of the timing control processes related to its continued production in absence of external cues might
be not needed after timed movement sequence has been overlearned. This resulted in the
significantly absence of activity during synchronization phase.
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3.9 The role of supplementary motor area in moving preparation
(Jenskin et al. 2000)
Using PTE (Positron Emission Tomography) scanning Jenkins et al. (2000) tested the postulation
about the responsibility of preparation preceding self-initiated and predictably cued movements for
equivalent levels of SMA (Supplementary Motor Area) activation in these two conditions. The
measure values of the distribution of radioactivity following the intravenous injection of the
positron-emitting tracer H2O was used as an index of relative regional cerebral blood flow (rCBF).
Statistical parametric mapping (SPM) was used to perform statistical analysis of rCBF images. Z-score
and cluster significance were approached to determine clusters for thresholding the images. Six
raised briskly the finger above a force touch sensor to made self-initiated right index finger
extensions and then returned the finger to the sensor in the first condition. An audible tone followed
the break of the contact. The extension movements were performed at random intervals between 2
and 7 s. Participants made the same movements of the generated intervals from the first condition
which were replayed in response to the tones. In the third condition subjects made no movements
after attending the same generated tones.
Figure 3-21: SPM projections of the sites of significantly increased rCBF for the comparison of self-initiated
movements with rest (threshold at P < 0.05 corrected for multiple comparisons). The activated areas are shown
projected onto single sagittal, coronal and transverse planes conforming to the stereotactic atlas of Talairach &
Tournoux (1988). For the purposes of illustration, the equivalent SPMs from the related study of Johanshahi et
al. (1995) (B) are shown alongside the data from the current study (A), demonstrating a very similar pattern of
activation in both. VPC = vertical line through the posterior commissure. (Taken from Jenkins et al. 2000)
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Table 3.3: Significant rCBF increases during self-initiated movement compared with rest. (Taken from Jenkins et
al. 2000)
Figure 3-22: SPM projections of the sites of significantly increased rCBF for the comparison of unpredictably
triggered movements with rest (threshold at P < 0.05 corrected for multiple comparisons). The activated areas
are shown projected onto single sagittal, coronal and transverse planes conforming to the stereotactic atlas of
Talairach & Tournoux (1988). For the purposes of illustration, the equivalent SPMs from the related study of
Johanshahi et al. (1995) (B) are shown alongside the data from the current study (A), illustrating that while
predictable triggering results in extensive activation of SMA and adjacent anterior cingulate cortex,
unpredictable triggering results in little activation of mesial frontal cortex while activation of contralateral
primary sensorimotor cortex and striatum is preserved. (Taken from Jenkins et al. 2000)
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Figure 3-23: SPM projections of the sites of significantly increased rCBF for the comparison of self-initiated
movements with triggered movements (threshold at P < 0.05 corrected for multiple comparisons). The
activated areas are shown projected onto single sagittal, coronal and transverse planes conforming to the
stereotactic atlas of Talairach & Tournoux (1988). For the purposes of illustration, the equivalent SPMs from
the related study of Johanshahi et al. (1995) (B) are shown alongside the data from the current study (A),
showing that when triggered movements are predictable, significant differences are limited to the DLPFC (in
this case only in the right hemisphere), but when they are unpredictable, differences are also found in mesial
frontal and parietal cortex. (Taken from Jenkins et al. 2000)
Figure 3-24: SPM projections of the sites of significantly increased rCBF for the triggered movements compared
with self-initiated movements (threshold at P < 0.05 corrected for multiple comparisons). The activated areas
are shown projected onto single sagittal, coronal and transverse planes conforming to the stereotactic atlas of
Talairach & Tournoux (1988). (Taken from Jenkins et al. 2000)
Table 3.4: significant rCBF increases during externally triggered movement compared with rest. (Taken from
Jenkins)
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Table 3.5: significant rCBF increases during self-initiated movement compared with externally triggered
movement. (Taken from Jenkins et al. 2000)
Foci of significant change in rCBF for comparison indicated in the table title. The coordinates in a standard
stereotactic space (Talairach & Tournoux 1988) are given in mm in x, y, and z for the maximally significant pixel
in each area, where x is the lateral displacement from the midline (- for left hemisphere); y is the
anteroposterior displacement relative to the anterior commisure (AC) (- posterior (PC) to this); and z is the
vertical position relative to the AC – PC line (- if below this). The vertical extent of each area activated (in mm
relative to the AC – PC line) is tabulated, and the level of significance is given by the Z score (where Z is the
standard deviation of the standard normal distribution). The mean percentage rCBF increase compared with
the rest is given for each area, measured at the pixel of maximal significance. L = left hemisphere; R = right
hemisphere; CMAr = rostral cingulated premotor area; CMAd = dorsal cingulated motor area.
Table 3.6: significant rCBF increases during externally triggered movement compared with self-initiated
movement. (Taken from Jenkins et al. 2000)
The axial extent of the areas activated, the peak Z scores and the percentage increases in rCBF are
given in table 3.3 for comparison of self-initiated versus rest, in table 3.4 for triggered versus rest, in
table 3.5 for self-initiated versus triggered, and in table 3.6 for triggered versus self-initiated. The
SPM projections of this comparison are shown in Fig. 3-21 for comparison of self- initiated versus
rest, in Fig. 3-22 for triggered versus rest, in Fig. 3-23 for self-initiated versus triggered, and in Fig. 324 for triggered versus self-initiated. The results showed that contralateral primary sensorimotor
cortex, caudal SMA and contralateral putamen were activated in unpredictably cued movements
compared with the rest condition. Rostral SMA, adjacent anterior cingulated cortex and bilateral
dorsolateral prefrontal cortex (DLPFC) additionally were activated in self-initiated movements
compared with cued movements. The primary role of rostral SMA in movement preparation and of
caudal SMA as motor executive area are suggested. DLPFC was activated only when decisions were
required about the timing of movements during the self-initiated task
3.10 Investigation of the daily pattern of eye-blink rate (Barbato et
al. 2000)
Barbato et al. (2000) assess blink rates of 24 healthy subjects at different times of the day.
Subjects were asked to sit silently in front of a blank, neutral wall. They recorded vertical and
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horizontal electro-oculograms (EOGs) on a polygraph. Subjects also performed a blink-suppression
test (BST) trying to avoid blinking for the longest time possible.
Table 3.7: Relationship between eye-blink rate (BR).and blink-suppression times (BST). BR: mean number of
blinks during two consecutive minutes. BST: time interval from the end of eye blink to the first eye movement
occurring during a blink suppression task. Blink suppression time was not available in the record of one of the
subjects. Rho: Significant negative correlations between eye-blink rates and ability to suppress eye blinks; P:
significant level (ANOVA). (Taken from Barbato et al. 2000)
Figure 3-25: Diurnal average profiles of blink rate blink suppression time, slow eye movements and Karolinska
Sleepiness Scale. (Taken from Barbato et al. 2000)
One-way ANOVA was used to assess the changes across time of blink parameters and sleepiness.
Blink rate increased significantly across the day (Fig. 3-25). No significant changes in eye-blink
suppression time and slow eye movements were found. A decreasing trend at the evening recording
time point was recognizable for eye-blink suppression. Significant negative correlations between eyeblink and ability to suppress eye blinks were found only at two time points (13.30 and 20.30 h) of
four time points examined (10.00, 17.00) (table 3.7).
The finding suggested an increase of central dopamine activity in the late evening. The
counteracting of the rising sleep drive against the dopamine-mediated activation is the possible
hypothesis reflected in the “forbidden zone for sleep”. In the evaluation of neuropsychological and
biological parameters, and in the choice of the drug treatment regimen the role of diurnal variation
of dopamine function should be taken into account.
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3.11 A brain Stem Reflex in Eye Blink (Evinger 1995)
Additional to transient lid movements by blinking, lid movements accompany eye movements
evolved so that the lid-closing muscle governs blinks and the lid-opening muscle determines lid
position. To avoid damage to the eye reflex blinks lowers the upper eye lid. Reflex blinks also occur in
response to drying to maintain tear film continuity on the cornea by spreading tears. Primates
produce spontaneous blinks in the absence of sensory stimuli. This blink rate is much higher than
that required to keep the cornea moist.
Figure 3-26: Movements of upper eyelid and their kinematics in humans. A: reflex blink evoked by electrical
stimulation of supraorbital branch of trigeminal nerve (▲, SO stim). Two bursts of orbicularis oculi
electromyogram (OOemg, RI and R2) activity rapidly lower upper eyelid, which rises slowly after end of OOemg
activity. Pos: position of upper eyelid; Vel: velocity of upper eyelid. B: family of upward and downward lid
saccades that accompany upward and downward saccadic eye movements. Unlike the case with blinks, up and
down maximum lid saccade velocities are nearly equal. C: maximum velocity the lid achieves as a function of
amplitude of lid movement for lid lowering during a blink (blink down) and with lid saccades (saccade down)
and lid raising during blinks (blink up) and with lid saccades (saccade up). (Taken from Barbato et al. 2000)
Blink amplitude and blink rate are changing dramatically with emotional state and cognitive tasks.
A task which requires vigilance reduces spontaneous blinking. Downward lid movements accompany
downward eye movements. Blinks exhibit a very rapid downward movement followed by a slower up
phase (Fig. 3-26A). For both upward and downward lid movements lid saccades reached in contrast
nearly equal velocities (Fig. 3-26B). For both down and up lid saccades and down and up phases of a
blink the relationship between the amplitude of the lid movement and the maximum velocity is
unique (Fig 3-26C). For blink up phases and upward lid saccades the best-fitting linear relationship
between maximum velocity and amplitude are virtually identical
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Figure 3-27: Interactions of passive downward forces and active contractions of lid-closing orbicularis oculi
(OO) and lid-raising levator palpebrae (LP) muscles in producing blinks and lid saccades. A: muscle and ligament
stretching that occurs when going from eyelid closed state (closed) to eyelid open state (open). B: lid position
(pos) and rectified EMG activity of levator palpebrae superioris and orbicularis oculi muscles with an up lid
saccade, a down lid saccade, and a blink. APO: aponeurosis of LP; PL: palpebral ligaments; STL: superior
transverse ligament. (Taken from Evinger 1995)
Force producing movement of the upper eyelid
The movements of the upper eyelid can be explained by the interaction of the following four forces:
1) the spring-like orbicularis oculi (OO) muscle wrapping around the upper and lower eyelids like a
purse string generates a downward force, 2) the levator palpebrae superioris (LP) muscle originating
at the back of the bony orbit exerts an upward force and inserts on the lower margin of the upper
eyelid, 3) stretching tendons and ligaments connected to the LP produces a passive downward force,
and 4) a smooth muscle bridging the belly of the LP and its tendon produces an upward force (Fig 327A). Energy state is lowest when eyelids are closed (Fig. 3-27A). Several structures having springlike properties are stretched by the contraction of LP when opening eyelids. The more LP contraction
the greater the stretching and the larger the passive downward forces created. A higher level of
tonic activity accords a burst of activity is produced by the LP (Fig. 3-27B).The lid is moved quickly to
a more elevated position by this burst and is hold in place against the increased downward forces by
the new tonic activity. Both the OO and LP participate in blinking (Fig. 3-27B). The 4.force acts on the
upper eyelid and produces very small effects on the blink or lid saccades.
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Figure 3-28: Orbicularis oculi EMG (OOemg) activity evoked by light flashes of different intensities and
durations in an alert albino rabbit. A: while stimulus duration was held constant at 25 ms, lights of 3 different
3
intensities were flashed in the eye. B: while intensity was held constant at 5 X 1 O /ft-L, lights of 3 different
durations were flashed in the rabbit’s eye. Each trace is mean of 5 rectified OOemg responses. (Taken from
Evinger 1995)
Figure 3-29: Self-Inhibition in human blink reflex. A: presenting 2 identical electrical stimuli to supraorbital
branch of trigeminal nerve (▲, SO Stim) with an inter-stimulus interval of 500 ms significantly reduces
magnitude of blink (lid pos) and orbicularis oculi EMG (OOemg) activity in response to 2nd stimulus relative to 1
st. B: magnitude of 2nd, test R2 component of the OOemg activity divided by magnitude of the 1 st, condition
R2 activity, as a function of time between condition and test stimuli for 3 normal human subjects. Even at 1 s,
OOemg activity remains reduced. Each point is mean of at least 5 trials. C: a patient with hemifacial spasm who
has lost much of his blink self-inhibition so that a voluntary blink initiates a spasm of blinks that holds lid closed.
Lid Pos: position of upper eyelid; Lid Vel: velocity of upper eyelid. (Taken from Evinger 1995)
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Blink reflex circuit in the brain
To create reflex blinks at least two parallel long and short circuits are involved. They exhibit
different physiological characteristics and produce distinct components of OO motoneuron activity.
The initial burst of OO motoneuron activity is generated by the short-latency circuit (Fig. 3-26A R1,
Fig. 3-28 initial response). A specific magnitude of OO motoneuron is produced by a stimulus. Even
though the well occurring of the entire response after completion of the stimulus, with duration
constant causes the increasing of stimulus amplitude larger responses from the short-latency circuit
(Fig. 3-28A). Even though the longer lasting of the stimulus than the short-latency circuit response
while leaving its intensity constant the increasing of stimulus duration produces no amplitude change
of the initial response. (Fig 3-28B). A larger response at a significantly longer latency in contrast is
produced by the long-latency circuit than the short one (Fig. 3-26A R2, Fig. 3-28, later response). The
response magnitude generated by the long-latency circuit is increased by lengthened stimulus
duration with hold constant amplitude but the change of the response of the short one is not
significant (Fig. 3-28B). Ensuing reflex blinks are suppressed (Fig. 3-29A) in a “refractory period”
which is initiated by blink evoked by wind rushes. The suppression process gradually recovers over a
1-to-2s period (Fig. 3-29B). The self-inhibition after each blink is the reason that a saccade of blinks is
not initiated. A spasm of lid closure can e simply produced by a blink when the nervous system loses
or reduces this self-inhibition.
3.12 The Blink Recovery Process in Patients with bell’s Palsy
(VanderWerf et al. 2007)
VanderWerf et al. (2007) examined whether the adaptive changes and normal kinematical
values of eyelid movement during blinking of patients with Bell’s palsy will be reached after recovery.
Figure 3-30: Simultaneously recorded OO-EMG activities, upper eyelid and eye movements during blinking in
one subject. (A) Superposition of six successive traces of voluntary blinks and their OO-EMG activities. (B)
Superposition of six successive traces of palsied corneal airpuff-induced blinks and their OO-EMG activities.
Lines 1 and 2: OO-EMG activities in the palsied and nonpalsied eyelid, respectively; lines 3 and 5: vertical
displacement; lines 4 and 6: the horizontal eyelid displacement at palsied and nonpalsied sides; lines 7 and 9:
represent the vertical eye displacement at palsied and nonpalsied sides; and lines 8 and 10: represent the
horizontal eye displacement at palsied and nonpalsied sides. Note the abnormal vertical displacement (lines 7
and 9) and the large horizontal displacement (lines 8 and 10) of the eyes during voluntary blinking. The bar in
front of lines 1 and 2 corresponds with a 200 µV OO-EMG signal. Vertical bar in front of lines 3 to 10
corresponds with a 50° rotation; duration bar: 100 ms. (Taken from VanderWerf et al. 2007)
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Table 3.8: OO-EMG and eyelid kinematics at four moments during recovery of voluntary and reflex blinking.
(Taken from VanderWerf et al. 2007)
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Figure 3-31: Profiles of six superimposed successive traces of simultaneous recorded eyelid and eye movement
during voluntary and reflex blinking measured from the same patient as in Fig. 3-30. (A) Eyelid movement
during voluntary blinking recorded at the onset of the affliction. (B, C) Eyelid and eye movement during
voluntary blinking recorded at 30 and 72 weeks, respectively. (D) Eyelid movement after a corneal airpuff on
the palsied side recorded at the onset of the affliction. (E, F) Eyelid and eye movement after a corneal airpuff
on the palsied side recorded at 30 at 72 weeks, respectively. Eyelid movement motility remained impaired in
both types of blinking throughout the study. Both eyes move in the same abnormal direction during voluntary
blinking, whereas in reflex blinking the direction of eye movement is normal after 72 weeks; however, the
amplitude on the palsied side remains smaller. (Taken from VanderWerf et al. 2007)
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Table 3.9: Differences in OO-EMG and eyelid kinematics between nonpalsied and palsied sides. (Taken from
VanderWerf et al. 2007)
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Table 3.10: Eye Movement Parameters after 30 and 72 Weeks of Recovery. (Taken from VanderWerf et al.
2007)
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Figure 3-32: Schematic representations of OO-EMG and eyelid kinematics during recovery. Shown are eyelid
movement start time (A), duration of the up phase (B), maximum amplitude (C), maximum velocity (D), and
time maximum velocity (E), along with the amplitude (F), the summed amplitudes (G), and the start time (H) of
the OO-EMG over the recovery time. Light blue: voluntary blinking, red: corneal air-puff–induced blinking on
the nonpalsied side; green: corneal air-puff–induced blinking on the palsied side; purple: acoustic-click–induced
blinking. Thick lines: values on the palsied side; thin lines: values on the nonpalsied side. In (A), (E), and (H),
values from voluntary blinking (light blue) are absent, as an exact start time of a “trigger” cannot be
determined for this type of blinking. (Taken from VanderWerf et al. 2007)
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Between all types of blinks a significant difference in eyelid kinematics is shown in Table 3.8. The
OO-EMG activity in the palsied eyelid was absent at the onset of the affliction. Both eye and eyelid
movements were disturbed. Within the first 18 weeks there was no recovery of eyelid movement on
the palsied side. A difference of eyelid motility (Fig. 3-30A, 3-30B, 3-30C, 3-30F) between eyelids was
found at the end of study. During voluntary blinking eye movements were still impaired (Fig. 3-31C).
The movement start times of both eyelids showed a clear time delay in the first 18 weeks (Fig. 332A, table 3.8). The start time of the nonpalsied eyelid was slightly shorted until 1 year. The start
times of both eyes were not significantly different.
In voluntary and air-puff conditions the prolongation of the downphase duration of the palsied
side was found. Except for acoustic condition the upphase duration of the nonpalsied eyelid was
always longer than that of the palsied one (Fig. 3-32B, table 3.8). On the palsied side the maximum
amplitude and velocity significantly increased at least 84 weeks (Fig. 3-32C, 3-32D, table 3.8). The
largest increase followed by a small continuous increase for the air-puff condition on the palsied side.
The time of maximum amplitude and velocity on the palsied side shortened after 18 weeks until the
end of the study (Fig. 3-32E, table 3.8). Voluntary blinks revealed the largest difference between the
downphase duration of palsied and nonpalsied eyelids at the end of study (table 3.9). The OO-EMG
of the palsied eyelid was continuously decreased in reflex blinks. In reflex blinking, after 1 year OOEMG of the palsied eyelid was reset and showed values similar to the OO-EMG of the nonpalsied
eyelid (Fig. 3-32F). Independent of the stimulation side the decrease of the integrated OO-EMG of
the nonpalsied eyelid was found until 18 weeks. Throughout the study the sum of the integrated OOEMG of both eyelids was almost constant (Fig. 3-32G). Until the end of study the start time of the
OO-EMG was measurable in reflex blinks (Fig. 3-32H, table 3.8). In all type of blinking, the maximum
amplitude of eye movements on the palsied side always remained two times smaller than that on the
nonpalsied side (table 3.10).
Throughout the study the velocities, the maximum amplitudes and the eyelid movement
remained smaller on the palsied side. During the study the impairment of eye movements in
voluntary condition is consistent and motility of eyelid is reduced. These indicate the altering of the
controlling higher brain structures caused by the affliction
3.13 Summary
The distribution of the temporal relationship between one stochastic process and the constant
periodic one can be mathematically figured out. If the interblink intervals are described by a
stochastic process (Greene 1986; Hoshino 1996; Bentivoglio et al. 1997) then it is interesting to
compare the observed phases describing the temporal relationship between tapping and blinking
with those observed for blinking without tapping. In musicians a tight coupling of motor domains and
the auditory even on sub-cogniive processing levels was suggested (Bangert et al. 2006). If there is
also a tight coupling of the rhythm in music cognition, then the tapping would elicit an eye blink
response more often than in non-musicians. During silence the blink rate was significantly lower than
during speech (Karson et al. 1981). How is the concurrency between the effects of speech and other
factors and the effects of tapping on blinking? The significance of visual cortex activation may be
associated with attention and arousal (Tsubota et al. 1999). The prePMC and rostral dorsal PMC
activities correlated with temporal complexity are absent during overlearned tapping (continuation
phase) (Lewis et al. 2004). Tapping with required timing involves attention and arousal. The basal
ganglia are an integral part of decision processes, operating as a threshold mechanism and dopamine
inputs to the striatum modulate threshold settings (Ivry & Spencer 2004). A rising sleep drive
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counteracting dopamine mediated activation may be reflected in the “forbidden zone for sleep”
(Barbato et al. 2000). Central dopaminergic activity indicated by dopamine gene polymorphisms and
spontaneous eye blink rate plays an important role in the modulation of this balance (Dreisbach et al.
2005). Correlated activity was observed in bilateral SMA, more caudal dorsal and ventral PMC, right
DLPFC and right primary motor cortex during synchronization of finger tapping with an external
auditory cue but not during continuation tapping. Increased activation of rostral supplementary
motor area and dorsolateral prefrontal cortex was found for endogenous blinks; similarly to hand
motor actions (Jenkins et al. 2000). I.e. there is a shared brain network for blinking and tapping and it
can create appropriate conditions for their coordination. Higher brain structure modifies eyelid and
eye movement control during blinking (VanderWerf et al. 2007). Spontaneous blinks can be good
markers of completion of cognitive tasks such as a solution of arithmetic problems (e.g. Evinger
1995). The representation of time for speech generation might be derived from an endogenous
timing process (or a pacemaker) linked to some type of counting device (Ivry & Richardson 2002). If
counting process during timing can be considered as such cognitive task then spontaneous blinks can
be good markers of its completion.
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4 Literature review on saccades and timing processes
description of this work, often original phrases were taken from the original papers without specifically labeling
them, because mostly they are optimal with respect to information density.
Visible parts of environment are registered by saccade and a cognitive map is built up. Saccades
are investigated in several ways such as towards a stimulus, anti-saccade, towards a remembered
point, etc. Pathophysiologic saccades are identified by deviation from a healthy condition such as
attention-deficit, hyperactivity disorder (ADHD), and Nystagmus4. Saccades are used in this study as
another motor output to investigate motor coordination in dual-task.
4.1 Visual saccade and memory processes (Claeys et al. 1999)
Claeys et al. (1999) studied parallel processes containing visual and memory information
processing. 30 healthy persons (13 males and 17 females, age from 18 to 83 years) were subjected to
six tasks: (1) the prosaccade, (2) the no-saccade, and (3) the antisaccade task, each in two conditions,
either (a) with or (b) without the random tap task. In the prosaccade task, the participants were
required to move eyes towards the cue as quickly as possible; in the antisaccade task, the saccade
had to be executed mirror-like in the opposite direction of the stimulus. In the no-saccade task,
subjects had to continue to fixate the center of the screen in spite of the appearance of a lateral
stimulus. In the random tap task, a tap had to be performed randomly at an unpredictable rhythm on
the zero key of the numeric keyboard by the dominant hand, at a rate of approximately one tap per
second. The aim of this “random time interval generation” (RIG) is to examine it as a secondary task
that interferes with executive function in dual-task studies, while keeping the load on other
processing systems to a minimum (Vandierendonck, De Vooght, & Van der Goten 1998). (RIG) task
requires that the intervals produced do not form a systematic and repetitive series.
4
http://saccadic.askdefine.com/
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Figure 4-1: Distribution of the number of errors made in the prosaccade (PS), the no-saccade (NS) and the
antisaccade task (AS) without and with tapping (median, 10-90 th percentiles). (Taken from Claeys et al. 1999)
Figure 4-2: Correlation between the difference in latency time and the difference in number of errors with and
without tapping in the antisaccade task (6 conditions*30 trials*30 stimuli; r = –0.43; n = 30; 95% confidence
interval -0.68 to –0.08; p =0.02). Because of the normal distribution of the latency times, the significance of
differences was assessed by the Student’s t-test. (Taken from Claeys et al. 1999)
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Table 4.1: Mean latency in msec and standard deviation in the prosaccade and the antisaccade task. (Taken
from Claeys et al. 1999)
The hypothesis is that an increased load to the central executive would decrease the ability to
inhibit prepotent saccades (prosaccade), where the random tap task is believed to be a pure central
executive task, i.e. this lead to a decreased performance in the antisaccade task, if the antisaccade
task would also be under central executive control because it is non-automatic activity. By
concurrent tapping a significant increasing of latency times was shown both in the prosaccade and
antisaccde task (table 4.1). The saccadic latencies in the antisaccade task both with and without
tapping were significantly longer than the latencies in the prosaccade task with and without tapping,
respectively. The number of saccadic errors was not significantly increased in all of the three saccade
tasks by the random tap task (Fig. 4-1). In the antisaccade task, subjects made significantly more
errors compared to the no-saccade and the prosaccade task both with and without tapping (Fig. 4-2).
Claeys et al (1999) concluded that the prosaccade task is brought under willed control of the central
executive because subjects were instructed to look as quickly as possible in the direction of the
stimulus. The antisaccade task is a more difficult task to perform than the no-saccade task. A more
sensitive parameter seems the latency to be than the number of errors.
4.2 Saccade with concurrent auditory task (Malmstrom, Reed, &
Weber 1983)
Divided attention and an opponent-process visual processing model were supported by
Malmstrom, Reed, & Weber (1983).
Figure 4-3: Flow diagram for sequence of primary visual tracking task and concurrent auditory task. (Taken
from Malmstrom, Reed, & Weber 1983)
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Figure 4-4: Representative raw data of a subject following jumpwise vertical target, frequency .5 Hz, concurrent
task difficulty 1.2. During task, subject misses approximately 4% of target movements. (Spikes are eye blinks).
(Taken from Malmstrom, Reed, & Weber 1983)
Figure 4-5: Representative raw data of a subject following jumpwise vertical target, frequency 1.0 Hz,
concurrent task difficulty 1.2. During task, subject misses approximately 56% of target movements. (Spikes are
eye blinks). (Taken from Malmstrom, Reed, & Weber 1983)
Figure 4-6: Mean saccadic eye movement magnitude plotted as a percent of target magnitude, concurrent task
difficulty, and task periods. (Taken from Malmstrom, Reed, & Weber 1983)
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Ten adult males either separately tracked the jumpwise target or identified dots and dashes that
they listened to on a headset by a thumb switch. A 10-s visual tracking practice period followed by 1
10-s auditory task dot-dash identification practice period was presented to participants (Fig. 4-3).
Subjects were instructed to visually track the jumpwise target only for the first 30-s period of actual
trial after practice. The concurrent task period then begun with the same visual target and the added
dot-dash task. At the end of the task period, the auditory task ceased, and the target continued its
motion for an additional 30 s visual tracking period. The concurrent task was presented at two
difficulties, DF .6 and DF 1.2. For the DF 1.2, there were approximately 36 tones per the 30-sec task
period (36/30 s = 1.2, SD = 2.1); for the DF .6, there were approximately 18 tones per the 30-sec task
period (18/30 s = .6, SD = .93). For both difficulty levels, the ratio was 1.4 dots to 1 dash.
Results showed both an elimination of discrete saccades and a shortening of eye movement paths
due to the concurrent auditory task (Fig. 4-4, 4-5, 4-6) and most important that there were joint
effects of the concurrent auditory task difficulty and the visual tracking task difficulty (frequency).
Dependent effects of the visual tracking task and the concurrent auditory task were confirmed. A
divided attention and an opponent-process visual processing model were supported.
4.3 Saccadic eye movement and manual control system (Megaw &
Armstrong (1973)
Megaw, & Armstrong (1973) tried to determine whether latency in a saccadic eye movement was
dependent on certain probabilistic properties of a random step input task, and to establish whether
any interaction existed between the eye movement and manual control system when performed
simultaneously. Six participants performed a discrete tracking task under conditions of separate and
simultaneous saccadic eye tracking and manual tracking in experiment 1. The target for step input
could arrive at one of 5 horizontal positions as a vertical line on a display oscilloscope. The interval
between each step input varied randomly among 1.5, 1.7, 1.9, and 2.1 sec. The subjects gripped with
their right hand a free-moving handle to control a follower line. Each subject was tested on 3
conditions: (1) only tracking the input with their eyes alone (2) position the follower line within the
current target area by operating a control handle (motor only), (3) control the follower line to travel
between adjacent target positions. The input characteristics of the third condition were the same as
for the eye-only condition and the travelling of the follower line were the same as in the motor-only
condition. Four other participants were tested on 3 different types of sequences involving only eye
tracking in experiment 2 to exclude the transfer effect to the eyes-only condition because all subject
had experienced all the 3 conditions. The significant dependence of eye latencies on direction
uncertainty might have reflected central facility by the motor system during simultaneous
performance. Each experimental run included blocks of trials, each block containing the possible
target steps in each direction.
They attempted to fit the set of observations by a simple linear model RTijkl= M + αAj+ βIi + Dk+ Bl +
Eijkl, where M = the mean effect; I = direction information (I i= -log 2Pi bits, where Pi = direction
probability); A = angle subtended by the target; D = direction of response; B = the effect of blocks,
and E = the residual error.
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Figure 4-7: Recording of simultaneous motor (displacement of the control lever and its acceleration) and eye
movements to a probable target step where angle of target presentation (A) = 20°. The recording delays of 22
ms, to the eye and acceleration traces have been removed. Over the whole experiment the corrected
acceleration RT (RTa) preceded the displacement RT (RTd) by an average of 39 ms, due to the relative
sensitivity of the accelerometer as a response device. (Taken from Megaw, & Armstrong 1973)
Figure 4-8: Reaction time as a function of angle of target presentation (A) where direction information (I) = 0,
based on mean estimates of α. (Taken from Megaw, & Armstrong 1973)
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Figure 4-9: Reaction time as a function of direction information (I) where angle of target presentation (A) =0,
based on mean estimates of β. (Taken from Megaw, & Armstrong 1973)
Figure 4-10: Examples of directional and anticipatory errors by the eye system. (Taken from Megaw, &
Armstrong 1973)
Figure 4-11: Stick diagram of the relationship between eye movement and initial acceleration phases of the
motor response during simultaneous performance. (Direction information [I] = .68 bits and angle of target
presentation [A] = 25°). (Taken from Megaw, & Armstrong 1973)
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Table 4.2: ten equiprobable target steps for experiment 2. (Taken from Megaw, & Armstrong 1973)
Table 4.3: estimates of standard errors (in milliseconds, df = 57) for each subject and for the four main sets
(RTe-Eo, RTe-Em, ) of reaction time data for experiment 1 based on the combined 2- and 3-factor interactions
(direction, blocks, and target steps represent the main factor) excluding the Target Steps * Direction term..
(Taken from Megaw, & Armstrong 1973)
Table 4.4: Estimates of response measurements at the four target angles obtained from the eyes-motor
condition of experiment 1. (Taken from Megaw, & Armstrong 1973)
Table 4.5: Means (in milliseconds), estimates of α (in milliseconds per degree) and β (in milliseconds per bit),
and standard errors for the four subjects and two conditions of experiment 2. (Taken from Megaw, &
Armstrong 1973)
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The accelerometer has a relative sensitivity and hence the corrected acceleration RT (RTa)
preceded the displacement RT (RTd) by an average of 39 msec. Four sets of data were analyzed, RTe
(relative latencies of the eye) from the eyes-only and eyes-motor conditions (RTe-Eo, RTe-Em) and
RTa (corrected acceleration RT) from the motor-only and eyes-motor conditions (RTa-Mo, RTa-Em).
Table 4.3 gives the estimates of the standard errors for each subject and for the 4 sets of data based
on the combined 1- and 3-factor interactions (direction*block*target step) excluding the Target
Steps * Direction term. Eye movement duration (Emt) and angle of eye movement (Ea), maximum
positive acceleration (Amax), time from the end of RTa to Amax (Am), time from Amax to zero
acceleration or maximum velocity (Ao), and the difference RTa – RTe (RTdiff) are summarized in table
4.4. Table 4.5 gives the results including estimates of α, β, and the standard errors. Fig. 4-7 illustrates
a typical response for a probable target step during the eyes-motor condition. Fig. 4-8 illustrates the
relationship between RT and angle of target presentation when (direction information) I = 0 based on
the estimates of α averaged over the 6 subjects, Fig. 4-9 the relationship between RT and direction
information based on the estimates of β when A = 0, and Fig. 4-11 the relationship between eye
movements and the initial acceleration phases of the motor responses based on the averaged
responses of the 6 subjects. Fig. 4-10 shows such a response where the eye nearly came to rest at 10o
and 30o before finally reaching the 40o target position. The result showed that both saccadic and
motor latencies were dependent on direction probability. The increase in mean saccadic latency
observed during simultaneous performance was not significant, i.e. the two systems are substantial
independent in information processing.
In experiment 2 four subjects who had not experienced manual tracking was tested on 3 different
types of sequences in eyes-only condition. Additional to the 5-position condition used in the first
experiment two other sequences were developed which used only 3 target positions, restraining
choice of response to the 2 remaining equiprobable target positions and reducing the number of
possible target steps in each direction from 10 to 3. The result confirmed the dependence of saccadic
latencies on direction information and further indicated that they were independent of extent
uncertainty in the manner shown by Megaw (1972) for motor latencies.
4.4 Cognitive load on saccadic eye movements (Stuyven et al.
2000)
Stuyven et al. (2000) studied the Random time Interval Generation (RIG) task on saccade latencies
and errors to test the hypothesis that antisaccades require controlled processing due to the
prepotent response in prosaccades that needs to be inhibited. The first possibility is that a central
component would involve the cognitive control over actions to be performed such as the inhibition
of a reflexive saccade, the start of a saccade, the creation of a random series of time intervals, the
second possibility is that motor interference would occur when eye movement and finger movement
are to be performed at the same time. To address these possibilities, a first experiment compared
saccade performance within prosaccade and the antisaccade task, executed alone and in
combination with the RIG task and fixed tapping task. The fixed tapping task was added to exclude
possible motor component interference explanations.
Saccade task was a between-subjects and load (control, fixed, RIG) a within-subjects variable in
the 2 saccade (prosaccade or antisaccade) *2 order of presentation (fixed-control-RIG and controlRIG-fixed)*3 load design. A white square appeared as the fixation point. To the left or to the right of
the fixation point another square of the same size appeared 70 mm indicating the direction in which
the saccade had to be made. Subjects were instructed to tap either an unpredictable sequence for
98
the RIG task, or at a fixed rate of one per second for the fixed tapping task on the computer key.
Possible errors are: (1) saccades in the wrong direction; (2) unperformed saccades; (3) reflexive
saccades in the antisaccade task.
Figure 4-12: Latencies in ms for prosaccades (PS) and antisaccades (AS) as a function of secondary task
conditions in experiment 1. (Taken from Stuyven et al. 2000)
The condition order had no effect on the latencies or on the number of error. No significant
interactions between saccade task and cognitive load and no significant difference between RIG and
fixed tapping were found. On the mean latencies, the main effect of saccade task was significant.
Prosaccades were faster than antisaccades. Load also showed significant effect. Task*load
interaction was significant. Fig. 4-12 clearly shows that this difference was smaller but significant in
the prosaccade than in the antisaccade task. The fixed tapping and the RIG task had comparable
effects on the eye movement latencies. The effect of load was significant in both prosaccade and
antisaccade task.
Table 4.6: Mean number of errors per condition in experiment 1. (Taken from Stuyven et al. 2000)
Table 4.6 shows the average number of errors in the prosaccade and antisaccade task under the
different secondary task conditions. More errors occurred in antisaccades than in prosaccades. The
effect of load and the task*load interaction were significant. The interaction of saccade task with the
contrast between fixed tapping and control was not reliable.
The larger impairment of antisaccade performance and the required executive control for
accuracy in fixed tapping would lead to possible cognitive interpretation. To test this hypothesis,
experiment 2 compared performance under strict instructions and more lenient instructions for fixed
tapping task on both prosaccades and antisaccades under single- and dual-task conditions. In the
lenient instructions were told to hit the key about 1 hit per second but it was stressed that small
deviations were not important (against a rate of 1 hit per second in strict instruction).
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Table 4.7: Mean number of errors per condition in Experiment 2. (Taken from Stuyven et al. 2000)
On the mean latencies, the load effect and of instruction load was significant for antisaccades
but not for prosaccades. The effect of fixed tapping under strict instructions was significant but not
under lenient ones. Table 4.7 presents the mean number of errors for the pro- and antisaccade task
under different instructions and under load and noload conditions. None of the effects were
significant.
A possible hypothesis is that the prosaccade execution also needs control rather than they are
automatic although they are faster. Experiment 3 was designed to show mere controlled execution,
without inhibition, is enough to obtain interference effects. Exogenous prosaccade (triggered by
stimulus) and a saccade task which can only performed in a controlled manner (without peripheral
stimulus) but an inhibition of prepotent saccades is not needed are two used tasks. The larger
peripheral stimuli and the smaller fixation point were used to increase the exogenous triggering of
the prosaccades. For the endogenous saccade task, this cross fixation point was replaced by an arrow
pointing to the left or right, indicating the direction of saccade to be made. Two very small squares
remained visible throughout the endogenous saccade to get comparable amplitude.
Figure 4-13: Latencies in ms for prosaccades (PS) and endogenous saccades (ES) as a function of load in
Experiment 3. (Taken from Stuyven et al. 2000)
Fig. 4-13 summarizes the main results. The endogenous saccades were significantly slower than
the prosaccades. The main effect of RIG load was significant however was much larger for the
endogenous saccades. Separately analyses revealed the significant effect of load on both saccade
tasks.
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4.5 Human head-eye coordination during tapping task (Herst,
Epelboim, & Steinman 2001)
The general principles underlying the way in which the head and eyes cooperate in the
performance of manual tasks is still far from understanding (Herst, Epelboim, & Steinman 2001).
They examined the ‘natural’ temporal coordination of head and eye of four subjects who tapped a
sequence of targets. This sequence was arranged in 3D on a worktable in front of them. The angular
separation of targets was random. Two criteria for a coordinated head/eye movements, were
examined: (1) the head and eye moved in the same direction; and (2) the horizontal components of
both the head and eye were larger than 10°.
Figure 4-14: Proportion (%) of coordinated head/eye movements in which the head led the eye, the head and
eye moved simultaneously and the eye led the head. The performance of each of the 4 subjects is shown
separately. (Taken from Herst, Epelboim, & Steinman 2001)
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Figure 4-15: (A) Distribution (%) of coordinated head/eye movements (7 ms bins) in which the head led the eye,
the head and eye moved simultaneously, and the eye led the head. Latency difference was calculated as headonset minus eye-onset, the convention introduced by Guitton & Volle (1987). The data were pooled across
subjects because individual differences were small (see Fig. 4-14). (B) Distribution (%) of coordinated head/eye
movements near (±20 ms) the temporal resolution limit, viz. 2 ms. This distribution shows when the head led
the eye, the head and eye moved simultaneously, and the eye led the head with respect to the smallest
temporal interval that could be measured. (Taken from Herst, Epelboim, & Steinman 2001)
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Figure 4-16: (A) Mean right gaze-shift-size (°) and standard deviations when gaze-shifts went to the right and
the head led the eye, the head and eye moved simultaneously, and the eye led the head. The performance of
each subject is shown separately. (B) Mean left gaze-shift-size and standard deviations when gaze-shifts went
to the left. See Fig. 4-14 for the color code of each subject. (Taken from Herst, Epelboim, & Steinman 2001)
Head and eye movements were considered to begin simultaneously if their onset occurred within
8 ms of each other. The results showed that that the head tended to start moving before the eyes 48
% of the time. Both the head and eye started to move ‘simultaneously’ 37% of the time. The eye
started to move before the head only 15% of the time. Fig. 4-14 summarizes the differences among
the three groups of proportions of coordinated head/eye movements which are all significant. Fig. 415A shows the distribution, Fig. 4-16A the distribution of gaze-shift sizes, Fig. 4-16B the distribution
of individual subject’s gaze-shift directions of the three types of coordinated head/eye movements:
the eye leading, eye and head starting simultaneously and head leading. Fig. 4-15B plots the
proportion of the data that fell near (±20ms) their temporal resolution limit (~2 ms). The finding that
the eye was least likely to lead in this tapping task is inconsistent with most prior work on human
head/eye coordination. More observations under natural conditions are needed before to
understand why when and how human beings coordinate head and eyes.
4.6 Distributed neural systems underlying the timekeeping (Rao et
al. 1997)
Rao et al. (1997) used the whole-brain functional magnetic resonance imaging to get more
knowledge of the neural systems underlying timekeeping operations of human in a simple tapping
task. A series of four consecutive activation conditions consisting of two experimental (S
(synchronization), C (continuation)) and two control tasks (L (listen), D (discrimination)), which were
preceded and followed by a rest period, was approached. Thirteen participants pressed a key by the
right index finger in time with a series of tones separated by 300 or 600 ms in the S phase and they
had to maintain the same tapping rate in the C phase. Participants attended the same pacing tone
but were instructed not to tap in the L phase. In the D phase, they listened the same series of tone
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pairs but with a transition in pitch and were instructed to press the key whenever a transition
occurred.
Figure 4-17: Mean (±SEM) inter-response interval (A) and total variability (B) as a function of pacing interval
(300 or 600 ms) and condition (Synchronization, Continuation). Total variability is expressed as SD. (Taken from
Rao et al. 1997)
Table 4.8: Activation foci as a function of task and pacing interval. (Taken from Rao et al. 1997)
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Figure 4-18: Areas demonstrating significantly increased MR signal intensity changes for each of the four
conditions (S, synchronization; C, continuation; L, listening; D, discrimination) and pacing tone intervals (300 or
600 msec) relative to rest. Functional activity (shown in color) is overlaid onto averaged axial anatomic scans
(right side of brain is on reader’s right). SMC, Sensorimotor cortex; STG, superior temporal gyrus; SMA,
supplementary motor area; IFG, inferior frontal gyrus; put., putamen; thal., thalamus; cer., cerebellum. z
indicates the number of millimeters above (+) or below (-) the anterior–posterior commissure line. (Taken from
Rao et al. 1997)
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Figure 4-19: A) Areas of increased MR signal intensity for the synchronization (S) and continuation (C)
conditions at two pacing tone intervals. The two sagittal slices are located 48 and 57 mm right of the
interhemispheric fissure. STG activation is observed in both conditions, despite the absence of a tone stimulus
in the C condition. IFG activation is observed only in the C condition. B) Areas of increased MR signal intensity
for the continuation (C) and discrimination (D) conditions at two pacing tone intervals. The sagittal slice is
located 3 mm left of the interhemispheric fissure. The horizontal green line indicates the intersection of the
anterior and posterior commissures (z = 0); the perpendicular vertical line crosses through the anterior
commissure (VAC line; y = 0). The functional activity for the C condition is located primarily within the SMA
proper (located posterior to the VAC line), whereas activity for the D condition is located largely within the preSMA region (anterior to the VAC line). (Taken from Rao et al. 1997)
Fig. 4-17 displays the reaction time from the S and C conditions of the tapping task. Table 4.8
shows the centers, volumes, and peak intensities of the activation foci, as well as the number of
subjects demonstrating significantly activated tissue within each foci. Within the left sensorimotor
cortex, the right cerebellum, and the right superior temporal gyrus the same activation was found in
both S and C conditions (Fig. 4-18, Fig.4-19). The caudal supplementary motor area (SMA), the left
putamen, and the left ventrolateral thalamus, the right inferior frontal gyrus were activated in the C
condition. The D condition activated the rostral caudal supplementary motor area. No activation was
observed in the left superior temporal gyrus (STG) for the L condition at the slower stimulus rate (600
ms interval) and the magnitude of activation within the STG produced less activation than conditions
requiring a sensory discrimination. They suggested that three interrelated neural systems were
involved. Putamen, ventrolateral thalamus, supplementary motor areas were involved in explicit
timing in C condition, dorsal dentate nucleus and sensorimotor cortex in sensorimotor processing
both in C and S conditions, and inferior frontal gyrus and superior temporal gyrus mediated auditory
sensory memory for discrimination task in D condition.
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4.7 Gaze effects on movement activation patterns (Baker,
Donoghue, & Sanes 1999)
Baker, Donoghue, & Sanes (1999) used functional magnetic resonance imaging (MRI) to
investigate the modification of gaze direction on the pattern of finger movement activation. With
the supinated right arm at the right body side laid eleven subjects supine. They either lay still or
tapped with the right hand on a tip of the thumb at a rate of two per second for 30 sec. In both
conditions they fixed gaze in one of three directions: leftward 10-15 o , central, or rightward 10-15 o
without rotating the head.
Figure 4-20: Cerebral cortical regions assessed and exemplar activation patterns. A, B, Color-coded illustration
of the cortical regions assessed for functional MR activation. MI in red, PMA in green, SMA in purple, SPL in
orange, and IPL in yellow. See Materials and Methods for additional details of sulcal and gyral landmarks
defining each region. VAC, Vertical plane through the anterior commissure perpendicular to a line between the
anterior and posterior commissures; VPC, vertical plane through the posterior commissure perpendicular to a
line between the anterior and posterior commissures. C, Functional MR labelling in two exemplars, slice
obtained from a single participant (slice planes indicated on whole brain volumes at right). The least activation
occurred for leftward gaze, whereas that for both central and rightward gaze exceeded that for leftward gaze.
The images with overlain label depict mostly portions of the left, contralateral hemisphere (L, left; R, right). Red
arrowhead indicates fundus of central sulcus (indicated by green lines). (Taken from Baker, Donoghue, & Sanes
