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Semantic Fluency in Mild and
Moderate Alzheimer’s Disease
Seija Pekkala
2004
Department of Phonetics
University of Helsinki
P.O. Box 35 (Vironkatu 1B)
00014 University of Helsinki
ISSN 0357-5217
ISBN 952-10-1645-0 (paperback)
ISBN 952-10-1646-9 (PDF, http://ethesis.helsinki.fi/)
Hakapaino Oy, Helsinki 2004
Copyright Seija Pekkala 2004
To my parents
ABSTRACT
Semantic Fluency in Mild and Moderate Alzheimer’s Disease
Seija Pekkala
University of Helsinki, FIN
Alzheimer’s disease (AD) is characterized by an impairment of the semantic memory
responsible for processing meaning-related knowledge. This study was aimed at
examining how Finnish-speaking healthy elderly subjects (n = 30) and mildly (n = 20)
and moderately (n = 20) demented AD patients utilize semantic knowledge to perform
a semantic fluency task, a method of studying semantic memory. In this task subjects
are typically given 60 seconds to generate words belonging to the semantic category
of animals. Successful task performance requires fast retrieval of subcategory
exemplars in clusters (e.g., farm animals: ‘cow’, ‘horse’, ‘sheep’) and switching
between subcategories (e.g., pets, water animals, birds, rodents). In this study, the
scope of the task was extended to cover various noun and verb categories.
The results indicated that, compared with normal controls, both mildly and moderately demented AD patients showed reduced word production, limited clustering
and switching, narrowed semantic space, and an increase in errors, particularly
perseverations. However, the size of the clusters, the proportion of clustered words,
and the frequency and prototypicality of words remained relatively similar across
the subject groups. Although the moderately demented patients showed a poorer
overall performance than the mildly demented patients in the individual categories,
the error analysis appeared unaffected by the severity of AD. The results indicate a
semantically rather coherent performance but less specific, effective, and flexible
functioning of the semantic memory in mild and moderate AD patients.
The findings are discussed in relation to recent theories of word production and
semantic representation.
Semantic fluency, clustering, switching, semantic category, nouns, verbs,
Alzheimer’s disease
Contents
Acknowledgements ................................................................................................ i
List of Tables ........................................................................................................ iii
List of Figures ....................................................................................................... iv
Abbreviations ......................................................................................................... v
1 Introduction ...................................................................................................... 1
2 Alzheimer’s disease ........................................................................................... 5
2.1 Etiology and pathogenesis of Alzheimer’s disease .......................................... 7
2.2 Clinical findings of Alzheimer’s disease ......................................................... 8
2.3 Staging the severity of dementia in Alzheimer’s disease ............................... 12
2.4 Semantic impairment in Alzheimer’s disease ................................................ 16
2.4.1 Impaired knowledge of the meaning representation ........................... 17
2.4.2 Impaired naming ................................................................................. 18
3 Frameworks of semantic knowledge ............................................................. 23
3.1 Principles of categorization ........................................................................... 26
3.1.1 Categorization of objects ..................................................................... 28
3.1.2 Subcategories and hierarchical structure of nouns .............................. 32
3.2 Feature-based models as accounts of the semantic representation of nouns . 34
3.3 Accounts of semantic representation of verbs ............................................... 39
3.3.1 Categorization of actions ..................................................................... 41
3.3.2 Subcategories and hierarchical structure of verbs ............................... 43
3.3.3 Feature-based models as accounts of the semantic
representation of verbs ........................................................................ 47
3.3.4 Scripts as the account of semantic
representation of verbs ........................................................................ 49
4 Spoken word production ................................................................................ 51
4.1 Theories of spoken word production ............................................................. 51
4.2 Two-stage interactive activation models ........................................................ 53
5 Semantic fluency performance in
elderly adults and Alzheimer’s patients....................................................... 55
5.1 Word production during the semantic fluency task ....................................... 56
5.2 Clustering and switching ............................................................................... 66
5.3 Activation of different associations and semantic dimensions ...................... 68
5.4 Error analysis ................................................................................................. 70
5.4.1 Intrusions ............................................................................................. 71
5.4.2 Perseverations ...................................................................................... 73
5.5 Performance in different semantic categories ................................................ 74
6 Causes of the semantic impairment in
Alzheimer’s disease........................................................................................ 77
6.1 Breakdown and loss of semantic structures ................................................... 77
6.2 Impaired processing ....................................................................................... 79
6.3 A multifactorial deficit ................................................................................... 80
7 Aims of the study ............................................................................................ 83
8 Method ............................................................................................................. 85
8.1 Subjects .......................................................................................................... 85
8.2 Method ........................................................................................................... 86
8.2.1 Procedure of the semantic fluency tasks ............................................. 86
8.2.2 Analysis of the overall performance on the semantic fluency tasks .... 87
8.2.3 Clustering rules ................................................................................... 89
8.2.4 Analysis of the contents of the responses on the semantic
fluency tasks ........................................................................................ 90
8.2.5 Inter-rater judgements ......................................................................... 92
8.2.6 Control tasks ........................................................................................ 93
8.2.7 Statistical analysis ............................................................................... 94
9 Results .............................................................................................................. 97
9.1 Overall performance on the noun fluency tasks ............................................ 97
9.1.1 Number of correct nouns ..................................................................... 99
9.1.2 Clustering and switching ................................................................... 100
9.1.3 Summary of the results and discussion ............................................. 103
9.2 Analysis of the contents of the responses on the noun fluency tasks .......... 109
9.2.1 Proportion of correct nouns ............................................................... 109
9.2.2 Proportion of intrusions and perseverations ...................................... 109
9.2.3 Clustering strategies .......................................................................... 112
9.2.4 Number and variety of different semantic subcategories .................. 114
9.2.5 Degree of prototypicality and frequency of the nouns produced ...... 118
9.3 Overall performance on the verb fluency tasks ........................................... 128
9.3.1 Number of correct verbs .................................................................... 128
9.3.2 Clustering and switching ................................................................... 131
9.4 Analysis of the contents of the responses on the verb fluency tasks ........... 135
9.4.1 Proportion of correct verbs ................................................................ 136
9.4.2 Proportion of intrusions and perseverations ...................................... 138
9.4.3 Number and variety of different semantic subcategories .................. 140
9.4.4 Degree of prototypicality and frequency of the verbs produced ....... 144
9.5 Summary of the overall semantic fluency performance .............................. 152
9.6 Performance on the control tasks................................................................. 154
9.6.1 Performance on the control tasks requiring verbal responses ........... 154
9.6.2 Performance on the control tasks requiring non-verbal responses .... 155
9.6.3 Correlations among scores on the semantic tasks ............................. 156
9.6.4 Discussion on the semantic tasks ...................................................... 157
10 General discussion ...................................................................................... 161
10.1 Semantic fluency performance in mild and moderate Alzheimer’s disease162
10.1.1 Decreased semantic fluency performance ....................................... 162
10.1.2 Errors as indicators of impaired semantic memory functioning ..... 165
10.1.3 Causes of the semantic impairment in Alzheimer’s disease ............ 167
10.2 Methodological considerations of the study .............................................. 171
10.2.1 Subjects ........................................................................................... 171
10.2.2 Considerations of the semantic fluency task ................................... 173
10.2.3 Limitations of the study ................................................................... 177
10.3 Clinical implications .................................................................................. 179
10.4 Implications for further study .................................................................... 180
11 Conclusions ................................................................................................. 183
References ......................................................................................................... 187
Appendix 1A. Cluster division for the noun categories .................................... 225
Appendix 1B. Cluster division for the verb categories ...................................... 228
Appendix 2 A sample of prototypicality ratings of the words produced
for the semantic fluency tasks ...................................................................... 231
Appendix 3 A sample of frequency ratings of the words produced
for the semantic fluency tasks ...................................................................... 233
Appendix 4A. Examples of the semantic fluency performance
given by a participant in each subject group : clothes .................................. 235
Appendix 4B. Examples of the semantic fluency performance
given by a participant in each subject group: vegetables ............................. 236
Appendix 4C. Examples of the semantic fluency performance
given by a participant in each subject group: vehicles ................................. 237
Appendix 4D. Examples of the semantic fluency performance
given by a participant in each subject group: animals .................................. 238
Appendix 4E. Examples of the semantic fluency performance
given by a participant in each subject group: preparing food....................... 239
Appendix 4F. Examples of the semantic fluency performance
given by a participant in each subject group: playing sports ........................ 240
Appendix 4G. Examples of the semantic fluency performance
given by a participant in each subject group: construction .......................... 241
Appendix 4H. Examples of the semantic fluency performance
given by a participant in each subject group: cleaning up ............................ 242
Appendix 5. Results of the post-hoc pair-wise analyses of
the noun fluency tasks .................................................................................. 243
Appendix 6. Results of the post-hoc pair-wise analyses of
the verb fluency tasks ................................................................................... 246
Appendix 7. Results of the post-hoc pair-wise analyses of the control tasks .... 249
i
Acknowledgements
This study was originally inspired by the learning experiences I gained in the early
1990s when writing my Master’s thesis about the semantic breakdown found to take
place in Alzheimer’s disease. I am very grateful to Professor Matti Lehtihalmes,
who first encouraged me to continue studying the field I had found fascinating, for
giving me good advice and support during these years. I am very thankful to Dr.
Timo Erkinjuntti for enabling this study to be carried out at the Department of
Neurology, University Hospital of the University of Helsinki. I also want to thank
Raija Ahlfors and Toini Nukari for their co-operation in many practical arrangements
that needed to be taken care of during data collection.
My warmest thanks are due to my supervisors. I want to thank Professor Anu
Klippi for her valuable comments on this manuscript and for her patience and
encouragement throughout this study. I would also like to cordially thank Dr. Minna
Laakso for her interest in my work and helpful advice during the later phases of this
dissertation.
I would like to express my sincerest thanks and greatest appreciation to
Dr. Inga-Britt Persson who so willingly shared her knowledge of semantics and
spread her enthusiasm for it. I will always remember the long hours of brainstorming, the vivid flow of ideas and intense discussions, which helped me understand
many theoretical issues and combine theory and practice.
I would especially like to thank Anu Airola for her friendship and significant
linguistic contribution in co-analysing the data of this study. I want to thank Professor
Anneli Pajunen, Dr. Marja-Liisa Helasvuo, and Dr. Tiina Onikki-Rantajääskö for
advising me on many questions requiring expertise in linguistics and, in particular,
the Finnish language.
I am very grateful to Dr. Kimmo Vehkalahti and Pekka Lahti-Nuuttila for all
their help and guidance with the statistical matters of this study. I want to thank the
statisticians Marjatta Mankinen, Leena Pussinen, and Leena Kuukasjärvi at the
University of Oulu for their contribution when clearing up the final statistical issues
that needed to be resolved in order to complete this thesis.
I wish to express my most sincere gratitude and appreciation to Professor
Loraine K. Obler, The City University of New York, and Professor Matti Laine, Åbo
Akademi, the pre-examiners of this dissertation, for their very constructive criticism
and good suggestions for improving the manuscript.
I am very grateful to Roderick Dixon for carefully revising the English of the
manuscript.
I cordially thank my colleagues and fellow-students Dr. Helena Heimo and
Dr. Eira Jansson-Verkasalo for sharing with me the ups and downs common to all
graduate students. I would also like to thank Jenni Holappa and Riitta Nauha, as
well as Minna Vanhala and Outi Kaleva, for exchanging ideas about the semantic
fluency task which turned out to be our common interest. I want to thank my
ii
colleagues at the Department of Phonetics, University of Helsinki, for their
encouraging attitude towards this study. I would like to express my gratitude to Dr.
Anna-Maija Korpijaakko-Huuhka for her support throughout all these years.
Furthermore, my thanks go to my new co-workers at the Department of Finnish,
Saami and Logopedics, University of Oulu, for all the help I received when finishing
this dissertation.
I am extremely grateful to Jarmo Herkman, Lic.Psych, for his friendship,
support and invaluable help with a number of stumbling blocks that I came across
when working on this thesis. Without his positive attitude and great sense of humor,
many things would have become insurmountable obstacles.
I am deeply grateful to all the participants who voluntarily made an invaluable contribution to this study. I want to thank them for giving me the great opportunity to learn about semantics and, in particular, to find out how Alzheimer’s disease
affects the semantic memory that is so fundamental to our linguistic abilities. I also
wish to thank the caregivers for their co-operation and positive attitude towards this
study.
I am privileged and happy to have many friends around me who form a very
important cornerstone of my life. I am very grateful to them for helping me in various ways during this study. I also thank them for always being there for me when I
needed them most. I want to express my special thanks to Kirsti Eskelinen and
Tuomo Härkönen, Matti Hämäläinen, Maarit Koivurova, Jaana Lamminperä, Kari
Lehtonen, Kirsi, Reijo and Ronja Leino, Riitta-Leena and Mikko Manninen and
their children, Elizabeth Milliman and Ernie Taylor, Nökö Mähönen, Sari Salmisuo,
Tatu Ulvila, and Marja Vuorinen.
I am also very grateful to my family and relatives who have unceasingly
encouraged me during all these long years of study. I want to thank my cousin Helinä
Vanhala for helping me code the data and sort out journal articles needed for this
study. Many thanks are due to my sister Kaisa and my brother-in-law Timo Leinonen
for all the care and help they have given me. I wish to thank my niece Sofia and my
nephews Ville-Petteri and Tuomas for bringing great joy into my life. I owe my
sincerest thanks to my parents, Lempi and Helge Pekkala, to whom this dissertation
is dedicated, for their understanding and willingness to support me not only in my
studies, but also throughout my life.
When working on this dissertation, I had a chance to participate in the activities of the Langnet, Finnish National Graduate School for Language Studies, which
I gratefully acknowledge. I wish to thank the Finnish Cultural Foundation, Finnish
Konkordia Fund, Vetenskapsstiftelse för Kvinnor, and the University of Helsinki for
the financial support which made this study possible.
Seija Pekkala
Helsinki, December 2003
iii
List of Tables
Table 1
Table 2
Table 3
Table 4
Table 5
Table 6
Table 7
Table 8
Table 9
Table 10
Table 11
Table 12
Table 13
Table 14
Table 15
Table 16
Table 17
Table 18
Table 19
Table 20
Table 21
Table 22
Table 23
Table 24
Page
Criteria of probable Alzheimer’s disease (AD)
6
Clinical findings in different stages of Alzheimer’s disease (AD) 9
Progress of language deficits and communication
changes in Alzheimer’s disease (AD)
11
Studies on the semantic fluency in normal control
subjects (NC) and patients with Alzheimer’s disease (AD)
60
Demographic features of the subject groups
87
Scoring of clustering and switching on the semantic
fluency tasks
88
Total number of words and number of correct nouns
produced in the noun fluency tasks
98
Number of correct nouns produced by the male and
female participants in the NC group
100
Clustering and switching in the noun fluency tasks
101
Data on the animal fluency task performed by normal
control subjects (NC) and Alzheimer’s patients (AD)
in different studies
107
Proportion of correct words, intrusions, and perseverations
in the noun fluency tasks
110
Number of semantically related and unrelated intrusions
111
Clustering strategies in the noun fluency tasks
113
Number of different subcategories produced
for the semantic categories in the noun fluency tasks
115
Degree of prototypicality and frequency of the nouns produced 119
Total number of words and number of correct verbs produced
in the verb fluency tasks
129
Number of correct verbs produced by the male and
130
Clustering and switching in the verb fluency tasks
132
Word forms produced in the verb fluency tasks
136
Proportion of correct words, intrusions, and perseverations
in the verb fluency tasks
137
Number of semantically related and unrelated intrusions
produced in the verb fluency tasks
139
Number of different subcategories produced for
the semantic categories in the verb fluency tasks
141
Degree of prototypicality and frequency of the verbs produced 145
Summary of the comparison of the semantic
fluency performance between the subject groups
153
iv
Table 25
Table 26
Table 27
Performance of the subject groups on the control tasks
requiring verbal responses
Performance of the subject groups on the control tasks
requiring non-verbal responses
Spearman rank-order correlation coefficients (ρ) between
the correct responses of the semantic fluency tasks and
the control tasks in the subject groups
155
156
157
List of Figures
Page
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
The distribution and mean number of the most common
subcategories of clothes in different subject groups
subcategories of vegetables in different subject groups
subcategories of vehicles in different subject groups
subcategories of animals in different subject groups
subcategories of preparing food in different subject groups
subcategories of playing sports in different subject groups
subcategories of construction in different subject groups
subcategories of cleaning up in different subject groups
116
116
117
117
142
142
143
143
v
Abbreviations
AD
ANT
BNT
miAD
MMSE
moAD
NC
Action Naming Test
Boston Naming Test
Alzheimer’s patients with mild dementia
Mini Mental State Examination test
Alzheimer’s patients with moderate dementia
Normal control subjects
vi
1 Introduction
Although increased knowledge and wisdom are believed to come with age, scientific evidence has indicated that a variety of mental processes decline with advancing age. Signs of cognitive aging include a decrease in the speed of performing
mental operations and limited working memory processes, such as retrieving and
storing information. Finding words and retrieving names of people and places may
cause trouble for elderly people, as well as remembering details belonging to past
episodes (Craik 2000; Park 2000). Sensory functions (e.g., vision and hearing), which
are fundamental to cognitive abilities, seem to decline, and focusing attention on
target information and inhibiting attention to irrelevant material may become difficult (Park 2000).
Aging does not always take place normally. A faster and more severe decline
in multiple cognitive functions is found in dementia, which is a common clinical
syndrome among elderly people. It was estimated that in 2000 approximately 30,
000 people suffered from mild dementia and 80, 000 from moderate or severe
dementia in Finland (Viramo & Sulkava 2001). As the population ages, the number
of people with dementia increases, which makes varying demands on the society in
terms of planning appropriate assessment and management and providing services
for those in need. Alzheimer’s disease (AD) is the most common cause of dementia
affecting a person’s behavior, personality, and social skills. AD has also been
characterized by a progressive decline in a wide range of cognitive functions, with
memory problems being one of the earliest, as well as the most disabling, of the
cognitive deficits (Nebes, Martin & Horn 1984; Martin, Browers, Cox & Fedio 1985).
In addition to a defective episodic memory, responsible for maintaining recent
experiences, there is evidence that also the part of long-term memory called semantic
memory, responsible for storing meaning-related knowledge, is particularly impaired
in AD (e.g., Warrington 1975; Bayles & Tomoeda 1983; Martin & Fedio 1983; Nebes
1989; Hodges, Salmon & Butters 1990; Huff, Corkin & Growdon 1986; Chan,
Butters, Paulsen, Salmon, Swenson & Maloney 1993). The role of semantic memory
is crucial for communication and language processing, including word production
and comprehension, which have been found to be impaired in AD (Nebes 1989).
The language processing deficits are known to be frequent and to take place at an
2
Introduction
early phase of the disease (e.g., Appell, Kertesz & Fisman 1982; Huff et al. 1986;
Nebes, Brady & Huff 1989; Chertkow & Bub 1990). However, all aspects of language
function do not appear to be equally impaired in AD. The most impaired domain of
language seems to be semantics and pragmatics, while phonology and syntax are
relatively well preserved (Nebes et al. 1989).
The semantic fluency task, in which a subject is asked to produce words for a
particular semantic category (e.g., animals) in a certain period of time, is a method
widely used by speech pathologists and neuropsychologists to investigate retrieval
of words from semantic memory. The task is considered to be a very simple, fast,
and sensitive clinical task that provides useful information about the functioning of
the semantic processes and the status of the semantic representations (Binetti et al.
1995). The performance of the subjects is usually analyzed by counting correct
responses and errors, such as category violations and repetitions of words. There
seems to be a strategy commonly applied by the subjects to perform the task that
involves a cycle of clustering semantically related words (e.g., farm animals) and
switching to another subcategory (e.g., birds, fish, exotic animals, etc.; Gruenewald
& Lockhead 1980; Laine 1989:4-5, 20-25; Troyer, Moscovitch & Winocur 1997).
There is evidence indicating that AD patients tend not to perform the task in the way
normal elderly control subjects do. The semantic fluency performance of the AD
patients is characterized by a reduction in word production and an increase in errors,
as well as a limited use of clustering and switching (e.g., Beatty, Testa, English &
Winn 1997; Troyer, Moscovitch, Winocur, Leach & Freedman 1998; Tröster et al.
1998). The impaired semantic fluency performance in AD is attributed to disruptions
in the processing and/or organization of semantic memory (e.g., Warrington 1975;
Martin & Fedio 1983; Troyer, Moscovitch, Winocur, Leach et al. 1998).
Although an extensive literature documents the performance deficits of patients with AD on the semantic fluency task, the previous studies seem to have some
limitations, which served as the motivation for the present study. First, only very
few studies have provided detailed theoretical descriptions of the cognitive processes (e.g., categorization, word production, clustering, and switching) necessary
for performing the task, and the “semantics” of the contents of the responses has not
attracted as much attention as it deserves. Furthermore, the nature and organization
of the knowledge contained in semantic memory have usually been defined in very
vague and general terms. Second, the more detailed analyses of semantic fluency
performance, in general, have been restricted solely to the semantic category of
animals or the things obtainable in a supermarket. Recent theoretical research (e.g.,
Moss, Tyler & Devlin 2002) suggests that different semantic categories have distinctive representations, which is why generalizations of the subjects’ performance
should not be made on the basis of one or two semantic categories. Third, very little
investigation has been done on the effects of the severity of dementia on semantic
fluency performance. Semantic deficits being one of the hallmarks of AD, it is worthwhile knowing about the nature of the progressive changes, which also includes
semantic fluency behavior. Such knowledge can be used when assessing the phase
Introduction
3
of the disease a patient is in. It may also be useful for differential diagnosis. Fourth,
reports on how the semantic fluency task can be extended to involve the processing
of other grammatical classes, especially verbs, are virtually nonexistent. Implications of the contents and structure of semantic memory have so far been predominantly made based on findings obtained from testing with nouns, which may be
easier to study but which do not represent the whole spectrum of lexical-semantic
information contained in semantic memory. Because AD has been found to result in
deterioration of the semantic structure of nouns, it can be presumed that other word
classes carrying meaning-related information are also impacted by the disease. The
present study attempts to address these issues.
Typical of the field of logopedics, searching for answers in the present study
required a multidisciplinary approach to the phenomenon, which turned out to be
quite a challenge. Although the semantic fluency task is a common method in logopedics, the findings obtained from the task have mostly been interpreted in the traditions of cognitive psychology and neuropsychology. These fields of study tend to
focus on explaining the functions and organization of memory, and to cover one of
the most fundamental semantic processes underlying the semantic fluency performance, categorization. In order to explain the nature and format of semantic representation, the notions of the current connectionist theories were also considered. To
account for the other essential lexical process involved in the semantic fluency task,
word production, it was necessary to focus on current theories on lexical retrieval in
psycho- and neurolinguistics.
As a consequence of bringing together different traditions and conventions,
matching the definitions for various concepts was difficult. In particular, defining
the scope of semantic memory and the mental lexicon was cumbersome and using
the terms object vs. noun and action vs. verb to refer to the mental representations
and to the lexemes was at issue throughout the study. When referring to different
lexemes in the text, single quotation marks are used to make the distinction (e.g.,
‘sheep’, ‘cut’). Examples of semantic features (e.g., ‘has-a-tail’) and parts of scripts
(e.g., ‘standing-in-a-line’) are also marked. When considering the structure of this
dissertation, a matter worth mentioning is that, in general, most of the research concerning semantic processing and representation has so far been focused on nouns,
and there is still relatively little experimentally obtained knowledge about how verbs
may be semantically represented and processed. Thus, in this study, it may appear
that the issues concerning verbs are discussed at a shallower level compared to those
concerning nouns. However, despite the diverse traditions, the different terminology in the fields, and the less explored field of the semantic representation of verbs
in relation to that of nouns, an attempt is made to provide an understandable synthesis of these approaches to explain the semantic fluency performance.
In addition to investigating the abovementioned theoretical issues, the purpose
of this study was to provide a systematic and detailed analysis about the way healthy
elderly control subjects and AD patients with mild and moderate dementia performed
the semantic fluency task. In the present study, the subjects were asked to produce
4
Introduction
nouns for several different categories including categories of clothes, vegetables,
vehicles, and animals. An attempt was made to apply the task to also cover verb
production. The subjects were asked to produce concrete verbs for the categories of
preparing food, playing sports, construction, and cleaning. Evaluation of the semantic
fluency performance included measuring the total and correct output for each
category, as well as the clustering of semantically closely related words and switching
between the semantic subcategories. An error analysis was conducted to determine
the proportion of outside-category words (intrusions) and repetition of previously
generated words (perseverations) produced during the tasks. The nature of the
semantic dimensions activated for different semantic categories by the subject groups
was examined. The results are discussed in relation to other studies on semantic
fluency tasks as well as to some control tasks designed to measure semantic
processing. The results are also discussed in light of the current theories on semantic
representation and word production.
2 Alzheimer’s disease
Alzheimer’s disease (AD) was named after a German neuropsychiatrist, Professor
Aloys Alzheimer (1864-1915), who was the first to discover symptoms of advancing difficulty in memory and language functions and disorientation in his patient,
Auguste D (Alzheimer 1907; Maurer, Volk & Gerbaldo 1997; see also Maurer, Ihl
& Frölich 1993:1-4). The symptoms progressed to severe dementia and caused the
patient to die in a few years after their appearance. Alzheimer related the symptoms
to the changes found in Auguste D’s brain at autopsy: atrophy of the brain, senile
plaques, and neurofibrillary changes which later were considered to be the characteristic neuropathological findings of the disease.
AD appears to be the most common form of dementia, a common clinical
syndrome that progressively affects multiple cognitive functions, such as memory
functions, language skills, praxis, visuospatial perception, and executive functions,
and causes a remarkable restriction of social and occupational competence among
elderly people (DSM-IV; American Psychiatric Association 1994). AD accounts for
65-70% of the moderately or severely demented patients while vascular dementia,
the second most common cause of dementia, is evident in 15-20% of the cases.
Other rarer diseases (e.g., Lewy’s body, frontotemporal dementia, Pick’s disease,
Parkinson’s disease, Hakola’s disease, Huntington’s disease, Creutzfeldt-Jacob’s
disease) and factors such as disturbances in cerebral blood flow, brain injury, brain
tumors, infection, drugs, and toxins have also been reported to result in a broad
dysfunction of the brain typical of dementia (Erkinjuntti 2001; Erkinjuntti, Rinne &
Soininen 2001; Maurer et al. 1993:5-14; Fratiglioni et al. 2000; Viramo & Sulkava
2001).
The criteria for the clinical definition of AD are introduced both in the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV; American Psychiatric
Association 1994) and the International Classification of Diseases (ICD-10; World
Health Organization 1993), and by the National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer’s Disease and Related Disorders Associations (NINCDS-ADRDA; McKhann et al. 1984). The NINCDS-ADRDA
criteria divide AD into definite, probable, and possible levels of diagnostic certainty.
The criteria of probable AD can be found in Table 1. These criteria are the most
6
Table 1. Criteria of probable Alzheimer’s disease (AD)
Clinical picture
Findings supporting AD
clinically diagnosed dementia,
confirmed by neuropsychological
examination
deterioration of cognitive functions
(aphasia, apraxia, agnosia)
disorder in at least two cognitive
functions
deteriorating dysfunction of
memory and cognition
normal level of arousal
Findings applicable to AD, unless
other etiology is found
periods of plateau
deteriorating skills in daily living,
changes in behavior
depression, sleeplessness,
incontinence, delusions,
hallucinations
family history
sexual disorders
normal spinal fluids
loss of weight
normal EEG or nonspecific
changes
tension, myoclonus, difficulty in
walking
brain atrophy in CT/MRI
epileptic seizures
age of onset between 40-90 years
no general or brain diseases that
could explain the dysfunction of
memory and cognition
normal age-related CT/MRI
findings
(Modified from Pirttilä & Erkinjuntti 2001:138)
broadly used and their reliability is estimated to be 85-90% when clinically diagnosing probable AD (Cummings, Vinters, Cole & Khachaturian 1998; Pirttilä &
Erkinjuntti 2001). In addition to the clinical picture and neuropsychological tests,
laboratory tests, imaging studies (e.g., computer tomography, CT; magnetic resonance imaging, MRI; positron emission topography, PET; single photon emission
computed tomography, SPECT), as well as neurophysiological studies (EEG), are
used to diagnose probable AD (Maurer et al. 1993:21-50; Bauer 1994:66-76; Pirttilä
& Erkinjuntti 2001). During the lifetime of a patient with AD, a brain biopsy may
support the clinical findings of AD, but the definitive diagnosis is made on the basis
of neuropathological findings at autopsy (Cummings et al. 1998; Alafuzoff 2001).
AD is an age-related and irreversible disease with an insidious onset and slow
and stable progression (e.g., Maurer et al. 1993:5; Pirttilä and Erkinjuntti 2001).
The most prominent symptom is the progressive dementia. AD results in a memory
loss, behavior and personality changes, and a decline in intellectual, professional,
social, and everyday functions. Different forms have been called presenile AD (onset
before 65 years) and senile AD (onset after 65 years), but today they are considered
the same disease (Sulkava 1982; Sulkava & Amberla 1982; Sulkava 1983:7-8;
Erkinjuntti 1988:13; Sulkava, Erkinjuntti & Palo 1989:42-44; Erkinjuntti, Rinne &
Soininen 2001). According to Pirttilä and Erkinjuntti (2001), the neuropathology
and the clinical symptomatology associated with AD are, with some rarely occurring
variants of AD excluded, similar in both groups of patients despite differences in the
age of onset, etiology of the disease, or gender. The course of the disease and the
rate of decline vary from person to person. In most people with AD, the first symptoms
seem to appear between 60 and 70 years of age. The age at which AD is most often
7
diagnosed is 70-80 years (Baumann, Tienari & Haltia 1999). The average life
expectancy after the diagnosis of AD is 10 years, but it can vary from 2 to 16 years
(Pirttilä & Erkinjuntti 2001).