1999)
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Figure 4-21: Functional activation. The number of activated voxels in each analysed brain region for each
direction of gaze. All areas exhibited the east amount of activation for leftward gaze. (Taken from Baker,
Figure 4-22: Spatial distribution for gaze-independent and gaze-dependent voxels. Exemplar activation patterns
from two participants (one slice each in left and right panels), illustrating the intermixing of gaze-independent
and gaze-dependent voxels across brain regions. Voxels indicated in white correspond to gaze-independent,
and those in black correspond to gaze-dependent. White triangle indicates the interparietal sulcus; white
triangle with a black outline indicates SMAc; black triangle with white outline indicates MIc. (Taken from Baker,
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Figure 4-23: Gaze-related MR signal intensity. The percentage increase in movement-related (vs no-movement)
in MR signal intensity is illustrated for MIc (A) and SMAc (B) for each of the gaze directions and for the two
classes of activated voxels; gaze-dependent and gaze-independent. No differences in MR signal were observed
for the gaze-independent voxels in either MIc or SMAc. By contrast, MR signal obtained from labelled voxels in
MIc and SMAc in increased for the gaze-dependent voxels when participants looked in a sector of visual space
that yielded more active voxels. (Taken from Baker, Donoghue, & Sanes 1999)
Table 4.9: Activation occurrence. (Taken from Baker, Donoghue, & Sanes 1999)
Table 4.10: Gaze direction exhibiting the greatest activation. (Taken from Baker, Donoghue, & Sanes 1999)
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Table 4.11: Areal congruence of the gaze direction effect. (Taken from Baker, Donoghue, & Sanes 1999)
The general location of these cerebral cortical regions is illustrated in Fig. 4-20A. Fig. 4-20B
depicts a single participant’s brain defined on two horizontal slices. One participant exhibited typical
functional MR labeling occurring in frontal and parietal cortex during finger movement and the
obtained distribution of functional activation is illustrated in Fig. 4-20C. For each cortical area the
labeled voxel counts are shown in Fig. 4-21. Two participants whose data indicated no clear
anatomic segregation of voxels with gaze-dependent activation from those with gaze-independent
activation in SMAc (contralateral Supplementary Motor Area) MIc (contralateral Motor Cortex),
PMAc (contralateral Premotor Cortex), or the parietal lobe areas examined are shown in Fig. 4-22.
For all subjects when participants directed gaze towards at least one location for labeled voxels
are yielded during finger movement in the left and to a less extent in the right hemisphere (table
4.9). During either rightward or central gaze the greatest amount of MR labels for MIc, MIi (ipsilateral
Motor Cortex), SMAc, and SPLc (contralateral superior parietal lobule) are exhibited (table 4.10). The
results showed that MRI signals from the hemisphere contralateral to the moving hand revealed
activation in primary motor cortex, lateral and medial premotor cortex, and a wide extend of the
lateral superior and inferior parietal lobules.
In summary, statistical significance was found in 9 of 10 of the paired comparisons between
contralateral cortical areas (table 4.11). Hand moving aligned with gaze increased the activation of
the related area compared to when gaze was directed away from the moving hand (Fig. 4-23). They
suggested that a large-scale of cortical networks related to finger actions exists, in which the
skeletomotor processing in the cerebral cortex is consistently modified by gaze direction.
4.8 Models for saccade generation circuitry (Girard & Berthoz
2005)
The computational models for the five main brain regions (Cerebellum, premotor cortical areas
(PMA), reticular formation saccadic burst generators (SBG), basal ganglia (BG), and superior colliculus
(SC)) was reviewed during saccade generation (Girard & Berthoz 2005).
4.8.1 Reticular formation saccadic burst generators
Activations generated by SBG are transmitted to horizontal and vertical ocular motoneurons.
Motoneurons have burst-tonic discharge pattern caused proportional amplitude of saccade. The
tonic neurons of two neural integrators (Moschovakis, Scudder, & Highstein 1996) providing these
tonic activities are located in the interstitial nucleus of Cajal (vertical integrator) and nucleus
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prepositus hypoglossi (horizontal integrator), in interaction with the vestibular nuclei. The burst of
activity are composed of a set of neuron classes having specific patterns of activity.
4.8.2 Superior colliculus
SC is a multilayered structure. The superficial layers receive direct retinal inputs. Their rostral
parts respond to visual stimuli close to the fovea and peripheral parts activate more caudal sites. The
activity of neurons of this layer follows a bell-shape tuning function centered on a preferred position.
The deeper layers called visuomotor exhibit small visual bursts before the motor bursts and usually
are not sufficient to generate a saccade.
4.8.3 Cerebellum
The cerebellum plays an important role in the integration of sensory perception, coordination and
motor control. There are many neural pathways linking the cerebellum with the cerebral motor
cortex which sends information to the muscles causing them to move and the spinocerebellar tract
which provides proprioceptive feedback on the position of the body in space. Using the constant
feedback the cerebellum integrates these pathway to fine-tune motor activity. The motoneuron
activity during saccades is significantly influenced by projections from the cerebellum. Hypermetria,
alteration of the main sequence and an increased variability of the amplitude are induced by the
damage or inactivation of the cerebellar afferent areas (Optican & Robinson 1980). Numerous
experimental results found that the lobules VIc and VII of the vermis are the areas of the cerebellar
cortex involved in saccade generation. Ipsilateral and topographically organized projections are
drawn towards a subpart of the caudal fastigial nuclei (a cerebellar central nuclei) called FOR
(Fastigial Oculomotor Area). FOR affects the saccade generation circuitry at the level of the saccade
generator.
4.8.4 Basal Ganglia
BG is a set of interconnected subcortial nuclei involved in large cortico-basal ganglia-thalamocortical loops which consist of motor, oculomotor, prefrontal (dorsolateral prefrontal and lateral
orbitofrontal) and limbic loops. These loops have similar internal connectivity but interact with
different cortical areas and brainstem nuclei. In saccade generation the oculomotor loop interacts
with the frontal eye fields (FEF) and the parietal posterior cortex (PPC) and projects to the SC, gating
the loci of activation on the collicular map. The prefrontal loop involving the dorsolateral prefrontal
cortex (DLPFC) enable the learning and restitution of sequences of saccades as it is involved in
working memory processes.
4.8.5 Premotor cortical areas
Many cortex areas are more or less implicated in the saccadic premotor activity. The posterior
parietal cortex (PPC) and lateral intraparietal area (LIP) modulate the stream of the cortical visual
processing by attentional processes for determination of location. The dorsolateral prefrontal cortex
(DLPFC) allows temporal organization of saccades and even saccade inhibition. The presupplementary eye fields (preSEF) projects to the supplementary eye fields (SEF) which can execute
saccades by sending saccade orders to the frontal eye fields (FEF). The FEF operates the final target
selection stage by interacting with the basal ganglia and sends the corresponding motor command to
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the SC and SBG. Finally, FEF, SEF and DLPFC receive projections from the anterior cingulate cortex
which provides them with motivational modulation.
4.9 Gaze and Hand Position Effects on Brain Activation (Bedard
& Sanes 2009)
Bedard & Sanes (2009) investigated the integration of the gaze and hand signals to generate
movements. They hypothesized that the involvement of parietal, frontal and subcortical regions
exhibit modulation of movements related activation as a function of gaze and hand positions. 15
healthy adults (aged 19-34 yr; 8 females, 7 males, all right-handed) were recruited in 6 runs of
scanning using functional MRI. During each run, participants has to fix gaze at one of three visual
targets (left, center, right of their body and only one annulus was visible) presented randomly. By
turning of the annulus center from white to black with frequency of 3Hz participants tapped thrice
with their right thumb for each gaze position. The right arm was fully extended and half-pronated
beside participant’s right side or the arm crossed the body midline in midflexion so that the right
hand became aligned with the left shoulder. A two-way ANOVA (2 hand positions * 2 gaze positions)
with repeated measures and the t-test revealed RT were significantly slower when gazing right with
the hand on the left. Several areas (sensory motor cortex, supramarginal gyrus, superior parietal
lobule, superior frontal gyrus, anterior cingulate, and left cerebellum) that exhibited activation
related to a mixture of these hand and gaze positions. Activation driven only by gaze orientation
were found in regions with the left insula, left cuneus, left midcingulate gyrus, left putamen, and
right tempo-occipital. The main effect of gaze position was significantly revealed by the ANOVA in
clusters exhibiting finger movement-related activation (contralateral primary motor cortex,
supplementary motor area, cerebellum, anterior cingulated gyrus and right supramarginal gyrus).
Clusters with hand position effects were also found in the cerebellum bilaterally. They suggested that
processing is superimposed specific to goal-directed movements based on the baseline conditions of
this study and a multiplicity of frames of references is used for movements.
4.10 Coordination of ocular and hand motor systems (Bekkering et
al. 1994)
Bekkering et al. (1994) measured reaction time (RT) latencies of saccade eye and hand motor
responses using dual-task methodology to investigate whether the both movements share processes.
When RT latencies in the dual-task conditions do not change the independent mediating of both
responses is assumed. In contrast, the two movements share processes when the RT latencies differ
from each other. In experiment 1 they required 12 right-hand students to move (eyes alone, hand
alone, or eyes and hand concurrently (dual)) as quickly as possible to a large target stimulus
appearing randomly either to the left or right of a central fixation point. An ANOVA yielded a
significant effect for task condition and a paired two-tailed t-test indicated that RT of the eyes was
significantly longer in the duals-task condition than in the single-task condition.
To discriminate between the two interpretations that the consequence of being required to
produce any two responses, or the sharing of specific processes in the context of pointing to a target
they manipulated the nature of the manual response in experiment 2. Instead of a goal-directed
hand movement to the target stimulus, subjects had to make a button-press response with either the
index (for the left target) or the middle finger (for the right target) of the right hand. They argued
that if the interference effect disappears, support is found for a specific interference effect caused by
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a sharing of processes associated with the control of coordinated aimed eye and hand movements.
The results showed that RT latencies of the eyes were significantly shorter than the RT latencies of
the finger. The most important result of experiment 2 was the absence of a significant interaction
effect between type of movement (eye vs. finger) and task condition (single vs. dual). The specific
interference interpretation caused by a sharing of processes associated with the control of the
coordinated aimed eye and hand movements is supported, i.e. the overhead costs due to
coordinating any two responses are excluded.
4.11 Theoordination of saccadic and manual movements (Binsted
& Elliot 1999)
Binsted & Elliott (1999) conducted two experiments to identify the type of information that
mediates any eye-hand coordination. They used a point-light array to generate Müller-Lyer
configuration target endpoints (in-Müller, out-Müller, ‘X’) for 30 cm aiming movements. The
endpoint was a 37 LED ‘X’ generated by the intersection of perpendicular lines. The “in” and “out”
Müller-lyer figures were established by illumination of ipsilateral arms of “X” figure. Vision of the
hand was removed by employing liquid crystal occlusion goggles. Manual movements were
measured by an IRED (infrared–emitting diode) attached to the participant’s right index finger. Eye
movements were measured by an Applied Science Laboratories, series 4000-SU HMO, and headmounted eye-tracking system.
The level of dependence of the manual system on selected channels of ocular/visual information
was examined in experiment 1. Eleven students were asked to move as quickly as possible from the
home position to the centre point of the presented figure. The home position and endpoint figure
were positioned 30 cm apart. The home position consisted of a 2-cm-diameter button.
The retinal and extraretinal information were manipulated by various vision of the limb and
target, eye position, and the concurrent of eye movement. In full information condition (FULL)
concurrent eye and hand movements presumably were made using all available information
normally presented for eye-hand coordination. In the NOEFF no concurrent eye efference5 was
available for modulating hand movements. The no-vision conditions (NOVIS) allowed neither
concurrent eye movement nor vision of the hand in motion. Vision was returned following the
completion of each trial.
Correlations were calculated between: (1) end location of the primary eye and hand movements,
(2) end location of the eye of primary saccade and at final position of the hand, (3) eye reaction time
and hand reaction time, (4) total time of the eye to reach the end of the primary saccade with total
time of hand to peak velocity, and (5) time to complete the primary saccade with hand time to peak
velocity (reaction times removed). Single sample t-test was used to obtain significant effects. On eye
movements a significant effect of endpoint configuration was found for the primary saccade and the
final location of the eye after corrective6 saccades. In hand movements the main effect for vision
revealed participants moved significantly less distance in the NOVIS trials than in either FULL or
NOEFF situations. Hand movements variability increased significantly when vision of the hand was
removed. In eye-hand coordination the transformed z-scores for eye-hand coordination revealed a
significant relation between eye and hand reaction time. However, when reaction was removed, this
5
The terms ‘efference’ and ‘efferent information’ are intended to include efferent copy, corollary discharge, and
movement intention/programing information.
6
Inaccuracies in the initial impulse visually detected during the final portion of movement are corrected
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correlation approached zero. Neither the correlation between the displacements of the primary
movements of the eye and hand nor those for the final displacements reached significant level.
To completely examine the illusory implications for efferent theory and eye-hand coordination
they reproduced the earlier findings by replicating specific procedural practices used by those
researchers (Elliot & Lee 1995; Gentilucci et al. 1996) who enabled subjects the free viewing of the
illusion endpoint prior to and during the planning phases of a response. In experiment 2 participants
were explicitly asked to place the end of the pointer over the centre point of the LED. They believed
that this instructional adaptation decreased the goal tolerance to successfully reach the target. Hand
initiation can turn off the LED display by an additional switch attached to the home position (TAR <->
NOTAR).
All forms of extraretinal feedback and feedforward were available as in experiment 1 for the FULL
condition. However, these information sources were biased when the illusory endpoints were
present. In biased proprioception conditions (BIAS) due to illusory endpoint concurrent efference
was removed, eye position was incorrect on selected trials. In non-biased proprioception (UNBIAS)
condition, concurrent efference was removed and the eye attained a veridical position prior to each
trial.
Again all levels of endpoint configuration were significantly different from each other on eye
movements. The results also reflected that the hand movements were far slower than eye
movements, the eye failed to overcome its predictable undershooting tendency prior to hand
movement completion. The undershooting was increased when the eye was to maintain a fixation
without a visual target. The interaction between endpoint and eye position at hand completion
resulted largely from the illusory bias in the FULL conditions as compared to the UNBIAS trials.
However, the BIAS condition failed to produce a biased position.
On hand movements the main effect for vision of target and eye position was elicited with
increasing of reaction time as a result of simultaneous eye-hand movement and removal of target
vision. In movement time large values were again found for the eye-hand condition (FULL). On the
final hand displacement endpoint configuration generated main effect and interacted with both
vision of target and eye position. The variability in final figure displacement revealed an effect only
for the vision of the target. There was again an interaction between vision of target and endpoint
configuration. Peak velocity of the aiming movements and peak hand deceleration increased with the
removal of vision of the target. Peak deceleration was also affected endpoint configuration and
specifically greatest by aiming at the in-Müller configuration.
On eye-hand coordination the correlations involving the location of the eye and that of the hand
at completion and at the end of the primary movement were significant in NOTAR-FULL trials
compared to zero. Inconsistent with these no-vision effects and correlations, however, there was a
small, although significant, eye-hand relation in BIAS-TAR trials.
Together the Müller-lyer illusion effectively biased saccade eye movements but did generate
biases in manual movements only when the target lights were extinguished at movement initiation.
The corrective eye movements could not overcome the biasing information. The hypothesis that the
biasing in primary eye movement leads to the biasing in hand movement is not supported. Common
information shared by the hand and eye or planning and control of manual system based on
efference from initial saccade are weakened. No spatial relations with respect to bias for both the
eye and hand developed. If the hand is more dependent on extraretinal information when the target
is extinguished, the degree of oculo-manual coordination should increase. Temporal relation
between both movements was not found.. Rapid manual aiming benefits clearly from the availability
of vision of both the limb and target
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4.12 Eye Position Effects on neuronal Activity (Boussaoud,
Jouffrais, & Bremmer 1998)
Based on the observation that visual inputs to the brain are mapped in a retinocentric
reference frame, whereas the motor system plans movements in a body-centered frame. Boussaoud,
Jouffrais, & Bremmer (1998) concerned about whether the premotor areas receive visual information
from the parietal cortex and studied dorsal premotor cortex (PMd) neurons in two monkeys while
they performed a conditional visuomotor task and maintained fixation at different gaze angles.
Two rhesus monkeys were trained to perform a conditional visuomotor task with the left hand to
receive liquid reward and their head was firmly fixed 32 cm in front of a computer screen. A panel of
three metal touch pads was located at the bottom of the screen. The central pad was aligned on the
monkey’s body axis and the other two 12 cm to the left and the right. A white square appeared at
one of nine locations forming a grid served as a precue (PC), which directs the monkey’ attention to a
given location. A second colored square of the same size presented at the previously cued location
after a variable delay (the instructed delay period or set-related or preparatory activity) served as a
motor instructional conditional cue (MIC), which guided the monkey’s motor response. A red MIC
instructed a movement to the left touch pad and a green to the right one independently of their
spatial location. Monkey 1 was allowed to move its eye after the go signal (offset of cue) whereas
monkey 2 was trained to fixate throughout the trial period up to the end of movement. A trial begins
after the monkeys put its hand on the central pad. A video monitor provided visual stimuli.
Four major epochs were measured for the analysis of neuronal activity. Precue activity: 100
ms after the presentation of the precue; post-MIC: 100 ms after the onset of MIC; set-related
activity: just before the offset of MIC (go signal); and movement-related activity: after the go signal.
The ANOVA of the response time (RT) showed no significant effect of gaze angle but affected by
both the location of MIC cues and the direction of limb movement. The data of the precue period
and the delay period did not show EMG activity. Eye movements showed comparable response times
with those of limb movements. In relation to gaze angle no significant variations of EMG activity was
found during the instructed delay period. To-the-left Movement revealed significant stronger phasic,
movement-related activity than to-the-right movement. This phasic and movement-related activity
also was varied with gaze angle. The lumbar paravertebral muscle was differentially active with gaze
angle. A minority of PMd cells discharge in relation to visual cue when they simply direct spatial
attention with no instructional meaning. When MIC is green a phasic, signal-related activity lasts for
~400 ms after the cue’s onset. The vast majority of PMd cells discharge preferentially following
motor instruction cues than after the precue. Irrespective of the task period analyzed the discharge
rate of a large proportion of cells are significantly affected by gaze angle. The retinal effect, i.e.
activity variations related to target location in retinocentric coordinates, was significant in a modest
number of cells depending on the task epoch (the highest proportion during the earliest epoch after
MIC onset). The proportions were relatively low for set-related and movement related. The direction
sensitivity was observed in all three task periods but relatively stronger during the set and movement
periods than in the signal period. Gaze effect was highly significant and typically large. Signal-related
activity was nearly twice higher when the monkey fixated in the preferred gaze direction and showed
significant variations with gaze angle for leftward movement. Set-related activity is nearly absent
when movement is to the left and dramatically varies with gaze angle. The weakest neuronal
discharge is observed for gaze when gaze and limb movement are coincided. The discharge rate is
significantly higher during preparation of a leftward movement when gaze is deviated to the right.
Analysis of PMd movement-related cells showed significant activity variations with gaze angle and
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the clear effect of limb movement-direction on the cell’s discharge rate. A regression analysis
showed that the neuron activity varied linearly with eye position. This provide evidence that eye
position signal modulate the neuronal activity beyond sensory areas. Arm movement and gaze
direction at least are the two direction parameters and limb movement directions are coded in a
head-centered reference frame.
4.13 The main function of the cerebellum and basal ganglia
(Dreher & Grafman 2002)
The challenge of the primary function in motor control of the cerebellum and the basal ganglia is
the evidence that they also are activated during the performance of cognitive and attention tasks
(Dreher & Grafman 2002). They tried to identify their specific roles by using magnetic resonance
imaging. They tested three hypotheses using a task-switching experiment with a 2*2 factorial design
varying timing (random relative to fix) and task order (unpredictable relative to predictable).
Switching attentional set is mediated and error signals are provided regarding stimuli or rewards by
the cerebellum (first hypothesis). The third hypothesis is that they operate as in internal timing
providing the precise representation of temporal representation across various tasks.
Eight healthy subjects responded to visually presented letters by pressing response buttons with
their right or left hand in 8 conditions. Each condition is cued by a distinct visual instruction. 2
conditions is used for the baseline (Task A, vowel-consonant discrimination; Task B, case
discrimination), 4 task switching conditions obtained by crossing the task order and timing factors,
and 2 conditions was used for control (Union task, A or B). If the letter was a vowel subjects had to
press the right button and if it was a consonant they had to press the left button in the “vowelconsonant” condition. If the letter was a vowel subjects had to press the right button and if the letter
was a consonant in the “vowel-consonant” condition the left. If the letter was in upper case subjects
had to press the right button and if the letter was in lower case the left in the “case discrimination”.
In the “switching” conditions, subjects had to perform the vowel-consonant condition if the letter
was red and the case discrimination condition if the letter was green. There were four switch
conditions: “fixed predictable”, “random predictable”, “fixed unpredictable”, and “random
unpredictable”. Fix indicates timing between stimuli. In the unpredictable condition a switch was
pseudo-randomized. In the predictable a switch occurred between tasks every three stimuli. In the
union condition as control condition if the letter was a vowel or was in upper case subjects and if
both were true subjects had to press the right button and the left button otherwise. In the first
control condition stimuli appeared in constant distance and in the second one was pseudorandomized. Task order (predictable vs. unpredictable) and timing (fixed vs. random) are the factor in
ANOVA for analysis of RT and percentage of errors.
A significant response time cost was found in all task-switching conditions. The unpredictable
order of the tasks worsened the performance. No main effect of timing was found. RTs were
significantly slower in the task-switching conditions averaged together than in the control condition.
None of the brain area (caudate nucleus, thalamus and cerebellum) was significantly activated in the
comparison between control and task-switching condition by examining the voxels. The main effect
of task-order unpredictability activated the anterior putamen bilaterally. No significant activation
was found in the cerebellum. The main effect of timing irregularity was to activate the right posterior
cerebellar hemispheres and the dentate nucleus. In the basal ganglia no significant activation was
found. Interactions between timing and task order factors showed in brain activated the right head
of the caudate nucleus and the posterior putamen bilaterally. No interactions were found in the
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cerebellum. The task-switching condition with unpredictable task order and random timing specially
activated the substantia nigra. The caudate activation correlated positively with activation of DLPFC
(dorsolateral prefrontal cortex) and the cerebellum by unpredictable task order. In addition when
timing was random, the caudate activation correlated with the pro-SMA and the IPS (intra-parietal
sulcus). The cerebellum activation positively correlated with the DLPFC when timing was random and
with the pre-SMA activation when both timing and task order were unpredictable.
All together their results supported distinctive roles of cerebellum and basal ganglia. Timing
irregularity primarily induced the activation of the cerebellum while task order unpredictability
induced the activation of the anterior striatum independent of reward delivery. Switching attention
was not specific in the cerebellum and basal ganglia.
4.14 Central Bottleneck of Information processing with fMRI (Dux
et al. 2006)
Dux et al. (2006) used fMRI (time-resolved functional magnetic resonance imaging) to study the
bottleneck occurring at the central, amodal stage of information processing when two response
selections have to be concurrently executed. Dual-task (experiment 1), single-task (experiment 2),
and response selection load (experiment 3) were approached. In experiment 1, subjects had to select
the appropriate manual response to a complex auditory stimulus in one task (AM task) and the
appropriate vocal response to a visual stimulus in other task (VV task). The first task is called task 1
and the second task 2.The stimulus onset asynchrony (SOA) between the two tasks was either short
or long. Eight visual and audio stimuli required distinct vocal response and distinct key press
response respectively. Concurrently processing two sensorimotor tasks presenting intrinsic
limitations in the procedures would ensure the dual-task costs. Two localizer tasks (each task
involved an eight alternative force choice (AFC)) were included to isolate the regions which have
been hypothesized to be involved in response selection. Subjects performed separate ordered blocks
of trials of single AM and VV tasks, dual-tasks, and fixation blocks (passively view a fixation square) so
that both localizer runs each block type preceded and followed one another an equal number of
times. Significant dual-task interference was expected at the short SOA because RT to task 1 was
generally shorter than the long SOA. In addition long RT and prolonged psychological refractory
periods (PRPs) were expected due to the high number of response alternatives.
A peak latency difference at the long but not at the short SOA was revealed in the left premotor
cortex. The data demonstrated robust dual-task interference but this interference is largely revealed
by a postponement of task 2 as predicted by the central bottleneck model and other capacity-limited
models of the PRP. The first model predicts that response selection for task 2 is postponed until
response for task 1 is completed. The data showed a strong correlation between two tasks at shot
SOA supports this prediction. The data also supported the independence of response selection of
task 2 on the response selection of task 1 because the mean RT to task 1 is shorter than the long SOA
and a marginal correlation between two task RTs is shown. The Slow-Fast RT latency difference of the
activation pattern in the left pLPFC (posterior lateral prefrontal cortex) was larger at the short than at
the long SOA. The time between the onset of stimulus 1 and the response to task 2 (S1R2) is strongly
correlated with task 1 RT at the short SOA but not at the long SOA. At the long SOA no significant
latency difference was found but a peak difference at the short SOA between short and long S1R2
RTs a. Thus, the role of pLPFC in 1 central bottleneck of information processing was evidently
revealed in task 1 RT and S1R2 RT.
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To avoid the vocal artifacts they scanned subjects during single AM task trials in experiment 2.
They again observed a peak latency difference between Slow and fast RTs but no difference in onset
latency. The comparison of the time courses in pLPFC for the dual-task and single-task conditions
provides further evidence for its key function as neural substrate of the central bottleneck. The peak
latency in the left pLPFC was greater under dual-task conditions than single-task conditions.
Manipulation of response selection load would provide the involvement evidence of pLPFC.
Subjects performing single AM tasks were required choosing between either two or six response
alternatives with the 2FAC and 6FAC (alternative force choice) trials separately blocked in experiment
3. RTs were shorter in the 2AFC condition than in the 6AFC condition. The left pLPFC’ activity was
stronger in the 6AFC condition than in the 2AFC condition and the difference between these
conditions arose at trial onset. Taken together a key role for pLPFC was again consistent. SMFC
(superior medial frontal cortex) also exhibited a peak latency difference between Slow and Fast RTs,
but no onset latency difference in the single-task condition. Dual-task conditions showed greater
peak latency than single-task conditions. In the response selection load experiment SMFC showed
greater activity in the 6AFC condition than the 2AFC condition. The inconsistent pattern of activity
observed across experiments in the cerebellum, the right IFG (inferior frontal gyrus), and the IPS
(intra-parietal sulcus) makes it difficult to ascribe to them any specific role in dual-task limitations.
Dual-task limitation possibly is revealed in the inability of pLPFC and of SMFC to process both tasks at
once.
4.15 The organization of eye and limb movements during reaching
(Fisk & Goodable 1985)
Fisk & Goodable (1985) examined the spatial and temporal organization of unrestricted limb
movements directed to small targets in two experiments. In experiment 1, subjects had to point
their index finger quickly and accurately immediately following illumination of the target (visual field
of target presentation: left or right, brief or persistent target duration) to the position on the screen
(10o or 20o eccentricity) after fixation at the central light and a ready command. Subjects were
instructed to look to the targets (eye movement condition) as well as point to them or maintain
fixation on the central light while pointing (no eye movement condition).
The results showed that subjects initiated a reach within 500 ms after target illumination. The
ratio of the minimum vector distance between the initial and final positions of the finger to the
actual distance travelled was not significant affected by variations in the experimental conditions.
The correction movement in vertical dimension was largely responsible for the deviation of the
trajectory from a straight line path. The acceleration phase constituted approximately the first third
of the total movement duration, while the longer deceleration included a very low velocity
movement just before the finger contact on the screen. The affection of the target laterality with
respect to the hand used to reach on the latency and kinematics of the limb was clear. The trend
toward shorter latencies for reaches with the right hand opposed to the left hand. The interaction of
the factors (visual field, target eccentricity) was found significant for movement latency, maximum
velocity, mean velocity, and duration. The increase in latency with increased target eccentricity was
much greater for contralaterally opposed to ipsilaterally directed reaches. The more eccentric targets
the greater maximum velocity, mean velocity during ipsilateral reach. The duration of contralateral
reaches increased dramatically with increased target eccentricity. The variation in the illumination
only affected the movement duration. The duration of target illumination, the laterality, and
eccentricity had significant effect on pointing accuracy. The absolute vertical error was smaller when
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subjects looked to the target than they did not. Variable vertical error was reduced for the eyemovement compared to no-eye-movement trials. Both absolute lateral and absolute vertical error
showed significant interaction between eye movement condition and the target duration. A similar
trend was also noted for the variable vector error (the standard deviations of the signed lateral and
vertical error scores and the standard deviation of the unsigned vector error scores). Thus, to ensure
an optimal level of pointing accuracy neither the eye movements nor the persistent target were
sufficient
The accuracy of final horizontal eye position was affected by all of the same factors in accuracy of
the limb movement. A significant correlation for the end point of movement by the eyes and the
finger was found. A trend toward an effect of the duration of target illumination on the strength of
this correlation was indicated.
There were similarities in the effects of the experimental conditions on the latencies of eye
and hand (ipsilateral target versus contralateral target). Right hand had shorter response latencies
than left hand. Eye movement latencies lower for right-handed movements than for left-handed
movements.
Figure 4-24: The four reaching conditions and two initial fixation points for a subject performing reaches with
the left hand. The filled triangles represent the position of fixation at the initiation of a trial while the open
ones represent the position of the target. All target positions shown are at 20 o eccentricity from the body axis.
Initial fixation varies between 0o (central) and 30o (eccentric). The position of target with regard to the visual
field and body space frames of reference are illustrated for each reaching condition. For the central fixation
trials these positions corresponded, but for the eccentric fixation trials this correspondence was eliminated.
The eccentricity of the targets and fixation points in these diagrams are exaggerated for the sake of clarity.
(Taken from Fisk & Goodable 1985)
In experiment 2, location of the initial fixation point was systematically varied to disembed the
effects of the visual hemifields from those of body-relative hemispace (Fig. 4-24). Significant effects
of target laterality and eccentricity were evident in the body space analyses.
Taken together the most consistent differences between reaches directed across the body axis to
targets presented in the contralateral visual field and reaches directed at ipsilateral targets were
observed. The findings suggested that the hemispherical organized neural systems are involved in
the programming of visually guided limb movements. A close relationship between movement
latency for both motor systems was found. An integration of both eye and hand movements was
involved. The laterality of the target position relative to the body axis modified the kinematics of
reaching movements.
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4.16 Modality pairing effects on dual-task (Hazeltine & Ruthruff
2006)
The Generic central bottleneck predicts three discrete processing stages: a prebottleneck stage
(Stage A), a bottleneck stage (Stage B), and a postbottleneck (Stage C). Both Stage A and Stage C but
not Stage B can proceed in parallel with any other processing stage.
Figure 4-25: Panel a: three stages of processing for Tasks 1 and 2 at a long stimulus onset asynchrony (SOA,
when the SOA is long, the bottleneck stages (Stage B) for the two tasks are required at nonoverlapping times,
so each task proceeds without interruption. Panel b: three stages of processing for Tasks 1 and 2 at a short
SOA. When the SOA is short, the bottleneck stage (Stage B) for Task 2 must wait for the bottleneck stage for
Task 1 to be completed, so Task 2 is slowed. (Taken from Hazeltine & Ruthruff 2006)
When the SOA (stimulus onset asynchrony) is long, the bottleneck stages are not overlapped,
and RTs (reaction time) for both tasks are determined only by the sum of durations of three
component stages (Fig. 4-25).
Stimulus categorization is implied in the stage A and response execution in the stage C. Although,
the modality pairings should not qualitatively change the way the bottleneck mechanism operates,
they might alter the duration of its stages. Hazeltine & Ruthruff (2006) used the psychological
refractory period (PRP) procedure to examine the effects of input/output modality pairings on dualtask performance to evaluate these predictions.
Table 4.12 Four task combinations used in the experiment (taken from Hazeltine & Ruthruff 2006). The first
two letters indicate the composition of Task 1, (AM: Auditory stimulus, Manual response; AV: Auditory
stimulus, Vocal response; VM: Visual stimulus, Manual response; VV: Visual stimulus, Vocal response). The
second two letters indicate the composition of Task 2, using the same abbreviations as for Task 1
Four groups of 96 participants differed in terms of the S (stimulus)-R (response) associations for
the two tasks (table 4.12). The AVVM (auditory-vocal/visual-manual) group responded by saying the
words “one” and “two” to the tone (task 1) and by pressing the “H” and “J” keys to the symbols “#”
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and “%” (task 2), respectively. The VMAV group performed the same two tasks except that the visualmanual task was task 1 and the auditory-vocal task was task 2. The AMVV group responded to the
tones by pressing the “H” and “J” keys (Task 1) and to the “#” and “%” symbols by saying the words
“one” and “two” (Task 2), respectively. Both the VVAM group and the AMVV group have same tasks,
except the exchange task number 1 for the visual-vocal task and the auditory-manual task. Three
different trial types according to the SOA duration (short, intermediate, and long) were provided.
Auditory-vocal task with a visual-manual task is referred to as the ‘‘standard’’ modality pairings and
auditory-manual task with a visual-vocal task to as nonstandard ones. RTs from the intermediate SOA
were divided into 140 s bins.
On Task1 RT (RT1) there were no significant main effects but statistically reliable interactions
between stimulus*response, SOA*stimulus, and SOA*stimulus*response. Standard modality pairing
had an advantage compared to nonstandard pairing. There were main effects of SOA reflecting the
PRP effect, and response reflecting advantage for vocal response but not stimulus in Task 2. There
was no advantage for the standard modality pairings at the long SOA although an overall advantage
for auditory stimuli and an overall advantage for manual responses existed. Task 2 RT (RT2) at the
short SOA showed much greater variation across groups. This variation was due to a reliable
stimulus*response interaction. The PRP effects were larger for the AMVV and VVAM groups than for
the AVVM and VMAV groups at the short SOA compared to the long SOA. On average, the
nonstandard groups showed more dual-task interferences than the standard groups but RT2s at the
long SOA were similar across the four groups. Furthermore the combinations of stimuli and of
responses were identical for all four groups. The nonstandard groups produced much larger PRP-RT1
differences than the standard groups. Thus, the dual-task costs on Task 2 were still significantly larger
for the nonstandard groups.
On the proportions of correct responses on task 1 there were significant main effects of SOA but
not stimulus or response. There were also reliable stimulus*SOA interactions and stimulus*response
interactions. On task 2 a significant main effect of SOA but not stimulus or response. The
stimulus*response and SOA*response were reliable. No three-way interaction for Task 2 and
stimulus*response interaction was robust at both SOA. On task 1 the RTs did not vary with SOA but
on Task 2 the correlation coefficients were reliable negative for all groups for the first two SOA bins,
and reliable negative for VMAV, AMVV, and VVAM groups for the thirst SOA bin.
Consistent with the GCB model, the slopes along the mean RTs tended to decrease as the SOA
increased. The two nonstandard groups showed steeply slopes at the short SOA than the standard
groups.
Taken together, modality pairings had large effects on dual-task reaction times. Because the task
demands were the same across the groups, the modality pairing effect cannot attributed to the
difficulty of stimulus classification or response execution. Beside the postponement of central
operations, the findings suggested that dual-task interference also arises from a slowing of central
operations whose magnitude is sensitive to the input/output modality pairings.
4.17 Summary
Natural experiments have to be made to understand how eyes and hand are coordinated in
everyday tasks (Herst, Epelboim, & Steinman 2001).
Reticular formation saccadic burst generators, superior colliculus, cerebellum, basal ganglia
and premotor cortical areas are the five main brain regions involved in saccade generation (Girard &
Berthoz 2005). In monkeys, gaze interactions occur in several arm movement related areas (e.g.,
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Boussaoud, Jouffrais, & Bremmer 1998) in addition to the visual cortical areas. Three interrelated
neural systems (one that is involved in explicit timing (putamen, ventrolateral thalamus,
supplementary motor area (SMA)), one that mediates auditory sensory memory (inferior frontal
gyrus (IFG), superior temporal gyrus (STG)), and another that is involved in sensorimotor processing
(dorsal dentate nucleus, sensorimotor cortex)) are suggested to be responsible for the internal
generation of precisely timed movements (Rao et al. 1997). Large-scale cortical networks are related
to finger actions and gaze direction signals modified skeletomotor processing in the cerebral cortex
(Baker, Donoghue, & Sanes 1999). Several areas that exhibited activation related to a mixture of
hand and gaze positions were found: the sensorimotor cortex, supramarginal gyrus, superior parietal
lobule, superior frontal gyrus, anterior cingulate, and left cerebellum.
On one side, the central bottle neck of information processing is suggested in a neural
network of frontal lobe area (Dux et al., 2006). Under visual control reaching toward a target implies
a common integration of eye and hand movements (Fisk & Goodable 1985). Divided attention model
predicts decrements on the visual performance to be offset by an increase in concurrent task
performance and opponent-process model predicts a shortening of saccade movement paths. Both
models are supported when subjects simultaneously visually tracked a jumpwise moving target and
identified randomly generated auditory dots and dashes (Malmstrom, Reed, & Weber 1983). On the
other side, an effect of central executive load was not expected in prosaccade task because it is an
automatic activity (Claeys et al. 1999). Interaction on prosaccades could arise from a controlled
execution of these saccades (Stuyven et al. 2000). Information processing for the eye tracking and
manual tracking substantially was independent. On the other hand both system share common input
processes (Megaw & Armstrong, 1973). The ocular and manual motor systems are not operating
independently when initiating saccadic eye and goal-directed hand movements but saccadic eye
movements and button-press responses could be initiated without delay (Bekkering et al. 1999).
Although in trials with concurrent eye movement and elimination of retinal target information on
limb movement initiation only manual bias in response to illusory targets occurred no temporal
relation between eye and hand movements was found (Binsted & Elliott 1999). Beside the
postponement of the central operation due to central bottle-neck, the effects of input/output
modality pairing slowed down its performance in dual-task (Hazeltine & Ruthruff 2006). With timing
irregularity the cerebellum is primarily activated while with task order unpredictability the anterior
striatum is activated. These support two forms of readjustment presenting the distinctive roles
(Dreher & Grafman 2002). For the gaze only regions within the left insula, left cuneus, left
midcingulate gyrus, left putamen, and right tempo-occipital and for the hand cluster in the
cerebellum were found (Bedard & Sanes 2009). The results indicate that at least two signals are
integrated in these areas for performing visual-motor actions.
The approached oculo-manual tasks will not focused on the visually guided eye–hand
coordination in space. Common spatial variables for both movements are avoided and the saccade
target is fixed to prevent any spatial uncertainty. An exogenously triggered reflexive oculo-manual
task together with an endogenously timed repetitive manual task would reduce probability of
temporal coupling between tasks in oculo-manual dual-task. Will concurrent operation of these two
very different effectors engaged in two independent tasks cause interference?
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5 Motor coordination framework: which gaps are addressed by
this study
For execution of voluntary movements is there a transformation of the activity in the central
nervous system of the spatial variable (direction) and mechanical variable (amplitude and velocity) of
the limb, as neural representation, into signals that activate the muscles moving the limb? Would the
interdependence between multi movements reflected in neural representation and also in signals
that activate the muscles be sufficient to give the answer?
5.1 The form of continuous movement trajectories
Classical evaluation of experimental tapping data simply determines the intertap interval
sequence (i.e. only the times of contact onsets of the finger tip to the ground plate are considered).
Both Yamanishi, Kawato, & Suzuki (1979) and Yoshino et al. (2002) used phase resetting experiments
to investigates the dominant periodic tapping disturbed by the non-dominant finger tap in response
to impulsive stimulus, Although they started from the dynamic system perspective, i.e. continuous
movement primarily is concerned, only information processing perspective was taken, i.e. discrete
events, which equals an abstraction of the continuous position signal, generated by the finger tap
was analyzed. Recording the finger positions as analogue signals is approached to improve the
classical evaluation. The motion of the fingers would be less harmonic in tapping on hard surface and
did not take the standard cosines form (Haken, Kelso, & Bunz 1985). The degree of Asymmetry in the
flexion and extension movement times would increase (Balasubramaniam, Wing, & Daffertshofer
2004). Semjen & Summers (2002) recorded Sample movement trajectories in the production of
bimanual rhythmic movements with a 1:2 frequency ratio. They showed that although both hands
often moved together, the slower hand alternated in full-amplitude and reduced-amplitude
movements.
5.2 The movement order
A prominent theme of motor control research is that complex movements are produced by
combining elements from fundamental classes of primitive movements (Hogan & Sternad 2007). One
compartmentalization in the research literature is the separation between rhythmic and discrete
movements (Wing & Kristofferson 1973a, b; Schulz 1978; Delignières, Lemoine, & Torre 2004;
Zelaznik, Spencer, & Ivry 2002). Each repetition is separated by a salient event in tapping on the hard
surface with hits are performed in a rhythmic fashion (Hogan & Sternad 2007). This periodic tapping
should be considered as a sequence of individual steps or continuous rhythmic movements (Hogan &
Sternad 2007)? We employed contact-free tapping to partly mask this salient event and isometric
tapping where the fingers were fixed. Isometric tapping would substantially mask the execution level
of motor and eliminate the discrete properties of sensory feedback. The recurrent periodic behaviour
at the cognitive level in the case of this reduced degree-of-freedom would be pronounced.
5.3 The effects of feedback
Perceived synchrony seems to depend on all available forms of sensory evidence (Aschersleben
2002). Central representation of the tap is formed by various feedback components (Mates &
Aschersleben 2000). More explicit temporal control is required when continuous movements
encompass a series of discrete contact (Delignieres, Lemoine, & Torre 2004; Zelaznik, Spencer, & Ivry
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2002). Rapid internal phase correction and a slow internal period correction of the tapping period
occurred (Repp 2001). In timing control if the actual tap is timed from the preceding tap then the
nature of the perceptual information on which period correction is based has to be considered.
Period correction is based on perception of discrepancies between the internal timekeeper period
and the sequence IOI duration (Mates 1994a, b). According to pattern formation and selforganization from synergetic the periodic timing in tapping as an oscillator will be self-sustaining and
their long-term behaviour will be periodically stable with a specific combination of the damping
terms and restoring terms (Haken, Kelso, & Bunz 1985). These terms have to be more complex in the
presence of discrete features in comparison with pure continuous movements.
5.4 The effects of amplitude (force)
Force fields generated by spinal cord compartments serve as primitives have been suggested
(Bizzi, Mussa-Ivaldi, & Giszter 1991; Giszter, Mussa-Ivaldi, & Bizzi 1993; Hart & Giszter 2004). Mirror
movement is a robust feature of the mature motor system which is reported in many studies (Carson
20059. This tendency not only occurred in association with specific disorders of the CNS but is also
observed frequently in normally developing children (Carson 2005). Motor irradiation, an increase in
the excitability of the (opposite) homologous motor pathways is visible when unimanual movements
are performed. Strong tapping is used in both ST and DT experiments (Carson 2005). The increased
force clearly leads to the increased accumulating evidence from different sensory channels
(Aschersleben, Gehrke, & Prinz 2001; Aschersleben 2002).
5.5 The effects of multiple effectors
Involuntary contractions emerged during intended unilateral engagement of the opposite limb
(Carson 2005). Simultaneous activation of homologous muscles during bimanual coordination
produces a more stable pattern than alternative activation (egocentric constraint; Swinnen et al.
1997). The transition-related activation that is distinct from motor execution activation not only