The prevalence (the number of people with the disease at any one time) of AD
is estimated to be 0.5% among people under 65 years, 1% among people aged 65-70
years, and 30% among those older than 85 years (Pirttilä & Erkinjuntti 2001). In
Europe, the prevalence of clinically diagnosed AD is estimated to be 4.4% among
people older than 65 years, the proportion being a little lower among younger people
and higher among females than males, especially among very old individuals (Lobo
et al. 2000). The incidence (the number of new cases) of AD among people older
than 75 is 10/1000 and among people over 90 63/1000 in Western and Northern
Europe, and 47/1000 in Southern Europe. In all age groups, females are more vulnerable to AD than males (Fratiglioni et al. 2000; Viramo & Sulkava 2001).
2.1 Etiology and pathogenesis of Alzheimer’s disease
Two types of AD exist: familial AD (FAD) and sporadic AD. FAD has an early
onset, usually before 60 years of age, and it is known to be caused by mutations in
three genes located in three different chromosomes (chromosomes 1, 14, and 21;
Bauer 1994:54-56; Cummings et al. 1998; Lehtovirta 2001). The mutations are
inherited in an autosomal dominant mode of transmission, and they account for less
than 10% of patients with AD. Most patients with AD have dementia syndromes
that have developed later in life and are sporadic in nature. Several risk factors have
been identified or are likely to cause sporadic AD, such as advanced age, female
gender, lower intelligence, lower educational level, depression, small head size,
advanced age of the mother at subject’s birth, Down syndrome, history of head
trauma, hyperthyreosis, vascular dysfunction of the brain, inflammatory processes,
high cholesterol, viruses, toxins, and yet undetermined environmental influences
(e.g., Maurer et al. 1993:69-74; Cummings et al. 1998; Erkinjuntti, Rinne & Soininen
2001; Soininen 2001).
The neuropathology typical of AD may start to develop even 20-30 years
before the appearance of the first symptoms. Histopathologically, the accumulation
of the phosphorylized tau protein and the formation of neurofibrillary tangles in the
neurons and the accumulation of toxic beta-amyloid in the extracellular neuritic
plaques are likely to destroy neurons in AD (Maurer et al. 1993:51-67; Bauer 1994:3049; Cummings et al. 1998; Baumann et al. 1999; Alafuzoff 2001; Partanen, Mäntylä,
Erkinjuntti & Rinne 2001; Tienari & Haltia 2001). Other changes include presence
of amyloid in the small blood vessels (amyloid angiopathy), granulovacuolar degeneration, and loss of synapses and neurons. Several systems of neurotransmitters
can be impacted, especially damage to the cholinergic transmitters in the basal ganglia. Microscopically, a global loss of neurons is found in the hippocampus and the
entorhinal cortex located in the medial temporal lobe. The pathways that connect
8
the hippocampus and the entorhinal region with the rest of the cortex and the cholinergic system in the basal forebrain—the neural systems that subserve learning,
memory, attention, and behavior—are vulnerable to AD. Later, the process of atrophy spreads to the neocortex of the temporal lobe, followed by a central and cortical
atrophy. Macroscopically, the brains of the AD patients show atrophy of the cerebral
gyri and associated widening of the sulci, dilatation of the lateral and third ventricles, and a decrease in weight. The areas most severely involved in the atrophic
process are the medial temporal lobe, especially the entorhinal cortex and the hippocampus.
2.2 Clinical findings of Alzheimer’s disease
Alzheimer (1907; see also Maurer et al. 1997) observed that Auguste D had problems in many cognitive areas and communication, such as memory function, praxia,
speech production and comprehension, conversational skills, reading, and writing.
While her articulation seemed to be normal, she tended to forget recently presented
material easily, to produce semantic substitutions for words, to repeat her speech,
and to use incomplete sentences and out-of-context utterances. In 1907, Alzheimer
documented the most typical cognitive deficits for the disease that was later to be
named after him.
After Alzheimer’s first observations, it has been discovered that the clinical
findings of AD are numerous and diverse. Abnormal neurological symptoms are
few and rare during the early stages of AD, but they are likely to appear towards the
more advanced stages (see Table 2). The most typical neurological findings of AD
seem to be slowness of motor movements, extrapyramidal signs (e.g., hypomimia,
hypokinesia, rigidity, posture and gait abnormalities), primitive reflexes (e.g., suck
and grasp reflexes), as well as involuntary and apractic movements (Bauer 1994:1415; Stern et al. 1996).
Behavioral and psychiatric symptoms are present in AD in the form of inappropriate verbal bursts, physical aggression, agitation, irritability, restlessness, and
difficulty in sleeping (Burns, Jacoby & Levy 1990a, b, c, d; Rubin, Kinscherf &
Morris 1993; Bauer 1994:15-16; Mega, Cummings, Fiorello & Gornbein 1996;
Saarela, Koponen, Erkinjuntti, Alhainen & Viramo 1997; Soininen 1997; Vataja 2001;
see Table 2). Suspiciousness, paranoid thoughts, visual and auditory hallucinations,
and delusional misidentification of people and events are common (Burns et al.
1990a, b; Binetti et al. 1993). Apathy, slowness of functions, and lack of initiative
are found (Saarela et al. 1997), as well as mood changes, carelessness, eating disorders, and disturbed sexual functions (Burns et al. 1990d; Soininen 1997). Depression is often associated with the phase when indications of cognitive decline and
memory dysfunction are identified, as well as with the neurochemical changes in
the neurotransmitters, such as deficiency of noradrelanin and serotonin (Burns et al.
1990c; Saarela et al. 1997; Vataja 2001).
9
Table 2. Clinical findings in different stages of Alzheimer’s disease (AD)
Early AD
MMSE 26-30
Cognitive skills
Functional ability
Behavior
deterioration of learning
slowness and uncertainty
at work
stress
difficulty in coping with
new and demanding
situations
low-spiritedness
impaired retrieval of
recently learned names
impaired skills in foreign
languages
Somatic
symptoms
exhaustion
reduced activity in hobbies
Mild AD
MMSE 18-26
difficulty in following
slowing down of executive conversation
reduction in reading
functions
apathy
loss of weight
withdrawal
slowing of motor functions
agitation
apraxia
withdrawal from
complicated hobbies and
deterioration of judgement interests
difficulty in planning
and problem solving
housekeeping tasks
deterioration of
anxiety
increased forgetfulness
decrease in initiation of
action
depression
delusions
jealousy
difficulty in handling
difficulty in finding words finances and shopping
difficulty in taking
difficulty in calculating
responsibility for own
medication
concentration
decrease in working skills
use of memory aids
Moderate AD
MMSE 10-22
poor memory for recent
events
impaired instrumental
activities (ADL)
delusions
loss of weight
hallucinations
apraxia
difficulties in speech
production
impaired cooking skills
paranoia
difficulty in dressing
appropriately
psychomotor
restlessness
extrapyramidal symptoms:
rigidity, slowness,
inexpressiveness of face
good physical condition
impaired conversational
skills
losing things
difficulty in perception
wandering
getting lost
disorientation
sleep disorder
need to be reminded of
things
depression
loss of insight
dyspraxia
visuospatial difficulties
poor concentration
Severe AD
MMSE 0-12
poor memory functions
poor speech production,
echolalia
very poor speech
comprehension
inability to concentrate
disorientation
severe apraxia
requires assistance in
managing basic functions
(ADL)
impaired driving skills
apathy
relatively well
preserved personality
and social skills
behavior problems
restlessness
requires much help with
basic ADL functions:
behavior problems
dressing, personal hygiene,
outbursts
going to the toilet, eating
agitation
incontinence
apraxia
extrapyramidal symptoms
poor walking (festinating)
sleep disorder
primitive reflexes: grasp,
snout and suck reflexes
depression
low blood pressure
deviant motor
behavior
(Modified from Erkinjuntti, Rinne & Soininen 2001:333 and Pirttilä & Erkinjuntti 2001:132134); ADL = Activities of Daily Living; MMSE = Mini Mental State Examination (Folstein et
al. 1975)
10
AD seems to affect a wide range of cognitive functions that deteriorate slowly
and selectively (Almkvist & Bäckman 1993; Pulliainen & Kuikka 1998). The speed
of the deterioration varies inter-individually (Braak & Braak 1991). The profile of
the cognitive decline is more severe and more biased to memory dysfunction in AD
than in the normal aging process (Soininen & Hänninen 1999). The most typical
and early feature seems to be memory dysfunction, especially in the domain of
episodic memory, leading to a loss of memory of recent things and experiences
(Huff et al. 1987; Almkvist 1996; Soininen & Hänninen 1999). Primary (short-term)
memory, as well as semantic and procedural memory, are likely to be better preserved
during the early phases of the disease (Morris 1994; Almkvist 1996; Soininen &
Hänninen 1999). However, AD may cause global amnesia in its more advanced
stages (Almkvist 1996). Disorders also tend to appear in verbal abilities, visuospatial
functions, attention, and executive functions. Furthermore, deficits in sensory-motor
performance, agnosia (disorder of recognition), apraxia (disorder of skilled
movements), and acalculia (disorder of arithmetic skills) are likely to occur in AD
(Travniczek-Marterer, Danielczyk, Simanyi & Fischer 1993; Bauer 1994:12;
Almkvist 1996, Carlomagno et al. 1999).
Language and communication disorders also seem to be among the most outstanding and early symptoms in AD, and the deficits tend to change during the
process of the disease (Obler & Albert 1981; Obler 1983; Bayles 1982; Martin &
Fedio 1983; Seltzer & Sherwin 1983; Cummings, Benson, Hill & Read 1985; Smith,
Murdoch & Chenery 1989; Bayles, Boone, Tomoeda, Slauson & Kaszniak 1989;
Bayles & Tomoeda 1991; Kempler 1995). The findings on language impairment
seem to be heterogeneous: More severe language impairment has been found in
early-onset than in late-onset AD (Seltzer & Sherwin 1983). Contrarily, Bayles (1991)
found that late onset AD patients were more impaired than patients with an early
onset of the disease. However, according to more recent investigations, the younger
and the older onset patients seemed to have somewhat similar language impairment
and the degree of language impairment was highly correlated with the severity of
the dementia, regardless of the age of onset (Cummings et al. 1985; Selnes, Carson,
Rovner & Gordon 1988; Murdoch, Chenery, Wilks & Boyle 1987). Furthermore,
some studies have reported gender differences among AD patients’ language skills,
with female AD patients performing significantly worse on language production
tasks than male AD patients (Henderson & Buckwalter 1994; Buckwalter et al. 1996).
The findings of a more recent longitudinal study, however, have shown that the
decline in language function and other domains of cognitive function in AD may not
vary by gender (Hebert et al. 2000).
The language and communication problems in AD are largely confined to
two aspects of language; semantics and pragmatics, with preservation of phonology
and syntax (see Table 3). These problems may be expressed as impaired speech
production and comprehension, deteriorated reading and writing abilities, and poor
conversational skills (e.g., Schwartz, Marin & Saffran 1979; Appell et al. 1982;
Bayles 1982; Cummings et al. 1985; Kempler, Curtiss & Jackson 1987; Murdoch et
11
Table 3. Progress of language deficits and commincation
changes in Alzheimer’s disease (AD)
Pragmatics
Symptoms in mild AD
Symptoms in moderate AD
Symptoms in severe AD
preserved conversational skills
compromised conversational
skills: poor topic maintenance
and use of reference, knows
when to talk, responds to
questions but has lost sensitivity
to conversational partners,
repeats ideas, forgets topic, talks
about the past or trivia, lacks
ideas, uses stereotypical
utterances (“ How are you?” )
poor conversational skills: lack
of coherence, poor maintenance
of eye contact and turn-taking,
few ideas, meaningless and
bizarre utterances, severe word
finding deficit
some difficulty in giving
instructions and storytelling
pronominal referencing may
cause confusion
expresses needs of clarification
and confirmation
difficulty in understanding
humor, analogies, sarcasm,
metaphors, and abstract
expressions
initiation of conversation may
be inappropriate and
unsuccessful
perseveration
mutism in final stage
vague, incomplete, and
irrelevant responses
lacks self-correction of speech
errors
difficulty in accounting for the
situational context
can digress from topic
generating series of meaningful
sentences may fail
vague, incomplete, and
irrelevant responses
uses compliments and
expressions of appreciation
Semantics
deteriorated word fluency and
word finding
poor word fluency and
confrontation naming
uses circumlocutions, gesture, or diminished vocabulary
associated words to compensate
uses circumlocutions, unrelated
for word finding difficulties
word substitutions, and empty
compromised comprehension of speech
abstract and/or complex
impaired comprehension of
concepts
cause-effect (sequential)
relationships
Syntax
no errors generally
grammatical mistakes,
simplified syntax
difficulty in comprehending
complex structures
paraphasia, echolalia, palilalia
poor comprehension
very limited vocabulary
jargon
grammar generally preserved
incomplete sentences
poor comprehension of syntactic
structures
Phonology
normal articulation, pitch,
volume, and speaking rate
phonological and articulatory
impairments may occur (e.g.,
false starts, paraphasias,
articulatory difficulty)
Reading
difficulty in reading for
comprehension, intact
mechanics for reading aloud
compromised coherent and well- severely impaired
formed reading, preserved
mechanical reading skills
Writing
difficulty in generating
spontaneous written language,
intact mechanics for writing
compromised coherent and well- severely impaired
formed writing, preserved
mechanical writing skills
more frequent errors, repetition
of nonsense sounds
(After Huff 1988; Fromm & Holland 1989; Kempler 1995; Ripich & Ziol 1998:476; Croot et
al. 2000, and Orange & Ryan 2000)
12
al. 1987; Horner, Heyman, Dawson & Rogers 1988; Fromm & Holland 1989;
Rapcsak, Arthur, Bliklen & Rubens 1989; Mentis, Briggs-Whittaker & Gramigna
1995; Kempler 1995; Ripich & Ziol 1998; Luzzatti, Laiacona & Agazzi 2003).
Morpho-syntactic abilities are relatively well preserved (Obler & Albert 1981;
Murdoch et al. 1987; Smith, Chenery & Murdoch 1989; Kempler 1995; cf. Obler &
Gjerlow 1999:94-95, 97-99) even in the moderate and late stages (Bayles 1982;
Kempler et al. 1987), even though the conveyance of meaningful information may
be poor (Appell et al. 1982; Murdoch et al. 1987). Phonologic deficits and motor
speech deficits (e.g., poor control of phonation, dysarthria) may be selectively present
in early and moderate stages (Croot, Hodges, Xuereb & Patterson 2000), but they
are usually not observed until the latest stages of the disease (Kempler 1995).
The impairment of semantic processing and functional communication can
have a devastating effect on the communicational skills of AD patients (Huff et al.
1986; Fromm & Holland 1989). Failing attention, memory dysfunction, anomia,
and more general pragmatic deficits may contribute to the discourse problems in
AD or AD may selectively affect specific discourse knowledge, that is, knowledge
of the rules and the type of information used in a discourse (Fromm & Holland
1989; Kempler 1995; Watson, Chenery & Carter 1999). The findings concerning
the deficits in language and communication abilities are presented in Table 3 in
conjunction with staging the severity of dementia in AD (see 2.3). A more detailed
description on the semantic difficulties found in AD is given in 2.4.
2.3 Staging the severity of dementia in Alzheimer’s
disease
Short tests for clinical use have been developed to assess the cognitive and functional capacity and social skills and to stage the severity of dementia of patients.
They do not replace a broad neuropsychological examination, but they give an estimation of the cognitive decline and severity of dementia, and they can be used to
follow the progression of the disease (Alhainen & Rosenvall 2001; Hänninen &
Pulliainen 2001). Such tests include the Global Deterioration Scale (GDS; Reisberg,
Ferris, De Leon & Crook 1982), Clinical Dementia Rating (CDR; Hughes, Berg,
Danziger, Coben & Martin 1982), and the Mini Mental State Examination (MMSE;
Folstein, Folstein & McHugh 1975; see also Erkinjuntti, Rinne, Alhainen & Soininen
2001:578-579). The MMSE is the method applied to define the severity of dementia
in this study.
The MMSE can be used to evaluate orientation, concentration, memory, language functions, and perception. It is a valid and reliable method to evaluate the
cognitive decline of the elderly (Rantakrans 1996:22-24; Hänninen & Pulliainen
2001), but it is not very effective in detecting the very mild cognitive decline in early
AD (Hänninen et al. 1999). The MMSE score correlates with the age and educational level. In studies conducted in Finland, it was found that the younger and more
13
educated tended to perform better in the test (Ylikoski et al. 1992; Rantakrans
1996:16-17), even with an apparent dysfunction of memory (Hänninen & Pulliainen
2001). Social class was also found to affect the MMSE score (Ylikoski et al. 1992;
Rantakrans 1996:16-17, 25).
The clinical findings typical of different stages of dementia in AD are gathered above in Table 2 in which the MMSE scores refer to the stage of dementia
severity. In Table 3, in which the findings of language and communication deficits
are presented, the staging of the disorders is based on several types of ratings found
in the literature.
Preclinical, very early, and mild stage of Alzheimer’s disease
In the preclinical phase of AD, when the clinical diagnosis has not yet been confirmed, very slight deterioration in attention, episodic memory, verbal abstractions,
and visuospatial construction can be identified fitting the criteria of Mild Cognitive
Impairment (Ylikoski et al. 1999; Pirttilä & Erkinjuntti 2001). The changes seem to
correspond to the pathological neurofibrillary changes that take place in the
transentorhinal and entorhinal cortex in the temporal lobe (Braak & Braak 1991,
1996; Pirttilä & Erkinjuntti 2001).
In the very early, clinically defined stage of AD, the cognitive impairment is
still mild but more obvious, and the patient may be aware of his or her forgetfulness
and difficulty in learning (Pulliainen & Kuikka 1998; Pirttilä & Erkinjuntti 2001;
see Table 2). The most important symptom of early AD is memory dysfunction
(Almkvist & Bäckman 1993; Petersen, Smith, Ivnik, Kokmen & Tangalos 1994;
Locascio, Growdon & Corkin 1995; Soininen 1997). Above all, episodic memory is
likely to be impaired and to involve limited capacity in both encoding and retrieval
of information (Masur, Sliwinski, Lipton, Blau & Crystal 1994), which leads to an
inability to learn new things, to remember recent events and experiences, and to a
compromised performance in memory tests, such as story recall, learning pairs and
series of words, word retrieval, and word fluency (Petersen et al. 1994; Locascio et
al. 1995; Herlitz, Hill, Fratiglioni & Backman 1995; Kempler 1995; Pulliainen &
Kuikka 1998; see 2.4, chap. 5). Planning and executing new and complex actions
may be impaired, causing slowness and uncertainty in work and hobbies (Pirttilä &
Erkinjuntti 2001). The cognitive decline seems to correspond to the limbic stages in
which the entorhinal region is severely destroyed and neurofibrillary changes appear in the hippocampus and the adjoining limbic area (Braak & Braak 1991, 1996;
Pirttilä & Erkinjuntti 2001).
In the mild stage of AD, a more severe decline in neurological and intellectual functions seems to take place (Almkvist & Bäckman1993; Pirttilä & Erkinjuntti
2001). Sensory functions, especially auditory discrimination, olfactory functions,
and tactile stimulation may be affected. Attention (focus, sustain, and attentional
shift), psychomotor speed, and executive functions (planning, flexibility, and monitoring) seem to be impaired, especially when more complicated mental tasks are at
14
hand. Decline in the short-term memory functions for verbal and visuospatial information can be manifested during the mild stage of AD, but the semantic memory
and the procedural memory tend to stay more preserved until the later stages of the
disease (Almkvist & Bäckman 1993; Pulliainen & Kuikka 1998; cf. 2.4). Visuospatial
(constructional) functions may also be impaired. Apraxia may hinder motor functioning (Travniczek-Marterer et al. 1993). Difficulty in concentration and orientation may appear, as well as psychiatric symptoms (e.g., depression, irritation;
Pulliainen & Kuikka 1998; Erkinjuntti, Rinne & Soininen 2001; Pirttilä & Erkinjuntti
2001). In the mild stage of AD, functional abilities and social functions usually
become restricted but the patient manages to live independently.
Studies have also found that in the mild stage of AD, conversational skills are
mainly preserved, but some patients may have difficulty with conversational initiation, storytelling, bringing up and maintaining a topic, and following the course of a
conversation (Obler & Albert 1981; Orange, Lubinski & Higginbotham 1996; Ripich
& Ziol 1998; see Table 3). Prosody, articulation, and rate of speech are relatively
well preserved (Appell et al. 1982; Hier, Hagenlocker & Shindler 1985; Huff 1988;
Orange & Ryan 2000; cf. Croot et al. 2000). Some patients may show reduction in
their verbal abilities, especially in word retrieval (Obler & Albert 1981; Huff et al.
1986; Bayles et al. 1989; Kempler 1995; Bucks, Singh, Cuerden & Wilcock 2000;
see 2.4), despite their verbose output (Appell et al. 1982). Semantically empty words
(e.g., ‘thing’, ‘stuff’, ‘do’, and deictic terms), circumlocutions, excessive use of
pronouns, gestures, and semantic paraphasias are used to overcome the word-finding problems in order to maintain the fluency of the conversation (Appell et al.
1982; Obler & Albert 1981; Nicholas, Obler, Albert & Helm-Estabrooks 1985;
Kempler 1995; Ripich & Ziol 1998; Bucks et al. 2000; Croot et al. 2000; Orange &
Ryan 2000). Comprehension of concrete and simple, well structured language is
preserved, but understanding abstract language, such as humor, analogies, sarcasm,
and metaphors, or complex grammatical structures may be difficult (Emery 1988;
Fromm & Holland 1989; Kontiola, Laaksonen, Sulkava & Erkinjuntti 1990; Kempler
1995; Grossman et al. 1996; Ripich & Ziol 1998; cf. Papagno 2001). Difficulty in
reading comprehension and creative writing may appear, although mechanical writing and reading skills are relatively well preserved (Kempler 1995; see also Platel et
al. 1993; Luzzatti et al. 2003).
The changes in cognitive abilities during the mild stage of AD likely correspond to the isocortical (neocortical) stages at which the entorhinal, hippocampal,
and limbic regions are severely damaged (Braak & Braak 1991, 1996; Pirttilä &
Erkinjuntti 2001). Excessive pathological changes may have taken place in the association areas of prefrontal and temporo-parietal cortices. Furthermore, the pathways connecting the different parts of the brain are likely to be affected, especially
the cholinergic tract (Pirttilä & Erkinjuntti 2001).
15
Moderate and severe stage of Alzheimer’s disease
In the moderate stage of AD, short-term memory is poor and patients tend to lose
things and repeatedly ask the same questions (Soininen 1997; Erkinjuntti, Rinne &
Soininen 2001; Pirttilä & Erkinjuntti 2001; see Table 2.). Memory aids do not facilitate coping with every-day functions. Orientation to time and places is poor. Agnosia, apraxia, and deterioration of executive functions become evident, followed by
an inability to perform activities of daily living. In the moderate and severe stages of
AD, most of the medial temporal lobe appears to be destroyed and the neocortical
pathology increases in the prefrontal and parietal areas. The increased severity of
the neuronal damage is followed by an increase in impairments in several cognitive
functions, which reduces the patients’ ability to live independently. In the moderate
stage, AD patients have severe difficulty in language production and comprehension (see Table 3.). The word-finding problems worsen, the vocabulary diminishes,
and the amount of empty speech, circumlocutions, and semantic paraphasias replacing content words increases (Obler & Albert 1981; Bayles 1982; Bayles & Tomoeda
1983; Hier et al. 1985; Nicholas et al. 1985; Ripich & Ziol 1998). The patients’
ability to participate in a discourse is usually poor due to pragmatic deficits, inattention, word-finding difficulties, or memory deficits (Kempler 1995; Mentis et al. 1995;
Orange et al. 1996; Ripich & Ziol 1998). Turn-taking, responding to questions, and
using stereotypical, socially ritualized utterances (e.g.. greetings and leave takings)
may occur successfully, but the content of speech may be vague and disordered.
People with dementia may frequently repeat the same words or ideas, and
they may easily forget the topic and talk about irrelevant topics (Bayles 1982; Nicholas
et al. 1985; Ripich & Ziol 1998; Croot et al. 2000). Self-correction of speech errors
or inappropriateness takes place less frequently. Their ability to follow conversation
is poor. Withdrawal from social situations in which communication is demanded
may occur (Bayles 1982; Mentis et al. 1995; Orange et al. 1996). The use of grammatically complex sentence structures seems to remain relatively preserved (Obler
& Albert 1981; Hier et al. 1985). Instead, comprehension of more complex language is likely to be deteriorated (Emery 1988; Fromm & Holland 1989; Kontiola et
al. 1990), and difficulty in grasping the meaning of common words may appear,
which can show up as confusion in using words, phonemic and semantic paraphasias,
and as a lack of ability to differentiate between semantically related words (Obler &
Albert 1981; Huff 1988). Coherent and well-formed writing is compromised, as
well as reading for comprehension (Huff 1988; Horner et al. 1988; Bayles et al.
1989; Kempler 1995), while mechanical writing and reading skills are preserved
(Cummings, Houlihan & Hill 1986; Huff 1988; Rapcsak et al. 1989).
In the severe stage of AD, the impairment of memory and language functions
is grave and perception, orientation and praxis are very poor (Erkinjuntti, Rinne &
Soininen 2001; Pirttilä & Erkinjuntti 2001; see Table 2.). Behavioral problems, apathy,
and restlessness are common symptoms. Neurological symptoms, such as apraxia
and extrapyramidal disorders, are very common. The patients need help and guidance
16
in all basic functions in their everyday life. They are likely to develop other illnesses
and infections. Most commonly, due to problems in swallowing, people with AD
die of pneumonia (Juva, Valvanne & Voutilainen 2001).
Conversational abilities in the severe stage are poor (Appell et al. 1982; Obler
& Albert 1981; Obler 1983; Kempler 1995; Ripich & Ziol 1998; see Table 3.). The
patient’s speech production may be incoherent, contain meaningless and absurd
utterances and consist of only a few ideas and a few words, due to poor vocabulary.
Phonemic and semantic paraphasias exist, but phonological errors seldom violate
the phonotactic constraints of the language. Echolalia (repetition of others) and
palilalia (repetition of self) may appear. The patient has difficulty in eye contact and
turn-taking in a conversation. Language comprehension, especially comprehending
grammatical structures, is very limited.
In the very advanced stage of AD, language production and comprehension
are very limited (Appell et al. 1982; Obler & Albert 1981; Bayles 1982; Bayles &
Tomoeda 1983; Kempler 1995; Ripizh & Ziol 1998). Verbal expressions are very
few and likely to be bizarre and uninterpretable, due to paraphasias and dysarthria.
Some patients may be mute. Language comprehension is impaired in all modalities
and may be limited only to very few, if any, concrete words, or the patient may
understand something from the emotional content or the speaker’s gestures.
2.4 Semantic impairment in Alzheimer’s disease
It is commonly thought that AD patients have a progressive semantic memory
impairment characterized by an inability to distinguish among words belonging to
the same semantic category, and a difficulty in producing names for them (e.g., Huff
et al. 1986; Kertesz, Appell & Fisman 1986; Shuttleworth & Huber 1988; Hodges &
Patterson 1995). There seems to be a wealth of findings both supporting and opposing
the notion (for a review, see Nebes 1989, 1992). However, strong evidence of this
decline has been obtained from AD patients’ performance on tasks tapping lexical
semantic processing, particularly on noun and verb recognition and naming tasks,
as well as on the semantic fluency tasks, in which different aspects of semantic
knowledge are called into play (e.g., Martin & Fedio 1983; Bowles, Obler & Albert
1987; Hodges, Salmon & Butters 1992; Grossman, Mickanin, Onishi & Hughes
1996; see chap. 5). In word recognition tasks, the AD patients were found to be
prone to select semantically related foils instead of correct targets (Huff et al. 1986;
see also Martin & Fedio 1983; Diesfeldt 1989; Hodges & Patterson 1995). Some
studies have provided information about the consistent and general pattern of semantic
impairment present in AD by a detailed item-to-item analysis of various semantic
tasks in and across different sensory modalities (Huff et al. 1986; Chertkow, Bub &
Seidenberg 1989; Chertkow & Bub 1990; Hodges et al. 1992; Hodges & Patterson
1995; Laine, Vuorinen & Rinne 1997; cf. Diesfeldt 1989).
17
2.4.1 Impaired knowledge of the meaning representation
Patients with AD have been reported to have difficulty in retaining different kinds of
semantic features (i.e., information concerning the superordinate category as well
as physical and functional features) that make up the meaning of words (see 3.1.2,
3.2). The semantic features are thought to be essential for, for example, encoding of
information, categorizing and differentiating closely related semantic items, such as
members of the same semantic category. Therefore, impaired processing of semantic features may be responsible for many types of semantic dysfunctions, such as
word finding difficulties and impaired maintenance of the associations between items
in semantic memory. However, studies that have found normal semantic representation in AD have also been reported (e.g., Nebes et al. 1984, 1989; Nebes & Halligan
1996; Grober, Buschke, Kawas & Fuld 1985; Smith, Murdoch et al. 1989; Bayles,
Tomoeda & Trosset 1990; Cronin-Golomb, Keane, Kokodis, Corkin & Growdon
1992; see 6.2).