might be because our preference for symmetry arises from a preference for perceptual symmetry
(Mechsner et al. 2001; Salter et al. 2004; Welsh, Almeida, & Lee 2005) but also from a strong
constraint derived from the interhemispheric anatomical coupling (Aramaki et al. 2006). The effects
of multiple effectors clearly leads to following effects: the increase of the integration degree of
different central control signals related to each effector’s movement (Drewing et al. 2004; Ivry,
Keele, & Diener 1988; Ivry RB & Hazeltine 1999; Helmuth & Ivry 1996); the increase of oscillatory
coupling degree (Beek, Peper, & Daffertshofer 2002; Haken, Kelso, & Bunz 1985); and again the
increased accumulating evidence from different sensory channels (Aschersleben, Gehrke, & Prinz
2001; Aschersleben 2002).
5.6 The effects of attention
With attention we can perfectly tap at one tempo while listening to an auditory sequence of
beats at a different tempo (Repp 2006). Period correction is sensitive to the attentional requirements
of the task and period correction is, at least in part, a higher level cognitive function (Repp & Keller
2004). Phase correction and period correction in response to perturbation are based on the
fluctuations of attentional energy entrained by a metronome sequence (Barnes & Jones 2000). Thus
rhythm perception is viewed as a form of covert synchronization and attentional dynamics are closely
linked to the motor system, with motor imagery or simulation of the sensory consequences of
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rhythmic action accompanying the burst of attentional energy (Barnes & Jones 2000). Additional
mental task or apply of multi effectors would require more attentional energy but the performance
could be rewarded or impacted. Focus on one movement would increase its performance but would
decrease the performance of other.
5.7 Single-task conditions
5.7.1 Basic considerations
5.7.1.1 Dynamical concept for control of periodic finger tapping
An oscillator is any system in which every state is constrained to recur at regular intervals. In
finger tapping we refer to the rest position of the finger as the “equilibrium position”. The
displacement of the fingertip during tapping equals the distance from the equilibrium position. We
can perform periodic finger tapping without any external pacemaker, which has lead to the
hypothesis of an “internal clock” in the nervous system generating the periodic rhythm (Wing &
Kristofferson 1973b; Ivry 1996). The periodic dynamics of the internal clock for the periodic finger
tapping control, its stability was demonstrated by the study of Scholz, Kelso, & Schöner (1987), can
be modeled by a so-called limit-cycle oscillator. The limit-cycle oscillators incorporate a dissipative
mechanism to damp oscillation that grow too large and a source of energy to pump up those that
become too small, i.e. as a result of this idealization, at the notion of conservative systems we
assume that the sum of potential and kinetic energy remains constant (Andronov, Vitt, & Khaikin
1996). For example as simplified systems with restricted questions the energy of the RC circuit
consisted of the energy of the electric field in the capacitor (q /2C), and the energy of the RL circuit
consisted of the energy of the magnetic field in the self-induction coil (=Li /2). The energy also
remains unvaried in the jump because the charge q of the capacitor in the first case and the current i
in the second do not vary (Andronov, Vitt, & Khaikin 1996).
The state of the motion of periodic process through phase space presented in its coordinates
is described in mathematic. The study of the dynamics of oscillatory systems had used at first the
phase plane. The motion builds a particular path in the phase space and after some perturbation
return to their accustomed rhythm if it is hit out (Andronov, Vitt, & Khaikin 1996). The orthogonal
Cartesian coordinates provides the independent variable x and its derivation y in the concept of the
phase plane and on this x, y plane the motion of a harmonic oscillator is studied. A point on the x, y
plane representing the state of the periodic process have the corresponding values of the coordinate
x and velocity y. There is one and only one state of the system which corresponds to each point on
the x, y plane. Totality of all possible states of the periodic process is represented. The so-called
"representative" point and its velocity is called the phase velocity. The representative point follows
on a path called a phase path. Note that this path is not the motion trajectory and the phase velocity
is not the motion velocity. The so-called integral curve is for example determined by the equation
dy/dx = -ω02 * x/y. A curve which over the whole time of its motion (from t = - ∞ to t = + ∞)
described by the representative point will be called a complete phase path. Singular points designate
the points where the direction of the tangent becomes indeterminate. Separatrices define the
isolated finite sections of phase curve passing through singular points (Andronov, Vitt, & Khaikin
1996).
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5.7.1.2 Information processing concept for timing mechanism
The judgments of temporal equivalence are based on a memory for prescribed interval
duration (Keele et al. 1989) that we learn during synchronization phase. The perceptual centre of the
perceptual or motor event we use as the reference point for synchronization. This perceptual centre
(Morton, Marcus, & Frankish 1976) is determined not only by stimulus length or stimulus intensity in
the perceptual events but also by factors such as the intensity of somatosensory feedback in the
motor events. We continue the external beat after the initial events and then use that movement as
a base for judgment and a timed interval is recycled from end to beginning, i.e. the central timer
generates at these specific times a motor command for the finger to tap to the peripheral
implementation system, which in turn generates the motor response (Wing & Kristofferson 1973b).
The central timer as the temporal pacemaker produces regular output pulses at this constant base
frequency (Treisman 1963; Treisman et al. 1990).
5.7.2 The effects of feedback
Bimanual advantage, Performance during multi-effector tapping benefit from integration of
various feedbacks and of several central command signals. Because the neuronal pathways that
connect sensory neuron to the central nervous system are different, the most benefit of feedback is
clearly in voice tapping. The performance of timekeeper clearly is improved. But breath keeping
during voice tapping would impact the task performance. The discrete feature of tactile feedback is
mostly masked in contact-free tapping. Thus the degree of asymmetry in the flexion and extension
movement reduced and the movement trajectory would be more proportional. The Performance of
timekeeper is expected to be suffered. The movement amplitude without constraint would lead to
increase of motor variance. Because both performances (timekeeper and motor implementation) are
suffered in voice tapping and contact-free tapping, the correlation of successive intervals is difficult
to predict. The discrete feature of tactile feedback also is partly masked in isometric tapping but the
peripheral motor implementation is excluded in isometric tapping. Thus a prediction of zero of the
correlation lag-1 between adjacent intervals is expected. A tight coupling of sensorimotor processes
in trained musicians extends to preattentively mediated reflexes (Bangert et al. 2006). This evidence
leads to the question whether a tight coupling of the required rhythm of the tapping and motor
domains in musicians exists (Bangert et al. 2006). Musicians have a stronger cognitive representation
of rhythm than non-musicians. Would this mental representation of rhythm have an effect on
blinking?
5.7.3 The effects of amplitude (force)
Separate timing signals are generated for each effector and then are integrated to produce
coordinated motor commands (Ivry, Keele, & Diener 1988). Increased finger force intensifies not only
internal (kinaesthetic/tactile) feedback but also central command signals. It also requires more
attentional resource and energy resource for motor implementation. Both the variance resources of
time keeper and motor implementation together with the role of feedback come into play. The task
performance clearly will benefit from the increase of available sensory information but whether it
would also benefit from the more needed energy for motor implementation due to its variance
source. A higher required force would increase neural activity on the shared brain network for
blinking and tapping and a stronger coupling would be expected to entrain the spontaneous blinking.
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5.7.4 The effects of multiple effectors
Analogously not only the more sensory information but also the more central command
signals are available and more attentional resource and energy resource for motor implementation
are required. Additional to the cognitive level and the implementation level together with feedback,
the integration of various components, come into play. Separate signals are generated for each
effector (Ivry, Richardson, & Helmuth 2002). Various feedback components (tactile, kinesthetic) are
linearly integrated to form one central representation (Mates & Aschersleben 2000). Motor
commands from the two hemispheres are integrated subcortically (Ivry RB & Hazeltine 1999). The
task performance would benefit from the subtle error correction that is well below the explicit
detection threshold led to effective adjustments in the timing of the motor response (Repp 2000).
But the asynchrony between different effectors also leads to this subtle correction. The question is
whether this correction process will improve or impact the timing control. The integration of various
command signals and the enhanced discrete properties of sensory feedback supporting the rhythmic
task create more appropriate conditions for the hand motor system to entrain the concurrent
spontaneous blinking.
5.7.5 The effects of attention
Timing control apparently requires attention. Phase correction was largely automatic and period
correction required conscious awareness and attention (Repp & Keller 2004). The attentional
resources can be varied by introducing the mental task. If additional mental tapping changes the
tapping performance then attention supports the timekeeper or there is a command signal for this
mental tapping that the better performance benefits form the integration of the two command
signals. Contra to or pro to the common central timer would be partly reflected. Focus of attention
on the nonpreferred hand impacts the dual-task performance (Peters 1985) and would also impacts
the bimanual advantage. More focus on the required task would modulate the spontaneous blink
rate.
5.8 Dual-task conditions
5.8.1 Basic considerations
The stationary motion can be first of all states of equilibrium in which accelerations reduce to
zero, i.e. disturbance has no effect on the periodic movement. In the vicinity of the states of
equilibrium force disturbance either bring back the periodic movement to the state of equilibrium or
remove it still farther away. In the first case we shall have stable states of equilibrium and in the
second unstable ones (Andronov, Vitt, & Khaikin 1996). The phenomenon of phase depending
resetting of the behaviour of the periodic movement would not only reveal the states of equilibrium
but also determine their stability with respect to small variations of the coordinates and velocities.
Stable states of equilibrium are in this case a necessary condition that the system might be found in
the vicinity after undergoing the disturbance duration.
Pulse coupling is common in biology. Fireflies interact only when they see the sudden flash of
another and shift their rhythm accordingly (Strogatz & Stewart 1993). Phase shift of periodic rhythm
by the sudden impulsive force is the paradigm of pulse coupled oscillator system. Force will attract
the periodic movement to the position of equilibrium or repels it away from a position of equilibrium
and both are proportional to the displacement of system (Andronov, Vitt, & Khaikin 1996). Thus we
will peek into the magical world of coupled nonlinear oscillators.
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5.8.1.1 The effects of discrete movement on periodic movement
Due to oscillator coupling the impulsive discrete movement subjected to a limit-cycle
oscillator moves the state point away from the limit-cycle, but it returns to the limit-cycle
asymptotically and therefore causes the phase shift (Yoshino et al. 2002). The degree of this phase
shift, demonstrated by the phase-resetting-curve, would reveal much information about the
underlying dynamical system. If there is no interaction (phase shift = 0) the tapping behaviour
complying with Type 1 Phase Reset and if the phase shift is outright Type 0 Phase Reset has to be
presented. Modification of the bottom-up effect such as somatosensory feedback and top-down
effect such as force, attention, multiple effectors would lead to different results. The physiological
parameters (force, attention, feedback, etc.) would determine the shifting degree so that a mixed
form of phase resetting is possible. As the parameter is varied, the integral curves or PRC curves will
vary. If we assume that the potential energy is an analytic function of the parameter, then these
variations will occur continuously (Andronov, Vitt, & Khaikin 1966). The general form of the curves
will undergo quantitative variations only, and only for certain special so-called "bifurcation" values of
the parameter shall we have qualitative variations of the character of the curves. The bifurcation
values of the parameter will be, in this case, the values of the parameter for which a variation of the
character of the singular points and separatrices occurs. Andronov, Vitt, & Khaikin (1966) gave the
following definition which is not connected with the conservativeness of the system: a value of the
parameter λ = λ0, will be called by us ordinary if such a finite ε (ε > 0) exists that for all λ satisfying the
condition | λ - λ0| < ε we have the same topologic structure in the mapping-out of the phase plane by
the integral curves. The other values of the parameters for which this condition is not satisfied will be
called bifurcation or branch values. This existence of these bifurcation or branch values would clearly
be reflected in our position signal.
5.8.1.2 The effect of periodic movement on discrete movement
In most reports, only one-sided-affect of periodic finger on discrete finger was studied.
Staude, Cong-Khac, & Wolf (2006) reported the mutual interaction between two different
movements on one finger. The movement trajectory during interaction and the reaction time of the
discrete movement would reflect the effect of the periodic movement on it. If this effect is found
then the question is whether this gating also exists in the same dual-task for movement coordination
but of different fingers.
5.8.2 The effects of feedback
The effectiveness of the conscious process involved in perceptual judgment and action
planning mentioned in 5.3 has to be taken into account in the case that if the clearly disturbance of
the periodic movement by the discrete movement is easily perceivable. Furthermore the dynamic
system approach does not include the role of perception in coordination and does not address other
possible stable phase differences. If the central representation of the tap through its sensory
feedback is used as a base for timing judgment then there is ample evidence for the importance of
sensory effects on the timing of movements and are the physiological parameters and dynamical
parameters (the restoring force and damping force) determine the shifting degree of the state point
on limit-cycle.
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5.8.3 The effects of amplitude (force)
Phase-shifting stimuli are posited to change the state variables in all research topics using
PRCs and their strength determines the new state. In fact, in every organism for which type 0
resetting has been found experimentally, a stimulus of reduced strength has been shown to lead to
type 1 (Kronauer, Jewett, & Czeisler 1993; Peterson 1980; Taylor et al 1982; Gander & Lewis 1983).
Force combined with the rate of tapping can shift the value of critical point at which antiphase
tapping becomes unstable (Loesby, Piek, & Barrett 2001). Intensified force requires attentional
energy and increases the command signal. Focus on nonpreferred hand damages the dual-task
performance (Peter 1985). Increasing force of the discrete movement acting as a force on the state
point on limit-cycle would increase the degree of the phase shift. The more required attentional
energy together with the enhancement of sensory information by increasing force on the periodic
movement would support the timing control but as mentioned above whether the more needed
energy resource for motor implementation would also have effect on the motor variance source.
5.8.4 The effects of attention
The hypothesis that physiological constraint which is responsible for the interaction is
affected by varying the instruction was proposed (De Rugy & Sternad 2003). The moment that is
physiologically optimal in combination of the discrete movement with the ongoing oscillation is the
synchronization. The intention to initiate the discrete movement as fast as possible revealed that the
concurrent effect of focused central control signal relating to discrete movement acting on the
rhythmic movement against its free landing choice due to time-stressed emerges (De Rugy & Sternad
2003). Dual-task performance suffered when attention was focused on the non-preferred hand
during bimanual dual-tasks (Peters 1985). Focus on one movement would entrain the other
movement, i.e. focus on periodic movement would improve the timing function and focus on
discrete movement would impact.
5.8.5 Eye-Hand movement coordination without common spatial target
Since possible perceptual and movement production bottlenecks (De Jong 1993) as well as
cross-talk (Navon & Miller 1987) may be avoided in a way that the stimuli (visual and auditory) as
well as the required responses (saccade and finger tapping, respectively) for the two tasks engage
different modalities. Eye movements towards a lateral visual target (prosaccades) are performed
automatically (Roberts, Hager, & Heron 1994). The significantly higher rate of gain of information of
the eye system over the motor system suggested that certain features of the information processing
occurred independently (Megaw & Armstrong 1973). Saccadic eye movements and button-press
responses could be initiated without delay (Bekkering et al. 1994). It is also consequent to ask what
happens when these two effectors (eyes and hands), now not sharing a target, are acting
independently but concurrently. Only few studies have explored eye-hand interaction in different DT
situations (e.g., Bekkering et al. 1994; Pashler, Carrier, & Hoffman 1993; Claeys et al. 1999; Stuyven
et al. 2000), and their results are ambiguous. In DT paradigms with two discrete tasks (manual
reaction and saccade, e.g., Bekkering et al. 1994), oculo-manual (OM) interference was found in case
of targets being unpredictable in time and space but not in case of some degree of predictability. DT
paradigms with combination of periodic and discrete tasks were also used (e.g., Claeys et al. 1999;
Stuyven et al. 2000) and showed some general small increase of saccade latency due to the
secondary (tapping) task, but, unfortunately, the tapping behaviour was not analyzed. This
demonstrates that the issue of interference between eye and hand movements has remained
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controversial. On the one hand, since both effectors share some common brain structures, OM
interaction effects in DT would not be surprising. On the other hand, this sharing of common
networks must not necessarily be effective in all DT conditions but can be redundant (i.e., not
basically necessary for the DT execution) allowing some independence of the two tasks. Therefore,
our study addresses the question of whether the strong DT interaction found in BM-DT condition is
also be present in an equivalent OM-DT condition.
5.8.6 Multiple effectors
How are the cortical areas involved in these motor processes? Within the primary motor
cortex, there is no overlapping or anatomical linking of hand and foot representations (Huntley &
Jones 1991); however, there are common upstream to the primary motor cortex from secondary
motor hand and foot areas (Murthy & Fetz 1996). Hand and foot motor representations considerably
overlap in the secondary motor areas (the dorsal and ventral premotor cortices, the supplementary
motor area, and the cingulate cortex) (Fink et al. 1997), where coordination-related interactions
between hand and foot movements should appear (Byblow et al. 2007). Baldissera et al. (2002)
showed that excitatory changes occur in the hand motor area during voluntary cyclic movements of
the foot even if the hand is resting during the experiment. Raethjen et al. (2008) showed that the
cortical motor areas are involved in the generation of voluntary rhythmic foot movements in a similar
way as they are during rhythmic voluntary hand movements (Pollok et al. 2005). When a hand and a
foot move together, a strong interaction takes place between their cortical areas (Liepert, Terborg, &
Weiller 1999). In summary, neurophysiological data supports the following two alternate hypotheses:
concurrent hand-foot responses in a DT situation can be performed either in an integrated or —
alternatively — in an isolated way.
Thus, analysis of previous reports does not allow clear conclusions of interaction and coordination
between periodic and discrete tapping movements in DTs executed by the combined effectors such
as the upper and lower limbs — neither from physiological nor from the observational point of view.
Synchronization emerges cooperatively in communities of oscillators. If a few oscillators happen
to synchronize, their combined signal exerting a stronger effect on the others, i.e. the additional
oscillators into the synchronized nucleus amplify the integrated signal (Strogatz & Stewart 1993).
Timing is better as more sensory information becomes available (Drewing & Aschersleben 2003), the
timer variance for both hands decreased systematically with increasing sensory of the extra (left)
hand. The bimanual advantage decreased for both hands when auditory feedback for the taps with
the extra hand was omitted. Person suffering from a complete and permanent loss of tactile and
kinaesthetic afferents and consequently having no peripherally originating feedback of movement
were involved. Drewing et al. (2004) compared their results with those –matched controls. This
person showed an even more pronounced bimanual advantage than controls. Hence the bimanual
advantage is not only due to actual sensory feedback but also profits in opposite direction from the
averaging of different central control signals that relate to each effector’s movements.
This kind of integration was also illustrated in the hand-foot overlap area in secondary motor
areas (Fink et al. 1997) and the originated upstream of primary motor cortex (Byblow et al. 2007) by
their coordination-related interactions. Thus on the one side the more amount of sensory
information in bimanual single-task support timing but on the other side the more intensity of
central control signals. According to the functional cerebral distance model (Kinsbourne & Hicks
1978) and the confirmed less stable ipsilateral hand–foot coordination patterns for anti-phase
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coordination a difference may be in number of anti-phase coordination between ipsilateral and
contralateral hand–foot coordination patterns has to be present.
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6 Methods – Experimental Concepts, Recorded signals
There are numerous examples of temporal synchronization and rhythmicity in group-living
organisms (Greenfield & Roizen 1993; Snedden & Greenfield 1998) and human (McClintock 1971).
Also, there are theoretical systems of coupled oscillators and corresponding experimental
observations on the behaviour of interacting oscillators in physics. Thus, asking for analogy in
biological systems motivated the present study: Can the phenomenon of phase depending resetting
also be observed in the human motor system? The answer must be given by experiments: the focus
is directed to the dual-task (DT) coordination of a simple discrete motor response with a periodic
action, which requires observation of the neuromuscular system in conjunction with the perceptual
system during DT execution. Three issues are mainly addressed: (i) the stationarity of the periodic
movement when disturbed by the discrete reaction, (ii) the reaction times measured by the discrete
responses during the concurrent execution of a periodic movement, and (iii) the time relationship
between both motor actions. Besides the timing parameter, the spatial information about the
movement trajectories is an interesting parameter.
Experimental work mostly reports about one-sided-effects; i.e. both motor components of a DT
experiment (the periodic movement and the discrete movement) were performed by the same limb.
E.g., Staude, Dengler & Wolf (2002) conducted experiments with subjects performing reactive rapid
index finger abduction movements during a secondary internally paced rhythmic (tremor-like)
adduction-abduction movement of the same finger. The results confirmed the ability of one
movement to constrain or even impede the execution of the other. Due to the physical binding of
both in the same effector, it remained unclear where this interaction takes place within the
sensorimotor chain. Also, this type of experiment is difficult for the subjects to perform and requires
high concentration. Another type of motor control experiment is tapping (i.e. up-down movement)
introduced by Stevens (1886) and mostly performed by the index fingers; it is much easier to do, and
tasks can be distributed between different effectors (both hands and both feet). In addition, the
neural pathways and control of these effectors are well known, which allows a physiological
interpretation of observed data. Thus, tapping was selected as the basic experimental concept for
this study.
6.1 Materials and method
6.1.1 Subjects and experimental setup
Twenty two right-handed young healthy subjects (ages 21 - 50, mean 27) took part in the different
experimental conditions (described later). . Their informed consent according to the international
Ethical Guidelines for Biomedical Research (2002) were given but the aim of the study was not. All
subjects did not have any disease or any history or signs of disease or neurological disorder and had
normal vision.
6.1.2 Experimental setup
The classical tapping setup is very simple, which leads to a widespread use of tapping in
experimental psychology: pacing is performed by some digitally controlled discrete visual or auditory
stimuli, and the finger taps activate micro switches when hitting the ground surface. Thus, only
recording of a few digital signals is required, and evaluation of these time series simply needs to
detect the state change of binary signals by some threshold procedure. Finally, the intervals between
state changes in signals are determined and statistically analyzed (Wing, Kristofferson, 1973b).
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Clearly, this basic setup can be improved significantly by recording the finger positions as
analogue signals, because they comprise much more detailed information about the kinetic process
of the finger movements, if sophisticated signal evaluation tools far beyond simple threshold are
used.
Figure 6-1: Experimental situation. Actual experiment investigates bimanual normal tapping together with
saccadic eye movement recording.
Figure 6-2: Two types of experiments: A) normal tapping with moving index finger; B) isometric tapping
without movement of the limb but force development only.
Figure 6-3: A) Schematic illustration of the experimental setup. B) Sensors and stimulators. The tapping of the
index fingers is recorded by the force transducers (embedded in the table top) responding to the hits, and by
vertical laser position sensors fixed above the finger. The pacing signal during the synchronization phase and
the go signal during the continuation phase are generated by the loudspeaker and/or the LED-flash in the
middle.
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Figure 6-4: EMG recording from the finger flexors and extensors. Left plot indicates the electrode positions
(arrows), the right picture shows the active electrode used.
Figure 6-5: Foot pedals for including feet as tapping effectors. Left: 3 pedals are located such that participants
can operate them comfortably. The left and right pedal are for tapping responses, the pedal between is for
requesting a break in the experimental procedure. Right: The return spring was adjusted such that the
counteracting force equals the gravity and the passive elasticity forces of the soleus muscle; therefore, the foot
remains in the upper resting position without muscle activity.
The general experimental situation is scheme of our advanced experimental setup is shown in Fig.
6-1: Participants were seated with elbows comfortably semi-flexed on a height-adjustable chair at
the table in a well illuminated room (luminance about 45 Lux), their forearms and palms rested on a
table (ulnar side down), which allowed effortless bimanual tapping with the index fingers. In the
normal tapping condition, tapping was performed by moving the finger tip up and down with the
upper position being the resting position (Fig. 6-2A). In contact-free tapping ("tapping in the air"),
elbows and hand palm were rested on rubber foam tailored such that the index finger cannot reach
the table surface during the tapping. In isometric tapping, the subject’s index fingers were fixed to
the force sensors by orthopedic finger splints. Thus, the subjects were not able to lift their index
fingers but only they could develop directional up and down forces (Fig. 6-2B). A schematic overview
of the tapping experiment is given in Fig. 6-3. It shows the sensors for recording the physical
quantities together with stimulus sources: Two force transducers embedded in the tabletop record
the tap hits (ground contact) of the index finger tip of the left and the right hand, respectively. Also,
there are two laser sensors to measure the vertical position of the index fingers, and, according to
Fig. 6-4, active EMG electrodes MYO 110 (Liberting Technologies Inc.) are applied to register the EMG
from the extensors and flexors located in the forearms. For the single discrete tap either acoustic or
visual stimulus, for the periodic tap a sequence of beeps were used.
For the ocular-manual (OM) experiment, the setup was supplemented by a chin-rest and two
light-bars each consisting of 4 bright, vertically aligned LEDs (bar size: 0.8 degree of width, 3.6 degree
of height, 38 degrees distance between bars); they were fixed on a white screen and served as
saccade target (right bar) and as the fixation mark (left bar). The white screen was placed at the
distance of 70 cm at eye level in front of the participant. For eye movement registration, the IRIS
infra-red light eye movement system (model 6500, SKALAR Medical b.v.) mounted on a helmet was
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employed. The device provides an optimal resolution of 2 minutes of arc within 90 degrees
horizontally and 35 degrees vertically, and it yields a bandwidth of 100 Hz (-3 dB).
In experiments without eye movement recording, eye blinks were monitored by
electrooculographic techniques (EOG). A single-channel high-gain differential input electrooculogram
amplifier module (EOG100B, Biopac Inc., USA) was used together with two Ag-AgCl electrodes placed
above and below the right eye referenced to linked mastoids (as used e.g., by Skotte et al. 2007) for
tracking the eye blinks.
For including tapping by feet, two foot-switches were added to the setup (Fig. 6-5). They were
fixed on the floor for recording right and left foot taps. Foot was placed on the switch in a resting
position, and, in response to the go signal, it was pressed downwards while the force and position
changes developed by the foot was recorded. For the force signal recording, a force transducer was
inserted between lower pedal surface and the reset spring, and for the position signal recording, the
angle of the pedal with respect to horizontal level was indicated by a potentiometer fixed to the
pivot of the pedal. Of course, these two signals contain almost the same information and work in
parallel.
Figure 6-6: Screen dump of Diadem configuration. The box on the right shows the configured input channels.
Finally, a microphone was installed for voice recording to allow "verbal tapping" by respectively
saying the same "word" like … tac tac tac tac …. with sequence timing according to the pacing
during the synchronization phase.
Loudspeakers provided the auditory pacing signals (50 ms tone duration, 1000 Hz) with an ISI of
600 ms (in some cases, other interval durations were used). Participants were also exposed to the
auditory go-signal (50 ms, 2000 Hz) (right bar) which triggered the discrete tap (or saccade). A
Pentium IV computer equipped with two input-output cards (PCI-MIO-16-4, National Instrument Inc)
controlled the experiments and recorded all relevant signals at a sampling frequency of 1 kHz using
DIADEM 9.1 software; Fig. 6-6 depicts a configuration scheme of the experimental control.
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6.2 Procedures and experimental paradigms
Tapping can be conducted in two basic kinds: (i) moving limb or (ii) fixed limb, both with periodic
antagonistic activation of the muscles serving the joint. When the limb is moving ("normal tapping"),
the position is recorded over time and the force sensor is recording the touch-down forces of the
"landing" limb; when the limb is fixed ("isometric tapping"), the force developed by the muscles is
recorded as a function of time. When "tapping in the air" is performed, no forces are recorded but
the finger positions only.
As already emphasized, the interaction in dual-task was focused by this study mainly. Thus, the
experimental work also concentrates on DT experiments. However, tapping in DT is based on the
undisturbed tapping behaviour which can be observed in single-task condition. Therefore, some ST
aspects were roughly investigated in addition to obtain a reference platform for interpretation of the
DT tapping behaviour. These ST experiments fit more to pilot study character, but they demonstrate
the versatility of the experimental design of this work and the promising perspectives for using it in
future research activities. The following sections will describe all the different ST and DT paradigms.
6.2.1 The Synchronization-Continuation paradigm
Figure 6-7: Time course of the tapping paradigm in dual-task condition. The basic element is a trial comprising
a synchronization phase and continuation phase. The latter is divided in 12 segments. All pacing occurs mostly
with an interstimulus interval ISI = 600 ms.
The basic experimental concept of this work is the synchronization/continuation tapping task as
introduced by Stevens (1886). The basic structure is depicted in Fig. 6-7. Beside the reaction time
single-task, the experimental session consisted of 24 trials, each trial was about 75 s, 22s or 9s and
starts with introducing the tapping pace by five or twelve pacing audio signals in the leading
“synchronization phase” which allows the participant to adjust his/her tapping to the pace
frequency; in principle, this represents a closed loop condition. Then, the participants proceed with a
self-paced tapping in the so-called “continuation phase” at the same rate as introduced by the
external pacer before.
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Figure 6-8: The concept of the feedback loop control the dynamic behaviour of the performance. The sensed
value is compared to the desired value to create the error for the controller. Other internal interferences arise
from other cortical processes like execution of cognitive tasks.
The salient point of this method is that this continuation tapping is performed in open loop
condition, which is very sensitive to any interference with other ongoing processes and sensorimotor
inputs (Fig. 6-8).
Figure 6-9: Time course of the tapping paradigm in single-task condition. The basic element is a trial comprising
a synchronization phase and continuation phase. The latter is divided in 12 segments each ending with a
resynchronization phase where a triad of paces were presented additionally (shown by a group of 3 vertical
arrows). This paradigm was the same for unimanual and bimanual tapping.
For some statistical analysis, larger deviations of the actual tapping frequency from the original
pace signal frequency are obstructive. Therefore, the original concept of Stevens (1886) was
extended: the continuation phase of each trial was additionally divided into 12 (bi-partite) segments;
the first part of each segment (with duration randomly selected between 6 s and 8 s) involved pure
self-paced tapping, whereas in the second resynchronization part (Fig. 6-9), an additional triad of
pacing signals helped to stabilize the tapping rate in self-pacing. The onset of the first pace within
the triad was synchronized with the actual tap, whereas the next two paces occurred with the
standard ISI as used in the synchronization phase (Fig. 6-9).
6.2.2 Single-task conditions
Basically, all tasks used as combined set in DT conditions should be investigated in isolated form,
too, and performed in ST conditions. There are two categories:
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 periodic tapping and its control
 discrete tap execution in reaction time condition
The group of the discrete tap was generalized to discrete motor responses like saccadic eye
movements and eye blinks, whereas the periodic tapping was extended to "voice tapping" with
vocalising "… tac … tac … tac …".
6.2.2.1 Reaction Time RT
These experiments allowed recording of undisturbed discrete responses. A single discrete tapping
experiment in which the observers performed 72 stimulus-induced discrete taps in the absence of
any periodic movement under normal and isometric conditions, respectively, was the basic scheme.
To preserve the bimanual tapping scenario of the DT experiment, subjects were required to respond
to the acoustic stimulus by a simultaneous discrete tap of both the left and the right index finger, but
only the left hand signal was used to determine the reaction time (RT).
The observers were required to response with a single saccade to the onset of the visual gosignal, the saccade latency was measured. The same spatio-temporal parameters as in the oculomanual dual-task were used for this simple saccadic task.
6.2.2.2 Periodic Tapping ST
In order to compare of the timing of the periodic tapping in isolated and dual-task conditions, a
single manual experiment in which participants tapped without a concurrent discrete response task
under normal and isometric conditions.
Figure 6-10: (A) Timing of the "blink experiment". Again, the trial comprised a synchronization phase and
continuation phase, the latter with resynchronization for stabilization of the tap frequency. The first pace of the
resynchronization triad was synchronized with the actual periodic tap. (B) Basically, the scheme represents a
ST concept because the blink is assumed to occur independently. The temporal relationship of the blink with
respect to the reference tap is defined by the interval tperturbation and the phase  = tperturbation / ISI.
For replicating Drewing & Aschersleben’s (2003) results in simple bimanual tapping, the same
design of synchronization/continuation procedure was used under three conditions: left hand only,
right hand only and both hands synchronously. To obtain modification of tactile feedback normal,
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contact-free, isometric and voice tapping were employed. The session consisted of three parts, each
containing blocks of nine trials of each experimental condition. The trial consisted of 12 taps for
synchronization phase and about 40 taps (15 taps) for finger tapping (for voice tapping) in
continuation phase. The order of blocks was counterbalanced within and between participants by a
latin square design (Bailey 1996). A Latin square is an n × n table filled with n different symbols in
such a way that each symbol occurs exactly once in each row and exactly once in each column. The
order was not changed in each part. Five participants were tested in these experiments. In all
conditions, trials containing at least one rather long or short intertap interval (outliers) or with a
linear trend of the duration of the intertap intervals were immediately repeated (the mean intertap
interval ± four standard deviations determined online in the actual trial served as tolerance range for
the outlier, and linear trends were defined by a significant (p < .05) correlation between the length of
an intertap interval and its sequence order within the trial showing a range of [1 . . 40] and [1 .. 15]
in finger tapping and voice tapping, respectively. The used ISI was 400 ms, in hand-foot experiments
also ISI = 600 ms was used but also other ISI values were tested in pilot experiments.
To check the spontaneous blinking during the periodic tapping, the following experiments were
conducted with the ISI of 550 ms (Fig. 6-10)
 Experiment 1: standard tapping. Participants could tap as they felt comfortable, without any
special instruction about finger movement and contact force.
 Experiment 2: strong tapping. Participants have got instructions to tap stronger, i.e., with
force more than in case of standard tapping but not exaggerating (to avoid fatigue).
 Experiment 3: impulse-like tapping. The finger taps had to be as short as possible. A specific
instruction was given: the upward and downward movements of the finger tip had to be
moved as fast as possible with the duration of the ground contact as short as possible. No
tap force restrictions were applied.
Each experiment was split into two parts: the first 12 trials were dedicated to unimanual tapping
with the index finger of the dominant hand only, followed by 12 trials of bimanual tapping with both
index fingers simultaneously. Two participants performed in the inverted order but no order effect
was found. In unimanual condition, the non-dominant (inactive) index finger rested on the force
sensor surface. Seven participants took part in all three experimental conditions and a control
condition.
6.2.2.3 Periodic limb tapping and mental (cognitive) tapping ST
As in 6.6.6.6 the same periodic tapping ST was performed concurrently with a simple periodic
mental task to replicate experiments of Drewing & Aschersleben’s (2003). Different peripheral motor
implementations were realized by different experiment conditions (normal, contact-free, isometric).
Unimanual tapping and bimanual tapping were approached. The correlation function between
adjacent periodic intertap intervals was analyzed. The multiple-limb advantage was concerned. As a
mental task, counting from 1 to 4 (4-grouping condition) and from 1 to 8 (8-grouping condition) was
used. Pilot experiments showed in the 4-grouping condition that counting " .. 1 .. 2 .. 3 .. 4 .. 1 .. 2 .. 3
.. 4 .. 1 .." can be substituted a simple rhythm like " .. tac .. tac .. tac.. tac.. tac.. tac .. tac .. tac.. tac.."
emphasizing the forth item in sequence. The imaginary (silent) counting was performed in mother
language without any additional motor activation but rather as a pure mental tapping.
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6.2.3 Dual-task conditions
Figure 6-11: Dual-task condition. During the periodic tapping, the participants are required to perform discrete
taps in response to go signals (stimuli) during the continuation phase. The temporal relationship with respect to
the reference tap is defined by the interval tperturbation and the phase  = tperturbation / ISI.
In DT experiments, participants had to concurrently perform a discrete motor task by the nondominant finger, by the feet or by the eyes, and a rhythmic motor task by the index finger of the
dominant hand, or both tasks by the dominant hand (Fig. 6-11). Within the continuation phase of
each trial, go-signals for discrete responses were interspersed at randomized interval of 2-5 s (Fig. 611); the participants had to respond to these stimuli by the execution of one or several discrete
motor reactions (e.g., a tap or a saccade). There were 12 go-signals with corresponding 12 discrete
responses within each trial. Thus, a total of 288 discrete responses were usually obtained within a
session. The ISI of 600 ms was used (according to Yoshino et al. 2002) as it was reported to be
convenient for participants, but also other ISI values were used in some pilot experiments.
In all DT conditions, participants were asked to react as quickly as possible by either a single tap, a
combination of hand/foot taps or a saccade, in response to a randomly presented audio stimulus,
whereas they should maintain the periodic movement as regular as possible. This basic paradigm was
used in eight specific experimental schemes:
1. Experiment 1: normal tapping with tactile feedback of finger tip landing. Participants could
tap as they felt comfortable, without any special instruction for the requested tapping
movement. The goal of this condition was to study the interaction between the discrete
event (e.g. finger beat) as disturbance and the periodic tapping events.
2. Experiment 2: isometric tapping without movement of the fingers. The mechanical
constraints of the executive limbs are not further effective, thus their consideration by the
executive brain level is not demanded. But it reduced the sensory availability of pronounced
discrete events as well as the kinesthetic feedback which both are used for explicit timing
control. Thus, an exaggerated instability of periodic movement was expected.
3. Experiment 3: contact-free tapping ("tapping in the air"). As a mixture of Experiment 1 and
Experiment 2, tactile feedback from the finger tip is avoided by removing of discrete sensory
events such as landing of the finger tip, but maintaining the mechanical movement and
demanding its brain control. Again, a reduced stability of periodic movement was expected
4. Experiment 4: strong tapping. The finger taps should be very pronounced. Peak contact force
aim was individually adjusted to participants (20 percent of the maximum voluntary force).
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The concurrent effect is an increased central control related to each effector’s movements,
and, in addition, an increased sensory information inflow (kinesthetic/tactile) which needed
to be processed.
5. Experiment 5: normal periodic tapping by the dominant hand competing with discrete
reactions including combinations of hand/foot reaction (multi effector responses):
 RH-LF - participants performed periodic tapping with right hand and executed discrete
responses with left foot.
 RH-RF - participants performed periodic tapping with right hand and executed discrete
responses with right foot.
 RH-LH-LF - participants performed periodic tapping with right hand and executed discrete
responses with left hand and left foot together.
 RH-LH-RF - participants performed periodic tapping with right hand and executed
discrete responses with left hand and right foot together.
 RH-RF-LF- participants performed periodic tapping with right hand and executed discrete
responses with right and left feet together.
 RH-LH-RF-LF – participants performed periodic tapping with right hand and executed
discrete responses with left hand and both feet together.
The increase of the motor control load through the demanded integration of multiple discrete
central commands caused an augmented load to central levels, as well as lateralization effects had to
be considered.
6. Experiment 6: as Experiment 1 but with more attention to one of the two movements, which
lead to a prioritization of a task. Subjects were instructed either to react as fast as possible
or to maintain the rhythmic movement as constant as possible.
7. Experiment 7: as Experiment 1 but both tasks were performed on the dominant hand.
8. Experiment 8: as Experiment 1 but as the discrete movement the participants had to perform
a goal directed saccade.
6.3 Signal analysis
Even if some registered data were online processed as required for the closed loop control of the
experiments (e.g. synchronization of the resynchronization pacing to the ongoing tapping), most data
evaluation was performed offline by Matlab scripts running on PC. Data evaluation concentrates on
two aspects:
The conventional timing analysis with extraction of the onset and offset times of motor acts (e.g.
ground contacts of the finger), and
The time course analysis of the analogue signals like position, force, and EMG activity, all of them
imaging the continuous motor control process of the motor activities.
The first task (1) is obligatory required by the aim of the study: DT execution means task
switching, which is assessed by analyzing the timing structure of the motor events. And tapping itself
is a sequence of motor acts (down-movement, up-movement, pause in resting position), which is
characterized by their timing. Thus, detection of these motor events must be performed to obtain a
segmentation of the continuous experimental recordings according to the different sensorimotor
processes.
The second task (2) mirrors the advanced study character of this work, also reflecting the
engineering thinking of causality that the continuous time course of the system outputs signal for a
specific input indicating the system's function. Therefore, time course analysis leading to a
classification of the transients between task states refines the pure time events analysis. Within the
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framework of this study, mainly the change dynamics (e.g. movement gradients, slew rates of slopes,
signal discontinuities, etc.) were investigated in the analogue signals like position and force signals.
All automatic evaluation results were visually checked before further statistical treatment to avoid
misinterpretations of results due to evaluation errors. During this interactive (time-consuming)
control, evaluation errors were corrected if possible, or they led to discard the respective single
response (less than 5% of the responses).
6.3.1 Event detection
The simplest method for event detection is the threshold-based event detection performed on
the sensor data; an event can be captured when the observed signal exceeds this predefined
threshold. As a more sophisticated approach, the maximum likelihood (ML) method computes the
likelihood of a change for each time instant, and the most likely one is taken as the estimate of
change onset time. Using simple amplitude threshold methods, particularly weak and abnormal
signal profiles may lead to high inaccuracy as well as systematic errors. For detection the change
onset of weak and highly variable signal profiles such as EMG-signals, a method bases on statistical
signal processing including a priori knowledge on the generator process of the monitored signal was
described by Staude (2001). In many applications, the change is rather smooth but not an abrupt
one. The linear regression method would miss a local change. For the non-abrupt changes, the rise
time of the change as an additional parameter was considered in a so-called 'step, ramp-step and
ramp profile model' (Hofer, Staude, & Wolf 2004).
The detection procedure used for the evaluation of the force data in normal tapping by this work
is based on conventional simple threshold analysis, because there is a binary structure in the signal:
(1) signal is at baseline level as long as the finger does not contact the sensor surface and (2) signal
shows some substantial component during the impulse-like contact. Therefore, the event detection
is simply achieved by using an adaptive threshold criterion (more details in 6.3.2.1).
Complicated signals such as finger position, isometric force, eye-blink, saccades, and foot tapping,
however, reflect analogue processes for which event detection requests more sophisticated analysis.
Therefore, a more complex event detection scheme based on template matching (Hofer, Staude, &
Wolf 2005) was applied.
6.3.2 The time course analysis of signals
Classical evaluation of experimental tapping data simply determines the intertap interval
sequence (i.e. only the times of contact onsets of the finger tip to the ground plate are further
considered), which equals an abstraction of the continuous position signal by an event time
sequence. Then, this sequence was statistically analyzed. Therefore, inspecting the finger tip position
data in normal tapping, leads to an extended analysis of the tapping movements which reflect the
motor process in more detail.
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Figure 6-12: Force signal time course and events in normal tapping. Note that force (in N) increase is shown
downwards. a) Force response of a single tap out of the periodic sequence; the contact duration L is split into
the landing phase Lp and the resting phase W2. The event markers M1, M2, and M3 are determined through
using two sliding windows W1 and W2 for a moving average approach; these windows are structured into two
parts x and y. For details, see text. b) Example of automatic event detection result which is interactively
controlled subsequently.
6.3.2.1 Simply Structured Signals
A typical force response in normal tapping is shown in Fig. 6-12. A single tap magnified in Fig. 612a looks consists of an initial large force stroke component resulting from the shock-like touchdown
of the finger mass on the inelastic sensor surface (inertia energy); it is followed by a period during
which the finger tip "rests" on the force sensor and the spring-like behaviour of the compressed
tissue becomes effective (second-order process). Both components are individually pronounced; the