Many studies, in which different semantic tasks (e.g., card sorting, category
membership judging) have been used to study the functioning of semantic memory,
have indicated that AD patients are progressively impaired in their ability to use
semantic information, the highest superordinate level information being more robust
to degeneration than the basic level and subcategory level information (Warrington
1975; Schwartz et al. 1979; Martin & Fedio 1983; Martin et al. 1985; Huff et al.
1986; Shuttleworth & Huber 1988; Chertkow et al. 1989; Chertkow & Bub 1990;
Cronin-Golomb et al. 1992; Hodges et al. 1992; Tippett, McAuliffe & Farah 1995;
Laine, Vuorinen et al. 1997; cf. Diesfeldt 1985; Smith et al. 1989; Bayles et al. 1990;
Funnell 1995; Bell, Chenery & Ingram 2000; see 3.1.2). However, Laatu, Portin,
Revonsuo, Tuisku, and Rinne (1997; see also Hodges, Patterson, Graham & Dawson
1996) found that, relative to the normal control subjects, their Finnish-speaking mildto-moderate AD patients were significantly impaired in comprehending the structural
hierarchies of concrete words, even at the superordinate level. The impairment
appeared when the AD patients were first asked to correct a hierarchy in which
words were misplaced at different levels of abstraction and then to construct semantic
hierarchies of written labels representing different hierarchical sub- and superordinate
categories (e.g., food was divided to three lower level items (fruit, rootcrop, vegetable),
each of which was further divided to two subordinate level items (‘lemon’ and ‘pear’,
‘carrot’ and ‘turnip’, ‘tomato’ and ‘cucumber’)). Supporting findings on impaired
superordinate knowledge were presented by Grossman, D’Esposito et al. (1996)
who concluded that AD patients were impaired in their superordinate membership
judgments when acceptance or rejection between category members and foils was
required.
There is ample evidence to support the notion that the subordinate features of
nouns are vulnerable to damage in AD. It has been indicated that AD patients tend to
have difficulty in grasping semantic relations between a noun and its defining
structural and functional features (Hodges et al. 1992, 1996; Chan et al. 1993; Laine,
18
Vuorinen et al. 1997; Laatu et al. 1997, Laatu 1999), as well as in determining the
salience of features to a specific item (Grober et al. 1985; Abeysinghe, Bayles &
Trosset 1990; Laatu et al. 1997). As a consequence, the boundaries of different
semantic items may become obscure. For example, in the study of Hodges et al.
(1992, 1996) and Laatu et al. (1997), words were defined by semantically related
features belonging to a neighboring word from the same semantic category or by
semantically unrelated features. Grober et al. (1985) suggested that a reduction in
the weight of the semantic features contained by words may take place in AD, possibly
leading to a change in the organization of semantic information (see 3.2, 10.1.3).
Contrasting findings have also been obtained from noun-feature verification studies
with the AD patients performing as accurately as the normal control subjects, leading
the authors to presume a normal organization of the semantic memory in AD (Johnson,
Hermann & Bonilla 1995; Smith, Faust, Beeman, Kennedy & Perry 1995). As far as
verb processing is concerned, equivalent difficulties to noun processing have been
presented by Grossman, Mickanin et al. (1996), who found that while the healthy
control subjects were able to understand and make associative judgments about the
semantic relations of verbs denoting motion, cognition, and perception, the AD
patients evidenced misunderstanding and anomalous associations of the semantic
relations between these verbs.
Many studies using the method of semantic priming tasks provided findings
supporting the notion of an intact semantic memory in AD. In these tasks, the time
was measured for a subject to process a semantically related (e.g., ‘doctor’ - ‘nurse’),
an unrelated (e.g., ‘pepper’ - ‘goat’) or a neutral (e.g., ‘blank’ - ‘baby’) stimulus
word or picture of an object occurring prior to the target word or a picture of an
object to be named or recognized. AD patients performed well on the priming tasks
and showed shorter response times when the word to be named or recognized was
preceded by a semantically related rather than a semantically unrelated or a neutral
prime. AD patients showed a tendency to have a normal spread of activation along a
normally structured semantic network, and a sensitivity to the semantic relations
between words, as well as to the knowledge of semantic attributes making up the
semantic representation of words (Nebes et al. 1984, 1989; Nebes & Halligan 1996;
Albert & Milberg 1989). However, more recent studies have indicated that AD patients
may express abnormal responses in the semantic priming tasks with both noun and
verb stimuli, implying a semantic memory degradation in AD (Ober & Shenaut
1988; Chertkow et al. 1989; Knight 1996; Bushell & Martin 1997; Bell, Chenery &
Ingram 2001). For example, Bushell and Martin’s study indicated that AD patients
failed to show priming for semantically related verbs denoting different types of
motions (e.g., ‘go’-‘come’, ‘tremble’-‘shake’).
2.4.2 Impaired naming
AD patients tend to perform worse than normal control subjects when producing
names for both objects and actions in confrontation naming tests such as the Boston
Naming Test (BNT; Kaplan, Goodglass & Weintraub 1983) and the Action Naming
19
Test (ANT; Obler & Albert 1979 in Bowles et al. 1987). AD patients are also impaired
in the semantic fluency task in which nouns are produced according to a category
constraint in a certain period of time (see Table 4). There is a wealth of controversial
results obtained from AD patients’ performance in object naming and semantic
fluency with categories of nouns. Moreover, the number of reports on their
performance in action naming is still very limited and published studies on the
semantic fluency task with verb categories are lacking.
Anomia, a disorder of producing or thinking of an appropriate word for an
object or an action, is a prominent feature in AD (Kirshner, Webb & Kelly 1984;
Kertesz et al. 1986; Bowles et al. 1987; Nebes 1989; Chertkow & Bub 1990; Bayles
& Tomoeda 1991; Laine, Vuorinen et al. 1997; Cappa et al. 1998; Bucks et al. 2000).
Difficulty in naming tend to appear at very early phases of the disease (Appell et al.
1982; Kirshner et al. 1984; Huff et al. 1986; Flicker, Ferris, Crook & Bartus 1987;
Shuttleworth & Huber 1988; Hodges & Patterson 1995; Goldstein et al. 1996; Laine,
Vuorinen et al. 1997; cf. Bayles & Tomoeda 1983; Faber-Langendoen et al. 1988;
Hodges et al. 1996) and severity of dementia tends to correlate with naming success
(Bowles et al. 1987; Robinson, Grossman, White-Devine & D’Esposito 1996; WhiteDevine et al. 1996; Cappa et al. 1998; Williamson, Adair, Raymer & Heilman 1998;
Kim & Thompson 2001). Naming ability may deteriorate rapidly in some AD patients, as indicated by follow-up studies on naming of nouns (Kertesz et al. 1986;
Hodges et al. 1990; Beatty, Salmon, Testa, Hanisch & Tröster 2000). Some studies
evidenced that semantic cueing appeared ineffective to prompt correct naming in
AD patients (Obler & Albert 1981; Chertkow & Bub 1990).
Normal healthy elderly tend to name high and low frequency words relatively
equally, whereas AD patients’ accuracy of naming low frequency words is remarkably affected (Kirshner et al. 1984; Shuttleworth & Huber 1988; Miller Sommers &
Pierce 1990; Goldstein, Green, Presley & Green 1992; Williamson et al. 1998; Kim
& Thompson 2001). Other factors, such as familiarity of the to-be-named target, as
well as its imageability and typicality, have also been shown to affect the naming
performance both among normal elderly and people with AD, with the higher familiarity, imageability, and category typicality corresponding to more accurate naming
(Gainotti, Di Betta & Silveri 1996; Bird, Howard et al. 2000; Bird, Lambon Ralph,
Patterson & Hodges 2000). The naming ability of the AD patients may also be affected by word length but not until later in the course of the disease (Kirshner et al.
1984). Furthermore, the semantic category, distinguishing between living and nonliving objects, is likely to have an impact on naming in AD. However, the findings
are contradictory. According to some studies, the semantic information corresponding to living entities may be better preserved in AD than that of nonliving entities
(Gainotti et al. 1996; see 3.1.2, 3.2), while some other studies hold the opposite
view (Whatmough et al. 2003).
The ability to name words according to their syntactic category may remain
unaffected late in AD. This was demonstrated by AD patients’ relatively well preserved ability to name nouns and verbs with paradigmatic responses consisting of
20
semantically related responses without violating the grammatical class boundaries
(Gewirth, Shindler & Hier 1984; see also Astell & Harley 1998). However, the findings concerning the difficulty of producing words for the grammatical classes of
nouns and verbs seem to vary: some studies found the naming of nouns to be more
difficult than that of verbs in the AD group (Cappa et al. 1998; Williamson et al.
1998; Fung et al. 2001), while some others have observed the opposite pattern
(Robinson et al. 1996; White-Devine et al. 1995, 1996; Kim & Thompson 2001).
The errors produced by normal control subjects and AD patients for both
noun and verb naming tasks tend to consist of semantically related errors, omissions, and “don’t know” responses (Huff 1988; Shuttleworth & Huber 1988; Smith
et al. 1989; Bayles et al. 1990; Goldstein et al.1992; Nicholas, Obler, Au & Albert
1996; White-Devine et al. 1996; Robinson et al. 1996; Williamson et al. 1998), as
well as perceptual errors (Kirshner et al. 1984; Bowles et al. 1987; Goldstein et al.
1992; Williamson et al. 1998). AD patients tend to describe the contextual and functional features of objects and actions more often than control subjects (Obler &
Albert 1981; Martin & Fedio 1983; Bayles et al. 1990; Miller Sommers & Pierce
1990; Robinson et al. 1996; White-Devine et al. 1996; Astell & Harley 1998;
Williamson et al. 1998). As the disease advances, the error rate keeps increasing,
and different types of erroneous responses occur, such as circumcolutions (tangentially related, nonspecific labels for the target), nonwords (anomalous combinations
of morphemes), unrelated words (a response without semantic, phonological, or
perceptual similarity to the target), utterances with empty syntax (i.e., noninformative
responses), and less logical, even bizarre responses (Obler & Albert 1981; Bowles
et al. 1987; Bayles & Tomoeda 1983; Smith et al. 1989; Bayles et al. 1990; WhiteDevine et al. 1996).
Phonologically related errors may also emerge for both types of tasks
(Williamson et al. 1998), but their proportion was found to be relatively low among
AD patients (Robinson et al. 1996; White-Devine et al. 1996; cf. Croot et al. 2000).
As far as the naming of nouns is concerned, the typical word errors are semantically
related errors, such as superordinate category labels (e.g., ‘bird’ for ‘pelican’; Martin & Fedio 1983; Hodges, Salmon & Butters 1991; White-Devine et al. 1996; cf.
Bayles et al. 1990; Astell & Harley 1998) and co-ordinates of the same semantic
category (e.g., ‘goat’ for ‘camel’; Martin & Fedio 1983; Bayles & Tomoeda 1983;
Miller Sommers & Pierce 1990; Hodges et al. 1991; Astell & Harley 1998). Errors
specific to action naming are likely to involve very general responses (Bowles et al.
1987) and naming the parts of the objects in the picture without naming the action
(Williamson et al. 1998). Nevertheless, it has also been observed that AD patients’
ability to produce verbs with a varying number of arguments tends to be preserved
(Kim & Thompson 2001).
All in all, there is a great but quite controversial body of research, some of
which provide evidence of the existence of semantic impairment in AD while others
argue for an intact semantic memory where only the access procedures are affected
(see chap. 6). One reason for the discrepancy in these results may be the lack of
21
consistency among the methods applied. The semantic tasks tapping the functioning
of the semantic memory seem to show a great variability across the studies. Even
though the tasks may have been similar, the test material (e.g., words in the naming
task) has differed. The type and level of detail in qualitative analyses varies between
studies, which makes them less comparable to each other. Different, even opposing,
findings of the integrity of semantic memory may also be due to some of the tasks
requiring more effort in processing than others (see the discussion in Nebes 1989,
1992). Moreover, the methods used in selecting the participants for the studies and
grading the severity of dementia of the AD patients vary, which adds up to the
difficulty in comparing the studies with each other. However, it is generally believed
that AD patients seem to have difficulty in retaining and using semantic information
at different levels of abstraction, which is reflected in their impaired performance on
several lexical-semantic tasks requiring a fine-grained differentiation between
semantically related items.
22
3 Frameworks of semantic knowledge
Semantic knowledge, one of the basic components of the language faculty, has mainly
been investigated in four disciplines: in psycholinguistics and cognitive psychology,
where the research concerns the language of neurologically healthy persons, and in
neurolinguistics and cognitive neuropsychology where the study deals with neurologically impaired individuals. The disciplines are interrelated. Psycholinguistic and
cognitive psychological findings provide information by means of which semantic
disturbances may be identified and analyzed. Neurolinguistic and cognitive neuropsychological evidence may, on the other hand, shed light upon the architecture or
processing of the healthy system. The reason is that damaged systems may work
more transparently and therefore possibly reveal aspects of the semantic knowledge
structure and its processing in general (Persson 1995:16-17; Obler & Gjerlow 1999:112). Due to the great interest in finding out how the brain works, and the new technological devices available, the number of studies on lexical-semantic impairment
in different types of brain damage has increased dramatically; the number of studies
concerning the semantic fluency performance of AD patients alone can be counted
in the dozens. Although the descriptions of the theoretical background of the semantic fluency studies are usually at a very general level, these studies seem to be primarily based on the notions of cognitive psychology and psycholinguistics, and more
recently, to a greater extent, on the notions of cognitive neuropsychology and
neurolinguistics. However, the major focus of these studies has been on memory
and executive functions, leaving the neurolinguistic perspective less explored.
Cognitive neuropsychology is an approach which “… seeks to explain the
patterns of impaired and intact cognitive performance [such as thinking, reading,
writing, speaking, recognising, and remembering] seen in brain-injured patients in
terms of damage to one or more of the components of a theory or model of normal
cognitive functioning and, conversely, to draw conclusions about normal, intact
cognitive processes from the observed disorders.” (Ellis & Young 1988:23, brackets
added; see also Shallice 1988:3-37; Coltheart 2001; Selnes 2001). Neurolinguistic
research, on the other hand, attempts to shed light on the fundamental components
of the human language system mainly by studying damaged language systems and
to model understanding of the complex processes of language (Westbury 1998; Obler
24
Frameworks of semantic knowledge
& Gjerlow 1999:1-2, 7-8, 12). Thus, the scope of study in cognitive neuropsychology is broader, whereas neurolinguistic research is concentrated on language functions. However, with regard to studying languages, these fields of study do not exclude one another. Rather, the approaches overlap to a great extent (e.g., Willmes
1998; Obler & Gjerlow 1999:2-3, 156-168).
The overlap notwithstanding, the terminology concerning the semantic knowledge applied by these traditions tends to differ, which may appear confusing to a
reader when interpreting what types of information are considered integrated in the
semantic system of the words (see Nickels 2001). For example, the terms essential
for the present study, semantic memory and mental lexicon (dictionary), are both
used for the semantic entity central to processing words, but they originate from
different scientific traditions, the former from cognitive (neuro)psychology and the
latter from psycho- and neurolinguistics (see below). Another pair of terms that may
cause confusion is object and noun, the former of which is used to denote a (concrete) entity in the real world and the latter of which is used to denote the linguistic
unit, that is, the word that refers to that entity. Respectively, action is used to denote
an action and an event that takes place in the real word, whereas the linguistic term
verb is used to refer to that action or event.
Semantic memory
In cognitive (neuro)psychology, as well as in psycho- and neurolinguistics, the
person’s internal, conceptual knowledge of the meanings of words is called the semantic representation (Ellis & Young 1988:114-115). A commonly accepted view is
that part of the long-term memory system, the semantic memory, is responsible for
the permanent storage of this meaning-related knowledge (e.g., Hodges 2000; Balota,
Dolan & Duchek 2000; Tulving 2000; Schacter, Wagner & Buckner 2000). Tulving
(1972) first defined semantic memory as a mental thesaurus which contained generalized and organized information about words and their meaning, as well as relations between the words, facts, and concepts, but which lacked information about
the autobiographical episodes and events and their temporo-spatial relations which
were considered part of the episodic memory. Later on, it was concluded that episodic knowledge and information derived from autobiographical events and experiences overlapped the contents of the semantic memory and that information originating from both memory systems contributed to the semantic representation of
words (Tulving 1983; see the discussion in Sartori, Coltheart, Miozzo & Job 1994
and Graham, Lambon Ralph & Hodges 1997). In the Oxford Handbook of Memory,
semantic memory was recently defined by Hodges (2000:442) as a “… permanent
store of representational knowledge including facts, concepts, and words and their
meaning—for example, knowing the meaning of the word panda, that Paris is the
capital of France, and that the boiling point of water is 100° C, and so on”. Semantic memory is claimed to be necessary for the use of language because the knowledge and beliefs about the world that people acquire, possess, and use is critically
dependent upon semantic processing (Schacter & Tulving 1994:28; Tulving 2000).
25
Although the definition of semantic memory is widely agreed upon, there are
several divergent theories as to the structure of semantic memory. The most influential theories are in favour of the hypothesis of multiple, modality-specific semantic
systems (e.g., Warrington 1975; Warrington & Shallice 1984; Shallice 1988, esp.
chap. 12; Powell & Davidoff 1995), or the hypothesis of a unitary semantic system
(e.g., Riddoch, Humphreys, Coltheart & Funnell 1988; Caramazza, Hillis, Rapp &
Romani 1990; Farah & McClelland 1991; Sheridan & Humphreys 1993; Klimesch
1994:171-172; Tyler, Moss, Durrant-Peatfield & Levy 2000; Tyler, Russell, Fadili
& Moss 2001). Another issue worth mentioning is that the human memory system is
not restricted to the semantic and the episodic memory system, but has been divided
more specifically into various subsystems on the basis of human behavior and cognition (see e.g., Tulving 1983, 2000; Schacter and Tulving 1994; Schacter et al.
2000; Squire 1987:151-174; 1994:204; Knowlton 1997; Squire & Knowlton 2000).
Because these themes are beyond the scope of this thesis, they are not dealt with in
more detail.
Mental lexicon
The mental lexicon, its architecture and functioning in language production and
comprehension, constitutes a pivotal area of research in psycho- and neurolinguistics
(Schreuder & Flores D’Arcais 1989). The semantic layer of the mental lexicon is
part of semantic memory and it contains information about the word’s meaning, that
is, the set of conceptual conditions that must be selected by the word (e.g., the meaning
of ‘eat’ is “to ingest for nourishment or pleasure”; see Levelt 1989:182) and
encyclopaedic (real-world) knowledge (Lakoff 1987b:168-171, 182, 206; Schroeder
& Flores D’Arcais 1989; Persson 1995:29-30). The mental lexicon also hosts the
syntactic properties of word, containing information about the grammatical category
and the syntactic arguments it can take, the word’s morphological specification, as
well as the phonological composition, and the ortography of the word (Levelt
1989:182; Aitchison 1994:10-14; Hagoort 1998; Pulvermüller 1999). The role of
the mental lexicon is to participate in language processing by mediating between
the form and the meaning of the word (Levelt 1989:182; Hagoort 1998; see 4.1).
This requires that the mental lexicon contains information about a large number of
words (Hagoort 1998). The lexicon of an adult Finnish speaker consists of about
100, 000 words (Niemi & Laine 1994), which can be inflected in 15 semanticsyntactic cases (Hakulinen 1979:97). Karlsson and Koskenniemi (1985) calculated
that a Finnish noun has about 2000 and a verb as many as 12 000 - 15 000 possible
different forms. Adding the derivatives and compound words, one word stem may
have an innumerable amount of lexical forms (Niemi & Laine 1994). Thus, the
mental lexicon is both large and complex (Aitchison 1994:15).
The classical view of linguistic semantics is not suited for the study of meaning
as a cognitive-psychological entity, that is, as a subdomain of memory. The classical
notion of linguistic semantics holds that the semantic representation of a word is
describable by means of sets of binary contrastive features (e.g., ‘woman’ described
26
as +HUMAN, -MALE, +ADULT; e.g., Lyons 1977:317-335). In such definitions,
various kinds of information that humans gain about the referent from their realworld experience (e.g., typical but not logically necessary properties, for example
that women typically are mothers) are not considered part of the semantic
representation (see Lakoff 1987b:74-76). In contrast, the most widely used definitions
of semantic memory and the semantic layer of the mental lexicon seem to overlap in
their notion about the semantic representation of a word by considering the lexical
meaning of the word, encyclopaedic information, and autobiographical events as
part of the semantic representation. The encyclopaedic knowledge mainly concerns
various kinds of factual information of the referents’ physical properties, what it is
typically used for (functional features), its typical location (thematic features), etc.
(see Persson 1995:29-30; see also 3.2, 3.3.3). Thus, the meaning of ‘panda’ may
contain the following information: a panda is an animal, it belongs to the subspecies
of bears, it has a black and white coat, its natural habitat is in China, it is mainly
herbivorous and it likes to eat bamboo and leaves, I have seen a panda at the zoo, etc.
In this study, this notion of the meaning of a word is applied. Subsequently, the terms
‘semantic memory’ and ‘the semantic layer of the mental lexicon’ are used to refer to
the semantic system responsible for the meaning representations. The term ‘mental
lexicon’ is used when referring to the system responsible for combining semantic,
grammatical, and phonological information for word production (see chap. 4).
3.1 Principles of categorization
The human mind seems to organize the phenomena of the real world in categories,
such as persons, objects, events, actions, states, emotions, times, places, directions,
and manners (Lakoff 1987b:6; Levelt 1989:74; Nelson 1996:111). The entities and
classes of items in the real world are likely to be represented mentally in a semanticconceptual format and expressed linguistically as words (e.g., nouns and verbs).
The human cognitive system, however, does not mirror the real world transparently,
but identification and classification of the real-world entities and actions are based
on the cognitive-perceptual system’s ability to selectively encode and store relevant
and purposeful information and to ignore irrelevant information (Persson 1995:2829, 80-84; Heit 1997).
The prerequisite for categorization is to recognize similarities and differences
in sensory-functional features regarding concrete objects and actions between an
input to be processed and the information in storage, which is fundamental to the
human thought processes (Tversky & Hemenway 1984; Fivush 1987; Persson
1995:58, 99; Nelson 1996:223; Hahn & Chater 1997; Smith & Jonides 2000). The
semantic-conceptual representation can be formed by virtue of visual and auditory
perception and by virtue of touching, feeling, smelling, and tasting (Miller & JohnsonLaird 1976:583-618; Fellbaum 1998b:8; Goldstone & Barsalou 1998; Saffran &
Sholl 1999; Bird, Howard et al. 2000; Vinson & Vigliocco 2002; for a developmental
view, see e.g., Nelson 1996; Gentner & Medina 1998), as well as performing and
27
observing actions (Huttenlocher, Smiley & Charney 1983; Engelkamp, Zimmer &
Denis 1989; Persson 1995:72, 59; Pulvermüller 1999). Vision is one of the most
important sensory modalities in perception-based concept formation: it plays a central
role in, for example, figure-ground distinction, detection of movement, and identifying
shape, contour, size, texture, and colour of objects, as well as categorization and
part-whole analysis of objects (Engelkamp 1975; Tversky & Hemenway 1984;
Marshall, Pring, Chiat & Robson 1996; Persson 1995:72-77; Pulvermüller 1999;
Bird, Howard et al. 2000; Riddoch & Humphreys 2001). Moreover, the system also
enables processing of the causal forces underlying similar properties between
instances (Keil, Smith, Simons & Levin 1998) and the ways instances interact with
each other (Tversky & Hemenway 1983, 1984; Lucariello & Rifkin 1986; Fivush
1987; Markman 1987; Nelson 1996:232-248, 252).
Consequently, categorization is based on prior knowledge structures which
facilitate the selection of relevant features of the input and which are reused in the
integration of new and old information (Persson 1995:61-62; Heit 1997; Kersten &
Billman 1997). After having perceived the incoming information and having mapped
it into the pre-existing knowledge structures, the system makes a decision about the
category membership (Lamberts 1997). The process of similarity detection, which
takes place in categorization, is thought to take place automatically and subconsciously by some researchers (Neisser 1987; Persson 1995:58-59; see also Nelson
1996:111), whereas others believe that a more controlled and conscious working of
the cognitive system is needed (Smith, Patalano & Jonides 1998; Sloman & Rips
1998). Categorization enables human perception, reduces the amount of information to be processed to manageable proportions, and allows inferences and predictions about imperceptible or additional properties and future occurrences. Categorization also enables memory functions and thought processes, as well as makes communication labels more economical through the usage of general category labels
(Tversky & Hemenway 1984; Lakoff 1987b:5-6; Heit 1997; Kersten & Billman
1997; Smith & Jonides 2000; cf. Small, Hart, Nguyen & Gordon 1995; Small 1997).
Categorization seems to underlie any representational knowledge. Thus, information stored in the semantic memory by the means of categorization serves as the
basis for semantic processing, including the performance on the semantic fluency
task.
The kind of semantic knowledge that is extracted by sensory and sensor-motor
systems from the external world can be called semantic-perceptual features or
exogenous features (i.e., physical, functional, and thematic information contained
by basic-level nouns, subordinate nouns with concrete reference, proper names, and
onomatopoeic items; Persson 1995:20, 70-90, Smolensky 1986; see also JohnsonLaird 1987; Bird, Howard et al. 2000). Semantic features that are not derived directly
from perceptual or kinesthetic information, but have more language-based knowledge,
can be called endogenous features because they make more use of system-internal
linguistic than perceptual-semantic features (i.e., some adjectives and specific
subclasses of verbs and superordinate nouns). Some of these features have a perceptual
28
back-up and they are called perceptually inferred endogenous features meaning that
their content is acquired from semantic-perceptual structures which are combined
with linguistic features. Purely endogenous features are totally derived from languagebased information and they lack semantic-perceptual backup (e.g., abstract nouns,
polysemous verbs, mental verbs, relational verbs, and adverbs). The nature of
semantic representation of concrete nouns is dealt with in more detail in 3.2.
Categorization of actions and the nature of semantic representation of verbs are
discussed in more detail in 3.3.
3.1.1 Categorization of objects
The classic theory of concept structure and category formation, dating from the
times of Aristotle’s (384-322 BC) philosophical inspirations, considers categories
as representing a summary of all of its members (e.g., all birds) rather than describing subsets or exemplars of the particular category. Different words and word classes
can be formed and distinguished according to the logical combination of necessary
and sufficient features that fit and apply to all the cases of the category. Every member of a category shares the necessary features with other members, and sufficient
(defining) features allow the membership in a given category. Categories have a
hierarchical structure, in which lower-level items inherit their defining features from
the higher level. The boundaries of the categories are clear-cut and only two degrees
of membership are permitted, member and non-member of a category (see the discussion in Labov 1973; Smith & Medin 1981:22-60; Medin & Smith 1984; Lakoff
1987b:12-57; Komatsu 1992; Klimesch 1994:75-79; Taylor 1995:21-37).
The view described above, which is established mainly in theoretical semantics, has been refuted by experimental studies. It has been shown that the semantic
features of words are likely to be of different importance (e.g., McClelland, Rumelhart
& Hinton 1986; Persson 1995:80-84; see 3.2) and that the meaning of words (e.g.,
polysemous words) may vary due to the influence of their context (Barsalou 1982;
Lakoff 1987b:74-76; Persson 1995:30, 87, 109-113; Smith & Samuelson 1997;
Sloman & Rips 1998). Categories can also include atypical cases, for example an
object that does not look or act like a bird (e.g., an object lacking the ability to fly or
sing) can be still considered one (Rosch 1975; Smith & Samuelson 1997). Items in
the common categories (e.g., fruit, furniture, and vehicles) may be cross-classified
across different categories to serve a special goal or purpose (Barsalou 1982, 1983;
Tversky & Hemenway 1984; Hampton 1998). For example, an apple may belong to
fruit, things to take on a picnic, things that could fall on your head, and so on (Barsalou
1983). Further, the experimental studies have also indicated that the boundaries of
categories are likely to be fuzzy rather than rigid and clear-cut. For example, it is
difficult to determine the boundaries between a cup, a bowl, and a mug (Labov
1973). Furthermore, Barsalou (1982, 1983) showed that new semantic categories
could be formed on the basis of representations established earlier (see below for
goal-derived and ad hoc categories).
29
Recent studies of cognitive neuroscience have provided support for a multiple-procedure view according to which different processes are used in concept
formation and categorization (Smith & Medin 1981:170-175; Medin & Smith 1984;
Small et al. 1995; Sloman & Rips 1998; Keil et al. 1998; Smith et al. 1998; Gentner
& Medina 1998; see also Barsalou 1982, 1983, 1987; Schwartz & Reisberg 1991:366405). Various strategies of categorization may be applied to the same items at different stages of a child’s development (e.g., Lucariello & Rifkin 1986; Nelson 1996:232236). Furthermore, multiple strategies may be used simultaneously but they may
have different neural bases, and they seem to be qualitatively different from one
another (Smith & Medin 1981:174-175; Hahn & Chater 1998; Smith et al. 1998;
Koivisto & Laine 1999; Smith and Jonides 2000). Some of the strategies may involve analytical and controlled processing, as well as strategic and selective attending to and weighting of the features of the object, whereas others require more holistic and automatic processing with less load on the working memory (Smith et al.