higher the velocity of the finger downward movement (i.e. stronger muscle activation), the higher is
the amplitude of the first peak. It determines also the amplitude of the second component but
together with tissue and mass parameters. Since there is no specific instruction on tap force and
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speed given in normal tapping, the overall shape of the force tap response can vary. The events to
detect are: (1) onset M1 indicating finger tip landing; (2) maximum amplitude of the second
component M2; and (3) offset M3 when the finger tip leaves the surface. The used algorithm for
event detection is similar to the approach of Hodges & Bui (1996). For detection of the force onset
M1, a sliding window W1 is shifted sample by sample along the sequence (Fig. 6-12a). The mean
value m of the first x samples of W1 is taken as the baseline level of unloaded force sensor and a
threshold h =  + k*  is defined, where k is a constant factor and  is the standard deviation
estimated from the initial x samples. As soon as the first signal value in the second interval y of W1
exceeds this threshold h, detection of an onset is assumed. Analogously, the offset point M3 is
determined, but now the signal is scanned in the reverse direction, starting at half a pacing period
(i.e. ISI/2) after M1 detection with the condition that the mean value m of the second x samples of
W3 approaches the mean value m of the first x samples of W1, otherwise an arbitrary event notated
by a question mark for this offset is set for visual checking. Finally, the time of the maximum M2 is
determined by estimating the duration of the initial landing period Lp and fitting a parabola to the
signal within window W2 (length L–Lp, ending at M3) to reduce noise effects. The vertex of the
parabola indicates the location M2 of the force maximum during this second contact period. The
algorithm is then restarted for detection of the remaining events. After automatic event detection
was performed throughout all 288 segments, results were visually controlled (Fig. 6-12b). In the case
of an error, the markers can be corrected interactively. This also yields feedback on the performance
of the algorithm and the algorithm can be adapted according to specific signal conditions. This option
is particularly necessary for patient data, for which central motor disorders such as tremors can
dramatically deteriorate the correct detection performance.
Figure 6-13: Position signals (panel a) and corresponding force signals (panel b) in normal tapping. The discrete
tap causes shortening of the corresponding intertap interval of the periodic task (marked) but does not affect
the overall shape of the individual movement, thus a simple detection algorithm would be sufficient.
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Figure 6-14: Position and force signals in normal tapping like in Fig. 6-13. But the shape of the periodic tap is
affected by the discrete tap, which prohibits simple threshold procedures to be effective. Note that the partial
tap during which the index finger does not reach the force sensor surface remains hidden, if simple ground
contact recording is used.
Figure 6-15: The Ramp-Step-Approach for event detection. a) Model function for a ramp-step-like change
which is used for change detection. b) Estimated movement pattern (dashed line) for the position signal shown
in Fig. 6-14a. The three ramp-step segments I, II, III consider the partial tap perfectly.
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6.3.2.2 Complicated signals
Data recordings from finger position, isometric finger tapping, eye blink, saccades, and foot
tapping signals are more complicated, because they reflect the total motor activity of the periodic
and discrete movement Fig. 6-13 shows position and force signals in normal tapping comprising four
periodic right hand taps with a discrete left hand tap between. Basically, the position signals also look
simply structured, even if there is some shortening of the up-phase of the corresponding periodic tap
by the discrete tap. But inspecting an equivalent situation depicted in Fig. 6-14a, a more complicated
signal shape can be recognized obviously due to some severe interaction between both taps.
Therefore, simple analysis with threshold decision will not correctly work in this case, and a more
sophisticated approach like a maximum-likelihood based algorithm (Hofer, Staude, & Wolf 2005)
must be applied. Interestingly to note that such partial taps shown also occur when subjects were
required to tap with both hands but with a 1:2 frequency ratio (Semjen & Summers 2002).
This more sophisticated algorithm is a sequential method which uses a ramp-step model for the
change to be detected. This model of the signal transient shown in Fig. 6-15a is a piece-wise linear
function composed of three concatenated segments describing a single change; the parameters are
the ramp duration τ and the ramp amplitude h as well as the times a, b defining the interval [a, b]
and k indicating the ramp onset. The detection process starts at a predetermined time (e.g. the
beginning of a trial) with a fixed window length (b-a) and optimizes the parameters τ, h, and k such
that the resulting model fits the real signal in the interval [a, b] best. If this fails, the interval is shifted
forward (for details, see Hofer, Staude, & Wolf 2005). The length of the interval [a, b] is crucial: it
must show a minimum length to avoid local optima of the fitting due to some occasional noise
structure, and it must be limited to meet the dynamics of the change sequence. The same template
is used for modeling flexion h<0 and extension movements h>0. Using this algorithm, it was possible
to successfully resolve all the interaction events as shown in Fig. 6-15b.
6.3.2.3 Surface myoelectric signals
The method for the determination of muscle activation changes from surface myoelectric signals
suggested in (Staude, Kafka, & Wolf 2000) is used for the analysis of the EMG recordings. Transitions
between muscle activation patterns are detected as characteristic changes in the variance of the prewhitened surface electromyogram (SEMG) signal.
Figure 6-16: Determination of muscle activation intervals from a SEMG recording. The identified muscle
activation levels are indicated by letters a, b, c. The bold line depicts the square root of the estimated variance
pattern.
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The algorithm comprises three major processing steps. In an initial step, the raw SEMG is
processed by an adaptive whitening filter in order to remove information from the signal which is not
relevant for detection. The whitened signal is then investigated by a decision rule based upon the
approximated generalized likelihood-ratio (AGLR) test which signals possible changes in the variance
pattern. It produces a sequence of change times indicating transitions between stationary epochs of
approximately constant variance. In order to identify different levels of muscle activation, a post
processor performs an unsupervised classification by grouping the detected segments according to
their variances. The variance of each newly detected segment is compared to the already identified
clusters by multiple application of the F-test. If the variance of the current segment significantly
differs from the already identified clusters, a new cluster is created. Otherwise, the segment is
merged with the previous segment. The classification procedure thus creates a sequence of
segments of constant muscle activation together with the variances of their corresponding activation
levels, as illustrated in Fig. 6-16. Using this information, duration and magnitude of muscle activation
patterns associated with the tapping process investigated can be simply derived.
6.4 Phase Resetting Curve (PRC)
6.4.1 General idea
The PRC reflects the principle and degree of interaction between the regular periodic taps and the
discrete taps. It shows the timing of the periodic tapping as a function of the phase within the
periodic tap cycle at which the discrete tap is initiated. If the periodic tapping is almost independent
of the discrete perturbation events, the PRC will show horizontal dot lines with the dots uniformly
distributed over the x-axis. Any deviation from such a pattern indicates some interaction between
the two motor tasks.
Figure 6-17: Construction of a phase resetting curve (PRC). The left diagram shows a part of a PRC magnified,
while the right diagram elucidates the selection of the data points. The shaded area on the right will be then
included to the PRC (indicated by the arrow) on the left.
6.4.2 Phase Resetting Curve construction
As shown in Fig. 6-10 and 6-11 as well as in Fig. 6-17, each segment contains one discrete (single)
tap (saccade) in response to a go signal (right bar), in ST one single eye blink. Within segments, the
following events were defined according to Yoshino et al. (2002):
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
The non-dominant hand (foot) tap (saccade) following the go signal or the eye blink is
considered to be the discrete response (perturbation event) (internally) triggered by the go
signal (eye blink control). The reaction time (RT) in dual-task condition is defined as the
interval between go signal onset and the corresponding response tap (saccade).
 The right hand tap of the periodic tapping occurring just before the single discrete nondominant hand tap or the blink represents the reference tap (reference event) which defines
the time origin (i.e. t = 0) within each segment.
 The onset time tperturbation of the perturbation event (discrete tap, saccade, blink) relatively to
the reference tap is normalized by the interstimulus interval (ISI) of pacing signal and
introduced as perturbation phase Φ = tperturbation/ ISI.
Φ describes the locus of the discrete movement (eye blink) with respect to the cycle of the
periodic tapping, with Φ = 0 (as well as Φ = 1) indicating simultaneous (in-phase) execution of taps
(eye blink) and Φ = 0.5 indicating that the discrete movement (eye blink) is executed in the midst of
the periodic cycle (anti-phase execution). But it should be noted that the definition of Φ does not
consider either the variation of the intertap intervals nor some gradual frequency deviation in
periodic tapping, since it refers to the constant value of ISI = 600 ms (550ms in eye blink
experiments). Thus, Φ values > 1 will be obtained in cases when tperturbation (as defined in Fig. 6-10,
6.11) will exceed ISI, which implicitly requires the actual periodic tap interval to exceed ISI, too. These
cases are very rare since subjects tend to fasten the tapping in the continuation phase, so those
segments were simply discarded (< 1 %). But nevertheless, this fixed normalization reference value
ISI provides some objective comparison between subjects.
For the single-discrete-task data, SRT was determined as the interval between go signal onset and
the corresponding discrete response tap of the non-dominant hand (foot, saccade).
To present the phase changes of the periodic rhythm as a function of the periodic phase that the
discrete tap (saccade) or eye blink is given, the so-called phase resetting curves (PRC) in the form
used by Yoshino et al. (2002) was applied; these curves are derived from the force or position sensor
signals. The PRC shows the intertap intervals (in s) of all segments on the ordinate (dimension: time t)
as a function of the phase Φ plotted on abscissa. The construction of a PRC is demonstrated by Fig. 617:
(1) Each symbol (dot, circle, cross, triangle) in the diagram represents one of the periodic (right
index finger) taps.
(2) Each symbol belongs to a group of six subsequent periodic taps aligned vertically like a column
as shown in Fig. 6-17 by the shaded area on the right; for each discrete tap (saccade) or eye blink,
such a group of six periodic taps is picked out from the sequence.
(3) To determine these six symbols of one group, the discrete tap (saccade) or eye blink is located
in the segment; the corresponding reference tap is determined as the last periodic tap before the
discrete tap (saccade) or eye blink (see Fig. 6-10, 6-11). The reference tap together with the two
periodic taps preceding it and the three periodic taps following it constitute the tap group for one
segment.
(4) This group of six symbols will be added to the PRC such that its reference tap is vertically
aligned to t = 0 (ordinate); the horizontal position of the tap group on the abscissa is determined by
the phase of the perturbation Φ, given by the onset of the single discrete tap (saccade) or eye blink
within this segment. This procedure is performed for all 288 segments of a session in DT condition
and for all number of eye blinks, resulting in a superposition of all the 288 (number of eye blinks) sixsymbol groups in a single graph.
(5) The inclined dashed line above the abscissa (t = 0) represents the occurrences of the single
discrete taps (saccade) or eye blinks (perturbation events).
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The spreading of the PRC-lines in the vertical direction reproduces the tap-to-tap variation of the
observed inter-tap interval - ITIs,. Since all reference tap onsets are aligned to time t=0 within the
frame of each single response, the first PRC-lines above and below the abscissa represent single ITIs
(and the variability of 1 interval), whereas the second PRC-lines above and below reflect the sums of
2 ITIs and, therefore, their larger scatter reflects the double variation span with respect to t = 0.
6.5 Periodic-discrete process Interaction Categories
A main objective of the work was directed to possible interaction between the two tasks which
are concurrently executed within the DT-framework. Interaction is visualized by the PRC
configurations on the basis on the raw data, but can be classified to specific types which were
derived experimentally. Classification can be simply performed on the basis of the event times, but
also by analyzing the movement parameters of the taps.
6.5.1 Classification based on discrete events
The strong interaction occurs in DT OM-DT experiments either as directed effect from one process
to the other, or both processes interact mutually. Both forms of interaction between the periodic
and the discrete tapping process were specified according to the timing structure of the taps given
by:
 the actual interval of the periodic tapping, during which the discrete tap occurs,
 the reaction time (RT) of the discrete tap, and
 the temporal relation between the periodic tap and the discrete tap.
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Figure 6-18: Phase resetting curve construction and basic interpretation. Construction: (A) ideally, if tapping is
not affected by the discrete taps and if discrete taps occur completely independent of the tapping process, the
taps before and after the reference tap would form horizontal lines. (B) If the reactive event fully resets the
tapping oscillator, the next periodic tap would occur with a delay of one full ISI after the discrete tap, thus
these taps build the first inclined post-reference-tap dot-line (parallel to the inclined dashed line). (C) For small
values of Φ, the nearly horizontal dot ‘line’ composed of the right hand taps next after the perturbation event
may indicate a MTI behaviour; but beyond this range of Φ, the next periodic tap for t > 0 occurs simultaneously
with the discrete left hand tap, which can be interpreted as a skip to the final state of the actual periodic
tapping process or hastening of the cycling. (D) Any systematic deviation of a uniform (horizontal) distribution
of the dots over phase indicates the reverse effect of (B): the periodic tapping process controls the discrete tap
timing. Panel D shows an example where the discrete tap onsets are synchronized with the undisturbed
periodic tapping; therefore, dot locations are restricted to particular phases (here: 0 < Ф < 0:2).
The interaction patterns were structured by visual inspection, which results in an abstract
definition of typical interaction categories. In a second step, formal criteria for the parameters which
allow assignment of the actual interaction pattern to a specific category were determined by cluster
analysis. These criteria are specified in the Appendix. (Note that this pattern recognition approach is
purely formal and does not include any physiological aspect.) The defined interaction categories are
(Fig. 6-18):
1) Marginal Tapping Interaction (MTI)
Segments show non visible or only very weak signs of interactions in the evaluation of force
signals.
2) Periodic Tap Retardation (PTR)
Segments show a distinct prolongation of the actual intertap interval of the periodic tapping
This type is further sub-structured into categories:
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- Tap Delaying (TD): The process of the periodic movement is shortly paused by the discrete tap,
- Tap Cancelling (TC): The execution of the ongoing periodic movement is stopped on the fly,
simultaneously with the execution of the discrete tap.
In both cases, the reaction time of the discrete tap is normal.
3) Periodic Tap Hastening (PTH)
Premature execution of the next scheduled periodic tap, i.e. the actual intertap interval
enveloping the discrete tap is shorter than usual.
4) Discrete Tap Entrainment (DTE)
Entrainment of the discrete tap by the periodic tap, i.e. the execution of the discrete tap is
delayed to be in-phase with the next periodic tap. In addition to the directed interaction categories
PTR, PTH (dominant effects on periodic task) and DTE (dominant effect on discrete task), often a
combination of cases PTR and PTH, respectively, with DTE reflecting a mutual interaction may occur.
This type is called
5) Mixed Tapping Strategies (MTS)
Segments show elements of two categories definitively. Detailed quantitative criteria used for
assignment of the segments to these categories are given in the Appendix. Certainly, this formal
separation of the classes is strict, but not really always unique like “black and white” but “grey” with
regard to subjective interpretation by the experimenters; nevertheless, it allows replication of results
and some basic statistical evaluation. It should be considered that the MTI set will also contain a bias
of those segments where the interaction leads to timing patterns looking just normal and unaffected.
6.5.2 Classification based on continuous trajectories of fingers
This approach is based on the a priori knowledge that rhythmic motor actions executed by
different limbs tend to be synchronized, either in-phase or anti-phase. Along this line, 7 different
exclusive conditions were defined as slope interaction schemes:
 down-slope synchronization (DS)
 up-slope synchronization (US)
 2-sided slope synchronization (TS)
 2-sided anti-phase slope synchronization (AP2S)
 1-sided anti-phase slope synchronization (AP1S)
 slope asynchrony in overlapping taps (SA)
 tap asynchrony (TA)
Criteria for this nomenclature will be presented below and are mutually exclusive; but note that
they are heuristically motivated, because the physiological background for these synchronizationconditions is not yet clear and demands for further basic investigations. But obviously, even if the
central go commands for both motor actions (discrete tap and periodic tap execution) are
simultaneously issued to the peripheral plant, variations in the neural processes and influences of
non-stationary biomechanical components will lead to different delays and slope profiles. Therefore,
criteria used in this study will mainly refer to a minimum overlapping period of both actions (i.e.
overlap in time of the periodic right hand tap and the discrete non-dominant hand tap must last a
specified duration at least); because the overlapping periods of the individual as well as of the pooled
data clearly show a non-uniform distribution, i. e an attraction evidently exists. The minimum
duration of this overlapping period used as a criterion for synchrony is determined by the 50% of the
minimum slope period of the two finger trajectories. This criterion is applied for all patterns. The
start and the end of the overlapping is determined by the last begin and the first end of the slope
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period out of two slope periods. For abbreviation the left one-sided coordination or the right onesided coordination (as the number) is used when the interaction is determined on the reference tap
(1) or on the next tap after reference tap (2) respectively.
1) Down-slope synchronization (DS)
Down-slope synchronization will be detected if the overlapping duration of both the down-slope
period of discrete tap and that of the periodic tap exceeds the criterion.
Figure 6-19: Down-slope synchronization (DS) of the periodic tap (blue) and the discrete tap (green) as a
response to the Go command. a) DS case in which the slope of the periodic tap precedes that of the single tap
(DS1), b) DS case with reversed slope sequence (DS2) c) Zoomed DS period (type DS1). The horizontal
continuous line segments (red) indicate the estimation of the actual tap duration, the dotted oblique lines (red)
shows the estimated linear regression lines of the movement trajectories as achieved by the ramp-step-model.
DS is rather rarely.
A sample of DS is shown by Fig. 6-19a, b. Clearly, both down-slopes (upward direction in the
graph) overlap significantly and elucidated by zoomed interval of concern (6.19c). This also applies
for the subsequent up-slopes, which will be discussed later.
As already mentioned in the protocol description above, the temporal relation between the
down-slope of the discrete tap response and the down-slope of the actual periodic tap determines
the definition of the so-called “reference tap” which is the last periodic tap before the discrete tap
response. Since the criterion for slope synchronization requires only some but not full overlapping of
both slopes, the sequence of both down-slope onsets is irrelevant for the DS-classification.
Nevertheless, the sequence is coded by a trailing “1” in case the synchronized periodic tap precedes
the discrete tap and a “2” in case the synchronized periodic tap succeeds to the discrete tap (i.e. DS1,
DS2). The same rules are applied for the other synchronization conditions.
2) Up-slope synchronization (US)
In principle, the decision process is symmetric to the DS condition.
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Figure 6-20: Up-slope synchronization (US). For details, see Fig. 6-19. a) US1, b) US2, c) Zoomed up-slope
synchronization period (type US1)
Fig. 6-20a, c depicts an example for US; the up-slope (downward direction in the graph) of both
the discrete tap (green line) and the concurrent periodic tap (blue curve) are vastly overlapping
(6.20b).
3) 2-sided slope synchronization (TS)
This total synchronization of the discrete tap with the concurrent periodic tap requires both the
DS criterion and the US criterion to be met.
Figure 6-21: a) Down-slopes and up-slopes of both the periodic tapping (thin curve) and the discrete tap (thick
curve) are vastly overlapping. b) Zoomed synchronization period elucidating the overlap period.
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It should be noted that 2-sided slope synchronization always requires that the corresponding tap
durations are of the same size (Fig. 6-21).
4) 2-sided anti-phase slope synchronization (AP2S)
The slope synchronization can happen cross-like, i.e. in anti-phase-relationship. Then, the
overlapping of the up-slope period of the discrete tap with the down-slope period of the concurrent
periodic tap (or vice versa) fulfilled the criterion.
Figure 6-22: 2-sided anti-phase synchronization. a) The downward onset of the discrete tap and the upward
onset of the reference tap started at the same time (or vice versa) on the next half period. b) Zoomed
synchronization period elucidating an overlap period.
The term AP2S1 is used when the up-slope of the periodic tapping synchronize with the downslope of the discrete tap, whereas AP2S2 indicates the reverse sequence (Fig. 6-22).
5) 1-sided anti-phase slope synchronization (AP1S1|AP1S2)
The cross-like slope synchronization can be one-sided.
Figure 6-23: 1-sided anti-phase synchronization: a) AP1S1 The upward slope of the reference tap is coordinated
with the downward slope of the discrete tap while the discrete upward slope was performed during the pause
of the reference tap, b) AP1S2: same as in a, but with reversed signs of the slopes, c) Zoomed synchronization
period elucidates the overlap period.
The other movement slope of the discrete response takes place during the pause of the periodic
finger (Fig. 6-23).
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6) Slope asynchrony in overlapping taps (SA)
In this remaining case, some overlapping of the taps exists but the criteria for slope
synchronization was not fulfilled.
Figure 6-24: slope asynchrony (SA) in overlapping taps. The number again indicates the sequence of slopes as in
DS and US: a) SA1, b) SA2.
Fig. 6-24 shows the overlapping of both tap duration but not the overlapping of the slope
duration.
7) Tap asynchrony (TA)
The total asynchrony is another case of tap asynchrony, referring to the condition that the
discrete tap occurs during the pause of the periodic tapping in the top of the up-position or between
the start of the upslope and the end of the downslope of the periodic tap.
Figure 6-25: Tap asynchrony. The discrete tap occurs in the resting phase of the periodic tapping.
Fig. 6-25a shows that the discrete tap is embedded during the pause of the periodic tap.
Sometimes, a small mirror tap in the right hand can be observed (Fig. 6-25b).
155
6.6 Statistical data analysis
One(multiple)-way analysis of variance (ANOVA) tests were applied to determine whether one
(several) given factor(s) (grouping variables), such as experimental conditions (contact-free,
isometric, strong tap, …) , coordination (unimanual, dual), task (single, dual), etc. have a significant
effect on the mean of certain behavior across any of the groups under study. The ANOVA returns F
statistic, p-value, the sums of squares (SS), degrees of freedom (df), and mean squares (SS/df). Any pvalue near zero casts doubt on the associated null hypothesis, which assumes that the effects of
factors are absent. However, if more than two groups are to be analyzed the one (multiple)-way
ANOVA does not specifically indicate which pair of groups exhibits statistical differences. To
determine which specific pair/pairs are differentially expressed Post Hoc tests multi-compare was
applied in this specific situation. To test for uniform distribution, the 2I-test (Sachs, 1984) or post-hoc
mean comparisons using Bonferroni-corrected t-tests (Sachs, 1984) were used. For instance, the eye
blink phases were divided among k equidistant classes, the 2I-test variable was calculated and
compared to the percentage point for significance level alpha from chi square distribution; the nullhypothesis of the test is that eye blink phases of k classes would occur with equal frequency in the
case that the test variable is smaller than the percentage point.
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7 Results
The obtained experimental data are basically presented using the same structure as for the
description of the experiments in Chapter 6. First, Chapter 7.1 reports the results of ST condition.
Specifically, the results of timing control in single periodic tapping are presented in 7.1.1. The mean
intertap intervals (ITI) and their standard deviation are reported for in all experiment conditions. The
correlations from lag zero to lag 5 of ITIs are presented. ANOVA and the ‘post ad hoc test’ were
applied to compare the performance with factor “coordination” (unimanual (UM) vs. bimanual (BM),
mental tapping vs. normal tapping) and effectors (left hand, right hand, voice). The entrainments of
spontaneous eye blinks by periodic tapping under different experimental condition are described in
Chapter 7.1.2.
Subsequently, Chapter 7.2, 7.3, and 7.4 report the results of DT condition in BM, OM, and handfoot combinations. Chapter 7.2.1 presents the basic interaction patterns illustrated by PRCs and the
time courses. Chapter 7.2.2 investigates reaction times, as well as distributions of discrete tap
occurrences over phase together with PRCs. Chapter 7.2.3 analyzes the continuous time course of
the tapping process by inspecting the shapes of the finger trajectories reflecting changes of the
timing control during interaction. Chapter 7.2.4 reports the effects of the experimental condition on
the tapping behaviors. Also, the BM-DT condition is extended to combinations of hand-foot tapping
which are reported in Chapter 7.3. The final Chapter 7.4 addresses the experiments with saccadic eye
movements as discrete responses; results of oculomanual (OM)-DT experiments are compared to
BM-DT experiments. All the results of the control experiments as references are reported in the
corresponding chapters. As stated in Chapter 6.2.2, the reference values obtained in ST (and also notask) control experiments serve for the comparison between them and the corresponding values in
DT-task (or ST, respectively) experiments. The ST control experiments for DT consist either of
stimulus-induced discrete taps (hand, foot), and saccades, respectively, in the absence of any
periodic movement under normal and/or isometric conditions, or of periodic tapping in the absence
of any discrete task.
7.1 Single-Task (ST) condition
7.1.1 Periodic tapping ST
As mentioned in Chapter 6, the periodic tapping had to meet specific inter-tap-interval (ITI)
criteria for statistical requirements (Mean intertap interval of the actual trial ± four standard
deviations are the boundaries for the mean of the intervals in continuation phase. Because of the
stationary assumption of the Wing-Kristofferson-model (1973 a, b) about the expected interval
length, linear trend was defined by significant (p<0.05) correlation between the length of an interval
and its position (Drewing & Aschersleben 2003)). Therefore, about 30% of four subjects’ trials had to
be repeated due to some ITI outlier. The repetitions were only performed to that extend that
subjects could endure. The result of every trial was considered as one observation of an experimental
condition for calculation of mean, standard deviation and ANOVA analysis.
157
Figure 7-1: One-way ANOVA and multiple comparison procedure were applied to every subject’s (P1,
P2, P3, and P4) tapping in periodic ST experiment. Each group mean is graphically illustrated by a
symbol and an interval around the symbol. The intervals of the means are disjoint or overlap
indicating significantly difference or not significantly difference respectively. The test compared the
variability between the different factors. A) left finger with factor “coordination” (UM, BM): all
experiments (47-54 trials) reveal a significant effect of BM advantage in isometric tapping, but not all
in normal and contact-free tapping. Three of four subjects show BM disadvantage in normal tapping
and two of four in contact-free tapping. B) Right finger with factor “coordination” (UM, BM): the
results reveal BM advantage in isometric tapping. Three of four subjects show BM disadvantage in
contact-free tapping and two of four in normal tapping. C) UM tapping and voice tapping
experiment, (73-81 trials) with factor “effector” (left hand, right hand, and voice): all subjects show
the same pattern that voice tapping has largest variance and - except for P4 - right hand is better
than left hand. Notation for the factor: LE: left hand, RI: right hand, VO: voice. D) (21-27 trials) with
factor “experiment” (normal tapping, normal + mental tapping), one subject performed the 4-group
and two subjects the 8-group condition with factor “experiment”: all subjects show the same pattern
that normal tapping together with mental tapping improve the tapping performance mostly
significantly. Notation for factor: N: normal, N+M: normal+mental.
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Table 7.1: Mean (standard deviation) of ITIs of left hand, right hand and voice in periodic ST experiments, all
values given in ms. Columns 3 and 4 show the left and right hand in UM and columns 5 and 6 in BM condition,
respectively. The mean (standard deviation) of each trial was considered as an observation from an
experimental condition (contact-free ("tapping in the air"), isometric, normal). Generally there is no significant
difference. Note that the used ISI was 400 ms.
Subjects
Manual
P1
Normal
Isometric
contact-free
Normal
Isometric
contact-free
Normal
Isometric
contact-free
Normal
Isometric
contact-free
P2
P3
P4
Unimanual (UM)
left hand right hand
387(12)
393(16)
389(23)
381(15)
382(8)
373(9)
380(10)
377(9)
384(12)
374(12)
375(10)
378(9)
401(6)
389(7)
390(12)
374(8)
386(5)
383(6)
400(12)
404(10)
412(18)
429(18)
386(11)
386(12)
Bimanual (BM)
left hand right hand
384(8)
384(8)
385(10)
385(10)
375(10)
376(10)
379(12)
379(12)
382(11)
382(11)
376(8)
375(8)
408(8)
409(8)
386(11)
386(11)
387(6)
388(6)
404(11)
403(9)
408(5)
408(6)
385(8)
384(8)
Voice
408(13)
384(10)
393(6)
378(5)
Table 7.1 shows mean and standard deviation (in parentheses) of ITI of the left hand, right hand
and voice. Although the antagonist muscle was not activated in isometric tapping, a strong EMG
signal of extensor muscle was found in this condition, too. Generally, no significant difference
between the different effectors was revealed. One-way ANOVA (47-54 trials, as stated that the
repetitions were only performed to such an extent that subjects could endure, the guideline number
was 54 trials (three phases*nine trials per phase) of two conditions (UM vs. BM) with factors
“coordination” (UM, BM) and the post hoc test were applied to every subject’s tapping sequence. In
Fig. 7-1A and 7-1B, the tests show the comparison of variability of the left and right hand between
UM and BM condition. All subjects show a BM advantage in isometric tapping experiments, but not
in all of the other two experimental conditions. Two of four subjects show rather a disadvantage on
the left hand in contact-free, three of four in normal tapping. The right hand data showed the similar
results. The same One-way ANOVA (73-81 trials) with factor effector (left hand, right hand, and
voice) and the post hoc test were applied to every subject’s UM tapping and voice tapping
experiment (Fig. 7-1C). Almost all subjects show the same pattern that voice tapping has largest
variance and, except for P4, right hand is better than left hand. In normal tapping combined with
mental tapping, the 8-group-condition caused a clear positive linear trend of the duration of ITI.
Participants were alerted by the experimenter to raise their slower tapping rate. Hence the criterion
for repeating an experiment due to linear trend was not applied in the comparison between normal
tapping with and without mental tapping because one participant’ BM tapping data still showed a
significant linear trend in 9 of 34 trials in normal tapping (without mental tapping) against 5 of 35 in
normal tapping with mental tapping (counting) on the left finger and 12 of 34 against 7 of 35 on the
right finger, only. The results show that normal tapping together with mental tapping all improved
the performance (Fig. 7-1D).
159
Figure 7-2: The autocovariance of the ITI in manual tapping A) left hand in UM tapping of all subjects. For lag 1,
only P1 shows negative correlation (i.e. direct compensation of a too short ITI by a subsequent longer ITI, and
v.v.) in three experimental conditions, P2 only in normal tapping. Three other subjects show rather some
higher trend to positive correlation (i.e. smooth shifts of the tapping frequency) in isometric tapping. B) right
hand in UM tapping of all subjects. For lag 1, P1 shows negative correlation in normal tapping and isometric
tapping, P4 in normal tapping but with reduced degree compared to the left hand. Again the three other
subjects show a higher trend to positive correlation in isometric tapping than in left hand. C) left hand in BM
tapping of all subjects. For lag 1, P1 again shows negative correlation in all experiment condition, P2 and P3 in
normal tapping and contact-free tapping and P4 in normal tapping. Again, P2, P3, P4 show a higher trend to
positive correlation in isometric tapping than in left hand. Generally, the left hand show stronger negative
correlation compared to UM condition. D) right hand in BM tapping of all subjects. For lag 1 P1 shows negative
correlation in normal and isometric tapping, P2 and P3 in normal tapping and contact-free tapping and P4 in
normal and isometric tapping. P2, P3 show a higher trend to positive correlation in isometric tapping than in
left hand. Generally, the right hand also show stronger negative correlation compared to UM condition.
As a further aspect, the binding between subsequent ITI was investigated by correlation analysis
of the ITI series. A high correlation would indicate an overall integral control of the tapping frequency
generator. Fig. 7-2A, B, C, and D show the ITI correlation functions from lag 1 (direct online control)
160
to lag 5 (more global control) for the left hand and right hand in UM and right hand in BM condition,
respectively.
Figure 7-3: The autocovariance of the ITI in manual, voice and mental tapping. A) The voice intervals pooled for
all show a clear negative correlation at lag 1. B) The intervals of 3 subject’s data in normal tapping combined
with mental tapping. For lag 1, minority of graphs shows a negative correlation at lag 1, in general there is no
specific trend. C) One-way ANOVA (21-27 trials) with factor experimental condition (normal tapping, normal +
mental tapping), and the post hoc test were applied to three subjects’ data. One subject performed 4-group
and two 8-group.The tests compared the mean of correlation lag 1 of successive ITIs between two experiments.
Almost all data show some bias to positive correlation in normal tapping with mental tapping compared to
normal tapping alone. Notation for factor: N: normal tapping, N+M: normal+mental tapping.
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In summary, correlation analysis of ITI (Fig. 7-2A, B, C, and D) shows stronger negative correlation
for both hands in BM condition than in UM condition at lag 1. Voice tapping clearly revealed a
negative correlation (Fig. 7-2A). Three subjects showed higher trend to positive correlation in
isometric tapping. Generally, mental tapping biases the correlation lag 1 to positive direction (Fig.
7.3B compared to 7-2A, B, C, and D). One-way ANOVA (about 27 trials) with factor experiment
(normal tapping, normal + mental tapping) and the post hoc test compared the mean of correlation
lag 1 of successive ITIs between two experiments and revealed a bias to positive correlation in
normal tapping with mental tapping (Fig. 7-3C).
Table 7.2 (A, B): Mean contact forces (Fmean in N) and mean contact durations (D mean in ms) of the right hand
finger tap in UM (A) and BM (B) tapping conditions. Values are shown for all participants (P1 … P7) executing
normal tapping (Exp1), strong tapping (Exp2), and impulse-like tapping (Exp3).
A
Unimanual
Exp. 1
Exp. 2
F mean
D mean
F mean
D mean
F mean
D mean
P1
P2
P3
P4
P5
P6
P7
1.50
1.85
1.18
1.95
1.05
1.23
0.57
115
140
177
119
217
183
116
6.10
4.16
7.59
2.55
2.43
7.83
5.48
174
154
237
131
231
239
154
0.75
1.57
0.80
1.24
0.98
0.82
1.24
46
65
78
77
29
48
61
B
Bimanual
Exp. 1
Exp. 2
F mean
D mean
F mean
D mean
F mean
D mean
1.25
1.60
0.72
2.06
0.78
0.69
0.63
110
117
160
111
127
155
121
7.30
3.60
5.73
2.68
4.40
5.84
6.34
205
137
241
132
227
244
154
0.60
1.52
0.69
1.81
1.01
0.69
0.79
34
62
77
79
23
42
57
P1
P2
P3
P4
P5
P6
P7
Exp. 3
Exp. 3
7.1.2 Periodic tapping and spontaneous eye blinks
The analysis of ST data investigates the question, whether – according to the idea of a possible
common central motor timing - spontaneous eye blinks and periodic tapping reveal a common root;
this should be more obvious in those tapping tasks which require more attention than normal
tapping (Exp.1 in Table 7.2) due to specific instructions like "strong"-tapping (Exp.2) and impulse-like
tapping (Exp.3). Table 7.2 shows the mean contact forces (in N) and the mean contact durations (in
ms) generated by the participants in these three tapping experiments. Capability of all subjects in
performing adequately the required tasks is apparent for both UM and BM tapping experiments: the
mean contact forces were largest in strong tapping whereas mean contact durations were shortest in
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impulse-like tapping. The longest contact durations in strong tapping show that in general, the
required larger peak forces are combined with longer contact durations.
163
Figure 7-4: PRCs for the BM tapping in ST experiments when the eye blink events are taken as discrete
response (disturbance of the periodic tapping process). The row headers indicate participant number and
Experiments 1 (normal tapping), 2 (strong tapping) and 3 (impulse-like tapping), respectively. The upper three
dot-lines of all PRCs stay horizontal indicating that the tapping process is not significantly disturbed by the
preceding spontaneous blinks. However, in particular in impulse-like tapping, the dot symbols are not
uniformly distributed over phase, but show a higher density at small phase values. Presence of such preferred
phases indicates that the onsets of the eye blinks are entrained to some extent by the periodic taps. Scaling of
abscissa is normalized phase, scaling of ordinate is s.
164
Figure 7-5: Phase histograms of eye blinks in BM tapping. Data of tapping experiments and of the reference
experiment are depicted for all participants. All histograms of tapping experiments show a clear shaping of the
distributions compared to the approximate uniform distribution of the corresponding reference experiment. Note
that the scatter of the peaks of the phase histograms indicates individual internal delays.
165
If the eye blink events are formally regarded as discrete motor responses (i.e. disturbance of the
periodic tapping process), PRCs can be constructed to reveal possible interactions between the blink
timing process and the tapping process. The PRCs of all participants for the three BM tapping ST
experiments are displayed in Fig. 7-4; all diagrams show that the six dot-lines are basically horizontal
indicating that the tapping process is not significantly disturbed by the actual spontaneous blink.
However, there is a trend that the dot symbols show a higher density at small phase values. These
preferred phases reveal the entrainment of the onsets of the eye blinks by the periodic taps. The
PRCs of the UM tapping experiments showed a similar horizontal orientation but less pronounced
concentration of dots at the preferred small phases. Differences between UM and BM conditions are
elaborated in more detail below.
The phases of the eye blink events (reflecting the blink timing control) can be depicted in
histograms; independence of the blink timing and the tapping process would predict a uniform
distribution. In Fig. 7-5 (column (2), (3), (4)), these phase histograms for the ST experiments are
shown and they reveal preferred phases of the blink events to occur during the tapping cycle: all
histograms of the BM ST-experiments show peaky distributions (although with different degrees of
modulation), which clearly differ from a uniform distribution as expected for independent processes.
The different locations of the peaks in Fig. 7-5 might indicate some individual internal delays (typical
for each participant). Also, the strength of interference varied between individuals: P1, P4, and P5
showed a very strong effect of tapping upon spontaneous blinking in all experiments. P7 had a less
pronounced shaping of phase distributions for standard (uninstructed) tapping, possibly due to very
light surface contact forces (Table 7.2).
166
Figure 7-6: PRC (A) and phase histogram (normalized by the total number of blinks) (B) of the reference
experiment of participant P1. The phase distribution (B) represents a possible template of a uniform
distribution which is scattered due to the limited observation period of the scattered ITI. Also, the horizontal
dot-lines in the PRC (A) are consistent with the expected behaviour in the case of independent processes. Note
that the scattered ITI causes the spreading of dots and decreasing density at phases Ф~1, too (see text for
details).
The first column in Fig. 7-5 shows phase histograms taken as reference, even if no real reference
experiment can be performed since blinking is an unconscious process. On the other hand, no ideal
statistics (which leads to the assumption of a uniform distribution of phases) can be expected due to
the limited observation period and the limited stationarity of the biological processes. Therefore an
estimation of a "real" reference histogram was achieved by an artificial “experiment”. For this
purpose, the phase analysis was performed with data from different experimental sessions thus
independence was given by principle: the blink event time series was combined with the tapping
time series of another experiment of the same subject (participant P1). The PRC in Fig. 7-6A proves
that the eye blink events do not entrain the periodic tapping in this case (also expected by principle).
The resulting phase histogram (normalized by the total number of blinks) shown in Fig. 7-6B is taken
as the reference for P1; it shows a basically uniform structure even if some structure due to the
variability of the measured ITIs can be recognized. Thus, both diagrams are consistent with the
theoretical expectation for independent processes.
167
Figure 7-7: Phase histograms of eye blinks during unimanual (A) and bimanual (B) tapping. Individual data of
participant P1 are depicted; the column headers (normal, strong, and impulse-like) indicate Experiments 1, 2,
and 3, respectively. Ordinate scaling shows the frequency of occurrence in all panels. All histograms show a
clear shaping of the distributions, being more prominent in bimanual tapping.
Table 7.3: 2I-test assertion of the null hypothesis of "uniform phase distribution", shown for all participants and
all tapping experiments. Larger values of the 2I-test statistic indicate stronger deviations from the uniform
distribution whereas the shaded values mark non-significant differences (p=0.05). The test indicated significant
deviations for the majority of the tapping experiments. Consistently, the expected uniform distribution was
confirmed for the reference experiments in all subjects.
P1
P2
P3
P4
P5
P6
P7
Ref.
21.54
12.87
18.06
17.83
10.85
15.17
14.27
Unimanual
Exp. 1 Exp. 2
36.55 29.64
29.45 38.16
20.09 32.15
40.59 38.34
96.78 115.64
14.51 29.16
17.70 39.01
Bimanual
Exp. 3 Exp. 1 Exp. 2
63.20 69.34 95.05
135.15 98.97 85.77
168.53 44.19 32.66
50.00 84.20 126.83
143.67 218.70 329.14
32.71 35.28 34.29
47.34 15.73
54.22
Exp. 3
101.78
168.51
172.75
94.49
328.74
81.10
44.55
Generally, the phase preference of blink events was more prominent in the BM tapping
experiments than in the UM tapping experiments. Fig. 7-7 demonstrates this difference between UM
(Fig. 7-7A) and BM (Fig. 7-7B) tapping for participant P1what is also typical for the other participants
P2-P7.
The 2I-test (Sachs, 1984) was applied for a quantitative analysis of deviations from the uniform
distribution of the phase. The test was performed separately for each experiment and each
individual. The test asserts the null hypothesis that the distributions are uniform; the decision is
taken on the significant level p<0.05. In order to prohibit false positive decisions due to scattered ITI,
the test was confined to phase values 0<Ф<0.8. The 2I-test values for all subjects are presented in
Table 7.3. Larger values indicate larger deviations from the uniform distribution. Shaded cells mark
2I-test values which did not reach significance while non-shaded cells indicate significant deviation
from a uniform distribution. The test expectedly proves the uniform distribution for the reference
data (blinking only) consistently showing small 2I-test values below the significance limit. By contrast,
for 38 of 42 phase distributions obtained in the ST experiments with tapping, the 2I-test indicated
significant deviations from the uniform distribution. While the four distributions for which the null
hypothesis was not rejected were all obtained in the standard (uninstructed) tapping situation
168
(Experiment 1), both the strong tapping (Experiment 2) and impulse-like tapping (Experiment 3) data
exhibited the non-uniform distributions in all participants. Moreover, with the exception of subject
P7, who generally showed a less pronounced shaping of phase distributions in uninstructed tapping
(cf., Fig. 7-5), 2I-test values were consistently larger in BM tapping compared to UM tapping
experiments. Thus, the concurrently active motor process stronger entrained spontaneous blinking in
case of the tapping task either (i) intensified by instruction, or (ii) performed in the BM condition.
Finally, it should be noted that the modification of Steven's (1886) original tapping paradigm by
introducing the re-synchronizing pace triads did not affect the basic result, namely that the tapping
entrains blinking; this was checked in pilot experiments without resynchronization. However, the
additional pacing signals significantly reduced the ITI variation and prevented frequency drifts in the
self-paced tapping such as drifts in the continuation phase would hamper the clarity of results since
phase is dependent on ITIs. Further, the mean values the three ITIs before, including and after the
blink were 522.37 ms (SD ± 34.00), 525.59 ms (SD ± 34.50) and 521.36 ms (SD ± 35.50), respectively;
thus, it is a clear indication that the blink events (occurring after the reference event) do not
influence the timing of the tapping.
7.2 Dual-Task (DT) condition
The first part 7.2.1 of this chapter will report on the overall tapping behaviour which is comprised
by the classical so-called ‘phase resetting curves’ (PRC) as originally used by Yoshino et al. (2002);
interaction between the periodic tapping process and the discrete response are described for 7
representative subjects performing normal and isometric tapping. The PRC information is
supplemented with the statistical analysis of these subjects’ individual tapping behaviour by Section
7.2.2; also, its effect on discrete-tap timing characteristics which took part in normal and isometric
conditions is addressed. Chapter 7.2.3 focuses on interactions between the periodic and discrete
tapping processes as observed during the transition period, when the finger moves from the resting
position and hit position (contacting surface) (and v.v.) in BM-DT conditions. The analysis used the
ANOVA and the complying multiple-comparison test with significance level of p<0.001. The effects of
experimental condition (contact-free, isometric, strong tapping) on this overall tapping behaviour
were reported in Chapter 7.2.4.
169
Figure 7-8: Typical phase resetting curves for the different tapping behaviours observed in the BM-DT
experiments. a) Marginal Tapping Interaction (MTI): the straight horizontal formation of the dots in the PRC
indicates almost no perturbation of the periodic tapping of the right hand due to the concurrent discrete tap of
the left hand. b) Periodic Tap Retardation (PTR): the inclined formation of the dots (somehow in parallel with
the dashed line) after the perturbation indicates a resetting of the periodic tapping process to the starting
state. c) Periodic Tap Hastening (PTH): similar to the PRC for PTR, the dot ‘lines’ after the perturbation due to
the discrete tap show an inclined orientation, but there is an additional branch starting around phase Φ = 0.26.
Dots on this line represent periodic right hand taps simultaneously executed with the discrete left hand tap. d)
Discrete Tap Entrainment (DTE) combined with Periodic Tap Hastening (PTH): The density of dots is essentially
raised in the phase range from 0 to 0.4, which indicates a preference for concurrent execution of the discrete
left hand tap with the periodic right hand tap. Since intervals of the periodic tapping (ITI) are almost stable
within this phase range, the execution of the discrete tap is delayed until the next periodic right hand tap in
DTE. For larger phase values, additionally a PTH like in Fig.7-8c can be observed. The inclined line indicates the
times of the discrete taps. Symbols: black dots - periodic taps preceding the discrete tap; circles – first periodic
tap after discrete tap; crosses – second periodic tap after discrete tap; triangles - third periodic tap after
discrete tap.
7.2.1 Basic interaction patterns and their PRCs
Because the PRC ordinate represents ongoing time and all reference tap onsets are aligned to
time t=0 within the frame of each single response (see Fig. 6-11), the first PRC-lines above and below
the abscissa (e.g. Fig. 7-8) represent single ITIs (and their variability), whereas the second PRC-lines
above and below reflect the sums of two ITIs and, therefore, their larger scatter reflects the double
variation span with respect to t=0.
Data analysis of the BM-DT experiments by PRCs results in four typical interaction patterns:
(i) Marginal Tapping Interaction (MTI),
(ii) Periodic Tap Retardation (PTR),
(iii) Periodic Tap Hastening (PTH), and
(iiii) Discrete Tap Entrainment (DTE).
They are demonstrated by the respective PRCs in Fig. 7-8. Even this nomenclature is a new result
of this work (prepublication of this work by Wachter et al. 2008), there is a correspondence to
170
Figure 7-9: Tap Delay (TD) in normal tapping (a) and in isometric tapping (b). The execution of the periodic right
hand tap is paused during the execution of the discrete left hand tap. This behaviour for the discrete tap
occurrences (the last two taps) is highlighted, whereas the first discrete tap seems not to cause any change in
the periodic tapping. This figure demonstrates pausing taking place in the up-position of the periodic tapping,
but it happens in down position as well. Ordinate scales are arbitrary. Abscissa: time in seconds.
Figure 7-10: Tap Cancelling (TC) in normal tapping (a) and in isometric tapping (b). When the execution of the
discrete tap starts, the continuation of the periodic tapping is stopped and the finger moves back to the upposition.
the previous state of the art established with the terms “Type 0 Phase Resetting” and “Type 1 Phase
Resetting” by Yoshino et al. (2002). MTI can be interpreted as Type 1 Phase Resetting, while PTR and
171
PTH correspond to Type 0 Phase Resetting. DTE which describes the directed effect of the periodic
tapping on the discrete tap is a novel aspect forwarded by this study, because Yoshino et al. (2002)
only investigated the directed effect of the discrete tap on the periodic tapping. DTE may occur in
combination with each of the other periodic tapping patterns MTI, PTR and PTH.
7.2.1.1 Marginal Tapping Interaction (MTI)
Fig. 7-8a shows PRCs typical for MTI; the occurrence of the discrete left hand tap is indicated by
the inclined dashed line from the origin to the point (abscissa: Φ = 1, ordinate: time = N (= 0.6s).
From the straight horizontal orientation of the upper dot ‘lines’ it follows that the timing of the
periodic taps is almost but not completely independent of the perturbing discrete tap. This
independence was decreased in other condition different from normal tapping.
7.2.1.2 Periodic Tap Retardation (PTR)
PTR is dominant in PRCs like in Fig. 7-8b. The occurrence of the perturbation (i.e. the discrete tap
by the left hand) is somewhere (uniformly distributed) on the inclined dashed line after zero time;
since the cycling of the subsequent periodic tapping restarts at the perturbation event, the next
following periodic tap of the right hand occurs always after some “pausing” interval following the
perturbation, independently of Φ, which results in the inclined dot ‘lines’ for positive times (upper
half of the diagram).
A retardation of the periodic right hand tapping is basically characterized by some pausing of the
periodic tapping process during the execution of the discrete left hand tap. Such a pausing mainly
occurs at two prominent states within the periodic tapping process and causes a distinct
prolongation of the actual ITI. PTR will be described by the following two different cases:
(i) Tap Delaying (TD): The periodic tapping is interrupted by the discrete tap. The subject is pausing
the periodic process either at the up-position (around phase 0.5 (Fig. 6-25a) or at the down-position
(around phase 0, 1 (Fig. 6-20a)) when starting the execution of the discrete tap. The pausing in the
first case causes a lengthening of the periodic tap duration. The periodic process continues later,
after the discrete movement will be finished. Subjects apply this strategy in all conditions (Fig. 7-9a
shows normal tapping, Fig. 7-9b isometric tapping).
(ii) Tap Cancelling (TC): The second kind of PTR is a more severe intervention in the periodic right
hand tapping process by the discrete left hand tap event (Fig. 6-25b: the subject stops the on-going
execution of the periodic movement on the fly at any position before ground contact, when the
discrete movement is launched, and the finger is moved down or up (phase 0.5; note that this
upward movement is against gravity thus needs muscle activation) together with it. Therefore, no
ground contact is observed by the contact force sensors, even if there is a clear tapping activity in the
position signal (Fig. 7-10a) and a strong buckling of the force amplitude in isometric tapping (Fig. 710b).
It should be mentioned that distinction of these two cases appears ambiguous since the
borderline between them is blurred. Especially in isometric tapping, the profiles of the time course
look similar, because the decision is taken on the buckling amplitude only (e.g. Fig. 7-10a right and
Fig. 7-10b left). Certainly, additional features for distinction can be derived from an extended data
set in order to assure the two different interaction patterns TD and TC, but nevertheless this is an
option, because TD and TC are subgroups of the well defined interaction pattern PTR.
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Note that no difference between Tap Delay (TD) and Tap Cancelling (TC) is visible in the PRCs,
because they are based on the timing structure only and TD is not the only cause for this behaviour in
the range of small phases.
Figure 7-11: Periodic Tap Hastening (PTH) in normal tapping (a) and in isometric tapping (b). A premature right
hand tap possibly renders its synchronized execution with the discrete left hand tap.
Figure 7-12: Discrete Tap Entrainment (DTE) in normal tapping (a) and in isometric tapping (b). Delaying the
discrete left hand tap possibly renders its synchronized execution in-phase with the next (periodic) right hand
tap. The plots demonstrate this behaviour by the delayed execution of the left tap with respect to the go signal
(vertical bar).
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7.2.1.3 Periodic Tap Hastening (PTH)
Dominant PTH is demonstrated in Fig. 7-8c und the time course is depicted in Fig. 7-11. For small
values of Φ, the nearly horizontal dot ‘line’ composed of the right hand taps next after the
perturbation event may indicate a MTI-behaviour in almost all subjects showing this pattern in
normal condition. But beyond this range of Φ, the next periodic tap for t > 0 occurs simultaneously
with the discrete left hand tap, which can be interpreted as a skip to the final state of the actual
periodic tapping process or hastening of the cycling. The transient between both states seems to be
limited by the minimum ITI which the individual subject can achieve, maybe due to biomechanical
limitations. The further periodic tapping continues with standard ITIs after this first right hand tap
after/with the perturbation event. This principle shows up either in all conditions (Fig. 7-11a, b show
normal and isometric tapping).
7.2.1.4 Discrete Tap Entrainment (DTE)
As will be shown in 7.2.3.4, the more expressed shortening of the last preceding periods of the
left one-sided coordination (i.e. the interaction is determined on the reference tap (6.5.2)) compared
to the right one-sided coordination (i.e. the interaction is determined on the next tap after the
reference tap (6.5.2)) makes the definition of reference tap difficult and hides the behaviour DTE
(more details in 7.2.3.4 and Fig. 7-28). DTE behaviour also manifests itself in the PRC as a distinct
aggregation of dots at small phase Φ (Fig. 7-8d). Small values of Φ mean that the execution of the
discrete tap is somehow synchronized with the periodic tap, which usually is achieved by delaying
the discrete tap until the simultaneous execution (Fig. 7-12). Thus, subjects with DTE dominance
execute both taps in-phase within a preferred range of phase Φ from 0 to 0.4, so the dot density in
this range is much higher than elsewhere. Note that the overall shape of the PRC in Fig. 7-8d shows
PTH behaviour like in Fig. 7-8c, too, which is another form of an in-phase execution of both taps. But
in addition to the dominant directed effect of the discrete task on the periodic task (PTH, Fig. 7-8c), a
clear entrainment effect of the periodic task on the discrete task can be observed in Fig. 7-8d and Fig.
7-12. This effect indicates a mutual interaction between both. A distinct but less pronounced DTE
component can also be recognized for the MTI subject in Fig. 7-8a.
7.2.2 Dominant tapping behaviour and discrete-tap timing characteristics in
normal tapping
In order to investigate the relationship between PRCs, individual tapping behaviour, and reaction
times in more detail, the distributions of discrete tap occurrences over phase Φ and average reaction
times in ST and DT experiments were determined for each of the 7 subjects in normal and isometric
conditions. Subjects were grouped according to their dominant tapping behaviour and multiple
comparisons of means were conducted to investigate possible effects of interaction patterns on
onset phase and reaction time.
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Figure 7-13: Histograms of onset phase Φ of the discrete taps. Data of the PRCs shown in Fig. 7-8 are shown in
histogram form, also indicating the shape parameters ‘average onset phase Φ av’ of Φ (more details in 7.2.2.2).
Note that even if diagrams show dominant interaction patterns, they all include the total of all 288 responses
of an experiment, i.e. the responses of the other interaction patterns, too. Data for all subjects are summarized
in numerical form by table 7.5.
Table 7.4 (A, B): Proportion of interaction patterns in BM tapping for all subjects depicted for normal tapping
(A) and isometric tapping (B). Values indicate the rate of occurrence in percent. Dominant tapping behaviour
was determined by the most frequent interaction pattern out of PTH, PTR, DTE, and MTS (see definition of
interaction pattern in Section 6.5.1 and Appendix). DTO (discrete tap omitted) represents an error class
indicating that execution of the discrete tap failed. If none of the patterns exceeded a threshold of 25%, the
dominant tapping behaviour was considered MTI.
A
Tapping behaviour [%] in normal tapping
Subject
PTR
MTI PTH TD
TC DTE MTS
Subj. 1 58,33 8,34 20,84 6,25 2,08 3,47
Subj. 2 50,00 8,33 9,03 2,08 13,19 17,02
Subj. 3 62,15 9,72 3,82 2,08 12,85 9,03
Subj. 4 34,03 51,04 9,72 1,04 1,39 2,43
Subj. 5 53,13 0,69 28,82 4,17 2,77 10,07
Subj. 6 79,51 3,82 3,13 0,35 7,29 5,21
Subj. 7 45,59 29,86 9,38 1,04 5,90 7,98
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Dominant behaviour
DTO
0,69
0,35
0,35
0,35
0,35
0,69
0,35
PTR
MTI
MTI
PTH
PTR
MTI
PTH
B
Tapping behaviour [%] in isometric tapping
Subject
PTR
Dominant behaviour
MTI PTH TD
DTE MTS DTO
TC
Subj. 1
Subj. 2
Subj. 3
Subj. 4
Subj. 5
Subj. 6
Subj. 7
52,08
28,82
73,61
35,07
12,15
70,83
35,07
6,94
2,78
2,08
35,42
1,39
4,86
4,86
19,10
32,99
2,08
6,94
64,23
4,17
28,12
10,07
11,11
1,39
6,25
10,42
0,69
9,38
4,17
6,60
11,46
2,43
0,35
8,68
3,13
7,29
17,01
9,03
12,85
11,11
10,42
18,40
0,35
0,69
0,35
1,04
0,35
0,35
1,04
PTR
PTR
MTI
PTH
PTR
MTI
PTR
7.2.2.1 Interaction patterns and dominant tapping behaviour
The percentages of interaction patterns for 7 subjects are presented in table 7.4. Generally, all
interaction patterns defined in Section 6.5.1 were observed in all subjects. A preference for one of
dominant tapping behaviours was expressed by a dominant occurrence frequency of this interaction
pattern. The subjects’ dominant tapping behaviour was determined by the criteria that it exceeded a
threshold of 25% out of PTH, PTR, DTE and MTS. If none of the patterns was found, the dominant
tapping behaviour was considered MTI.
7.2.2.2 Discrete taps: Distributions of their phase Φ and reaction times
If the execution of the discrete taps is independent of the periodic tapping, the distribution of the
onset phase Φ (within this cycle of periodic tapping) of the discrete taps is expected to be uniform
because the imperative stimulus triggering the discrete tap was presented independently of the cycle
of the periodic tapping. Any deviation from the uniform distribution indicates some interaction of the
discrete and the periodic motor task. Fig. 7-13 depicts distributions of onset phase Φ of the discrete
taps of the same data in Fig. 7-8. The relative phase-independent effect of the periodic task on the
discrete task from Subject 5’s data is presented by the phase distribution somehow near to a uniform
one (Fig. 7-13b). By contrast, the strict preference of small phase values in Fig. 7-13d indicates a
strong DTE component, i.e. the execution of the discrete tap happens concurrently with the
execution of the (pacing) next periodic tap for Subject 7. Also, a tendency to in-phase and (less
expressed) anti-phase synchronisation of the discrete tap can be recognized in Fig. 7-13a.
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Table 7.5 (A, B): Average onset phase Φav, skewness S of onset phase distribution, and average reaction time
RTav of DT and SRTav of ST conditions for all subjects and experiments.
A
Subject
Subj. 1
Subj. 2
Subj. 3
Subj. 4
Subj. 5
Subj. 6
Subj. 7
B
Subject
Subj. 1
Subj. 2
Subj. 3
Subj. 4
Subj. 5
Subj. 6
Subj. 7
normal tapping
Φav S
RTav SRTav
0.41 0.15 227 197
0.41 0.10 395 191
0.42 0.19 331 211
0.44 0.10 240 240
0.51 -0.08 258 195
0.40 0.29 281 219
0.27 0.85 322 213
isometric tapping
Φav S
RTav SRTav
0.37 0.54 182 161
0.43 0.15 282 196
0.48 0.14 227 230
0.27 0.90 197 174
0.50 0.25 199 168
0.35 0.60 210 219
0.40 0.28 250 217
dominant behaviour
PTR
MTI
MTI
PTH
PTR
MTI
PTH
dominant behaviour
PTR
PTR
MTI
PTH
PTR
MTI
PTR
The 2I-test (Sachs, 1984) revealed significant deviations from the uniform distribution for all
subjects and experiments (p < 0.01) except for the normal tapping data of Subject 5 (Fig. 7-13b). The
m3
average onset phase Φav and the skewness S =
3
of the phase distribution would make the
m2 2
statement about task interaction and were determined for each DT trial, where m3 is the sample
third central moment and m 2 is the sample variance. A perfectly symmetric distribution corresponds
to shape parameters Φav = 0.5 and S = 0. Phase values deviating from Φav = 0.5 and a positive or
negative skew S indicate an asymmetric distribution and, consequently, task interaction. These shape
parameters Φav and S are summarized for all subjects and experiments in table 7.5. Moreover,
average reaction times RTav of the DT experiments and SRTav of the ST reference experiments are
shown (table 7.5).
A 2-way ANOVA (24 repetitions with 12 responses per subject and experiment, 7 x 24 x 2 = 336
samples) with factors subject (1…7) and experiment (normal, isometric) revealed a significant main
effect of subject on all variables (Φav: F(6, 322) = 22.70,S: F(6, 322) = 7.37, RTav: F(6, 322) = 76.62, all p
< 0.0001). Moreover, there was a significant main effect of experiment on RTav (F(1, 322) = 318.77, p
< 0.0001) and skew S (F(1, 322) = 9.17, p < 0.01) but not on onset phase Φav (F(1, 322) = 0.61).
Post-hoc mean comparisons using Bonferroni-corrected t-tests (Sachs, 1984) revealed that both in
normal and in isometric tapping six out of the seven phase distributions were roughly uniform.
Average values of Φav = 0.43 and a slightly positive skew S = 0.23 of these subjects’ data is consistent
with a small peak at Φav < 0.4 in the corresponding histograms and the increased dot density in
corresponding PRCs (e.g. Fig. 7-8a, Fig. 7-13a). This indicates that the majority of discrete taps was
initiated independently of the phase of the periodic movement in these subjects, even if there was
some tendency to in-phase and anti-phase tapping of the right and left hand. A significantly more
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pronounced asymmetry and a strict preference of in-phase tapping were found for Subject 7 in
normal tapping and for Subject 4 in isometric tapping. The shape parameters of these two subjects
changed between experiments (p < 0.001) while differences in phase distributions of the other
subjects did not reach significance. Interestingly, Subject 4 showed a close to uniform distribution
(Φav = 0.44, S = 0.10) in normal tapping but changed to a more asymmetric distribution (Φ av = 0.27, S
= 0.90) in isometric tapping. By contrast, Subject 7 showed a positive skew (Φ av = 0.27, S = 0.85) in
normal tapping (c.f. Fig. 7-13d) but a roughly uniform distribution (Φav = 0.40, S = 0.27) in isometric
tapping. This decreasing asymmetry was accompanied by a change in dominant tapping behaviour
from PTH to PTR (table 7.4). By contrast, despite of the significant change in shape parameters,
Subject 4 showed dominant PTH both in normal and isometric tapping, while Subject 2 switched from
MTI to PTR behaviour without remarkable changes in shape parameters. Thus, an alteration of
dominant tapping behaviour was not necessarily accompanied by a corresponding change in shape
parameters and vice versa.
For the statistical analysis of reaction times, ST data SRTav were grouped in 24 repetitions with 3
responses per subject and experiment. Together with the DT reaction times RTav, they were analyzed
by a 3-way ANOVA (Sachs, 1984) with factors subject (1…7), experiment (normal, isometric), and task
(single, dual) which revealed significant main effects of all factors (subject: F(6, 644) = 74.73,
experiment: F(1, 644) = 330.32, task: F(1, 644) = 526.87, all p < 0.0001) on RT. Post-hoc mean
comparisons revealed that, generally, RT was shorter in isometric tapping than in normal tapping.
This may reflect the fact that in isometric tapping the first detectable change in the force signal
almost directly images the changes in muscle contraction while in normal tapping the onset in the
force signal indicates the time of first ground contact of the finger tip. Depending upon the subject's
individual initial (‘resting’) position of the finger above ground level, the latter occurs with some
delay which is reflected by the prolonged average RT.
Figure 7-14: Multiple comparison of average reaction times of single-task (empty circles) and dual-task (filled
circles) experiments for normal (a, b) and isometric (c, d) tapping. Horizontal bars represent the tolerance
intervals. Disjoint intervals indicate that mean values are significantly different (p < 0.05).
Average reaction times RTav and SRTav for all subjects are depicted in Fig. 7-14. Generally, RTav of
the DT experiments were longer than SRTav of the ST reference experiments (normal: 293 ms > 209
ms, isometric: 221 ms > 195 ms, p < 0.001) indicating a clear effect of task complexity on RT. As can
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be seen from Fig. 7-14a, in normal tapping the prolongation of RT was more pronounced for the
subjects with dominant MTI behaviour compared to the subjects with PTR or PTH. Despite his
dominant PTH behaviour, Subject 7 shows a larger delay similar to that of the MTI subjects which is
consistent with the asymmetric phase distribution indicating a strong tendency to in-phase tapping
and a distinct DTE component for this subject. The effect of directed interaction on RT is even more
obvious in Fig. 7-14b where subjects were grouped according to their dominant behaviour.
Interaction patterns PTR and PTH which are assumed to predominately affect the periodic task only
show a small delay of 46 ms and 55 ms, respectively, while dominant MTI behaviour is associated
with a significantly larger prolongation of 128 ms, which indicates some (hidden) mutual interaction
of both tasks. Interaction effects were less pronounced in isometric tapping (Figs. 7.14c and 7.14d).
Generally, prolongation of RT due to the additional periodic task (26 ms) was smaller than in normal
tapping (84 ms). Moreover, this prolongation is mainly caused by Subject 2 who reported a general
difficulty to coordinate the two motor tasks; maybe, this problem is reflected by the fact that this
subject had the longest RT in DT both in normal and isometric tapping. For four subjects, there were
no significant differences between SRT (ST) and RT (DT).
It should be noted that, despite the obvious statistical significance of the differences in RT shown
in Figs. 7.14b and 7.14d, conclusions on the causal relationship between dominant tapping behaviour
and response latency have to be drawn with care. While the estimates of Φ av, S, RTav, SRTav are based
upon a sufficiently large number of samples (24 repetitions with 12 responses per subject and
experiment), the classification according to the dominant tapping behaviour is based on the
comparably small number of seven subjects being assigned to the three classes PTR, PTH and MTI.
Therefore, latencies associated with a particular tapping behaviour may be biased by the individual
RT of the corresponding subject(s). Nevertheless, the general consistency of the results obtained by
heuristic analysis of tapping behaviour and the results obtained by statistical analysis of the
"objective" variables Φav, S, RTav, SRTav indicate a close relationship between dominant tapping
behaviour and discrete-tap timing characteristics.
Note that even if each diagram in Fig. 7-8 presents a dominant tapping behaviour, it always
comprises all data of an experiment, i.e. all occurrences of the other interaction patterns, too.
Moreover, since the four typical appearances of PRCs and corresponding phase distributions for
normal and isometric tapping look very similar, only data of normal tapping are presented.
7.2.3 Time course analysis of the tapping process
Analysis of tapping behavior based on the continuous trajectories of fingers is another possibility
to assess the motor coordination problem. The nomenclature for the shape conditions was described
in 6.5.2 and should be shortly repeated; 7 different exclusive conditions were defined as slope
interaction schemes:
1. down-slope synchronization (DS)
2. up-slope synchronization (US)
3. 2-sided slope synchronization (TS)
4. 2-sided antiphase slope synchronization (AP2S)
5. 1-sided antiphase slope synchronization (AP1S)
6. slope asynchrony in overlapping taps (SA)
7. tap asynchrony (TA)
If the interaction is determined on the reference tap, a “1” follows the letters (e.g. DS1), and on the
next tap after the reference tap, a “2” is written (e.g. DS2).
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Phase
Figure 7-15: Phase distribution of pooled data: a) the left one-sided coordination (TS1; DS1; US1; SA1; AP1S1)
cover small phases b) the rest (TA; AP2S) cover the middle phases c) the right one-sided coordination (TS2; DS2;
US2; AP1S2, SA2) cover the large phases.
Phase
Figure 7-16: pooled phases separated in two classes of fast reactions (upper diagram) and slower reaction
times (slower diagram). The strict preferred left one-sided coordination has almost fast reaction time whereas
the right one-sided coordination has the delayed reaction for the next preferred coordination.
Fig. 7-15 shows the phase distribution pooled for all subjects for the left one-sided coordination
(TS1, DS1, SA1, US1, AP1S1), the right one-sided coordination (TS2, DS2, SA2, US2, AP1S2), and the
rest (TA, AP2S) and Fig. 7-16 all together separated by fast and slow RT in two classes. The hidden
mutual interaction of both tasks is now more pronounced. On one side, the effect of the discrete tap
on the periodic tapping is clearly indicated by preferred phases (Fig. 7-15a, c). On the other side, the
effect of the periodic tapping on the discrete tap resulting in a faster RT is mainly appearing for small
phases (Fig. 7-16, upper panel), and delayed reactions mainly happen with phases between 0.3 and
0.8 (Fig. 7-16, lower panel). Data were pooled to obtain large amount of data for every coordination
pattern. Inspecting the finger tip position data in normal tapping and the force data in isometric hand
(foot) tapping revealed the phase relationships between the discrete left hand tap and the periodic
right hand taps more clearly, and the coordinated movement behaviour can be specified. Generally,
all trajectory coordination patterns can be observed in all subjects but with different probability of
occurrence depending on which interaction pattern is dominant. Based on the sided interaction of
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discrete taps with two neighbouring periodic taps, the coordination patterns are grouped in four
classes. The first most frequent class is the left one-sided coordination (DS1, SA1, US1, and AP1S1),
the second one is the right one-sided coordination (DS2, SA2, US2, AP1S2). These two most
frequently classes reveal the synchronization even without any detailed analysis,. The other two less
frequently occurring conditions are two-sided cross-like coordination (AP2S) as well as the total
asynchrony (TA).
7.2.3.1 Tap duration adjustment
The tap durations of the affected periodic taps and the other neighbouring taps and the discrete
taps were mutually compared for the different coordination patterns.
Figure 7-17: Distribution of the discrete tap durations and the four neighbouring periodic tap durations. PTD:
periodic tap duration; DTD: discrete tap duration; index: 0 the reference tap, -1 the last periodic tap preceding
reference tap, +1, +2 the 2 succeeding periodic taps. The shorter discrete tap durations are accompanied by
shorter reference tap durations (left diagram for (TS1, DS1)) and shorter durations of the periodic taps
succeeding the reference taps (right diagram for (TS2, DS2)), respectively.
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Figure 7-18: ANOVA was used to compare the means of the observations in the tap duration of the four
neighbouring taps (two preceding ptd  2 , ptd 1 , one succeeding ptd  1 and the reference tap td 0 ) and the
corresponding discrete taps (dtd). The test asserted that the affected periodic tap duration (reference taps in
(A (TS1), upper panel) and taps succeeding reference taps in (B (TS2), lower panel) have means significantly
different from the others tap durations.
Figure 7-19: Distribution of the discrete tap durations and the four neighbouring periodic tap durations, same
as in Fig.7-17 but now for the conditions US1 and US2. PTD: periodic tap duration; DTD: discrete tap duration.
The reference tap durations were prolonged (left diagram) for embedding of the discrete taps whereas the
discrete taps were prolonged for embedding of the periodic taps succeeding the reference taps. The embedded
taps were slightly shortened in both cases (PTD 0 in left diagram and PTD 1 in right diagram).
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Figure 7-20: Distribution of the discrete tap durations of TA. A well-formed distribution (~ normal distribution)
indicates the preferred combination of both tap duration to obtain the required timing of periodic task.
Generally, the short discrete tap durations due to their impulse-like characteristic equalized the
affected periodic ones in many subjects. This gives evidence for synchronisation. This effect is more
pronounced in (TS1, TS2) (Figs. 6.21 7.17) and also in part of (SA1, SA2) when the both fingers are
near enough from each other. The ANOVA revealed that the affected periodic taps have mean
durations significantly different from those of neighbouring taps but within them do not (Fig. 7-18).
For the embedding of the discrete tap into the periodic tap for a common upward movement,
prolongation (TD) of the enclosing tap duration and shortening of the enclosed tap duration is the
used strategy in (US1, US2) (Fig. 6-20, 7-19). The quick inserting of the discrete tap during the resting
phase (up-position) of the periodic finger to obtain stable timing reflected their well-form
distribution and their variability in other cases (Fig. 6-25b, 7-20). This corresponds to the most
preference of simple related frequency ratio 2:1 in the performance of polyrhythms (Summers 2002).
7.2.3.2 Slope duration adjustment
The comparison of the periodic slope durations between the affected taps and other
neighbouring taps and the comparison of discrete slope durations between different coordination
patterns were also performed.
Figure 7-21: Distribution of the discrete downslope durations and those of the four neighbouring periodic
downslope durations. PD: periodic downslope; DD: discrete downslope; index: 0 the reference downslope, -1
the last periodic downslope preceding reference downslope, +1, +2 are the corresponding for the succeeding
periodic downslopes. The fast discrete downslope durations shortened the reference downslope durations (left
diagram for (TS1, DS1)) and the periodic downslope durations succeeding the reference taps (right diagram for
(TS2, DS2)).
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Figure 7-22: Distribution of discrete upslope durations. The longer periodic upslope durations prolonged the
discrete upslope durations when they were overlapping (upper diagram) compared to cases when they do not
(lower diagram).
Figure 7-23: Distribution of periodic upslope durations in one-sided cross-like coordination. The shorter
discrete downslope durations accelerated the coordinated reference upslope durations (AP1S1, upper panel)
compared to the non-coordinated case (AP1S2, lower panel).
The faster discrete downslopes shortened the synchronized periodic downslopes in (TS1, DS1,
TS2, DS2) (Figs. 6-19, 6-21, 7-21), whereas the faster discrete upslopes were prolonged by the slower
periodic upslopes in (TS1, US1, TS2, US2) (Figs. 6.20 a,b, 6-21, 7-22). Again, this trend was also found
in part of (SA1, SA2). Upward movements often ended together although they started at different
time or at different amplitudes. In one-sided cross-like coordination the upslope duration of the
reference taps were shortened by the fast discrete downslopes (Fig. 6-23a, 7-23).
7.2.3.3 Trajectories adjustment
In two-sided cross-like coordination (AP2S) the attraction of the faster downslope duration of one
finger on the slower upslope duration of the other one leaded to a more harmonic form of both
trajectories (Fig. 6-22). A speedup of the periodic tapping in PTH was presented by a stronger
deflection (as an ahead abort of its process) and entrained by the discrete one (Fig. 6-20b). The
184
insertion of the discrete tap into the periodic tap was often combined with the shortening of the
discrete tap duration and lengthening of the periodic one (Fig. 6-20).
Figure 7-24: Distribution of affected ITIs (middle Diagram) and two neighbouring ITIs (upper and lower
Diagrams) in TA. The affected ITIs were strong retarded or even reset.
Figure 7-25: Distribution of affected ITIs in (SA1, US1) separated in small phases and large phases. The larger
the phases are the farer the discrete taps from the reference tap and the longer are the periods.
185
Figure 7-26: Distribution of affected ITIs in (AP1S1) separated in small phases and large phases. The larger these
phases are the farer the discrete taps from the reference tap and the more instable are the periods.
Figure 7-27: Distribution of affected ITIs (2nd Diagram) and two neighbouring ITIs (1st and 3rd Diagrams) in twosided, cross-like coordination. The affected ITIs were contracted on both sides.
186
Figure 7-28: Distribution of ITIs preceding the discrete taps separated in two groups of coordination patterns.
Diagram a) and b) contain ITIs separated in two groups of coordination patterns (TS1, DS1, AS1, US1) and (TS2,
DS2, AS2, US2), respectively. Diagram c and d contain the first group separated in two classes of lower reaction
times (<mean (RT) – standard deviation (RT)) and of higher reaction times (>mean (RT) + standard deviation
(RT)), respectively. The left one-sided coordination (TS1, DS1, AS1, and US1) had more shortened ITIs than the
right one but these ITIs with longer RT were shorter than ones with shorter RT.
7.2.3.4 Periodic timing adjustment
The strong PTR caused by TD or TC and almost found in TA is shown in Fig. 7-24. In normal
tapping, although the range of small phases enclosing most (TS1, DS1, SA1, US1, and AP1S1) cases
shows that timing is relatively stable, a weak PTR is hidden in (SA1, US1). The subsequent cycling was
started at the perturbation events. This is indirectly shown in the distribution of affected periods
over phases  separated in small and large phases (Fig. 7-25). The affected periods were longer when
the discrete taps are farer from the reference tap. The instability of AP1S1 is shown in Fig. 7-26. In
two-sided cross-like coordination (AP2S) the affected periods were often shortened (Fig. 7-27) due to
the attraction of the faster down periods on the slower up periods as mentioned above.
Depending on reaction time, the right one-sided coordination, where the both movements are in
the same direction, could result in PTH or a mix of PTH with DTE behaviour. In PTH, the launching of
the discrete tap reset the phase of the periodic tapping process and causes a premature execution of
the next periodic tap. Instead of PTH of the right hand tap to maintain the prescribed period stable,
187
the execution of the discrete tap is postponed until the next possible stable coordination emerges,
which leads to DTE. A trade-off between reaction time and timing requirement would yield in the
mixed behaviour (DTE with PTH). The more shortening of the last preceding periods of the left onesided coordination compared to the right one indicates the challenge of the definition of reference
tap and hides the behaviour DTE (Fig. 7-28).
7.2.3.5 Force adjustment
The overall difference in forces of five periodic taps around discrete taps was estimated by
ANOVA and the complying multiple comparison test. Force of the periodic finger was increased when
it was performed together with the discrete finger on the hard surface (ANOVA, F(1, 288)=7.78,
P<0.0001).
7.2.4 Effects of physiological parameters
ANOVA and the complying multiple-comparison test were used to determine the overall
difference in amplitudes of five periodic taps around discrete taps in the contact-free condition of 5
subjects’ data. Movement amplitude of the synchronized periodic finger was often decreased in
comparison with other periodic amplitudes around (ANOVA, F(1, 288)=7.78, P<0.02). Visual
inspection of position signals shows a marked asymmetry in normal tapping compared to the
contact-free-tapping particularly during the continuation phase, i.e.. the flexion or downward phase
of the movement has a much steeper slope than the extension or upward phase in normal tapping
while they are more harmonic in contact-free condition. Thus, the biomechanical differences
between flexion and extension are not significantly attributable to the difference between the two
phases of downward and upward movement in a given cycle. The following results also are apparent
from position signals and traces in PRCs without any complicated analysis. The TD and TC were
increased in isometric and particularly TC in contact-free condition. This stopping clearly shows up as
a strong buckling of the amplitude. The tap delay and cancelling yielded in an incline to higher
degree of PTR, i.e.. it trends to Type 0 Phase Reset even in the first half of the periodic tap interval. It
is opposite in strong tapping that within the second half of the periodic tap interval TC was reduced
and the premature execution of the next periodic tap together with launching of the discrete tap was
increased. Even in strong PTR behaviour where the inclined formation of the dots are in parallel with
the dashed line of the discrete tap after the perturbation in normal tapping, These dots are now
falling down on the dashed line The reduction of TC was combined with DTE and PTH and a clearer
synchronization was presented by an increased density of the dots around small and large phases in
PRC. The MTI-behaviour hence degraded in isometric and contact-free conditions.
188
Phase
Figure 7-29: Phase distribution of 5 subjects in the attention condition (focus on both tasks (left column), focus
on periodic task (middle column), and focus on discrete task (right column)).
189
Time (ms)
Figure 7-30: disturbed ITI distribution of same 5 subjects in the attention condition (focus on both tasks (left
column), focus on periodic task (middle column), and focus on discrete task (right column)).
190
Time (ms)
Figure 7-31: Reaction time distribution of the same 5 subjects in the attention condition (focus on both tasks
(left column), focus on periodic task (middle column), and focus on discrete task (right column)).
The phase distributions in the attention condition performed under one of the two timing
instructions ‘‘focus on periodic’’ and ‘‘focus on reaction’’ show the 2-modal form with higher
clustered distribution in the first condition (Fig. 7-29). The I2-tests established that almost all
distributions were significantly different from a uniform distribution (α < 0.01), however the test
variables of the first condition were higher as well as the constraint on the discrete movement onset
was visibly more pronounced. ANOVA (24 repetitions with 12 responses per experiment, 24 x 2 = 48
variance samples) with factor instruction (focus on periodic, focus on reaction) was performed on the
191
variation of the ITIs around disturbance for every subject. The ANOVA showed a significant main
effect of instruction (α < 0.05). The variability of the first succeeding ITI was higher when the subjects
with strong PTR paid attention on periodic movement than when they paid attention on discrete
reaction. The affected periods of the other subjects were more shortened causing the mean value
highly lower than the pacing frequency when they focused on reaction whereas the mean value
approached the pacing frequency when they focused on periodic movement (Fig. 7-30). All subject
approximately reached the same mean reaction time when they focused on the task (Fig. 7-31),
The comparison between normal tapping and strong tapping showed higher clustered phase
distribution of the 2-modal form, the larger 2I-test variables for uniform distribution of phases as
well as the constraint on the discrete movement onset was visibly more pronounced and the reduced
variability of the succeeding ITIs in strong tapping, the affected period of subjects with strong PTR
behaviour were shortened such that a change to PTH is obvious.
7.3 Hand-Foot condition
Analogously to the LH condition, the participants of the response conditions LF, RF, LH-LF, LH-RF,
RF-LF, and LH-RF-LF reproduced each of the different types of coordination patterns and also the
dominant behaviour while the other types also appeared in some trials and furthermore they can be
observed with discrete foot responses, too.
Table 6: The Proportion of interaction patterns MTI, PTR, PTH, DTE, and MTS (in %) in different response
conditions for all participants. The amount of discrete taps (segments) considered is between 276 and 288 for
each effector combination (variation is due to discarded erroneous segments). The dominant tapping pattern
values are highlighted.
LH
P MTI
1 33.2
2 69.7
3 48.5
4 36.9
5 27.8
6 90.2
LH-RF
P MTI
1 43.4
2 54.9
3 25.5
4 37.6
5 30.0
6 90.6
PTR
18.2
18.5
18.8
55.8
29.9
0.3
PTR
17.4
11.3
31.2
15.4
39.2
7.7
PTH
41.3
11.1
30.3
0.0
40.3
8.7
PTH
28.8
32.3
39.6
31.1
29.0
1.2
DTE
2.1
0.7
1.7
0.3
0.7
0.4
DTE
2.8
1.0
0.4
3.1
0.4
0.2
MTS
5.2
0.0
0.7
7.0
1.3
0.4
RF
MTI
38.0
38.0
36.8
52.1
38.3
96.9
MTS
7.6
0.5
3.3
12.8
1.4
0.3
LH-LF
MTI PTR
41.0 17.8
40.3 33.7
34.7 45.0
44.5 43.9
30.3 34.4
88.5 11.3
PTR
10.5
20.6
42.0
14.2
24.0
1.4
PTH
20.9
39.7
19.1
27.1
34.5
1.0
PTH
29.5
25.5
18.4
9.3
31.7
0.0
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DTE
8.0
0.0
0.7
2.8
0.7
0.7
DTE
3.8
0.5
0.5
0.2
1.8
0.0
MTS
22.6
1.7
1.4
3.8
2.5
0.0
LF
MTI
42.0
63.2
45.6
47.6
24.6
97.9
PTR
10.3
18.7
39.3
43.7
38.9
0.3
PTH
9.2
18.1
14.7
1.4
34.5
0.4
DTE
15.2
0.0
0.0
0.7
0.3
1.4
MTS
23.3
0.0
0.4
6.6
1.7
0.0
MTS
7.9
0.0
1.4
2.1
2.8
0.2
RF-LF
MTI PTR
41.0 25.3
42.7 20.2
46.0 46.8
36.8 31.3
31.2 31.0
91.0 7.4
PTH
18.4
35.3
4.3
20.1
30.3
0.9
DTE
4.0
0.9
0.6
2.1
0.7
0.5
MTS
11.3
0.6
2.2
9.7
6.8
0.2
LH-RF-LF
P MTI PTR
1 41.1 13.8
2 44.7 17.6
3 33.9 33.3
4 41.0 16.2
5 28.5 34.8
6 85.2 14.2
PTH
31.3
37.2
27.8
38.8
25.5
0.6
DTE
2.0
0.0
0.8
0.7
4.4
0.0
MTS
11.8
0.5
4.0
3.2
6.8
0.0
Table 6 shows the percentage of each of the interaction types for all participants and all conditions.
Figure 7-32: Phase resetting curves (PRC) for single response conditions (see list in text): (A) PRC for LH (in LH
condition), (B) PRC for LF (in LF condition), and (C) PRC for RF (in RF condition), all for Participant 3. In all cases,
the dot lines above abscissa (i.e., taps after the reference tap) are clearly inclined, revealing a strong interaction
between the periodic tapping and the discrete motor responses.
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2.5
A1
LH
A2
LF
2.0
1.5
Time (s)
1.0
0.5
0
- 0.5
- 1.0
- 1.5
2.5
B1
LH
B2
RF
C1
LF
C2
RF
2.0
1.5
T ime (s)
1.0
0.5
0
- 0.5
- 1.0
- 1.5
2.5
2.0
T ime (s)
1.5
1.0
0.5
0
- 0.5
- 1.0
- 1.5
0
0.2
0.4
0.6
0.8
1
1.2
Phase
0
0.2
0.4
0.6
0.8
1
1.2
Phase
Figure 7-33: Phase resetting curves (PRC) for double response conditions (see list in text): (A1) PRC for LH (in
LH-LF condition), (A2) PRC for LF (in LH-LF condition), (B1) PRC for LH (in LH-RF condition), (B2) PRC
for RF (in LH-RF condition), (C1) PRC for LF (in LF-RF condition), and (C2) PRC for RF (in LF-RF
condition), all for Participant 3. Again in all cases, the dot lines above abscissa (i.e., taps after the reference tap)
are clearly inclined, revealing the same strong interaction effect for double discrete responses like observed with
single responses shown in Fig. 7-32.
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Figure 7-34: Phase resetting curves (PRC) for triple response condition (see list in text): (A) PRC for LH (in LH-LFRF condition), (B) PRC for LF (in LH-LF-RF condition), and (C) PRC for RF (in LH-LF-RF condition), all for
Participant 3. Also for triple discrete responses, the same strong interaction effects like for single and dual
discrete responses are observed.
It is apparently that the increased proportion of MTI responses originates from the effect that
many responses with phase values Φ around 0 and around 0.9 are classified as MTI because of the
"regular" periodic ITI. It should be considered that the numerical criteria for interaction pattern
classification are not absolutely tight but they yield classification rates of about 90% compared to the
classification of an expert (i.e., 100% level); nevertheless, this effectiveness is sufficient for a general
grouping.
The PRCs plotted for each participant in each experimental condition indicate PTR and PTH as
dominant interaction patterns for Participants 1 – 5. None of the participants have shown DTE as a
dominant behavior, but Participant 6 turned out to belong to the rare group of absolute MTI
dominant people. In Fig. 7-32, 7-33, and 7-34, the PRCs of Participant 3 are displayed, who is
representative of PTR/PTH-dominant Participants 1 - 5 (i.e., in total, 12 PRCs are plotted for each
condition; in case of combined responses, PRCs are presented separately for each participating
effector (Fig. 7-33 and 7-34). The PRCs of Participant 6 (with MTI dominance) are not informative
since they are monotonically constructed of a horizontal dot lines as a result of the lacking
interaction between the periodic tapping and discrete responses in the DT conditions.
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Table 7: table shows correlation coefficients between limb RTs in different effectors’ combinations. Data are
obtained from DT experiments with triple discrete responses.
Participant
1
2
3
4
5
6
Mean
LH /LF
0.88
0.62
0.88
0.90
0.90
0.77
0.83
LH /RF
0.86
0.79
0.82
0.88
0.85
0.80
0.83
LF /RF
0.96
0.85
0.91
0.96
0.91
0.88
0.91
For underlining the coupling effects of the effectors within the discrete responses, the correlation
coefficients between the participating limbs were calculated for the triple response RTs in DT (Table
7). Evidently, correlation coefficients are high for all participants and in all considered pairs, ranging
from 0.62 to 0.96. The pooled means showed values above 0.8 for all effector combinations, with a
maximum of 0.91 for the LF-RF couple. These high correlation coefficients are also reflected in the
corresponding scatter diagrams (Fig. 7-36). The three subplots A, B, and C again show the pooled
data of all participants; clearly, all distributions scatter around the diagonal (representing a
correlation coefficient of 1.0) with the LF-RF case showing the tightest clustering.
Left Hand
Left Foot
Right Foot
180
200
220
240
260
Mean RT (ms)
280
300
320
Figure 7-35: Mean RTs together with their confidence intervals for the left hand (LH), left foot (LF) and right
foot (RF) with task condition as parameter (dots: ST; squares: DT). The relationship between the three limbs is
the same in ST and DT, but RTs for DT are longer reflecting the DT costs. Different widths of confidence
intervals are due to the different sample sizes of ST and DT conditions.
All PRCs in Fig. 7-32, 7-33, and 7-34 visualize that discrete hand and foot responses basically share
the same timing structure. Fig. 7-32 depicts PRCs for the single discrete responses (either LH, RF, or
LF) of Participant 3. The BM DT experiment (Fig. 7-32A) revealed more scattered periodic tapping
after the perturbation (at t = 0) as compared to the DT experiments employing foot responses (Fig. 732B and 7-32C); however, the general interaction pattern does not depend on the effectors. Fig. 7-33
presents the data with the paired discrete responses; interestingly to note that the phase value at
which the transition from PTR to PTH behavior occurs is reduced in case when a contralateral
response of LH and RF is required (0.5 → 0.3). This is also true for left hand behavior in the triple
response conditions (Fig. 7-34A).
Statistical analysis of RTs was performed for all participants and for all conditions, but results of
discrete triple responses in both DT and ST conditions were most informative. Three-way ANOVA of
RT with the factors participant, task and limb revealed highly significant main effect of all factors on
RT: participant, F(5, 5487) = 98.97; task, F(1, 5487) = 44.85; limb, F(2, 5487) = 445.07, all p’s < 0.001.
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ANOVA also revealed significant interaction effects in pairs participant – limb (F(10, 5487) = 32.41)
and participant – task (F(5, 5487) = 76.64) (all p’s < 0.001). However, there were no significant
interaction effects between limb and task, F(2, 5487) = 0.01. Post hoc mean comparisons (Fig. 7-35)
revealed that mean RT for DT is significantly longer than mean RT for ST, indicating an effect of the
DT complexity on RT. RTs for the left hand were significantly shorter than RTs for either foot on
average. RTs for the right foot were slightly shorter than RTs for the left foot, but this difference was
not statistically significant. Even though individual RT behavior showed some variations among the
participants (e.g., slower and faster RT compared to the standard) all the results fit to the RT scheme
depicted in Fig. 7-35. This figure also indicates the tight relationship between the RTs of the different
effectors: their mutual relations within the ST group and the DT group are almost the same showing
about 25 ms longer RTs for the feet. RT differences between ST and DT did not significantly vary
across the limbs: for LH = 56.0 ms (confidence interval [43.5 – 68.5 ms], for LF= 56.7 ms [44.7 – 68.6
ms], and for RF= 56.0 ms [44.6 – 67.5 ms]).
Analogous to the strong condition the trend to Type 0 Phase Reset in the first half of the periodic
tap interval was also found particularly with the combination of left foot and left hand but within the
second half of the periodic tap interval TC was reduced and the premature execution of the next
periodic tap together with launching of the discrete tap was increased. This result was pronounced in
subjects with strong PTR behaviour. In many cases a higher density of the dots around small and
large phases in PRC and the MTI-behaviour is obviously degraded.
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Figure 7-36: RT scatter diagrams for all limb combinations (A: LH-LF, B: LH-RF, C: LF-RF) in DT triple discrete
responses. The high correlation shown by the narrow clustering of dots around the diagonal indicates a
common motor command to all limbs, even if the timing of the command itself can be affected by DT costs. In
A and B, the slight leftward shift of the dot clusters corresponds to the shorter neural propagation delay to the
hand muscles compared to the delay of the foot muscles.
7.4 OM-DT condition
Although the eye and hand movements share some common brain structure, the issue of their
interference has remained controversial. The PRCs clearly indicate an interaction between hands and
between hand and foot. The sharing of common networks must not necessarily be effective in all DT
conditions but can be redundant in this condition.
198
a. Oculo-manual dual-task
2.0
Time (s)
1.0
Reference taps
0
-1.0
-2.0
b. Bimanual dual-task
2.0
Time (s)
1.0
Reference taps
0
-1.0
-2.0
0
0.2
0.4
Phase
0.6
0.8
1
Figure 7-37: Typical examples of phase resetting curves (PRCs): Results of participant N5 obtained in the oculomanual and BM DT experiments (“a” and “b”, respectively) are presented. Inclined solid line shows the onsets
of discrete events. Note that different symbols (square, circle, and triangle) in the PRC-lines above the abscissa
indicate the first, second, and third taps executed after the reference tap, respectively. The occurrence of the
reference taps is at t=0. More details of PRC construction can be seen in the Section 6.4.1. (a) The horizontal
orientation of all PRC-lines indicates that saccade execution does not disturb the periodic tapping. (a) The
changed orientation of the PRC-lines above the abscissa (representing post-perturbation taps) in comparison to
the lines below the abscissa (comprised of pre-perturbation taps) clearly indicates an interaction between the
two motor tasks.
The results of these DT experiments were evaluated based on the phase resetting curves. Fig. 737 shows typical examples from the oculo-manual (Fig. 7-37A) and BM DT (Fig. 7-37B) experiments,
both performed by participant N5. The major finding of these experiments drawn from PRC profiles is
that the periodic tapping timing is nearly unaffected by execution of the saccade as the discrete
event. In Fig. 7-37A, the three successive taps executed after each perturbation event above the
abscissa are approximately parallel to the abscissa as well as parallel to the two preceding taps
executed before the reference tap independent of the phase ϕ of the perturbation event. This type
of behavior is termed weak interaction, which corresponds to ‘type 1 phase resetting behavior’
according to Yoshino et al. (2002). (Note that the usage of the terms “type 0 reset” and “type 1
reset” in Yoshino et al., 2002, is not entirely compatible with the original definition of Winfree, 1980.)
The inclined line above the abscissa arising from the origin (t=0, ϕ=0) of the graphs in Fig. 7-37
represents the locus of the onset times of the discrete responses. This line represents no measured
data but is setup by definition: the perturbation onset phase value depicted on the abscissa is
directly proportional to tperturbation (shown on ordinate) according to Φ = tperturbation/ N. as defined in
Section 6.4.2.
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a. (N3)
2.0
Time (s)
1.5
1.0
0.5
a1
a2
b1
b2
b. (N13)
Time (s)
2.0
1.5
1.0
0.5
c. (N11)
Time (s)
2.0
1.5
1.0
0.5
c1
0
0.2
0.4
0.6
0.8
1
c2
1.2 0
Phase
0.2
0.4
0.6
0.8
1
1.2
Phase
Figure 7-38: Results of DT experiments selected to visualize inter-individual variability: Phase resetting curves
(PRCs) of three typical participants (N3, N11, and N13) obtained in the BM and the oculo-manual experiments
show the timing of first three periodic taps executed after the reference tap as function of the discrete
response onset phase. Inclined solid line represents the onsets of discrete events. Different symbols (square,
circle, and triangle) indicate the first, second, and third taps executed after the reference tap, respectively.
Note that the occurrence of the reference taps is at t=0. Results plotted in (a) show an example representative
for the group of nine participants with strong BM and weak oculo-manual interaction (shown in a1 and a2
plots, respectively). In (b), again the PRC-lines are tilted with respect to the abscissa in the BM conditions (b1),
whereas in the oculo-manual task (b2) they are horizontal, which indicates only a slight interaction in the oculomanual experiments. In the BM DT (b1) there is a noticeable scatter and a down-jump of the PRC-lines which
tend to incline at the end, indicating periodic tap hastening; this tapping behavior was predominantly shown by
two participants. It corresponds again to a strong interaction in BM DT conditions. Another representative
participant in c shows a rare case (only two in our results) where PRC-lines of both plots have approximately
the same horizontal orientation (i.e., no significant interaction in BM (c1) as well as in oculo-manual (c2) tasks).
In striking contrast to this oculo-manual performance, the periodic tapping in the BM DT (7.35B)
shows a distortion of performance by the left hand tap: the three PRC-lines above the abscissa
(which represents t=0) appear to be inclined, being parallel to the inclined line (i.e., discrete tap
onset time) in Fig. 7-37A. This indicates that the periodic tap following the perturbation event occurs
1 cycle after the perturbation event. For larger values of ϕ, in several participants the next periodic
tap is premature and occurs together with the discrete response indicated by a down-step in the
course of the respective PRC-line (e.g., at ϕ≈0.6 in Fig. 7-37B as well as at ϕ≈0.2 in Fig. 7-38B1). This
strong interaction between the two tasks in the DT scheme has been termed as “type 0 reset”
behavior by Yoshino et al. (2002). Our findings are in perfect agreement with study of Yoshino et al.
(2002) who also found strong interaction for BM DT tapping.
Fig. 7-38 presents the three PRC types of the experimental oculo-manual and of the control BM
DT being observed in the 13 participants. The PRCs are reduced to the first three taps executed after
the reference tap (i.e., to the three PRC-lines above the abscissa), for graphical purposes. 9 of the 13
200
participants build a homogenous group being represented by Fig. 7-38A; they exhibited strong
interaction in BM finger tapping (left graph) and weak interaction in oculo-manual DT conditions
(right graph). Two other participants behaved in the same way, but they showed the hastening effect
in addition (i.e., if the interval between the reference tap and the discrete tap exceeds some
minimum (“refractory”) period, the next periodic tap occurs simultaneously with the discrete tap
(see Fig. 7-38B). Fig. 7-38C gives an example of the rare case of weak interaction in BM DT (Fig. 738Cc1), too, observed in two participants (such tapping behavior was discussed in the context of
well-trained musicians by e.g., Franek et al. (1991) and Watson (2006)). However, this finding still
follows the line of very weak interaction in oculo-manual DT as observed in the other participants.
Thus, in summary, we conclude that saccade discrete task does not strongly modulate the periodic
manual tapping in oculo-manual DT situation, which is in high contrast to a manual discrete response
in BM DT finger tapping experiments (Yoshino et al., 2002).
The performance ITIs of the periodic tapping in a ST as control was checked for task coupling
within a DT situation. The mean ITI values were 564.8 ms (±46.8 ms SD) in the right hand tapping
task, 532.8 ms (51.6 ms SD) in the BM DT, and 552.2 ms (±45.8 ms SD) in the oculomanual DT.
ANOVA did not reveal a significant difference between the ITIs (p>0.05) of single manual and oculomanual DT conditions, i.e. the periodic tapping in the oculo-manual DT experiment is not strongly
modulated.
Finally, the timing of the saccadic reactions was analyzed. Again, reference values for saccade