1998; Sloman & Rips 1998).
The different processes involve classification strategies such as classification
by rule, prototype, or exemplar. Classification by rule holds that categories are
represented by a set of pre-existing mental decision rules that an object has to satisfy
in order to be classified as a member of a given category (e.g., Heit 1997; Lamberts
1997; Hahn & Chater 1998; Smith et al. 1998; see also Keil et al. 1998). For example,
the rule for the category of cars may include that the object possesses the following
features: it has four wheels and an engine, and it can be driven on the road.
Classification by prototype requires retrieval of prototypes (the most typical members
or members with the most inter-correlated features of a category, e.g., a robin) or
clusters of inter-correlated features of various categories (e.g., ‘has-feathers’, ‘haswings’, ‘is-able-to-fly’) from the long-term memory, systematic comparison of
features, and selection of that category whose prototype is most similar to the test
object. Items that are similar to the prototype tend to be classified most readily as a
member of a category, while items that are further from the prototype are less likely
to be included as a category member (Rosch 1975, 1978; Rosch and Mervis 1975;
Smith & Medin 1981:61-101; Smith et al. 1998; see also Storms & De Boeck 1997).
On the other hand, classification by exemplar is a view holding that categories are
represented by their exemplars rather than by a rule or an abstract summary of features
(Smith & Medin 1981:143; Storms & De Boeck 1997). This procedure includes
retrieval of prestored knowledge as a set of various subsets (e.g., a robin and a sparrow)
or specific instances (the pet canary “Tweety”) that are similar to the test object,
comparison between the object and the exemplars, and selection of that category
whose retrieved exemplars are most similar to the object (Smith & Medin 1981:143161; Smith et al. 1998; Smith & Jonides 2000; Heit 1997; Lamberts 1997; Storms &
De Boeck 1997).
According to the connectionist view, the categorical organization in the
semantic memory is understood as an emergent property based on processing the
sensory-functional and associative feature information about objects (Klimesch
30
1994:144-145, 153; Small et al. 1995; Small 1997; Persson 1995:63; Gonnerman,
Andersen, Devlin, Kempler & Seidenberg 1997; Garrard & Hodges 1999).
Categorization can be described as detecting the saliency of and similarity between
incoming and existing semantic information. The processing of features causes
activation to spread through various connections in the network of features and finally
to settle on the target pattern of features (see 3.2). The more familiar the combination
of features of the word, the higher frequency of occurrence of the word, or the greater
the overlap among the features to be matched, the faster the word can be categorized.
Unfamiliar words are identified and classified with less strength and slower integration
of features. Flexible and dynamic combination of these features allowed by the
cognitive system may bring about a change in the previously existing organization
of semantic information, an appearance of a whole new category, and even a
disappearance of some entities of knowledge (Hinton, McClelland & Rumelhart
1986; McClelland, Rumelhart et al. 1986; Persson 1995:60-61, 64-67; Small 1997).
Flexibility in feature combination also lies beneath the use of different words in
terms of crossing category boundaries (e.g., a knife is both a weapon and a kitchen
utensil), as well as differences found in semantic representations among different
people and the individual change of classification principles over time (Small et al.
1995; see also Barsalou 1983, 1987).
A somewhat different approach to knowledge representation and categorization focuses not only on how objects are perceived, but also on how they are functionally, spatially, and temporally related to each other in activities and routines
(Lucariello & Rifkin 1986; Fivush 1987; Nelson 1996:232-248; see also Schank &
Abelson 1977; see 3.3.4). Infants, as well as adults, often seem to group things
around themselves on the basis of a common function of objects which tend to occur
in the same position or “slot” in a given routine and structure of daily events (e.g.,
food to eat at lunch), and which can be substituted by other functionally similar
items (e.g., spaghetti, salad). These “slot filler categories” (e.g., food to eat at breakfast, food to eat at lunch) seem to be typical of the organization of early semantic
memory and they may serve as a basis for the development of hierarchical taxonomic categories (e.g., food and clothes), into which these event based subcategories can be combined later during the child’s development at the age of 7 or 8
(Lucariello & Rifkin 1986; Nelson 1996:232-236; cf. Keil et al. 1998). The associations between the items in the slot-filler categories can be explained by substitutability rather than by similarity among the items (Nelson 1996:235). Categorization
can also be based on contiguity or a thematic relation in which objects involved
temporally and spatially in the same events and routines can be related to each other
(e.g., spaghetti and plate are part of the lunch event; Lucariello & Rifkin 1986;
Nelson 1996:234).
Additional evidence concerning the principle of non-taxonomic categorization was produced by Barsalou (1982, 1983; see also Smith & Samuelson 1997)
who observed that people could construct new semantic categories in order to achieve
different goals. The goal-derived categories may include such categories as things
31
to eat on a diet and things to take on a camping trip. The ad hoc categories, on the
other hand, tend to consist of such categories as birthday presents and things that
can be used to hold a door open, and so on. These categories are composed of crossclassified items. They appear to be less familiar, less central to cultural knowledge,
and less established in the semantic memory than the common taxonomic categories, due to not having as strong a correlational structure of semantic features among
the items (see 3.2). Nevertheless, once constructed and frequently used, goal-derived and ad hoc categories may be well established as a common semantic representation, which enables a rapid activation of information. Barsalou (1983, 1987)
also noted that, as well as common categories, goal-derived and ad hoc categories
tended to have a graded structure with more and less typical items which, however,
seemed to vary considerably in different contexts and cultures (see 3.1.2). Barsalou
noticed that different people seemed to classify objects differently, and that people
tended to change their classification over time.
Relationships between different manners of categorization may be dynamic,
complementary, and constructive, resulting in interacting planes of knowledge organization (Nelson 1996:235). Evidence to support this notion has been brought
forward, for example, by Keil et al. (1998) who indicated that even young children
at the age of five were able to use typicality and similarity-based judgements, as
well as explanatory procedures based on rules and causal principles, in order to
classify animals and machines. Furthermore, amnesic patients were observed to
perform poorly on tasks that required retrieval of exemplars from the long-term
memory store that in their case had been impaired. However, at the same time they
performed better on tasks requiring the use of prototype and rule-based categorization which did not seem to stress long-term memory (Smith et al. 1998 and the
references therein). Further, the study of Koivisto and Laine (1999) indicated that in
normal conditions, the right hemisphere seemed to process lexical-semantic knowledge in the prototype manner when previously learned words were encoded and the
left hemisphere processed lexical-semantic information analytically by features and
was called in to play when new and less known words were encoded (cf. Medin &
Smith 1984).
Contrary to the classical view in linguistic semantics, according to which a
concept or a word can be defined by necessary and sufficient features, supporters of
the psycho- and neurolinguistic, as well as the cognitive psychological and cognitive neuropsychological, accounts assume that a set of necessary and sufficient features is not able to define the members of a category or the category boundaries
(Aitchison 1994:43-45, Taylor 1995:40-41; Ungerer & Schmid 1996:22) or to mirror the psychological reality (Rosch 1975; Rosch & Mervis 1975). Rather, it is emphasized that there is no fixed, basic meaning for all words or clear boundaries
between all categories, but they have fuzzy edges so that new entities and experiences can be easily associated with a category (Labov 1973; Rosch 1975, 1978;
McClelland & Kawamoto 1986; Aitchison 1994:39-41; Taylor 1995:53; Ungerer &
Schmid 1996:16-20, 29, 38; Hampton 1998; cf. Persson 1995:85 for words with
32
fixed core-meanings). Categorization is also influenced by contextual and cultural
factors (Barsalou 1983; Aitchison 1994:92; Taylor 1995:40-41; Lakoff 1987b:7476; Ungerer & Schmid 1996:49-52; Hampton 1998), as a consequence of which the
semantic representations are likely to differ between individuals and between different cultures.
3.1.2 Subcategories and hierarchical structure of nouns
Nouns, generally speaking, can be divided into common (e.g., ‘furniture’) and proper
nouns (e.g., ‘Mount Everest’) according to their semantic contents and referential
purposes (Levelt 1989:196-197; Karlsson 1998:191). Common nouns specify several semantic-conceptual properties that need to be activated in order for a word to
be identified (see 3.2). Proper nouns mainly merely point to a specific referent in the
world. Common nouns can be further subdivided into count nouns (e.g., ‘dog’) and
mass nouns (e.g., ‘water’). Count nouns usually refer to entities that may be countable, whereas mass nouns refer to substances. In some languages, such as German
and Spanish, nouns can also be divided according to grammatical gender (Crystal
1987:93). Dixon (1991:76-77) divided nouns into nouns with a concrete reference
(e.g., ‘horse’), an abstract reference (e.g., ‘time’), nouns denoting states and properties (e.g., ‘joy’), activities (some basic nouns e.g., ‘war’, and nouns derived from
verbs, e.g., ‘decision’), and speech acts (‘question’). In this thesis, the main focus
concerning nouns is on nouns with a concrete reference.
The semantic knowledge concerning nouns that refer to concrete objects can
be organized into two broad domains, living and non-living items, which are also
referred to as biological, animate, or natural categories vs. artifacts, inanimate, or
man-made categories. These domains can further be divided into distinct semantic
categories (e.g., Keil 1989:25-57; Karlsson 1998:191; Tyler et al. 2000). However,
there are varying notions of how the semantic-conceptual knowledge contained by
the semantic memory can correspond to the different semantic domains and categories of concrete objects (see 3.1.1, 3.2). The domain-specific knowledge hypothesis
assumes that evolutionarily, it is important for a human’s survival that specialized
innate neural mechanisms for recognizing and understanding such categories as
animals, plant life (fruit and vegetables), conspecifics, and perhaps artifacts (e.g.
tools), are developed and represented in specific regions of the brain (Caramazza &
Shelton 1998; Caramazza 2000; Shelton & Caramazza 2001; Santos & Caramazza
2002; for a critical view, see Tyler & Moss 2001; Devlin et al. 2002). However, most
of the recent semantic theories hold the view that the division into living and nonliving categories is based on the shared properties of the items, and further distinguished as different semantic categories (e.g., birds and fruit vs. vehicles and tools)
due to a unique set of clustered semantic features, which are not shared by other
categories of the domain (Farah & McClelland 1991; Gonnerman et al. 1997, Small
1997; Moss et al. 2002; Garrard, Lambon Ralph, Hodges & Patterson 2001; McRae
& Cree 2002; see also Warrington & Shallice 1984; Warrington & McCarthy 1983,
33
1987). Semantic features and their constellations in different semantic categories
are discussed in more detail subsequently in 3.2.
Categories of both living and non-living items form hierarchies of different
levels of specificity which are tightly interconnected in the semantic memory and
which can be relied on in order to be able to generalize the semantic knowledge
productively (Persson 1995:92-94). Categories can be vertically divided into the
superordinate (e.g., animals, furniture), basic (dogs, chairs), and subordinate level
(e.g., Dalmatians, kitchen chairs), according to their level of abstraction (Smith,
Shoben & Rips 1974; Rosch, Mervis, Gray, Johnson & Boyes-Bream 1976; Tversky
& Hemenway 1984). Rosch et al. strongly claimed that the internal structure of the
categories is horizontally considered graded, meaning that categories tend to have
more central or more prototypical and more marginal members (e.g., a robin vs. an
ostrich; see 3.1.1). The better or more typical of a category a member is, the more
features it has in common with other members of the category and the faster it can
be identified as a member of a particular category. Conversely, the marginal, “poor”
members share only few attributes with other members of the category (Rosch 1975,
1978; Rosch & Mervis 1975; see also chap. 1 in Ungerer & Schmid 1996; cf. Barsalou
1982, 1983; Lakoff 1987a, b).
Categories at the superordinate level are fairly large, very distinctive from
each other (e.g., animals vs. furniture), and they contain general information that
can be applied to the whole category. Nouns denoting categories at the superordinate
level (e.g., ‘furniture’, ‘vehicle’) have a rather sparse semantic feature-structure compared to the nouns at the basic and subordinate levels (Smith et al. 1974; Rosch et al.
1976; Tversky & Hemenway 1984, Persson 1995:93). The semantic features of the
superordinates tend to be abstract and functional, lacking a direct visual or motor
base. Nevertheless, some superordinate nouns seem to embody perceptual information regarding the referents’ shape structure (e.g., many animals have four legs;
Tversky & Hemenway 1984; Schreuder & Flores D’Arcais 1989; Persson 1995:93).
Therefore, some superordinates denoting biological categories may function as basic-level terms (e.g., ‘bird’) and do not form a clear three-level hierarchy (Rosch et
al. 1976; Tversky & Hemenway 1984; Persson 1995:93). Furthermore, nouns such
as ‘thing’, and ‘stuff’, form the uppermost level of the noun hierarchy, called the
super-superordinate level (Persson 1995:94). These super-superordinates may denote almost any kind of concrete and abstract referents, which often become specified in a particular context. While the superordinate nouns (e.g., ‘tool’) contain very
generalized information, summarized from different categories at the basic-level,
the semantic structure of the super-superordinate nouns is very sparse, schematic,
and derived from the superordinates. In other words, the structure of the superordinate
nouns embodies perceptually inferred endogenous features, whereas the structure
of the super-superordinate nouns is purely endogenous, that is, lacking a perceptual
back-up (Persson 1995:71, 94).
34
The basic level nouns (e.g., ‘dog’, ‘cat’, ‘car’, ‘bus’, ‘chair’, ‘sofa’) denote
cognitively the most important categories and they constitute the cornerstone of
different taxonomies. Categories at this level are very familiar, informative, and
distinctive from each other. For example, if one knows that the object in question is
a dog, one can better predict more about its appearance (e.g., it has four legs, it has
fur) and behavior (e.g., it barks and eats meat) than if one only knows that it is a
living thing. At the basic level, categories tend to be mutually exclusive, owing to
the breaks in their correlational structure of features in the environment (e.g., one
knows that dogs are different from the other categories at the same level, such as
cats, horses, cows, and other mammals; see 3.2). The basic level nouns are
perceptually transparent, that is, their semantic structure closely overlaps the
perceptual characteristics of the referents. The structure of the subordinate nouns
does not tend to differ significantly from the basic level nouns in their general physical
(e.g., shape, parts) or functional features. However, subordinates can provide more
information for a more accurate disambiguation because they often contain many
specific details (e.g., sensory-perceptual features) in addition to attributes that overlap
with other subordinates (e.g. ‘Dalmatian’, ‘German shepherd’ and other breeds of
dogs; Rosch & Mervis 1975; Rosch et al. 1976; Rosch 1978; Smith et al. 1974;
Tversky & Hemenway 1983, 1984, Persson 1995:84-85, 92-95; see also Ungerer &
Schmid 1996: chap. 2 and Murphy & Lassaline 1997). Thus, the structure of the
basic-level and subordinate nouns is exogenous, that is, perceptually encoded (Persson
1995:70-71).