latencies were collected in ocular experiments where the periodic tapping was omitted. The mean
values obtained in the ocular ST and the oculo-manual DT experiments were 229.9 ms (±60.6 ms SD)
and 258 ms (±74.5 ms SD), respectively. Saccade latencies in both experiments were independent of
the task conditions, and no systematic interaction was identified across subjects.
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8 Discussion
8.1 ST condition
8.1.1 Timing task
Our results in isometric tapping replicated and confirmed the bimanual advantage which was
presented for each hand (Fig. 7-1 A,B). Our results did not show the improvement of timing during
bimanual tapping without elimination of asynchrony by means of a firm mechanical coupling of the
index fingers of both hands as in Drewing’s experiments (Drewing, Hennings, & Aschersleben 2002)
in normal tapping (Fig. 7.1 A,B). This bimanual advantage was often absent in contact-free tapping or
even the disadvantage was found in normal tapping (Fig. 7-1 A,B). The bimanual performance did not
benefit from the asynchronous reafferences as compared to using only sensory reafferences of one
hand. These reafferences are enhanced by tactile-kinaesthetic feedback. The benefit of reafferences
and the integration of central commands in timing control would improve the timekeeper. In our
experiments, participants tapped on the hard surface of the force sensor case and in Drewing’s
experiment on a moveable spring-controlled key. The moving swing of the finger in our experiment
might yield more freedom for movement development than in Drewing’s experiments, and, hence, it
contained larger variance resource of mechanical implementation and larger asynchronous error.
The results suggest that the conscious decision processes of temporal judgment involved two
correction processes based on sensory reafferences and asynchronous errors. It is possible to
speculate that the first correction process is the source for the bimanual advantage, whereas the
second correction process as a function of asynchrony size is the source for bimanual disadvantage
and has a contribution to the correlation function between successive ITIs. The smaller the timer
variance, the larger the variance of motor delay will bias the correlation lag 1 between successive ITIs
to -0.5 (Wing & Kristofferson (1973b)). The first correction process would reduce timer variance but
mechanical variance would increase peripheral motor variance. Without mechanical
implementations (i.e. without peripheral motor variance) the trend to the zero correlation has to be
present. These suggestions were confirmed in isometric tapping compared to normal and contactfree tapping (Fig. 7-1 A, B). The most benefit of reafferences for the timekeeper would be in the case
of voice tapping (Fig. 7-1C) although breath keeping is needed for the motor process and peripheral
motor delay might be the largest one. Taken together, the bias of correlation lag 1 to -0.5 had to be
present in voice, normal and contact-free tapping. It is not sure to suggest that the peripheral motor
variance in isometric tapping is absent because the EMG showed large amplitude of antagonist. The
bimanual advantage again was found not only in multiple effectors but also in mental tapping in
combination with normal tapping (Fig. 7-1 D). If the memory capacity of 20 taps (Yamada 1996) and
temporal memory for interval duration (Steven et al. 1988) are used for timing task in ST then the
clock-counter mechanism according to which successive intervals are random is rejected. The slower
trend and positive correlation of successive intervals in mental grouping condition confirmed this
contradiction.
8.1.2 Timing task and spontaneous blinking
The results of eye blink behavior during distinct rhythmic finger tapping tasks (standard, strong,
and impulse-like tapping) compared to the reference data without tapping revealed that
spontaneous eye blink (SB) behavior is affected by self-paced finger tapping (Fig. 7-5), whereas the
tapping behavior seems to be unaffected by the eye blinks (Fig. 7-4). It is possible to suggest that the
blink behavior reflects not only psychological and perceptual factors (such as attention, stress,
202
fatigue, etc.) but also concurrently active simple motor processes. Theoretically, Poisson distribution
of IBI histograms of SB observed over longer periods would leads to uniform distributions in phase
histograms as shown in Fig. 7-5 (column 1). However, for the overwhelming majority of tapping data,
the 2I-Test rejected the null hypothesis (uniform distribution over phase) (Fig. 7-5 (column 2, 3, 4)).
Hence blinking in these conditions cannot be considered as being purely spontaneous but is rather
dependent on the tapping process, too.
The relationship between blink behavior and other centrally controlled monotonous motor
actions like gait and respiration has not been so far systematically investigated (e.g. Wilson,
Fullenkamp, & Davis 1994). Speech increased blink rate during verbal tasks (tasks (Schuri & von
Cramon 1981; Von Cramon & Schuri 1980). This report is in line with the results of the present study
showing a 30 percent average increase of blink rate during tapping compared to the reference
experiment without performing a cyclic motor task. However, cognitive functions for word selection
and motor control for pronunciation are required. Representation of time for speech generation
might be derived from an endogenous timing process or pacemaker linked to some type of counting
device (Ivry & Richardson 2002). Von Cramon & Schuri (1980) reported that counting loudly up to
100 increased blinking significantly, whereas reciting the alphabet had no significant effects on
blinking. A coupling or decoupling of the time-structured simple motor tasks like in this study from
blinking, depending on the task requirements, can be suggested. Additional to logistically demand for
synchronous activation of both hands (Fig. 7-7), the strong and the impulse-like bimanual tapping
physically require higher force and rate of movement (Fig. 7-5 (column 3,4)). Thus, stronger
triggering of motor commands as well as higher attentive load are expected and, indeed, resulted in
a stronger coupling (phase synchronization caused by phase entrainment) than uninstructed
unimanual tapping (Fig. 7-5 (column 2)). Supporting the aforementioned, evidence was found that
instructed force production requires cognitive resources (Zijdewind et al. 2006) as well. Further
framework such as the existence of a central bottleneck, resonant properties of eyelid motor system
and its widely distributed brain network can create appropriate conditions for the hand motor
system to synchronize the onset of its motor events with onsets of concurrent spontaneous
movements (blinks).
On the one hand, the suggestion is that motor commands for bimanual tapping coming from two
hemispheres are integrated for control of the coordinated behavior (Ivry & Richardson 2002), and
the eyelid movements are centrally originated and controlled, but influenced via certain “secondary
paths” (Ponder & Kennedy 1927). On the other hand, the neural pathways responsible for tapping
may cross-talk to these “secondary paths” leading to entrainment of blinks. A major role in these
processes can be assigned also to dopaminergic regulation of motor actions (e.g. Dreisbach et al.
2005). Thus, the internal representations of motor commands indicate their "strength" not only
through changes of the blink rate but also through specific timing. The entraining effect of strong
tapping and impulse-like tapping on blinking due to probably more pronounced motor commands is
equally obvious as its increased form observed in bimanual tapping (compared to unimanual tapping)
(Fig. 7-7).
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8.2 DT condition
8.2.1 BM
In this study, the introduction of a discrete movement might be considered as an external
perturbation on the ongoing rhythmical movement. On the other hand, the ongoing movement
affected some characteristics of the evolving discrete movement. Thereby, the interaction of
rhythmical and discrete movements may occur at the central control and/or peripheral levels. We
determine the synchronization reflecting the stable phase locking and the unstable one; the specific
effect of discrete movement on periodic one was directed to the underlying control mechanism
leading to the typical PRCs (Fig. 7-8). The mutual interaction was reflected in continuous trajectories
(6.5.2, Fig. 7-11, Fig. 7-12). We evaluated not only the data of the ground contact times, the force
and trajectory developing (isometric tapping, foot tapping, finger movement) process, but also the
physiological parameters realized by different experimental conditions (7.2.4).
Synchrony is the most familiar mode of organization for coupled oscillators in secular interactions
(Blasius & Stone 2000, Bonabeau, Theraulaz, & Deneubourg.1998, Delgado & Sole 2000, Glass 2001
...). Transient phase shift subjected to impulsive force is synchrony in episode interactions. The
behaviour of communities of oscillators depends on the strength of coupling among them (Strogatz
& Stewart 1993). If their interactions are weak, the oscillators will be unable to reach synchrony,
whereas if the interactions are strong, the oscillators will overcome their individual differences.
Transient phase shift is the episode effect leading to synchrony in secular trend during interaction.
The data showed in fact no perfect dissociation between trajectories. With a view on continuous
movement trajectories, the coordination constraints are reflected in the fact that the position,
velocity, and acceleration of both fingers are to some extent interdependent even if timing is stable
(Fig. 6-19b, 6-21a, 6-25b). The time course of one finger affected the time course of the other one.
These restrictions indicate limitations in central processing resources or occurrence of neural crosstalk between sensory and motor structures controlling each finger i.e. control signals producing
different movement patterns interact at a central level and modify each other. Basically, the
obtained experimental results in normal tapping are consistent with the results of previous
investigations on the coordination of discrete movements and periodic movements (e.g., Yamanishi,
Kawato, & Suzuki 1979; Yoshino et al. 2002). Since the basic experimental paradigm was cloned to
that of the study of Yoshino et al. (2002), not surprisingly ‘Periodic Tap Retardation’ (PTR) behaviour
(Type 0) (Fig. 7-8b) and ‘Marginal Tapping Interaction’ (MTI) behaviour (Type 1) (Fig. 7-8a) like in this
target study could be observed. Type 0 Phase Reset during the second half of the periodic tap
interval can be explained by the stronger coupling factor between two signals of the same direction
(exhibitory coupling) and Type 1 Phase Reset within the first half by weaker coupling factor between
two signals of the opposite direction (inhibitory coupling) and a correction process based on discrete
feedback.
8.2.1.1 Effect of discrete movement on periodic movement
Heuer & Klein (2005) discussed a coupling between concurrent movements generally as cross talk
mechanism which appears if the movement (i.e. motor activity) is already "on the way" when the
other movement will be launched. Correspondingly, the ongoing periodic tapping movement which is
continuously "on the way" is subject to be affected by the upcoming discrete tap most probably. The
suppression in Tap Delay (Fig. 6-20a, 6-25a) and the replacement in Tap Cancelling (Fig. 6-25b) of
rhythmical activity by the discrete movement suggest that the oscillation probably inclined and
declined respectively owing to the elastic and damping properties of muscles and inertial mechanics.
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The frequency of oscillation was not only usually greater after than before the discrete movement
(Adamovich, Levin, & Feldman 1994) in single limb condition but also in interlimb coordination as in
our study. Levin et al. (1992) and Feldman (1993) explained in terms of the strong coactivation
command accompanying fast discrete movements, but it might be again the episode effect leading to
synchronization just as the hypothesis of the existence of a transitional frequency suggested for
interlimb coordination during rhythmical movements (Kelso, Southard, & Goodman 1979).
Figure 8.1: Limit-cycle model. F: external force. Ft: tangential component of F; Fr: centripetal component of F
8.2.1.1.1 Stable and unstable states of equilibrium
In the vicinity of the stable states of equilibrium (phase range (0-0.3, 0.75-1), remember the cyclic
repetition!), the state point on the limit-cycle (Fig. 8-1) remained in the vicinity of a stable state
(attractor) after undergoing the disturbance duration (Ft ~ 0, Fr ~ F) because the effective force (Ft) is
null or ignorable. In the vicinity of the unstable states (repeller) of equilibrium (0.3-0.6), the state
point was repelled away (Ft ~ F, Fr ~ 0) forwards or backwards to the stable states because the
effective force is maximal. The limit cycle interpretation afforded by the present experimental
findings can be used to address the critical phase problem in mechanistic terms, further resolving the
PRC similarity puzzle. The stable states of equilibrium form the attraction regions and the unstable
ones the repelling region. The various mixtures of locking were reflected in trajectory coordination,
where the rate of change is zero because the current state is located at stable fixed point. The
attraction reflected in non-uniformly distribution of the overlapping of the (down-) upslope as well as
of the cross-like period slope when the homogeneous and the non-homogeneous muscles are
activated together, the occasional insertion of the fast discrete in the slow periodic finger’s path and
then moving up together. The tap duration of one finger inclined to equalize the other one, the two
affected periodic taps were contracted in two-sided cross-like coordination, the well-formed
distribution of the discrete tap duration by intertwining in two periodic taps. The constraints of
temporal relationship associated with attraction are presented under discrete events by the dense
distribution or under continuous movement trajectories by the higher number of sided coordination
(TS1, US1, SA1, TS2, DS2, SA2, and US2) around stable states. The repelling reflected in the rest phase
range for instance in PTH and PTR and presented in compact distribution or low number of TA and
AP2S coordination. PTH is described in position data more appropriate and reflects the stronger force
of stimulus on the state point pushing it quickly on the way to attraction region (Figure 6.20b). This
pushing force was intensified in multiple effectors and in strong condition through the absence of TC
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in PTR, which usually were observed, i.e. the oscillation was set to death. The critical phase is not a
property of the oscillator alone: it is a function as well of the mode of action of the perturbing pulse.
The rate of advance or retardation through the cycle of the periodic movement is conditioned jointly
by an external influence of discrete central command signal and the actual state (current phase). The
ring device runs faster or slower, depending on its current phase as long as exposed to this external
influence. The rate of change is not zero when the current state is located anywhere between two
fixed points. The oscillator could be set to death in tap delay (TD) or was set to death (PTH). A
cessation of oscillation would happen if the current state is directed away from the attraction
stagnation point and towards the repelling stagnation point and a hastening if the current state is
directed away from the repelling stagnation point and towards the attracting stagnation point. If the
external influence is not large enough to cause a complete resetting a transient resetting leading to a
mixture behaviour would be present.
In normal tapping condition, the chance to affect the periodic tapping movement causing PTH is
higher, particularly in strong and multiple effectors condition, than in isometric and contact-free
tapping whereas in isometric and contact-free tapping the chance causing PTR is higher; tapping with
movement follows the rules of a second order mechanical system comprising a memory due to
inertia and feedback-based regulation approach, whereas a simple proportional mechanical system
and lacked discrete feedback are reflected both in isometric tapping and in contact-free tapping. A
specific definition of the concepts about the generation of control variables (CV) was used
(Adamovich, Levin, & Feldman 1997; Asatryan & Feldman 1965; Feldman & Levin 1993; cf. Stein
1982; Berkinblit, Feldman, & Fukson 1986; Gottlieb, Corcos, & Agarwal 1989; Latash 1993). The
control level issues CVs, whereas the peripheral level responds to the CVs as well as to other
variables via proprioceptive feedback and mechanical interaction of the joint with the load. Thus, a
pronounced resetting at representation level which did not reach the execution level (TC) was
observed more often in contact-free and isometric tapping. Moreover the non-harmonic movement
due to asymmetry between the flexion and extension phases suggests limitations on autonomous
limit cycle oscillators as models of timed repetitive movements because they are inherently
symmetric.
8.2.1.1.2 The dissipative mechanism and a source of energy of limit-cycle oscillators
Central signals control the muscles in a predicted manner to compensate for interaction torques,
motion about one joint leads to load arising at other joint (Gribble & Ostry 1999). To compensate for
elastic loads on objects, movement induced inertial, and viscous subject adjust control signals to
finger muscles in a predictive manner (Flanagan & Wing 1997) during rapid arm movements with
hand-held loads in a study of grip force adjustments. Just as in the case of multi-joint motion, control
signals must be coordinated appropriately to muscles to stabilize the periodic movement, when
awaiting interaction with other mechanical components/systems.
In the dissipative mechanism the damping and restoring force are used to pump up oscillation
that becomes too small and to damp those that grow too large, respectively (Andronov, Vitt, &
Khaikin 1996). The restoring and the damping forces are applied to bring the system back to the
given state. Some subjects showed PRCs which start like MTI in a certain phase range up to 0.4 in
normal condition. This would be explained by the restoring force generated with the support of the
discrete events of periodic finger (reference tap) used as a resuming point for timing of the affected
period. Without this support, different degrees of resetting were observed just as in contact-free and
in isometric condition. In contact-free and isometric conditions, time judgment could not be well
performed because the discrete event of the reference tap was absent or was blurred by constraints
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of biomechanical movement. The restoring force also was exhibited in repelling and in attraction
region. The trajectory deflecting in TC reflected the restoring force because the downwards
movement indicates the oscillation become too small and the subsequent upwards movement is
against gravitation. The amplitudes of the periodic movement when it moved together with the
discrete one were smaller in contact-free condition whereas larger force in normal and isometric
tapping. This would indicate that the damping force is needed to avoid the large growing of the
oscillation which was observed when it is limited by the hard surface because the amplitude of
downward movement in normal tapping is not needed to be controlled but in contact-free and
isometric tapping.
8.2.1.2 Effect of periodic movement on discrete movement
The ongoing high-frequency oscillations constrained the time of initiation of the evolving motor
task (Adamovich, Levin, & Feldman 1997). Goodman & Kelso (1983) reported that the probability of
the initiation of a discrete elbow movement depended on the phase of physiological tremor in
normal subjects. This constraint emerged even when the both motor tasks are performed by
different limbs. A novel aspect in tapping is the phase entrainment (Staude, Dengler, & Wolf 2002)
effect (DTE). The DTE behaviour belongs to the most preferred “intrinsic” coordination facilitating
stable cooperation. Obviously, DTE is a directed effect of the periodic on the discrete process which
introduces a delay in the discrete tap reaction and thus an increased RT. DTE and its combination
with PTH were not yet discussed in tapping studies. It becomes not directly apparent in PRCs since
the onset time of the discrete tap is coded in the inclined dashed line but the corresponding RT
(betraying DTE) is hidden; the increased density of data points in the range of small phase values (Ф <
0.3) and the more shortening of the immediately preceding interval with corresponding higher
reaction time indicate the entrainment. It is most obvious from Figure 7.28d revealed the onset
phase of the discrete tap not within the affected cycle of the periodic tapping.
8.2.1.3 Mutual interaction
8.2.1.3.1 Phase entrainment and coupled periodic processes
Coupling of two processes can occur in one direction or in mutual interaction of processes by
which one task predominantly affects the other. Tapping in-phase and anti-phase, respectively, with
anti-phase being less stable and thus less frequent (Kelso 1984; Haken, Kelso, & Bunz, 1985) are the
preferred tapping behaviours and represent synchronized actions, which seem to be easier for the
system than arbitrary asynchrony. Speeding up the periodic tap (PTH) and delaying the discrete tap
(DTE) are the combined strategy to establish the in-phase and anti-phase coordination. The detection
of both, PTH and DTE tapping behaviour, is confined to cases where the actual scheduled phase
relationship of both taps is not compatible, and, hence, they cannot be detected by principle in cases
where phases match occasionally. Further, the mutual interaction was presented in mixed forms of
DTE combined with PTH and PTR, respectively; i.e. at the same time when the launching of the
discrete tap is delayed, the periodic tap process is advanced or it is paused in order to achieve a
synchronized timing. This may explain why none of the subjects tested showed pure dominant DTE
behaviour (Table 7.4). The hidden mutual interaction may be reflected in subjects classified as
dominant MTI who consistently showed prolonged dual-task RT compared to PTR and PTH (Figure
7.14, which may reflect such situations in these subjects. By the way, the analysis of continuous
trajectories revealed the directed effect of the periodic on the discrete process in the modification of
their slope periods and tap durations. Some small mirror activity was observed in the slow rhythm
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hand during the pauses when subjects had to produce a bimanual tapping rhythm with a 1:2
frequency ratio, when the other hand performed the additional tap to obtain the double tapping rate
(Semjen & Summers 2002). All these reports point to the fact that entrainment of two motor task
represents a basic mechanism in motor coordination, independent from the temporal characteristic
(periodic, discrete) of the motor task. Thus, future research will concern the development of a model
connecting the “phase entrainment” scenery with the “coupled oscillator” and “tapping” sceneries.
8.2.1.4 Coordination strategies
Another issue is whether the kind of coordination is selected at random, or is there a principle?
Although every individual shows a dominant tapping behaviour, all interaction patterns can be
observed. Obviously, the unspecific instruction ‘to accurately execute the periodic tapping as well as
to react as quickly as possible to the go signal’ contains a leaky element, as the paradigm of this study
follows that of several other tapping studies (e.g. Yoshino et al. 2002). The ambiguous instruction
forced the subject to give preference to one of the two competing motor tasks in case they cannot
be served at the same time by the sensorimotor system. But it should be emphasized that even if this
"instruction" argument sounds reasonable, it represents a hypothesis and is verified by the
instruction specific data In the case of unspecific instruction, all subjects were not able to execute the
two concurrent tasks totally independently and somehow managed the competing tasks by using
either MTI, PTR, PTH, DTE or mixed forms of strategies (MTS). The assumption that the motor control
system can usually handle only one task at a time and it will give dominance to one of the two tasks
in the dual-task condition (Greenwald 1972; Klapp 1979) is considered. This assumption is verified by
change of the conventional management of the competing tasks, i.e. it could be overcharged by
physiological parameters. Temprado et al. (2002) and Temprado et al. (1999) reported that the
subjects are not able to execute dual-task conditions with shared attention. The motor control
system takes a decision for managing spontaneous coordination in favour of one of the tasks.
Moreover, Ivry & Richardson (2002) suggested a multiple timer model assuming that timed bimanual
movements are controlled by separate but mutually coupled timers. Future experimental work will
clarify whether the hastening (PTH) and retardation (PTR) effects can be explained by those intervalbased gating and resetting processes as suggested by the multiple timer model (Ivry & Richardson
2002), or whether they are better modeled by a system of continuously coupled clocks as described
in the dynamical systems literature (Winfree 1980; Schoener 2002, Ariaratnam & Strogatz 2001).
8.2.1.5 Information processing theory versus dynamic system theory
In the information processing perspective, the notion of time keeping is inherently connected
with abstract mental representations. Schaal et al. (2004), using fMRI, reported contralateral activity
in several non-primary motor areas and in the cerebellum during discrete wrist movements that was
absent during their rhythmic counterparts. Timing as a property originated from a disturbed neural
network (Rao et al. 1997; Jantzen et al. 2007). The neural basis underlying timing still need to be
elucidated. The perceptual centre of a perceptual or motor event seem to be used as the reference
point for synchronization. Beside the factors such as stimulus length or stimulus intensity in the
perceptual events the intensity of somatosensory feedback of the motor events also has an effect on
this perceptual centre. Rhythmic timing reflecting stochastic and dynamical properties in terms of
discrete timing were considered in Information processing perspective. The view of timing and
coordination in dynamical system approach is the properties arising from (self-organized) pattern
formation processes. Winfree (1967) proposed that self-entraining communities of this sort possibly
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exist within individual metazoan animals and plants. On this basis of the diurnal coordination of their
physiological process was observed. The dynamical system approach concerns the special
phenomena arising from the smooth rhythmical interaction of whole populations of periodic
processes or the episode interaction between a discrete and a periodic process . There may also be a
level of ‘‘strategic’’ components for timing. Stable periodic timing but with interdependence such as
TC with acceleration and non retardation, discrete tap entrainment could indicate that the closedloop control strategy allowing a certain amount of "sloppiness" in timing control may have evolved to
take account of the inherent time delays of feedback loops. A full stochastic-dynamical system
accounting for a complete spatiotemporal pattern will encompass the one accounting for a discrete
timing.
8.2.2 OM-SM condition
Dual-task costs can simply emerge from coordination of the two ongoing tasks even if they share
neither perceptual nor motor resources. Further overlapping neural processing resources for the
control of ballistic eye and hand movements are known. Hence, we expected strong DT interference
effects in our OM-DT paradigm in a similar way as it was reported for BM-DT. However, with the
same experimental concept, the results disproved this assumption. In comparison to the BM-DT, the
DT costs of tapping observed in the OM-DT experiment turned out to be really weak, if existing at all
– in general, a simple goal-directed saccade to a fixed target did not disturbed significantly the finger
tapping (e.g., Fig. 7-37A) whereas the BM-DT experiment replicated the already known remarkably
strong interference (exemplified in Fig. 7-37B). The weak interference in OM-DTis not in
contradiction to the vast number of studies showing eye-hand-movement interference but rather
confirms the response-selection bottleneck model. For the internal timekeeper controlling the
periodic tapping a cognitive stage is not required. According to Dux et al. (2006) the posterior lateral
prefrontal cortex and, possibly, the superior medial frontal cortex are the suitable candidates for an
amodal bottleneck of information processing but they seem not to participate in a tapping task (Rao
et al. 1997). Our results also support the hypothesis of Wei, Wertman, & Sternad (2003) that the
discrete and rhythmic actions belong to two different control regimes.
8.2.2.1 Neural substrates of eye-hand movements - related studies
In continuous tapping, active involvement of the bilateral supplementary motor areas, basal
ganglia (Rao et al. 1997; Lewis et al. 2004) and cerebellum (Dreher & Grafman 2002; Ivry & Spencer
2004) was found as well as activity in the left putamen, the left ventrolateral thalamus, and the
caudal supplementary motor area (Rao et al. 1997; Jenkins et al. 2000). Further, a distributed
network of areas is involved in many studies concerning saccades, e.g. the reticular formation,
superior colliculus, frontal eye fields, posterior parietal cortex, cerebellum, premotor cortex and
basal ganglia (e.g., reviewed in Girard & Bertolz 2005). Gaze direction signals consistently modified
skeletomotor processing in the cerebral cortex (Baker et al. 1999). In monkeys, gaze interactions
occur in several arm movement related areas (e.g., Boussaoud et al. 1998) in addition to the visual
cortical areas. In humans, Baker, Donoghue, & Sanes (1999) measured neural pattern of finger
tapping related activation in conditions where participants (with the head fixed) maintained gaze
either on the tapping hand or away from it; brain images were taken while participants performed
sequential finger movements of the right hand (each of the finger tips was successively touched by
the thumb). In comparison to the condition when gaze was directed away from the tapping hand the
more aligning of gaze with the tapping hand increased activation in a wide extent of the lateral
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superior and inferior parietal lobules of the hemisphere contralateral to the moving hand, in the
primary motor cortex and in lateral and medial premotor cortices. Gaze direction as a salient variable
for the neural systems controlling hand motor actions likely modify activation patterns dynamically
and instantaneously (Baker, Donoghue, & Sanes 1999). These results provide a solid ground for eyehand interaction. From this point of view, a strong influence of the saccades on tapping should be
expected but was not found in the OM-DT condition of our study.
The nearly non-interaction as observed in our OM-DT situation indicates either that the same
brain resources are not required by both tasks or the load is too small for bottleneck effects to
become apparent. Schaal et al. (2004) reported that rhythmic movements activated a small number
of unilateral primary motor areas whereas discrete movements activated additional contralateral
non-primary motor areas showing strong bilateral cerebral and cerebellar activity. These results
mirror findings of Lewis & Miall (2003) on task timing related brain activity and support our results.
Thus, the discrete task characterized by the need for cognitive control recruits prefrontal and parietal
areas, while the rhythmic task accomplished via autonomous control is mostly based on primary
motor circuits not demanding the frontal areas: e.g., the dorsolateral prefrontal areas associated
with “higher-level” cognitive functions (Rao et al. 1997) were not active during tapping. Such a
concept can explain the non-significant OM-DT costs in our experiments.
8.2.2.2 Behavioural observations in combined eye-hand movement studies
Indications of OM-DT interferences but in discrete-discrete DT situations (e.g., Pashler, Carrier, &
Hoffman 1993; Baedeker & Wolf 1987; Bekkering et al. 1994) were reported in a large amount of
psychophysical studies on combined eye-hand movements. E.g., in studies concerning visually guided
pointing, two tasks were combined by the shared target, which may elicit task grouping (preprogramming). Saccadic DT studies should employ a concurrent “task that is logically independent of
the eye movement” (Pashler, Carrier, & Hoffman 1993) to avoid task grouping. On the other hand,
Pashler, Carrier, & Hoffman (1993) explained the results that certain types of saccades (e.g., to a
colored spot) do not show marked DT interference with the duality of the saccade control system
(involving the superior colliculus and the frontal eye fields). They assumed that, contrary to the
frontal eye field signals, collicular signals are not affected in the sensorimotor DT task.
So far, fewer studies have been devoted to discrete-rhythmic DT behavior involving eye
movements. Rhythmic step-jumping saccades in DT with a go/no-go type competing manual task
exhibited some interference in studies of Malmstrom, Reed, & Weber (1983), but the degree is
difficult to judge as saccadic latencies were not analyzed. Claeys et al. (1999) as well Stuyven et al.
(2000) explored perceptual and memory processes related to antisaccades and prosaccades in terms
of a different load imposed on the central executive. They combined random finger tapping as a
secondary task and saccade execution in a DT paradigm. As a result, prosaccades as well as
antisaccades showed some small general latency increase due to the secondary (tapping) task - a
tendency also observed in our study, especially in non-experienced participants. Unfortunately, these
reports did not include an analysis of the tapping behavior, thus no direct comparison with our
findings is possible.
The interference of eye and hand movements has to be presented in either the concurrent timing
or the shared spatial target information, i.e. the resulting trajectories will reflect the interference.
Sailer et al. (2000) used different eye movement tasks in combined eye and hand movement
experiments to study temporal aspects of motor coordination. They concluded that i) both motor
systems rely on common information to initiate movement, and ii) temporal coupling is stronger for
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intentional tasks than for reflexive tasks. These findings relate to our study in two ways: First, an
important point to be emphasized is that, in our study, the OM tasks were planned to be as
“temporal” as possible realized by the focus was on concurrent operation of two very different
effectors engaged in two independent tasks without common spatial variables and with fixed points
for the saccade. Second, an exogenously triggered reflexive OM task together with an endogenously
timed repetitive manual task reduces probability of temporal coupling between tasks in OM-DT
according to Sailer et al. (2000). In the real-life conditions, there are similar situations of undisturbed
eye-hand performance in which the eyes monitor the visual scenery while the hand is involved in a
pure temporal task. A pianist successfully performs rhythmical strokes on the keyboard while reading
the notes using visually controlled scanning saccades, and further examples can found in aviation,
driving, etc. Our “weak/no-interaction”-finding fits well to the results of Sailer et al. (2000) and
emphasizes their statement that “eye-hand coordination differs with the task employed” and that
“dual-task interference is content-dependent” (Hazeltine, Ruthruff, & Remington 2006).
8.2.3 Multiple effectors
The DT condition for hand-foot coordination comprised the discrete responses of the upper and
lower limbs to a single acoustic go signal in parallel to a periodic right index finger tapping; seven
discrete response types were analyzed: single (experiments LH, LF, and RF), double (experiments LHLF, LH-RF, LF-RF), and triple responses (experiment LH-LF-RF). The interaction and coordination
patterns reported for bimanual DT conditions (Yoshino et al. 2002) are also observed in case of the
foot responses. The interaction patterns also contralaterally or ipsilaterally occur in these
heterogeneous discrete hand-foot couples. From the essential result showing up in the PRCs, it is
possible to suggest that a unique motor control for the upper and lower limbs exists in the neural
mechanisms located within those higher level brain areas. It may refer to motor coordination
strategies that motor control in these DT conditions is not effector specific but rather task-based, the
system decides according to demands of the pending tasks independent of effectors involved in the
execution of these tasks, which is supported by the results of the ANOVA on RTs.
The distinct neural mechanisms which are responsible for discrete-rhythmic DT coordination
involving distinct effectors are not yet quite clear although DT interaction devoted to discretediscrete task tandems attracted huge attention (Greenwald 1972; Klapp 1979; Staude, Dengler, &
Wolf 2002; Sternad, Dean, & Schaal 2000; Swinnen et al. 1997) and periodic-discrete task tandems
are also gaining special attention (Swinnen & Wenderoth 2004; Sharikadze et al. 2009; Wachter et al.
2004; Yoshino et al. 2002). The two different classes of movement — rhythmic and discrete
movement — executed in parallel or in sequence are essential aspects of multi-tasking which
requires a high degree of motor coordination between effectors. Studies using fMRI showed that
different cortical areas are involved in the rhythmic and discrete movement control (Schaal &
Sternad 2001; Schaal et al. 2004). Also, psychophysical observations led to the conclusion that, in
bimanual task, rhythmic and discrete movements are two different classes of behaviors, and their
coupling occurs at a higher level of the central nervous system (Ronsse, Sternad D, & Lefevre 2009;
Sternad, Dean, & Schaal 2000; Wei, Wertman, & Sternad 2003). The supplementary motor area
(SMA) plays an equally significant role in preparation of single and repetitive voluntary movements
(Shibasaki et al. 1993). SMA is involved in the organization and control of coordination between the
homologous as well as the non-homologous limb segments (Debaere et al. 2003). A similar
assumption can be drawn from the direct interaction of hand and foot movements in our DT tapping
according to the known neural structure outlined in the introduction.
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The coordination of the upper and lower limbs is required during typical rhythmic motor tasks
such as walking, running, or swimming. How are different limb movements coordinated, and how do
they interact with each other? The arm musculature still rhythmically contracts even when the
upper limbs are restrained (Ballesteros, Buchtal, & Rosenfalck, 1965). On the other side muscular
activity is established in the movement of the arms during walking and it is not simple passive
pendulum movement (Jackson 1983; Jackson, Joseph, & Wyard, 1983a, b). Keele et al. (1985) and
Keele, Ivry, & Pokorny (1987) showed in tapping experiments that different effector systems could
share the same timing mechanisms for rhythmic movement generation. In their studies, participants
who were consistent in finger tapping were more consistent in foot tapping, too. Although during
gait and other natural activities the existence of flexible, task-dependent neuronal coupling between
the leg and arm movements was suggested based on much evidence the specific interaction between
legs and arms remains unclear (Dietz, Fouad, & Bastiaanse, 2001; Wannier et al. 2001).
We found that the RT for left hand about 25 ms shorter than RTs for either foot, on average
(Figure 7.33). This can be explained by the longer length of the reflex pathways of the foot than of
the hand. Propagation delay of neural impulses towards the hand muscles is significantly shorter
than towards the foot muscles and accordingly, RT for hand responses should be smaller in
comparison to foot responses. Single response correlation analysis between limbs (Table 7, Figure
7.34) shows that all limbs are timed similarly, even in the DT condition, where the timing of discrete
response command is subject of influence of the DT interaction. Further, ANOVA on RT has shown no
effects between effector and task condition. These two results may indicate that the DT interaction
occurs at a central level and the command for the discrete response is effector-unspecific. In
addition, the subsequent response selection for the required hand and foot action is driven from
symmetric pathways. This is in good agreement with the assumptions that there is at least one
central pattern generator (CPG) for each limb, that these CPGs are located in the spinal cord level,
and that strengths of coupling between the CPGs of homologous limbs (of hands or of feet) are larger
than between the CPGs of non-homologous limbs (hand-foot) (Zehr & Duysens 2004). The individual
differences in task performance would explain the significant interaction effects for the pairs of
participant and limb, and of participant and task. Although each participant performed the task and
set up the sequence structure of acting limbs in simultaneous responses in ‘own way’, all of these
behaviors still fit to a global conventional DT behavior.
212
9 Summary and outlook
9.1 Summary
The improvement of the classical setup which now includes force and position recording as a
substitution of the previous simple ground contact recording is reasonable and discloses some
internal processes like the phase locking phenomenon and timing control reflected by interactive
changes of trajectories. High velocity downward movements to obtain accuracy in synchronization
(Balasubramaniam, Wing, & Daffertshofer (2004)) are reflected in all tapping experiments (normal,
contact-free, and isometric). Not only one movement constrains or even impedes the execution of
the other in dual-task executed by a single limb but also when execution is distributed on two
different limbs like two fingers. The abstraction of the continuous position signal by an event time
sequence showed a stable global timing of periodic tapping but the continuous signal revealed the
mutual dependence in details. Including of partial taps and transient event-related changes in the
signal profile of single taps do not simplify the modelling aspects but can lead to a more profound
insight of motor timing mechanisms. The new techniques with highly accurate position recording by
laser equipment will now allow to simply extend the experimental program to big proximal muscles
like biceps and also to other kinds of tapping like tooth tapping (Nagasawa et al. 1993) in order to
again assess the classical problem: to which level of the neural information processing hierarchy
these timing processes of motor control can be assigned.
The importance role of sensory feedback in timing control was demonstrated by several authors
(Repp 2000, 2006; Aschersleben & Prinz 1995, 1997; Aschersleben, Gehrke, & Prinz 2001), The
bimanual advantage (Helmut & Ivry 1996; Drewing, Hennings, & Aschersleben 2002; Drewing &
Aschersleben 2003; Drewing et al. 2004) profits from the integration of different central control
signals and the amount of sensory information (Drewing et al. 2004; Drewing, Hennings, &
Aschersleben 2002; Ivry & Keller 1989; Ivry & Richardson 2002; Aschersleben & Prinz 1995). This
advantage was replicated in isometric tapping. The assumption of a common timing system that
might be violated (Vorberg & Hambuch 1984) when a performer tries to compensate for the
asynchrony between the hands by triggering the early hand only after some delay. The bimanual
advantage was suffered in normal and contact-free tapping by this correction process. Not only has
the integration of different central control signals that are related to each effector but also additional
mental tapping contributed to the bimanual advantage.
The smaller variance of motor delay which will bias the correlation lag 1 between successive ITIs
to 0 (Wing & Kristofferson (1973b)) was confirmed in isometric tapping in comparison with normal
tapping and contact-free tapping. The dominance of motor variance over the reafferent sensory
feedback in voice tapping resulted in clearly biasing of the correlation lag 1 to -0.5. Successive
intervals according to clock-counter mechanism are random. This hypothesis is rejected by the
gradually slower speed and positive correlation of successive intervals in mental tapping.
Spontaneous blinking as a concurrently active motor process is entrained by the finger tapping.
Opposite effect was not clear, i.e. the tapping behavior seems to be unaffected by the eye blinks.
Stronger tapping triggering stronger motor command as well as higher attentive loads and the
integration of motor commands for bimanual tapping are expected and indeed resulted in a stronger
entrainment.
The understanding of the synchronization process allowed us to formulate a model based on the
concept of limit-cycle such as the attracting and the repelling area and the interaction behaviours.
Furthermore, to complete the formulation of the model, the physiological parameters can be
determined by different experimental conditions. The resulting model is a differential equation with
213
two parameters specifying the intensity and the duration of disturbance and it is easily solved for the
simplest case in which some simplifying hypotheses are assumed. An impulsive force with constant
direction acts on a state point running with constant velocity on the limit cycle. Under these
conditions the system exhibits the two singular states, one stable is the attraction area and the other
unstable is the repelling one. The initial conditions would determine the achievement of one of these
states. It is not easy to know exactly the initial conditions because we can only measure the
observable events. Obviously, the in-phase synchronization is most probable and the preferred state
by which the best performance can be obtained because it is stable.
The model considerations of limit cycle synchronisation (Wei, Wertman, & Sternad 2003) can be
extended by now including the trajectory information in timed repetitive movements in the context
of weak and strong perturbations. This concept was mainly inspired by Winfree (1980) and based on
simple discrete event observation (e.g., contact timing). The introduction of a discrete movement
might be considered as a perturbation on the ongoing rhythmical movement. The particular aspects
are the nonlinear shortening or lengthening of the periodic process due to the external discrete
movement. Both shortening and lengthening of the periodic process running on the background
subjected to a perturbation of discrete movement would operate on the acquisition of robust
synchronization. Influence and sensitivity of physiological parameters are phase-dependent. The
effects of discrete tap on periodic one carries the state point to a new point in phase space.The
resetting of the state point to a new phase depends on the phase and magnitude of the perturbing
event represented by the phase position and size of the vector in model. Weak inputs (small vector)
perturb the system less and small Type 1 phase shifts will be observed. Strng inputs (large vector)
carried the system across the unstable equilibrium point and very large Type 0 phase shifts will occur
as a consequence. Based on the simple Winfree model and on the experimental observations of the
impulsive effects of the discrete movement on the periodic one, principally a perturbation produces
a phase delay when the both movements are on the opposite direction and produce a phase advance
when both movements are in the same direction.
The obtained experimental results in normal tapping are consistent with the results of previous
investigations on the coordination of discrete movements and periodic movements (e.g., Yamanishi,
Kawato, & Suzuki 1979; Yoshino et al. 2002). Beside ‘Periodic Tap Retardation’ (PTR) behavior (Type
0) and ‘Marginal Tapping Interaction’ (MTI) behavior (Type 1), “Periodic Tap Hastening” (PTH) and
“Discrete Tap Entrainment” (DTE) could be observed. The speed of periodic movement was usually
greater after than before the discrete movement. Phase range (0.3-0.6) was very often the range in
which the state point was repelled away. The attraction was found in synchronization of both fingers
and also in the cross-like coordination. Based on the discrete events, this attraction was reflected in
dense distribution of phase, and based on continuous trajectories this attraction was reflected by the
overlapping period of down-upward movements as well as the equalization of both the slope
durations and the tap durations. Furthermore, the embedding of discrete tap into the two
surrounding periodic tap with well-formed distribution of their tap duration to obtain stable timing
by the simplest related frequency ratio 2:1 also was found.
The PRC indicates excitatory coupling presented in PTH or inhibitory coupling presented in PTR
behaviour. PTR and PTH reflect the repelling effect pushing the state point to the stable state. This
repelling effect was clearer when force or multi-effectors were required for discrete movement. The
strength of phase-shifting inputs to the limit-cycle also is reduced when discrete events used as
feedback in the closed-loop control for the restoring force. The trajectory deflecting in TC of PTR
might reflect the restoring force, whereas the reduced periodic amplitude in contact-free condition
by synchronization might reflect the damping force. More PTH but fewer PTR in normal tapping was
214
found in comparison with isometric and contact-free tapping. Tapping with movement comprises a
memory due to inertia and feedback-based regulation approach. A simple proportional mechanical
system without regulation based on discrete feedback approximately is reflected both in isometric
tapping and in contact-free tapping. The pooled data even showed PTR in phase range up to 0.4,
some subjects showed PRCs which start like MTI in this small phase range in normal tapping but then
like PTH. This MTI can be explained by the support of the discrete events of reference tap used as a
resuming point for timing of the affected period for the first case because this MTI was absent in
contact-free and isometric tapping. For PTH, a more comfortable stronger force is the reason.
TD+PTR might represent pronounced resetting at representation level which does not reach the
execution level.
On the other hand, the ongoing movement affected some characteristics of the discrete
movement. The time course of one finger affected the time course of the other one. The discrete tap
reaction was delayed (DTE) to obtain preferred coordination. The ambiguous instruction forced the
subject to give preference to one of the two competing motor tasks and hence all interaction
patterns and mix forms can be observed.
Eye-hand coordination generally does not always cause DT costs. Sharing neural pathways might
reduce redundancy and/or resources could be spared. In the modern high-end computers, only
actually required resources are activated and those which can provoke interference are spared. One
can speculate that the neural system uses similar scaling methods, too. The chance for strong
interference during execution of two simple independent OM tasks will be low, as our results
demonstrate for this specific condition if only more complicated tasks require more complex and
cross-functional modules. It should be noted that this finding supplements but does not oppose the
reports showing eye-hand-interactions in complex dual-tasking (e.g. Pashler, Carrier, & Hoffman
1993).
The investigation of hand-foot combination imposingly showed the similar interaction pattern as
in case of a periodic right index finger tapping and single discrete responses executed by the other
hand. The specification of this DT behavior was even extended by demonstrating that this interaction
pattern is basically independent of which upper or lower limb is selected for the discrete response.
This fact holds also for our (musically trained) participant showing almost no interaction between the
rhythmic activity and the discrete responses (MTI behavior): this behavior is not changed when using
the foot instead of hand for the discrete response. However, these rare cases of participants (who
were discussed by Yamanishi, Kawato, & Suzuki (1980)) apparently demonstrate that the DT
interaction mechanism can be outperformed by other factors like intensively trained motor
sequences as discussed for musicians – thus the window for future research is still open.
9.2 Outlook
9.2.1 Effect of force, attention and external feedback
There are reports about the influence of sensory reafferences on continuation tapping such as
feedback tones for taps decreased intertap interval variability as compared to tapping without tones
(Barratt et al. 1981; Kolers & Brewster 1985). Aschersleben et al. (2002) confirmed that the temporal
control of repetitive movements is based on sensory information i.e. timing becomes more precise
the more sensory information becomes available. In a series of finger-tapping tasks consisted of nine
combinations of pace and force strong interactions between the two factors pace and force under
high pace conditions were found although motor timing was independent of force control in controls
of weak forces and slow pace (Inui et al. 1998).
215
The difference between the “reaction time” and the “self-paced” instruction is worthy to study with
clear emphasize on one task and selection of subject behaviour (strong PTR and strong MTI) under
further force and feedback variations.



Force requirement on one hand
Extern feedback (audio, visual) on periodic taps
Only Contact-free periodic tapping
9.2.2 Mental task instead of motor task
Information processing capacity is limited for any individual. If parallel execution of the two tasks
require more than the total capacity their performance on either or both deteriorates because
performing every task requires a given portion of capacity (Shumway-Cook & Woollacott 2000;
Neumann 1984; Wickens 1989). Research for studying attention and posture control has used dualtask paradigms in which the primary task postural control and a secondary task were performed
together. The extent of the declination of the performance on either task declined indicated the
interference between the processes controlling the two task, and thus the extent to which the two
tasks shared attentional resources (Kerr, Condon, & McDonald 1985).
Yardley et al. (1999) studied the postural stability with competing demands for attentional
resources by articulation.36 normal subjects were engaged for studying the postural sway. Subjects
had to repeat a number aloud (articulation), count backwards multiples of seven aloud (articulation
and attention), count backwards silently (attention), and neither articulation nor attention was
require. A significant increase in sway was reported in articulation condition, whereas no effect of
attention was observed.
Demand for attentional resource such as silently counting has no effect in postural sway maybe
because it presents a periodic process. It is interesting to study a mental response such as
imagination of a discrete movement.
9.2.3 Checking memory limit of time interval
The conclusion that judgements of temporal equivalence are based not only on synchrony of events
with internal beats, but on a memory for interval durations (Keele et al. 1989) needs still further
support. Yamada & Yonera (2001) estimated the temporal control mechanism of tapping with
rhythmic patterns. Subjects who were at intermediate levels of musical performance made equalled
interval tapping in several tempos (180, 370, 800ms/tap). The results of this study show that the
memory is able to govern 20 taps to control of equalled interval tapping.
The 20-taps-memory capacity, the judgements of temporal equivalence are based on a memory
for prescribed interval duration that we learn during synchronization phase and the motor event of
the reference tap we use as the reference point for temporal judgement are the factors leading to
the question whether the 20-taps-memory capacity can still be maintained in this bimanual dual-task
at larger periods such as 800ms, 1000ms, 1200ms.
9.2.4 Audiomotor overlearned in musical trained people
A tight connection between action and perception of the sensory feedback in Musicians was
suggested (Bangert et al., 2006). Even when the physical presence of the sensory feed back is
experimentally suppressed a mental representation is still generated. It is interesting to check
216
whether the stronger cognitive representation of rhythm in musicians also has a strong entrainment
on eyeblink.
217
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238
10 Appendix
10.1 Classification of the interactions based on discrete events
The basic idea of this classification is to categorize the tapping behavior of the subjects into the
following typical interaction patterns (for description of patterns, see Section 6.5.1):
1) Marginal Tapping Interaction (MTI),
2) Periodic Tap Retardation (PTR),
- Tap Delaying (TD),
- Tap Cancelling (TC),
3) Periodic Tap Hastening (PTH),
4) Discrete Tap Entrainment (DTE),
5) Mixed Tapping Strategies (MTS).
The additionally introduced error class “Discrete Tap Omitted” (DTO) contains all segments in which
the discrete tap was not executed.
For the statistical evaluation in Section 7.2, these groups are defined by quantitative estimation
resulting in a motor response scheme for the periodic taps and the discrete taps. Such a simple response
scheme simply is derived from statistical observations. Actually, decision for a tap behavior category is
taken according to rules which are based on the length of the disturbed interval between the reference
tap and the next following periodic tap which encloses the discrete tap as well as on the deviation of the
dual-task discrete taps RT from SRT reference value determined in single-task experiments. These rules
were applied for the automatic classification of the segments of real data recorded in the experiments.
Determination of the SRT references
First, the individual mean reaction time SRTav and the corresponding standard deviation σsrt are
determined from the left hand taps responses of these reaction time sessions; the accompanying right
hand responses are not further considered.
The lower bound SRTmin and the upper bound SRTmax was set to:
SRTmin = SRTav – σsrt fsrt
SRTmax = SRTav + σsrt fsrt
The factor fsrt was empirically determined but equal for all subjects.
Determination of the periodic tapping interval references
The mean periodic tapping interval Nav and the corresponding standard deviation σN were derived
from the data of the normal tapping experiment. In order to get representative values (undisturbed
intervals), not only the intertap interval preceding the reference tap but also the two further preceding
intervals was considered. The mean Nav and the standard deviation σN were determined for each
experiment of each subject.
The lower and upper bound Nmin and Nmax, respectively, for the tapping period between reference tap
and first periodic tap after the reference tap was set to:
Nmin = Nav – σN fN
Nmax = Nav + σN fN .
The factor fN was empirically determined but equal for all subjects and experiments.
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Definitions of the interaction patterns
The dual-task data of every valid trial is divided into 12 segments when it contains one discrete
single tap. 5 periodic taps around the discrete tap are taken for each segment and shown on the
ordinate of PRC.
(1) Marginal Tapping Interaction (MTI)
A sequence is assigned to this category, if the segment does not show any signs of the following
interactions
- RT of the discrete tap lies within the range limited by the lower bound SRTmin and the upper bound
SRTmax, and
- the duration of the periodic tapping interval lies within range limited by the upper bound N max and
the lower bound Nmin.
(2) Periodic Tap Retardation (PTR)
a) Tap Delaying (TD)
A sequence is assigned to this category
- if RT of the discrete tap is within the lower bound SRTmin and the upper bound SRTmax, and
- if the duration of the periodic tapping interval enclosing this discrete tap exceeds the upper bound
Nmax.
b) Tap Cancelling (TC)
- If RT of the discrete tap is within the lower bound SRTmin and the upper bound SRTmax, and
- If the execution of the ongoing periodic movement is stopped on the fly simultaneously with the
execution of the discrete tap; in normal tapping no ground contact is observed and in isometric tapping
the amplitude of the tap is about one third of the usual amplitude, and
- If the duration of the periodic tapping interval enclosing this discrete tap exceeds the upper bound
Nmax.
(3) (PTH)
- If RT of the discrete tap, which simultaneously is executed together with the first periodic tap after the
reference tap, is within the lower bound SRTmin and the upper bound SRTmax, and
- If the duration of the periodic tapping interval after the reference tap is shorter than the lower
tapping period bound Nmin.
(4) Discrete Tap Entrainment (DTE)
- If the duration of the periodic tapping interval after the reference tap is within the lower bound N min
and the upper bound Nmax of the tapping period, and
- If the discrete tap is executed simultaneously together with the periodic tap, and
- If its RT exceeds the upper bound SRTmax.
It should be noted that phase entrainment of the discrete tap can be combined with every value of RT:
with very short RT and normal RT as they also occur in single-task reaction time experiments, but in
addition with unusual long RT which are most probably due to the entrainment. The criteria used here
only select trials with those distinctly prolonged RT, i.e. their rate is certainly underestimated.
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(5) Mixed Tapping Strategies (MTS)
- If the RT of the discrete tap exceeds the upper bound SRTmax and
- If the duration of the periodic tapping interval enclosing this discrete tap exceeds the upper bound
Nmax or
- If the duration of the periodic tapping interval enclosing this discrete tap is shorter than the lower
bound Nmin of the tapping period or
- If the execution of the ongoing periodic movement is stopped on the fly simultaneously with the
execution of the discrete tap; in normal tapping no ground contact is observed and in isometric
tapping the amplitude of the tap is about one third of the usual amplitude.
(6) Discrete Tap Omitted (DTO)
A sequence is assigned to this error class, if the discrete tap is missing, independent from the periodic
tapping interval duration.
Determination of reference values (change bias)
Subjects showed a preference for a dominant tapping behavior reflected by a dominant occurrence
of the corresponding interaction pattern. But a specific interaction pattern can simply occur occasionally
due to the asynchrony of the two tapping processes, since the timing criteria for a specific category (as
listed above) can be matched just by chance and not due to any specific interaction. Therefore, a lower
bound reference value which indicates this chance bias of occurrence was determined for each subject
and experiment; so any value significantly different from this chance bias will indicate some interaction
of both tapping processes. Chance bias values were estimated by simulating the motor event sequence:
(i) the periodic tap intervals were generated at random with a mean interval and standard deviation
matching those of the subject data, (ii) the go signal sequence was randomized between 3000 and 5000
ms, and (iii) the reaction times again were generated at random with the mean and standard deviation
matching those of the subject data. Thus, the periodic events and the discrete event were totally
independent in this simulation. The generated event sequence for both (simulated) motor tasks was
input to the same simple motor response scheme as described above which was used to assign the
recorded segments to the interaction pattern categories. The resulting bias values are depicted in Fig.
10.1 as “expectation values”. Again to emphasize that neither this event simulation nor the motor
response scheme does incorporate any complex physiological and psychological background, but is
rather mechanistically derived from statistical observations only. Note that calculation of these values is
restricted to categories MTI, DTE, TD and PTH by principle, because this formal categorization described
above does not include position signal evaluation but only event times.
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Figure 10-1: Distribution of interaction patterns in bimanual tapping and chance bias (expectation) for Subj. 4.
Left from the dashed vertical line, groups of 3 bars show the proportion of each interaction category for the
three cases: (i) normal tapping (left bar), (ii) isometric tapping (middle bar), (iii) chance bias (no-interaction
prediction, right bar). Right from the vertical dashed line, only experimental data are given, since change bias
estimates cannot be obtained by principle.
Comparison between the experimental results and chance bias is illustrated by Fig. 10.1 which shows
three bars for each category on the left side of the diagrams: (i) normal tapping, (ii) isometric tapping,
and (iii) chance bias (no-interaction prediction) for Subject 4. Clearly, the chance bias for MTI (left group)
being near to 100 % is dominant, because all segments not being assigned to any specific interaction
type are designated to the MTI group; i.e. the criteria for the different categories are very specific and
their timing structures will almost never appear by chance.
10.2 Software
Experimental control schemes were realized using mainly control system DIAdem (National
Instrument Inc.) and additional MATLAB for short computation during pause between experimental
trials. DIAdem offers interactively inspecting data during experiment and a wide range of mathematical
routines for analyzing data. DIAdem uses a built-in script language which contains normal programming
constructs such as loops and case statements. Body limb movements are separately recorded for each in
several channels and saved in a binary data file. Meta-file containing information such as name of
subject, used condition file, date, comment, channel names, binary file names, etc is generated. This
meta-file is used in MATLAB-program for reading binary data and offline-analysis and is saved in Header
as program variable (more details below).
MATLAB is a high-level language and interactive environment for performing computationally
intensive tasks. MATLAB is used for off-line analysis. There are two main MATLAB modules Showdata
and AnalyzeTapResults. The main function of Showdata is to display experiment data and to provide
interactive user interface. The graphic user interface is realized by event listener with callback function
and dialog windows. Action is evoked by mouse click on menu, undermenu, button, label, and graphical
objects such as marker (more deltails later). Example for the menu DIAdem file:
hfile = uimenu(hfig, 'Label', 'DIAdemFile');
With the callback function for the undermenu open:
uimenu(hfile, 'Label', 'Open file', 'Accelerator', 'D', 'Callback', callback);
Where callback = [s1, usermode, s2, s3]; and
242
usermode = 'UserData.mode = ''open file''; ';
s1 = 'UserData = get(gcf, ''UserData'');'
s2 = 'set(gcf, ''UserData'', UserData);'
s3 = 'uiresume;'
The built-in MATLAB variable UserData is set by the callback function. This variable is queried for the
actual action (open file (more details later)) and control is given back to graphical user interface
(uiresume).
The recorded channels can be selected in multiple modes. The characteristic events of signal changes
such as onset, offset, and maximum are detected by two algorithms. For simple signal such as force
signal linear regression method and simple threshold algorithm, and for complex signal such as finger
position signal, sophisticated ramp-step model are approached. These events are presented by vertical
or tangential red lines and are named marker as program variable. These detected markers can be
manually set, deleted, changed and saved. The data structure of marker is a tree-structured data type
called Result and is saved in a MATLAB-format file, which is used for analysis in AnalyseTapResults.
Additional information are added to this marker structure and used to display the classification of
interaction in DT situation. Saving, loading, and modifying of data structure in any order is possible.
Offline analysis is mainly performed in AnalyzeTapResults based on the result file generated in
Showdata. The experimental condition (eye-hand tapping, hand-hand-hand, hand-foot tapping, etc.)
and task condition (dual-task, single-task) branch off the analysis process into different functions such as
ProcessPeriodicRightHand, ProcessPeriodicRightHandAndRightFoot, ProcessEyeHand, ProcessRT, etc. In
these functions timing of characteristic events selected by user on interactive dialog window are read
from the result file and checked for consistency. The user is notified about the missing events. These
missed events have to be checked and corrected in Showdata. The correct timing of events is saved in a
MATLAB-format for later use by loading it if the same timing structure is needed for other analysis.
Timing structure can be changed for instance by modified or new added marker. The analyzed result
also is saved in a MATLAB format files called AnalysedData. For plotting this analyzed result also can be
loaded if new analysis is not needed for the new plotting actions. Different specific plot functions and
these functions are realized by undermenu.
To start both programs, simply type the program name in MATLAB command line and press the
return button. Both programs run in an endless loop waiting for an action on the menu and can be exit
by the menu exit or by the default close operation of MATLAB. The MATLAB switch instruction branch
off to the corresponding action based on program variable UserData.mode(=usermode) set by callback
function. Both modules use common custom dialog AutoDetectUIInput(detectAnalyse) and default
MATLAB dialog uigetdir(path, 'Please select directory') for getting the user input parameters. The input
parameter detectAnalyse has following elements for three dialog arts text dialog, list box dialog and
radio button dialog:
% the header name for the text box field
defaultValueHeader = 'default';
customValueHeader = 'custom values';
% the input parameter names for the text dialog
defaultNames = {'Bin Step'; 'XLim2'; 'YLim2'; 'Phase from'; 'Phase to'};
% the default input parameter values for the text dialog
defaultValues = {'30', '500', '60', '0.35', '0.45'};
% the input parameter names for the list box dialog
namesToSelect = {'Reaction limb', 'Periodic limb', 'signal event'};
% the input parameter values for the list box dialog
valuesToSelect =
[{'left hand'; 'right hand'; 'left foot'; 'right foot'; 'go'}, ...
{'left hand'; 'right hand'; 'left foot'; 'right foot'; ''}, ...
{'force'; 'position'; 'agonist'; 'antagonist'; ''}];
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% the header name for the radio button dialog
boxHeaders = {'Select channels'};
% the input parameter names for the radio button dialog
boxNames = {'channelName1'; 'channelName2'; 'channelName3'};
% the input parameter values for the radio button dialog
boxValue = {'0'; '0'; '0'};
Example for getting input values:
qqAnalyse = AutoDetectUIInput(detectAnalyse);
binStep = str2num(char(qqAnalyse.customValues(1)));
xlimit2 = str2num(char(qqAnalyse.customValues(2)));
ylimit2 = str2num(char(qqAnalyse.customValues(3)));
phaseFrom = str2num(char(qqAnalyse.customValues(4)));
phaseTo = str2num(char(qqAnalyse.customValues(5)));
reactionLimb =
deblank(char(detectUiParam.valuesToSelect(qq.selectedValues(1), 1)));
periodicLimb =
signalEvent =
if qqAnalyse.boxValues(1, 1)=='1';
10.3 Showdata
10.3.1 Variable
10.3.1.1 Global variable
Global variable are considered as static variable and accessible in all functions
Header: contains meta information about the experiment data in following fields
2.1 element
2.3 Filename
2.5 FullFilename
2.7 FilePath
2.9 NotANumber
2.11 Name
2.13 Comment
2.15 Author
2.17 Date
2.19 Time
2.21 TimeFormat
2.23 Channel
2.25 NChannels
2.27 NDataChan
2.29 FileType
2.31 NumberMarkers
2.33 Version
2.35 DataSize
2.37 DataFormat
2.39 ChannelDataPos
2.2 meaning
2.4 experiment data file name
2.6 path and file name
2.8 path
2.10 minimum invalid value (exceed data type float)
2.12 participant name
2.14 additional information about experiment
2.16 experimenter
2.18 performing date of experiment
2.20 performing time of experiment
2.22 'dd.mm.yyyy hh:nn:ss.ffff'
2.24 vector of channels
2.26 number of channels
2.28 index of data channel (not time channel)
2.30
2.32
2.34 'DIAdem9'
2.36 size of recorded data
2.38 data format
2.40 unused
244
Example to access meta data:
Header.Channel(i).Length:
%data length of the channel number i;
Header.Channel(i).SampleTime
%sample time (1ms)
Header.Channel(i).Name
%channel name
for i = 1:Header.NChannels
%loop through all channel
 Result: contains data structure of characteristic events of all signals which were detected by custom
algorithms
2.41 Element
2.43 Status
2.45 NDataChan
2.47 Channel
2.49 Channel structure
2.42 Meaning
2.44 status of Header
2.46 number of channels
2.48 channel list
2.50 Meaning
2.53 Channel name
2.56 Trial number
2.59 Marker list
2.52 Name
2.55 Trial
2.58 Marker
2.61 Marker structure 2.62 Meaning
2.65 YLocation
2.69 XLocation
2.73 Name
2.77 Magnitude
2.81 RiseTime
2.85 Character
2.63
2.66 y-coordinate of marker 2.67
2.70 time of marker
2.71
2.74 marker name
2.75
2.78 y-coordinate
of 2.79
tangent
2.82 x-coordinate
of
tangent
2.86 interaction class name
2.51
2.54
2.57
2.60
2.64
2.68
2.72
2.76
2.80
2.83
2.84
2.87
2.88
Example accessing marker:
for cidx = 1:length(Result.Channel), % for every channel
ms = [];
% initial markers for this channel
if isfield(Result.Channel(cidx), 'trial'),
trialTo = length(Result.Channel(cidx).trial); % number of trial
if trialTo>0
for tidx = 1:trialTo, % for every trial
if ~isempty(Result.Channel(cidx).trial(tidx).Marker)
midx = 1;
% initial marker index
% as long as the marker index is valid
while midx <=
length(Result.Channel(cidx).trial(tidx).Marker)
m = Rsult.Channel(cidx).trial(tidx).Marker(midx);
% check if the marker name is valid
if isContainedString({'s', 'm', 'e', '1', '2'}, m.Name)
% trial number for this marker
m.trial = tidx;
array
end
midx = midx + 1;
end
end
end
end
end
245
ms = [ms m];
% append to marker
end
UserData: default global MATLAB variable specifying data that associate with actual graphic object
(window) and usable in other m files.
2.89 Element
2.91 Warntxt
2.93 Mode
2.95 showCharacteristicsOfInteraction
2.97 trial
2.99 datstart
2.101 plot.dattime
2.103 plot.datsize
2.105 plot.showchan
2.107 plot.zoomstart
2.109 plot.zoomsize
2.111 markerstatus
2.113 selectedChannelsToShowMarkers
2.115 markerNames
2.117 StartSignal
2.119 PaceSignal
2.121 GoSignal
2.90 Meaning
2.92 hint for setting marker operation
2.94 execution mode generated by action on menu.
2.96 indication whether interaction classification
should be displayed
2.98 actual trial in Showdata
2.100 start time of actual segment in Showdata
2.102 maximum time vector of all channels
2.104 segment time range
2.106 selected channels for displayed
2.108 start time of zoom if it is activated.
2.110 zoom size
2.112 marker status
2.114 selected channels for marker display
2.116 marker name vector
2.118 control signal trial
2.120 control signal pace
2.122 control signal stimulus
For example accessing data in function PlotAcqData:
if ~isempty(UserData.plot.zoomstart) & (UserData.plot.zoomsize ~= 0)
10.3.1.2 Local variable
actualReactionIndex, actualPauseIndex are current index of actual discrete movement and pause signal
currently displayed on the screen providing jump on the desired go and pause signal number.
10.3.2 Menu
Menu and undermenu are created in MATLAB by the function uicontrol. Default menu from MATLAB are
file, edit, view, insert, tools, desktop window help. Other menus are custom menus.
246
Figure 10-2: dialog for selecting the experiment data file
247
Figure 10-3: dialog shows the list of all experiment data files
10.3.2.1 DIAdemfile: Menu for experiment data file.
10.3.2.1.1 Unternenu
open file:
Click on menu open file, a dialog created by calling [ffiles, fpath] = ListFilesForSelect; for selecting the
experiment data file (Fig. 10.2). The function ListFilesForSelect uses the default MATLAB function
uigetdir which return the directory path and all file names in the selected directory. This list of all
experiment data files is shown in the next dialog window (Fig. 10.3) by calling the default MATLABfunction listdlg.
Figure 10-4: dialog for selecting of channels
248
Figure 10-5: the result file is loaded and the markers are displayed in Showdata
Fig. 10.4 shows dialog window by calling AutoDetectUIInput(detectAnalyse) for selecting of channels.
The input parameter detectAnalyse consists of only radio button dialog. The channel names are read
from the variable Header. The variable Header is created from the meta-file created by DIAdem for
global usage. All the channel data of control signals (go command, pacing) are read and saved in variable
UserData. The result file is loaded and the markers are displayed (Fig. 10.5). Displaying signals and
characteristic events is implemented in function PlotAcqData. It calls the function GetDiaData to read all
the binary data of selected signals with the help of variable Header.
save result file: save result file containing markers
exit: exit Showdata
249
10.3.2.2 Options
10.3.2.2.1 Untermenu

Select channels: select the channels to be displayed (Fig. 10.4). Unit display in Newton can be
selected.
Figure 10-6: dialog for selecting the maximum time duration to be displayed.

Select segment size: select the time duration to be displayed on the screen (Fig. 10.6)

Header of content file: print the structure of Header containing information about
experiment.

Reverse signals: display the reversed signal
Figure 10-7: shows the numbered onset of the registered extern clock

Show extern takt: show the registered extern clock which onset is numbered (Fig. 10.7)
10.3.2.3 Characterize

Show characteristics of interactions: display the classified interaction in DT tapping

Hide characteristics of interactions: hide the classified interaction in DT tapping

Print number of interaction characteristics: print the number of classified interaction in DT
tapping
250
10.3.2.4 Event marker
Figure 10-8: dialog for selecting the new individual marker name


Change marker name: change individual marker name
Activate on the menu and click on the marker leads to a dialog window for selecting the new
marker name (Fig. 10.8):
Delete marker within area: delete all the markers within the area masked by the left mouse.
Press and drag a rectangle inclosing the markers to be deleted.

Set marker: single setting new marker

A cross line appear on the mouse cursor for setting the new marker. The dialog for selecting the
marker name appears.
Set marker per slope: set the tangent to the down (up) ward movement

Press and drag from the start point to the end point of the tangent on the slope then release the
mouse.
Change marker: replace the individual marker.
Analog to the menu set marker.
251
Figure 10-9: dialog for deleting all markers within the time range given by user

Delete marker: single deleting marker

Delete marker from to: delete all markers within the time range given by user (Fig. 10.9)
2.123 Parameter
2.125 From
2.127 To
2.129 YLoc
2.131 DeltaY
2.133 Slope

2.124 Meaning
2.126 start time
2.128 end time
2.130 y-coordinaten of markers
2.132 tolerant values for both direction of YLoc.
2.134 down(up)-slope or all (none).
Change marker name from to: replace all marker names by the new name within time range
given by user
Dialog window is analog to menu “delete marker from to”
252

Show only markers: display the marker whose names are selected by user

Show slope: display the tangent to the down (up) ward movement

Get time duration: display time duration masked by the left mouse
10.3.2.5 Detection
Figure 10-10: dialog for selecting the detection algorithm

Auto detection: let the characteristic events in signal to be detected. User has to select the
algorithm (Fig. 10.10)
Figure 10-11: dialog shows the parameter for the simple detection algorithm
Dialog for simple threshold algorithm (Fig. 10.11):
2.135 Parameter
2.137 Minimum length of an event
2.139 Distance of next peak
2.141 Noise in force channels
2.143 Distance of next peak
2.145 Minimum length of an event
2.136 Meaning
2.138 minimum event duration
2.140 minimum distance from signal onset to the
real force peak.
2.142 tolerance in ms from the baseline
2.144 distance from signal onset to the force
peak
2.146 minimum event duration
253
Figure 10-12: dialog shows the parameter for the ramp-step detection algorithm
Dialog for ramp-step algorithm (Fig. 10.12)
10.3.3 Control buttons







GoNr: jump to the go number given in the input text field
Prev. go: jump to the previous go number
Next go: jump to the next go number
Prev. pause: jump to the previous pause number
Next pause: jump to the next pause number
Go to: jump to the time point given in the input text field
Next miss.: jump to the next location where marker is missed which is checked and saved in
AnalyzeTapResults
10.4 AnalyzeTapResults
10.4.1 Variable
10.4.1.1 Global variable


resultFileName: full result file name used for other m files as title for diagram.
AnalysedData: analysed data structure
254
All analyzed information is stored in the variable AnalysedData. AnalyzedData is structured into four
layers. AnalysedData is the root. The second layer is combined by the periodic and discrete limb in
order. The third indicates the signal event (force, position, etc.). For example
AnalysedData.rightHandLeftHand.force.referenceOnsets is the onset timing vector of the reference tap
of force signal in the case that the periodic movement is the right finger and the discrete limb is the left
finger. For continuous analysis (slope analysis) the signal event is replaced by the classification because
up to now it is implemented for the dual-task experiment with periodic right hand and discrete left
hand.
 ds1Us1 (ds2Us2): 2-sided down (up) slope synchronization at the reference tap (next tap of
reference tap)
 ds1 (ds2): 1-sided downslope synchronization at the reference tap (next tap of reference tap)
 sa1 (sa2): 1-sided slope asynchronization at the reference tap (next tap of reference tap)
 ap1 (ap2): 1-sided slope antiphase synchronization at the reference tap (next tap of reference
tap)
 us1 (us2): 1-sided upslope synchronization at the reference tap (next tap of reference tap)
 ap: 2-sided slope antiphase synchronization at the reference tap (next tap of reference tap)
 ta: tap asynchronization
% the periodic limb is the right, the discrete limb is the left finger and signal is the force channel.
AnalysedData. rightHandleftHand.force.
Following results are saved:
% force onset of periodic tap after reference tap where the discrete limb is the left hand
referenceOnsets
% the trial number vector where the discrete limb is the right foot
trialNrs
% time duration from the discrete right tap to the reference tap
timeElaps
% phase defined for the discrete tap normalized by constant ITI
phases
% compliment phase defined for discrete tap normalized by constant ITI
complPhases
% dynamic phase defined for the discrete tap normalized by mean of 3 preceding ITIs
dynamicPhases
% compliment phase defined for discrete tap normalized by mean of 3 preceeding
complDynamicPhases
ITIs
% reaction time vector
RTs
% time duration between the 5 periodic taps around the reference tap
thirdITIsPrecedingDiscrete
secondITIsPrecedingDiscrete
lastITIsPrecedingDiscrete
ITIsClosingDiscrete
firstITIsSucceedingDiscrete
secondITIsSucceedingDiscrete
% Analog time duration between the 8 periodic taps around the go command and phase are defined analogously
for the go signal
255
% The corresponding go timing
goSignals
% Intertap intervals separated into the non-disturbed and the disturbed one for the periodic limb. The onset, trial
numbers are also saved.
AnalysedData rightHandleftHand.force.unaffectedITIs
AnalysedData.rightHandleftHand.force.unaffectedOnsets
AnalysedData.rightHandLeftHand.force.unaffectedTrialNrs
% And the IPT for the heart messurement (down: downslope onset)
AnalysedData.puls.down.unaffectedIPIs = unaffectedITIs;
AnalysedData.puls.down.unaffectedOnsets = unaffectedOnsets;
AnalysedData.puls.down.unaffectedTrialNrs = unaffectedTrialNrs;
For the slope analysis:
% the coefficient estimates of the discrete upward movement
reactionUpEstimates
% onset and offset of the discrete upward movement
reactionUpslopeOnsetsOffsets
% the coefficient estimates of the discrete downward movement
reactionDownEstimates
% onset and offset of the discrete downward movement
reactionDownslopeOnsetsOffsets
% the coefficient estimates (slope gradient and y-intercept) of the 3th, 2sd, 1st periodic upward movement
preceeding (succeeding) the reference
thirdPrecedingUpEstimates
secondPrecedingUpEstimates
lastPrecedingUpEstimates
firstSucceedingUpEstimates
secondSucceedingUpEstimates
% analog for the downward movement
thirdPrecedingDownEstimates
secondPrecedingDownEstimates
lastPrecedingDownEstimates
firstSucceedingDownEstimates
secondSucceedingDownEstimates
% onset and offset of the 3th, 2sd, 1st periodic upward movement preceeding (succeeding) the reference
thirdPrecedingUpslopeOnsetsOffsets
secondPrecedingUpslopeOnsetsOffsets
lastPrecedingUpslopeOnsetsOffsets
firstSucceedingUpslopeOnsetsOffsets
secondSucceedingUpslopeOnsetsOffsets
thirdPrecedingDownslopeOnsetsOffsets
secondPrecedingDownslopeOnsetsOffsets
lastPrecedingDownslopeOnsetsOffsets
firstSucceedingDownslopeOnsetsOffsets
secondSucceedingDownslopeOnsetsOffsets
256
% time duration between the succeeding (preceding) periodic taps and the reference tap
intervalsAroundDisturb
% phase, dynamic phase and corresponding complement phase (1-phase) defined for the discrete tap
phases
complPhases
% reaction time vector for the force and position signal
forceRT
posRT
% indexes Of reference tap where its downward movement offset still before the go
indexesOfRefTapBeforeGo
% timing of 6 periodic taps around the discrete tap
periodicForceOnsetsOffsets
% timing of the discrete tap
discreteForceOnsetsOffsets
% amplitudes of 6 periodic taps around the discrete tap
periodicForceAmplitude
% amplitudes of the discrete tap
discreteForceAmplitude
% phase of go as disturbance but the reference tap is defined based on discrete tap
goAsReferencePhases
% time duration from the reference tap to the go as disturbance
timeElaps
% time duration from the go to the next tap of reference tap
timeLeft
% phase, dynamic phase and corresponding complement phase (1-phase) defined for the go and the go timing
phases
complPhases
goSignals
10.4.2 Menu:
Default menu from MATLAB: file, edit, view, insert, tools, help.
10.4.2.1 Resultfile:

Open file: open experimental data file for analysis
All interactions over dialog window are analog to the menu DIAdem file in Showdata. Input parameters
are the experimental condition and periodic limb.
detectAnalyse.namesToSelect = {'Experiment', 'Periodic limbs', ‘event to analize’};
detectAnalyse.valuesToSelect =
[{'hand-foot';
'ST ITI';
257
'ST reaction';
'saccade-hand'; ''; ''; ''; ''; ''; ''}, ...
{'left hand'; 'right hand'; 'left hand und right hand'; ...
'left foot'; 'right foot'; 'left foot and right foot'; ...
'right hand und right foot'; 'right hand und left foot'; ...
'left hand und left foot'; 'left hand und right foot'}, …
{'discrete event'; 'slope'; ''; ''; ''; ''; ''; ''; ''; ''}];
If the old analyzed data should be used then AnalysedData or AnalysedITI is loaded and plotting is
available. Otherwise the global variable Header and Result are loaded and depending on the selected
experimental condition, task condition and event to analyze one of different function calls is invoked:
1.
2.
3.
4.
ProcessRT: single-task discrete reaction experiment
ProcessEyeHand: occulo-hand dual-task experiment
ProcessPeriodicRightHandSlope: analysis based on the tangent to the down(up)ward movement.
ProcessPeriodicRightHand: hand-hand or hand-foot dual-task experiment by which right hand is
periodic
5. ProcessRightHandAndRightFoot: hand-hand or hand-foot dual-task experiment by which right
hand and right foot are periodic
6. …
The running processes of all the modules ProcessX in principal are similar. Control signal and markers of
all channels are at first read from binary file and result file respectively in GetTimingVectors() and
assigned to local variables for analysis. The timing of the control signal (go-command, pacing signal) are
read only once from the binary data file described in global variable Header and signal onsets are
extracted. This control timing is stored in a MATLAB format file “experimentFileName_controlTime.mat”
for later loading
controlTimeFile = [resultFile(1:end-7) '-controlTime.mat'];
if ~exist(controlTimeFile, 'file')
for i=1:Header.NChannels,
if (strcmp(Header.Channel(i).Name, 'TimeGoSignal'))
file = fullfile(Header.FilePath, Header.Channel(i).Filename);
FID2 = fopen(file, 'r');
if FID2 == -1, % no file opened.
error([' ### PorceeITI: datafile ', 10, ...
' ', file, 10, ...
' not found!', 10]);
else % file is successfully opened - get data of old data set
if old_data_set == 0; % control varaible aready indicates new data set
error(' ### ProcessITI detects old and new data set indicator concurrently (goSignal)');
end
old_data_set= 1; % set control of data set version to old data set
fseek(FID2, (Header.Channel(i).StartIndex - 1 ) * 8, -1);
aLength = Header.Channel(i).Length;
goSignal = fread(FID2, aLength, 'float64', 8);
goSignal = goSignal*1000; %software clock times are in s - adapt them to ms
fclose(FID2);
end
% Pacing signals to adapt periodic tapping
258
elseif strcmp(Header.Channel(i).Name, 'TimePaceSignal')||strcmp(Header.Channel(i).Name,
'TimeTapSignal')
if old_data_set == 0; % control varaible aready indicates new data set
error(' ### ProcessITI detects old and new data set indicator concurrently
(TimePaceSignal)');
end
% presumably, here the same clock problem is given as before with go signals !!! Must be corrected as
well!
paceSignal = GetDiaTrigger(i, 1, Header.Channel(i).Length)*1000;
% now the new data sets are addressed
%
elseif strcmp(Header.Channel(i).Name, '1000GoSignal')
if old_data_set == 1; % control varaible aready indicates old data set
error(' ### ProcessITI detects new and old data set indicator concurrently (1000GoSignal)');
end
if ~isempty(goSignal)
error(' ### ProcessITI detects new and old data set indicator concurrently (1000GoSignal-2)');
end
% load data and determine timing onsets
[amplitude, xnew] = GetDiaData(i, 0, Header.Channel(i).Length);
goSignal = (find(diff(amplitude)>2.5) + 1); % '+1' because 'diff' shows the time of last sample
being 0
if ~isempty (goSignal)
%
goSignal = [goSignal(1); goSignal(find(diff(goSignal)>1500)+1)];
else
error(' ### ProcessITI detected new data set but no Go-signals! (1000GoSignal-3)');
end
%
% procedure for the subsequent channels are similar than for the
% '1000GoSignal'
elseif strcmp(Header.Channel(i).Name, '1000PaceSignal')
paceSignal = (find(diff(amplitude)>2.5) + 1);
paceSignal = [paceSignal(1); paceSignal(find(diff(paceSignal)>500)+1)];
elseif strcmp(Header.Channel(i).Name, '1000RhythReproIn')
startSignal = (find(diff(amplitude)>0.5) + 1);
startSignal = [startSignal(1); startSignal(find(diff(startSignal)>30000)+1)];
elseif (strcmp(Header.Channel(i).Name, '1000StartSignal'))
259
startSignal = (find(diff(amplitude)>2.5) + 1);
startSignal = [startSignal(1); startSignal(find(diff(startSignal)>500)+1)];
% specific part for start signal decoding (see description above)
if ~isempty(find(diff(startSignal)<20000))
pauseSignal = startSignal(2:end);
endSignal = startSignal(2:2:end);
tmp % temporary vector
tmp = startSignal(1:2:end); % copy times where trial started
startSignal = tmp;
if length(endSignal) ~= length(startSignal); % check for same length
error(' ### ProcessITI detects diffent length in endSignal and startSignal');
end
end
elseif (strcmp(Header.Channel(i).Name, 'TimePauseResume'))
pauseSignal = GetDiaTrigger(i, 1, Header.Channel(i).Length)*1000;
elseif strcmp(Header.Channel(i).Name, '1000LDoldtFB') % Synch-signal from Matlab-PC to
diadem-PC
landoldtAmplitude
landoldtFBSignal = (find(diff(amplitude)>0.5) + 1);
landoldtFBSignal = [landoldtFBSignal(1); landoldtFBSignal(find(diff(landoldtFBSignal)>1500)+1)];
REACT = 1; STOP = 2; NOTHING = 3;
for i = 1:length(goSignal)
ampl = max(amplitude(landoldtFBSignal(i)-100:landoldtFBSignal(i)+100));
if (ampl>0.5) && (ampl<1.5) landoldtAmplitude(i) = REACT;
elseif (ampl>1.5) && (ampl<2.5) landoldtAmplitude(i) = STOP;
elseif (ampl>2.5) && (ampl<3.5) landoldtAmplitude(i) = NOTHING;
else
error(['Unexpected landoldt amplitude ' num2str(ampl) ' at landoldt number ' num2str(i)]);
end
end
elseif strcmp(Header.Channel(i).Name, '1000Landoldt')
goSignal = find(amplitude<5);
goSignal = [goSignal(1); goSignal(find(diff(goSignal)>1500)+1)];
end
end
260
% End of channel search loop in case when control file does not exist
%saving data structure for control signal
if exist('goSignal', 'var') control.goSignal = goSignal; end
if exist('startSignal', 'var') control.startSignal = startSignal; end
if exist('paceSignal') control.paceSignal = paceSignal; end
if exist('pauseSignal') control.pauseSignal = pauseSignal; end
disp([controlTimeFile ' saved!!']);
save(controlTimeFile, 'control');
% control file exists, thus read it
else
load(controlTimeFile);
disp(['Loading ' controlTimeFile]);
end
The GetTimingVectors() return the variable timing containing all event timing.
timing.saccade1Onsets;
timing.saccade1StartMagnitudes;
timing.returnSaccade1Magnitudes;
timing.saccade1Offsets;
timing.returnSaccade1Onsets;
timing.returnSaccade1Offsets;
timing.blinkDownOnsets;
timing.blinkDownOffsets;
timing.blinkUpOnsets;
timing.blinkUpOffsets;
timing.leftForceOnsets;
timing.leftForceMaxs;
timing.leftForceOffsets;
timing.leftPosDownOnsets;
timing.leftPosDownOffsets;
timing.leftPosUpOnsets;
timing.leftPosUpOffsets;
timing.agonistLeftOnsets;
timing.agonistLeftOffsets;
timing.antagonistLeftOnsets;
timing.antagonistLeftOffsets;
timing.rightForceOnsets;
timing.rightForceMaxs;
timing.rightForceOffsets;
timing.rightPosDownOnsets;
timing.rightPosDownOffsets;
timing.rightPosUpOnsets;
timing.rightPosUpOffsets;
timing.agonistRightOnsets;
timing.agonistRightOffsets;
timing.antagonistRightOnsets;
timing.antagonistRightOffsets;
timing.leftFootForceDownOnsets;
timing.leftFootForceDownOffsets;
timing.leftFootForceUpOnsets;
timing.leftFootForceUpOffsets;
261
timing.leftFootPosDownOnsets;
timing.leftFootPosDownOffsets;
timing.leftFootPosUpOnsets;
timing.leftFootPosUpOffsets;
timing.leftTibialisOnsets;
timing.leftTibialisOffsets;
timing.leftSoleusOnsets;
timing.leftSoleusOffsets;
timing.rightFootForceDownOnsets;
timing.rightFootForceDownOffsets;
timing.rightFootForceUpOnsets;
timing.rightFootForceUpOffsets;
timing.rightFootPosDownOnsets;
timing.rightFootPosDownOffsets;
timing.rightFootPosUpOnsets;
timing.rightFootPosUpOffsets;
timing.rightTibialisOnsets;
timing.rightTibialisOffsets;
timing.rightSoleusOnsets;
timing.rightSoleusOffsets;
timing.pulsDownOnsets;
timing.pulsUpOnsets;
A query dialog for timing vector (whether it should be generated in case of marker change or the
existing one should be used):
timingFile = [resultFile(1:end-11) '-timing.mat'];
alt = YesNoQuestion(' ', 'New timing?', 'yes', 'no');
if strcmp(alt, 'yes') isGetting = 1;
else
if exist(timingFile, 'file') isGetting = false;
else
Notify('Timging vector does not exist!! quit');
return;
end
end
Missing markers of all characteristic events from the result file are checked
if isGetting
% start reading the marker from result file
for cidx = 1:length(Result.Channel),
ms
if isfield(Result.Channel(cidx), 'trial'),
trialTo = length(Result.Channel(cidx).trial);
if trialTo>0
hwaitb = WaitBarN(trialTo, 0, ['Getting markers of ' Result.Channel(cidx).Name '. Please
wait']);
for tidx = 1:trialTo,
WaitBarN(hwaitb, tidx);
if ~isempty(Result.Channel(cidx).trial(tidx).Marker),
midx = 1;
while midx <= length(Result.Channel(cidx).trial(tidx).Marker),
262
m = Result.Channel(cidx).trial(tidx).Marker(midx);
if isempty(m.XLocation)|isempty(m.Name)
Result.Channel(cidx).trial(tidx).Marker(midx)
midx = midx - 1;
Result.Status = 'changed';
elseif isContainedString({'s', 'm', 'e', '1', '2'}, m.Name)
m.trial = tidx;
ms = [ms m];
end
midx = midx + 1;
end
end
end
delete(hwaitb);
end
end
if length(ms)>0
% temporary timing
times = [ms(1, :).XLocation];
% sort the markers according to timing
[times indexes] = sort(times);
ms = ms(indexes);
% initialize the number of markers for every channel
startNr = 0; maxNr = 0; endNr = 0;
% missing marker timing will be saved in file MissingMarkers for use in
showdata
faultTime
% excluding indexes of timing during pauses
indexes
ok = 1;
if pauseLen>0
% excluding timing during pauses
times = [ms.XLocation];
for i=1:2:pauseLen
if i==1 idxs = find(times<pauseSignal(i));
else
if i<(pauseLen-1) idxs = find( (times>(pauseSignal(i-1)))&(times<pauseSignal(i)) );
else idxs = find( (times>(pauseSignal(i+1)))|...
((times>(pauseSignal(i-1)))&(times<pauseSignal(i))) );
end
end
indexes = [indexes idxs];
end
end
if ~isempty(indexes) ms = ms(indexes); end
numberOfTimePoints = size(ms, 2);
% asign timing to local variables according to the channel
% and check missing markers. Save the missing markers in
% file MissingMarkers.txt, notify user to correct them in
% showdata and return if any marker is missing
if strcmp(Result.Channel(cidx).Name, '1000ForceLeft')
263
for i = 1:numberOfTimePoints
if strcmp(ms(i).Name(1), '1')|strcmp(ms(i).Name(1), 's')
leftForceOnsets = [leftForceOnsets ms(i).XLocation];
startNr = startNr + 1;
elseif strcmp(ms(i).Name(1), '2')|strcmp(ms(i).Name(1), 'e')
leftForceOffsets = [leftForceOffsets ms(i).XLocation];
endNr = endNr + 1;
end
if (startNr-endNr)>1
faultTime = [faultTime ms(i).XLocation];
disp(['Missing right force end marker at time: ' num2str(ms(i).XLocation) ' ms']);
ok = 0;
endNr = endNr + 1;
elseif (endNr-startNr)>0
faultTime = [faultTime ms(i).XLocation];
disp(['Missing right force start marker at time: ' num2str(ms(i).XLocation) ' ms']);
ok = 0;
startNr = startNr + 1;
end
end
if ~ok
MissingMarkers;
return;
end
…….
The missing timing is saved in file “MissingMarkers.txt” used for correction in Showdata.
function MissingMarkers;
% dialog for selecting the location for the file MissingMarkers.txt
detectUiParam.prompText = 'File path and file name for missing marks';
detectUiParam.defaultValueHeader = 'default';
detectUiParam.customValueHeader = 'custom values';
detectUiParam.defaultNames = {'Path'};
detectUiParam.defaultValues = {'C:\BioCybCodes\MissingMarkers.txt'};
qq = AutoDetectUIInput(detectUiParam);
if ischar(qq)
return;
end
file = char(qq.customValues(1));
FID = fopen(file, 'w');
for i=1:length(faultTime)
fprintf(FID,'%s\n', num2str(faultTime(i)));
end
fclose(FID);
return;
end
If there is any missed marker of any channel the program (function MissingMarkers()) will notify the
user and exit. Correction has to be done in Showdata. Open the experiment file in Showdata and click
264
on the button MissingMarker. The next missed one is present on a click. The click without moving on the
screen indicates the last missed marker is reached.
After successful reading of correct markers, the next valid index of the discrete tap is searched for
every go command (for j = 1:goLen). If several discrete taps are found, only the first after the go
command is selected and the user is notified.
if j < goLen,
leftHandForceReactionIdxs = find((leftForceOnsets >
goSignal(j)) & (leftForceOnsets < goSignal(j + 1)));
leftHandPosReactionIdxs = find((leftPosDownOnsets >
goSignal(j)) & (leftPosDownOnsets < goSignal(j + 1)));
leftAgonistReactionIdxs = find((agonistLeftOnsets >
goSignal(j)) & (agonistLeftOnsets < goSignal(j + 1)));
leftAntagonistReactionIdxs = find((antagonistLeftOnsets >
goSignal(j)) & (antagonistLeftOnsets < goSignal(j + 1)));
leftFootForceReactionIdxs = find((leftFootForceDownOnsets >
goSignal(j)) & (leftFootForceDownOnsets < goSignal(j + 1)));
leftFootPosReactionIdxs = find((leftFootPosDownOnsets >
goSignal(j)) & (leftFootPosDownOnsets < goSignal(j + 1)));
rightFootForceReactionIdxs = find((rightFootForceDownOnsets >
goSignal(j)) & (rightFootForceDownOnsets < goSignal(j + 1)));
rightFootPosReactionIdxs = find((rightFootPosDownOnsets >
goSignal(j)) & (rightFootPosDownOnsets < goSignal(j + 1)));
else
leftHandForceReactionIdxs = find(leftForceOnsets> goSignal(j));
leftHandPosReactionIdxs = find(leftPosDownOnsets> goSignal(j));
leftAgonistReactionIdxs = find(agonistLeftOnsets> goSignal(j));
leftAntagonistReactionIdxs = find(antagonistLeftOnsets>
goSignal(j));
leftFootPosReactionIdxs = find(leftFootPosDownOnsets> goSignal(j));
leftFootForceReactionIdxs = find(leftFootForceDownOnsets>
goSignal(j));
rightFootForceReactionIdxs = find(rightFootForceDownOnsets>
goSignal(j));
rightFootPosReactionIdxs = find(rightFootPosDownOnsets>
goSignal(j));
end
265
And the actual index of the periodic tap corresponding to the actual discrete tap is calculated by:
if ~exist('numberPeriodicForcePrecedingLeftHandForce', 'var')
numberPeriodicForcePrecedingLeftHandForce =
length(find(rightForceOnsets <= leftForceOnsets(leftHandForceReactionIdx)));
periodicForcePrecedingLeftHandForceIdx =
numberPeriodicForcePrecedingLeftHandForce;
else
numberPeriodicForcePrecedingLeftHandForce =
length(find(rightForceOnsets(periodicForcePrecedingLeftHandForc
eIdx+1:end) <
leftForceOnsets(leftHandForceReactionIdx)));
periodicForcePrecedingLeftHandForceIdx =
periodicForcePrecedingLeftHandForceIdx +
numberPeriodicForcePrecedingLeftHandForce;
end
The
variables
numberPeriodicForcePrecedingLeftHandForce,
numberPeriodicForcePrecedingLeftFootForce, etc. are the corresponding processed number of periodic
tap which serve skipping the already processed periodic tap in the next searching.
periodicForcePrecedingLeftHandForceIdx, periodicForcePrecedingLeftFootForceIdx, etc. are the current
processing indexes for the periodic tap before discrete left hand and discrete left foot etc. respectively.
Five periodic taps around this discrete taps are then extracted. The reference tap is defined as the
last periodic tap succeeding discrete tap or go command (as stimulus). The interaction results such as
trial numbers, phases, distances between reference tap to these five taps, etc. are calculated in function
GetInteractionResults with the following input parameter:
2.147 Parameter
2.149 Path
2.151 periodicOnsets
2.153 discreteOnset
2.155 goSignal
2.157 periode
2.159 trialNr
2.148 Meaning
2.150 the saving path
2.152 5 periodic taps around the reference tap
2.154 discrete event timing
2.156 the actual go timing
2.158 standard period
2.160 the actual trial number
ITIsAround = zeros(3,1);
for k = [3 2 1]
ITIsAround(k,1) = periodicOnsets(4-(k-1)) - periodicOnsets(4-k);
end
N = mean(ITIsAround);
if (strcmp(path, 'rightHandLeftHand.force'))
AnalysedData.rightHandLeftHand.force.goSignals =
[AnalysedData.rrightHandLeftHand.force.goSignals goSignal];
AnalysedData.rightHandleftHand.force.referenceOnsets =
[AnalysedData.rightHandleftHand.force.referenceOnsets periodicOnsets(4)];
AnalysedData.rightHandleftHand.force.trialNrs =
[AnalysedData.rightHandleftHand.force.trialNrs trialNr];
266
AnalysedData.rightHandleftHand.force.RTs =
[AnalysedData.rightHandleftHand.force.RTs discreteOnset - goSignal];
AnalysedData.rightHandleftHand.force.thirdITIsPrecedingDiscrete =
[AnalysedData.rightHandleftHand.force.thirdITIsPrecedingDiscrete
(periodicOnsets(1) – periodicOnsets(4)];
AnalysedData.rightHandleftHand.force.secondITIsPrecedingDiscrete =
[AnalysedData.rightHandleftHand.force.secondITIsPrecedingDiscrete
AnalysedData.rightHandleftHand.force.lastITIsPrecedingDiscrete =
[AnalysedData.rightHandleftHand.force.lastITIsPrecedingDiscrete
AnalysedData.rightHandleftHand.force.ITIsClosingDiscrete =
[AnalysedData.rightHandleftHand.force.ITIsClosingDiscrete
AnalysedData.rightHandleftHand.force.firstITIsSucceedingDiscrete =
[AnalysedData.rightHandleftHand.force.firstITIsSucceedingDiscrete
AnalysedData.rightHandleftHand.force.secondITIsSucceedingDiscrete
[AnalysedData.rightHandleftHand.force.secondITIsSucceedingDiscrete
=
AnalysedData.rightHandleftHand.force.timeElaps = …
[AnalysedData.rightHandleftHand.force.timeElaps discreteOnset –
periodicOnsets(4)];
AnalysedData.rightHandleftHand.force.timeLeft = …
[AnalysedData.rightHandleftHand.force.timeLeft periodicOnsets(5) - discreteOnset];
AnalysedData.rightHandLeftHand.force.phases =
[AnalysedData.rightHandLeftHand.force.phases
AnalysedData.rightHandLeftHand.force.timeElaps(end)/periode];
AnalysedData.rightHandLeftHand.force.complPhases =
[AnalysedData.rightHandLeftHand.force.complPhases
AnalysedData.rightHandLeftHand.force.timeLeft(end)/periode];
AnalysedData.rightHandLeftHand.force.dynamicPhases =
[AnalysedData.rightHandLeftHand.force.dynamicPhases
AnalysedData.rightHandLeftHand.force.timeElaps(end)/N];
AnalysedData.rightHandLeftHand.force.complDynamicPhases =
[AnalysedData.rightHandLeftHand.force.complDynamicPhases
AnalysedData.rightHandLeftHand.force.timeLeft(end)/N];
ITI that happened outside the pacing signal and not contained the discrete taps are recorded as
undisturbed ITI (AnalysedData.rightHandleftHand.force.unaffectedITIs). The two outer pacing events are
extracted for every grouped pacing signal.
267
paceOuterSignal
firstPaceOfGroupFound = 0;
for i=1:length(paceSignal)-1
if (paceSignal(i+1)-paceSignal(i))<(periode+100)
if firstPaceOfGroupFound==0
paceOuterSignal = [paceOuterSignal paceSignal(i)];
end
else
paceOuterSignal = [paceOuterSignal paceSignal(i)];
end
end
paceOuterSignal = [paceOuterSignal paceSignal(end)];
The excluded periodic interval considered as disturbed for several discrete taps in multiple limb
condition is the maximum interval inclosing all theses discrete taps. The process runs through every
periodic tap. For the first pacing it checks the current ITI whether the first and second tap are before this
pacing. If they are before then the current ITI and trial number are saved.
for j=1:rightForceLen-1
if (paceIdx==1)
trial = find(startSignal<=paceOuterSignal(paceIdx));
trialNr = trial(end);
if (rightForceOnsets(j)<paceOuterSignal(paceIdx) &&
rightForceOnsets(j+1)<paceOuterSignal(paceIdx))
AnalysedData.rightHandleftHand.force.unaffectedITIs(j) =
rightForceOnsets(j+1) - rightForceOnsets(j);
AnalysedData.rightHandleftHand.force.unaffectedTrialNrs(j)
= trialNr;
Otherwise it increases the pacing index and goes to the next periodic tap
else
paceIdx = paceIdx + 1;
continue;
end
For the next pacing the process checks whether the two taps of current ITI are between the last
pacing of the previous trial and the first pacing of the next trial or after the last pacing.
else
% for the next pacing
if (paceIdx<paceLen)
if (periodicEvents(j)<=paceOuterSignal(paceIdx) || periodicEvents(j+1)<paceOuterSignal(paceIdx) )
% go to next periodic tap if it is still before the previous pacing
continue;
elseif
(periodicEvents(j)>=paceOuterSignal(paceIdx+1)
periodicEvents(j+1)>=paceOuterSignal(paceIdx+1) )
% increase the pacing index if the second tap of
% current ITI exceeds the next pacing
paceIdx = paceIdx + 2;
continue;
end
elseif (periodicEvents(j)<=paceOuterSignal(end) || periodicEvents(j+1)<=paceOuterSignal(end))
% go to next periodic tap if it is still before the previous pacing
268
||
continue;
end
end
If the two periodic taps still lies between the two pacing then they will be checked for their inclosing
in the maximum interval which contains all the possible discrete taps. If none of discrete tap was found
then the actual ITI is saved. In the case that three discrete movements exist
if (sizeOfDiscreteEvents2>0 && sizeOfDiscreteEvents3>0
% if 3 discrete movements exist
rightLimit = discreteEvents1(discreteIdx1);
rightLimit = min(rightLimit, discreteEvents2(discreteIdx2));
if (periodicEvents(j)<rightLimit && periodicEvents(j+1)<rightLimit)
% if the current ITI is still before all discrete movements then the current ITI and trial number are saved
unaffectedITIs(j) = periodicEvents(j+1) - periodicEvents(j);
unaffectedTrialNrs(j) = trialNr;
else
Otherwise the index of the relevant discrete is increased.
% otherwise
if (periodicEvents(j)>=discreteEvents1(discreteIdx1) ||
periodicEvents(j+1)>=discreteEvents1(discreteIdx1))
% if any periodic tap of the current ITI exceeds the current first discrete tap then increase this index
if discreteIdx1<sizeOfDiscreteEvents1
discreteIdx1 = discreteIdx1 + 1;
end
end
if (periodicEvents(j)>=discreteEvents2(discreteIdx2) ||
periodicEvents(j+1)>=discreteEvents2(discreteIdx2))
% if any periodic tap of the current ITI exceeds the current second discrete then increase the index
if discreteIdx2<sizeOfDiscreteEvents2
discreteIdx2 = discreteIdx2 + 1;
end
end
The analyzed results are saved in a MATLAB format file.

exit: exit program
10.4.2.2 Plot

ITI Histogram: plot intertap intervals histogram

Phase resetting curve: plot phase resetting curve in DT experiment

Reaction time in simple experiment: plot reaction time in experiment where only discrete
movements of the same limbs were required.

Reaction time in combination experiment: plot reaction time in experiment where discrete
movements in different limbs in any combination were required.
269

ITI before and after reference tap: plot histogram of 5 intertap intervals around the discrete
reaction.

Phase: plot phase histogram.

PeriodicTapDurationHist: plot the histogram of five periodic tap durations and the discrete
tap duration.

DiscreteTapDurationHist: plot the histogram of discrete tap duration. The plot can be listing
(one subplot per class) or combined (divided into synchronization and asynchronization).

Go phase: plot phase histogram of go signal.

PlotTapDuration: plot tap (slope) duration of discrete tap and five surrounding periodic taps.
The first interaction is displayed and the left mouse click leads to the next interaction.
From the piece-wise linear function composed of three concatenated segments that models a single
change of ramp-step-model we obtain the change onset and the change offset. The down (up)-slope is
the tangent starting from the change onset to the change offset. Slope duration is defined as time
duration from its onset to its offset.
Duration of the taps is determined by the horizontal distance between its downslope and upslope at
certain y-coordinate. This y-coordinate is chosen for example in the case of synchronization the upslope
offset of the discrete tap with the assumption that it is the end of the driving force because the upslope
is usually higher than the rest position of the finger before downward movement. This assumption is
justified by visual inspection. In the case of asynchronization the upslope offset of the reference tap is
selected. As a mean for visualization the function b = robustfit(X,Y) is used. It uses robust linear
regression to fit Y as a function of the columns of X, and returns the vector b of coefficient estimates.
b(1)+b(2)*X is the slope, i. e b(1) is the y-intercept and b(2) is the slope gradient. This algorithm gives
lower weight to points that do not fit well. The results are less sensitive to outliers in the data as
compared with ordinary least squares regression.
10.5 Interaction classification in DT-experiment
The classification of interaction in DT experiment is performed in function CharakterizeInteraction.
The result is stored in variable AnalysedData with following elements which also are used for
visualization:
% standard deviation, mean and sigma factor given by user of the 3 preceding ITIs
AnalysedData.interaction.std3PrecedITI;
AnalysedData.interaction.mean3PrecedITI;
AnalysedData.interaction.itiSigmaFactor;
% interactions by which the reactions were before the go commands
AnalysedData.interaction.predictedInterval;
% times of these predicted reactions
AnalysedData.interaction.predictedTimePoint;
% coressponding reaction times
AnalysedData.interaction.predictedRT;
% interactions by which the reactions were delayed for synchronization
AnalysedData.interaction.entrainmentInterval;
% times of these reactions
AnalysedData.interaction.entrainmentTimePoint;
% coressponding reaction times
AnalysedData.interaction.entrainmentRT
% periodic tapw were hastened for synchronization
270
AnalysedData.interaction.hasteningInterval
AnalysedData.interaction.hasteningTimePoint
AnalysedData.interaction.hasteningRT
% periodic tap was interrupt and resumed after reaction
AnalysedData.interaction.resetPauseInterval;
AnalysedData.interaction.resetPauseTimePoint;
AnalysedData.interaction.resetPauseRT;
% reaction was delayed and periodic process was resumed
AnalysedData.interaction.entrainmentResetInterval;
AnalysedData.interaction.entrainmentResetTimePoint;
AnalysedData.interaction.entrainmentResetRT;
% reaction was delayed and periodic tap was hastened
AnalysedData.interaction.entrainmentHasteningInterval;
AnalysedData.interaction.entrainmentHasteningTimePoint;
AnalysedData.interaction.entrainmentHasteningRT;
% no sign of interaction or weak interaction
AnalysedData.interaction.stableInterval;
AnalysedData.interaction.stableTimePoint;
AnalysedData.interaction.stableRT;
% the corresponding number of classes
AnalysedData.interaction.numberOfPhaseEntrainment;
AnalysedData.interaction.numberOfPhaseHastening;
AnalysedData.interaction.numberOfPhaseResetPause;
AnalysedData.interaction.numberOfInterruption;
AnalysedData.interaction.numberOfPhaseEntrainmentReset;
AnalysedData.interaction.numberOfPhaseEntrainmentHastening;
AnalysedData.interaction.numberOfStableCoordination;
AnalysedData.interaction.numberOfPredictedStimulus;
Following are the input parameters:
detectUiParam.defaultValueHeader = 'default';
detectUiParam.customValueHeader = 'custom values';
detectUiParam.defaultNames = {'ITISigma', 'RTSigma', 'Interrupt percentage'};
detectUiParam.defaultValues = {'3', '5', '0.3'};
detectUiParam.namesToSelect = {'Subject', 'Reaction limb'};
detectUiParam.valuesToSelect = [{'AM'; 'BJ'; 'CB'; 'CKD'; 'DS'; 'JS';'KhP'; 'RN'; 'WW'}, ...
{'left hand'; ''; ''; ''; ''; ''; ''; ''; ''}];
% getting input values
qq = AutoDetectUIInput(detectUiParam);
if ischar(qq) return; end
% sigma factor for ITI
itiSigmaFactor = str2num(char(qq.customValues(1)));
% sigma factor for RT
rtSigmaFactor = str2num(char(qq.customValues(2)));
% percentage for interruption of finger position
interruptPercentage = str2num(char(qq.customValues(3)));
subject = deblank(char(detectUiParam.valuesToSelect(qq.selectedValues(1), 1)));
reactionLimb = deblank(char(detectUiParam.valuesToSelect(qq.selectedValues(2), 2)));
And all experiment data file names of every subject wherein the first is the single-task reaction
experiment.
files = {...
271
'261208AMReactionTime', ...
'230209AMBimanual_rep', ...
'050109AMRhandLhandRfoot', ...
'030109AMRhandLhandLfoot', ...
'050109AMRhandLhandLfootRfoot', ...
};
Reaction time and timing are read from the analyzed data for calculating the mean, deviation values
and the upper and under bound respectively. i. e the experiment data has to be analyzed at first. All the
markers of the selected reaction limb are read from the result file of the first data file.
if fileIdx==1
if length(file)>0
if ~isempty(findstr(reactionLimb, 'left hand'))
RT = AnalysedData.leftHand.force.RTs;
elseif ~isempty(findstr(reactionLimb, 'left foot'))
RT = AnalysedData.leftFoot.force.RTs;
elseif ~isempty(findstr(reactionLimb, 'right foot'))
RT = AnalysedData.rightFoot.force.RTs;
end
rtMean = mean(RT);
rtStd = std(RT);
underRTBound = rtMean - rtSigmaFactor*rtStd;
upperRTBound = rtMean + rtSigmaFactor*rtStd;
end
Every interaction is checked for the next discrete tap.
if isfield(Result.Channel(forceLeftIdx), 'trial'),
faultTime
for tidx = 1:length(Result.Channel(forceLeftIdx).trial),
trialNr = trialNr + 1;
if ~isempty(Result.Channel(forceLeftIdx).trial(tidx).Marker),
times =
[Result.Channel(forceLeftIdx).trial(tidx).Marker.XLocation];
[times indexes] = sort(times);
Result.Channel(forceLeftIdx).trial(tidx).Marker =
Result.Channel(forceLeftIdx).trial(tidx).Marker(indexes);
% extract the reaction timing
for midx =
1:length(Result.Channel(forceLeftIdx).trial(tidx).Marker),
mname =
Result.Channel(forceLeftIdx).trial(tidx).Marker(midx).
Name(1);
if mname=='b'|mname=='m'|mname=='1'
mtime =
Result.Channel(forceLeftIdx).trial(tidx).
Marker(midx).XLocation;
% check the consistency between
analyzed timing and timing reading from result file
if abs(leftHandMaxsPoint(rtId)-mtime)>1
disp(['Mismatch between reaction time point and
max marker at time: ' num2str(mtime), 10,
'reaction time point: '
num2str(leftHandMaxsPoint(rtId)) ' from
file ' file]);
272
continue;
else
………
If RT is larger than the upper bound and the corresponding intertap interval is normal then the
interaction is classified as entrainment.
if (interactionRT(rtId)>upperRTBound)
if(intervalsContainReactionTaps(rtId)<=upperITIBound)&...
(intervalsContainReactionTaps(rtId)>=underITIBound)
…………….
See Wachter et al. (2008) for more details about this process.
Name list of ST-Experiments replicating Aschersleben’s Experiments
P1: Cornelia Budach
P2: Cong Thi Phuong Thuy
P3: Thach Thanh Thao
P4: Wolfgang Weber
273

UNIVERSITÄT DER BUNDESWEHR MÜNCHEN Fakultät für

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