3.2 Feature-based models as accounts of the
semantic representation of nouns
In the philosophical tradition called associationism, originating in the 17th century,
it was thought that the human knowledge of the world was built by accumulated
sensory information and ideas, which became linked on the basis of temporal contiguity and repetition. More precisely, when two or more experienced entities occurred repeatedly, associative links were established between them and a new idea
was compounded. In this way, the knowledge structure was believed to form a huge
associative network. The notion that memory can be represented as a vast associative network in which episodes and facts are connected to each other has been very
influential in cognitive psychology. It has had a strong impact also on the modern
theories of memory (e.g., connectionism) in which knowledge is represented as a
dynamic, effective, and interconnected network (Schwartz & Reisberg 1991:5-7;
406-427; see also Pulvermüller 1999). Another notion about semantic organization,
shared by many theories and supported by experimental evidence, is that knowledge
is represented in different hierarchical structures, as discussed previously (e.g., Collins
& Quillian 1969; Warrington 1975; Warrington & McCarthy 1983; Klimesch 1994:71,
81; Persson 1995:92-94).
35
Theories of semantic representation are divided into two broader groups
according to the notion they hold about the format of the semantic knowledge.
According to the atomistic view, words are regarded as indivisible conceptual wholes
(i.e., without an internal structure) that form an associative network between each
other (Collins & Loftus 1975; Watkins & Gardiner 1979; Roelofs 1992; Levelt 1999a,
2001; Levelt, Schriefers et al. 1999; see discussion in Persson 1995:57, 101).
Alternatively, the semantic representation of the words can be defined by
decomposing the meaning into smaller units (e.g., Smith et al. 1974; Miller &
Johnson-Laird 1976; Allport 1985; Stemberger 1985; Dell 1986; Dell & O’Seaghda
1992; Persson 1995:57-61). These latter theories, commonly called decompositional
or feature-based theories, can be divided further into two distinct approaches by the
way the information is stored in the semantic memory. According to the hypothesis
of multiple copies, words are supposed to consist of a rather fixed set of wordspecific semantic features, which are stored one-to-one for each lexical item. In the
distributed accounts, rather than being stored over and over again for all relevant
words, the semantic features common to many different words are shared by the
words in question (see the discussion in Allport 1985; Stemberger 1985:136-145;
Hinton, McClelland et al. 1986; Persson 1995:57, 118-119; Pulvermüller 1999; Tyler
et al. 2000). Thus, for example the feature ‘has-a-mane’ is represented only once in
the semantic memory and it is shared by, for example, a lion and a horse (Garrard &
Hodges 1999).
Nouns form the largest group of words in the Finnish lexicon (Saukkonen,
Haipus, Niemikorpi & Sulkala 1979:8-21; Karlsson 1998:132). The early productive vocabulary of children seems to consist mainly of concrete nouns, whereas
verbs and other word classes appear later in their active use of words (Guasti 2002:8081, Reyna 1987; cf. Tomasello 1992:9-10). The advantage of concrete nouns over
verbs in a child’s early vocabulary may lie in the way in which the meaning of some
concrete nouns can be fixed by relying on a word-to-world mapping procedure,
whereby the word is transparently mapped onto the sensory and motor features of
an object to which it refers (Persson 1995:29, 84; Guasti 2002:81). A concrete noun
tends to have a conceptually and semantically independent structure, meaning that
its existence at a given moment does not depend on any other object or on its participation in an interaction (e.g., nouns such as ‘knife’ and ‘tool’ can be understood
independently of other structures; Schank 1972; Huttenlocher & Lui 1979; Langacker
1991:14; Persson 1995:96). Verbs, on the other hand, have a dependent conceptualsemantic structure, meaning that they are closely related to other words (see 3.3).
The classical view has been adopted in many experimental feature models,
according to which the semantic structure of a word denoting a concrete object can
be explained by decomposing its meaning into smaller units. These elements have
been called by different terms, such as properties (Collins & Quillian 1969), semantic features (Smith et al. 1974; Smith & Medin 1981), and attributes (Rosch 1975;
for a review on feature-based models, see Chang 1986 and Komatsu 1992). In the
connectionist models, the semantic representation is not a static or fixed cluster of
36
features but rather a dynamic feature conglomerate. It is believed that the item’s
final constellation is tuned, and thus determined, by the verbal content of the item.
In this sense, the semantic representation is not believed to consist of semantic features as such but of a pattern of activation across semantic microfeatures (McClelland
& Kawamoto 1986; see also Persson 1995:57, Tyler et al. 2000). The microfeatures
do not necessarily have a linguistic correspondence but are subsymbolic and unconscious elements, which may act connected to each other flexibly and at various
levels of the semantic processing system (e.g., Rumelhart, Hinton & McClelland
1986; Persson 1995:57-61; Schyns & Rodet 1997). Though the feature-approach is
empirically supported, critical views have been raised (e.g., Levelt 1989:201, 1999a;
Roelofs 1992; Aitchison 1994:39-50; Harley 2001:284-288). In this study, terms
such as property, semantic feature, and attribute that refer to the decomposition of
the word meaning are used interchangeably, and no distinction is made among them.
Semantic features
Semantic representation is believed to consist of physical, functional, and thematic
information. Some researchers seem to share the view that different kinds of semantic information are housed in topographically distinct regions in the brain and that
there is a relationship between the manners in which the information is acquired and
the format in which it is stored (e.g., Warrington 1975; Warrington & McCarthy
1983, 1987; Warrington & Shallice 1984; Allport 1985; Pulvermüller 1999;
Pulvermüller, Mohr & Schleichert 1999; Pulvermüller, Härle & Hummel 2001;
Saffran & Scholl 1999). Some other researchers stress that semantic information is
likely to be represented in an undifferentiated cortical network (Tyler et al. 2000;
Tyler & Moss 2001; Tyler et al. 2001). Although there are plenty of theories and
they seem to stress different aspects of semantic representation, they need not be
mutually exclusive.
Perceptually encoded physical features are thought to be essential components of meaning carrying information of the shape (form and part-structure), texture, size, colour, taste, smell, etc. of the objects (Persson 1995:73-76; Tyler et al.
2000). Semantic-perceptual processing can be mediated by several sensory modalities (e.g., vision, sound, smell, taste, touch), by sensations of subject-internal states
(e.g., pain and cold), and by sensorimotor (kinesthetic) percepts (Miller & JohnsonLaird 1976:583-618; Persson 1995:72; Goldstone & Barsalou 1998; Saffran & Sholl
1999; Bird, Howard et al. 2000; Vinson & Vigliocco 2002; see 3.1).
The role of the functional features, which concerns the manner and the rules
(cause and effect) by which objects move or interact with the environment or how
humans move their bodies when manipulating objects, is considered important in
semantic representation (Marshall, Chiat, Robson & Pring 1996; Persson 1995:7779; Bird, Howard et al. 2000, 2001; Tyler et al. 2000; see also Labov 1973; Rosch et
al. 1976, and Tversky & Hemenway 1984). For example, the semantic representation of a telephone is not only about what it looks and feels like, but about the
37
sensory-perceptual features being associated with the specific functional information about what a telephone does and is used for, and what significance it has in the
environment (Allport 1985; Tyler et al. 2000). Functional features can be encoded
by imagining or observing actions performed by others, or through the motor modality involving self-enactment or simulated self-action (Persson 1995:77; see also
Engelkamp et al. 1989). As far as the basic-level objects are concerned (e.g., a chair),
the functional features seem to refer to actions that are regularly and typically (canonically) associated with the object (a chair is for sitting rather than painting or
buying; Persson 1995:77).
Thematic features (also called contextual or associative features), such as spatial locations and causal and interactional relationships between the objects in a
scene (who does what to whom, etc.), as well as cultural experiences, may be attached to the representation of words (Barsalou 1982, 1983, 1987; Lucariello &
Rifkin 1986; Nelson 1996:234). The perceptual and motor systems, as well as emotional impressions experienced in connection with objects and words, are involved
in mediating thematic information (Persson 1995:79). Many objects share thematic
contexts, that is, thematic features often fit many different words (e.g., books and
pencils can be found on shelves or in stores and offices). The information of referents not gained through direct perceptual or motor experiences, such as facts of a
referent learned from books (e.g., lions live in Africa and they are often called “The
king of the jungle”), is also part of the semantic representation (Tyler et al. 2000).
Such information is called the encyclopaedic knowledge.
Shared and distinctive features
In the more recent connectionist semantic theories, it is thought that the semantic
features making up the semantic representation of a word involve distributed or
shared features and distinctive features. Shared features tend to be features common
to many semantically related words (cf., the multiple copy hypothesis), whereas
distinctive features may be activated only for one or a few members in a category
thus enabling differentiation among co-ordinates (Persson 1995:118-124; Gonnerman
et al. 1997; McRae, de Sa & Seidenberg 1997; Devlin, Gonnerman, Andersen &
Seidenberg 1998; Tyler & Moss 2001; cf. Rips, Shoben & Smith 1973; Smith et al.
1974). Most lexical items, such as nouns, seem to form semantic categories of various kinds of similarity in meaning (see 3.1.2). On a distributed account, semantically similar items share a varying number of features (the greater the similarity, the
higher the number of shared features), and their semantic representations thus have
overlapping zones for shared features. As a consequence of overlap, a shared feature, when activated, spreads activation not only to the semantic representation of
the target, but also to representations having features in common with the target.
Because successful selection requires that activation settle on one representation,
the semantically related items need to be deactivated, and the target-specific, distinctive features must be activated in addition to the shared features (Persson
38
1995:118-124). For example, lions and tigers share such features as ‘has-4-legs’,
‘has-fur’, and ‘has-a-tail’, but only tigers have the feature ‘has-stripes’, by which
they can be differentiated from lions (Gonnerman et al. 1997).
The features that co-occur in a systematic fashion can predict another (e.g.,
things that have legs typically also have ears and eyes and are able to move) and
support each other with mutual activation. These features are called correlated features (e.g., Rosch et al. 1976; Rosch 1978; Keil 1989:154-155; Caramazza et al.
1990; Klimesch 1994:135; Persson 1995:118-124; Vandenberghe, Price, Wise,
Josephs & Frackowiak 1996; Gonnerman et al. 1997; McRae et al. 1997; Keil et al.
1998; Pulvermüller 1999; Pulvermüller et al. 2001; Bird, Howard et al. 2000, 2001;
Caramazza 2000; Tyler et al. 2000; Tyler & Moss 2001; see also Smith & Medin
1981). The shared and distinctive features include physical and functional features,
as well as thematic information. The relative importance of the features may vary.
Some features (i.e., physical and functional features) are likely to be more discriminative of their semantic structures than other features (i.e., thematic features) thus
helping the disambiguation and selection of items take place. This is called the principle of weighting (Farah & McClelland 1991; Persson 1995:72-84; Gonnerman
1997; Bird, Howard et al. 2000, 2001; Tyler & Moss 2001; see also Rosch et al.
1976). Thematic features being shared by many items may not be very discriminative and, therefore, they are likely to be more lightly weighted than sensory-perceptual and functional features, having, therefore, less impact on the selection of items
(Persson 1995:80-82). It is not yet clear if thematic features form correlated patterns
with other semantic features (Tyler et al. 2000).
The degree of correlation of features tends to be higher in the living than in
the non-living categories (Tyler et al. 2000; Tyler & Moss 2001; Moss et al. 2002;
Garrard et al. 2001). Living entities (most typically, animals) share many perceptual
features that frequently co-occur and thus are strongly correlated. Their distinctive
features, which distinguish one category member from another, tend to be weakly
correlated with other semantic features (e.g., tiger: ‘has-stripes’, lion: ‘has-mane’).
Unlike artifacts, the distinctive features of the living things are not usually closely
related to a specific type of function with the environment (cf. Persson 1995:81 for
the biological categories that are strongly associated with functions: donkeys for
transporting, horses for riding). Rather, the shared perceptual features closely interact with their biological functions (e.g., ‘has-wings’&‘flies’; ‘has-eyes’&‘sees’; ‘haslegs’&‘walks’, etc.) that are typical of the category and shared by other category
members (Tyler et al. 2000; Tyler & Moss 2001; Moss et al. 2002). Artifacts tend to
have fewer shared but more distinctive properties than the members of the living
domains. There seems to be a strong relationship between the distinctive physical
(perceptual) form and the specific function of the object (e.g., ‘has-blade’&‘cuts’).
Artifacts are mutually discriminative, that is, their semantic representation tends to
have a unique function that is associated with distinctive perceptual features. For
example, a knife, a spoon, and a fork all have a different shape and a different function. The features denoting form and function in artifacts tend to form more strongly
39
connected clusters than those in the category of living entities (Tversky & Hemenway
1984; Persson 1995:80-81; Tyler et al. 2000; Tyler & Moss 2001; Moss et al. 2002;
see also Labov 1973; cf., Garrard et al. 2001).
Basing on the varying patterns of feature conglomerations and the degree of
feature correlation, the semantic representations for the categories and the category
members seem to have different internal structures (Tyler et al. 2000; Tyler & Moss
2001; Moss et al. 2002; cf. Pulvermüller et al. 2001). For example, animals tend to
have many shared, correlated features with relatively few distinctive features, whereas
fruit and vegetables, although they are semantically closely related, tend to have
fewer shared and fewer and poorly correlated distinctive features. Respectively,
vehicles, which are all used for transportation, seem to have a higher ratio of shared
features but less distinctive features than, for example, tools. Tools seemingly have
more distinctive functional than shared properties for the purpose of their usage
(e.g., for cutting: ‘has-a-sharp-edge’; for raking: ‘has-long-tines’).
Strongly correlated sets of features tend to be heavily weighted and thus they
are likely to be more resistant to damage than features with weaker correlation.
Consequently, distinctive features that are weakly correlated for living things tend
to be more vulnerable to damage than the highly correlated shared features that are
protected by mutual correlations. Therefore, as a result of damage to the vulnerable
distinctive features of living things, naming pictures and matching words to pictures
may cause problems, whereas sorting items into semantic categories is preserved
due to shared semantic features. The man-made objects seem to be more robust
against damage than the living entities because their feature correlation is formed
between distinctive perceptual feature denoting form and functional features (Tyler
& Moss 2001; Tyler et al. 2000; Devlin et al. 2002; Moss et al. 2002; see 9.2.6,
10.1.3).
3.3 Accounts of semantic representation of verbs
Although the proportion of verbs in the lexicon seems to be smaller than that of
nouns, at least in Finnish (Saukkonen, Haipus, Niemikorpi & Sulkala 1979:9-11)
and English (Miller & Fellbaum 1991; Aitchison 1994:110), verbs probably are the
most important, as well as perhaps the most complex lexical category of a language.
The importance of verbs lies, for example, in their pivotal position in clauses because they seem to motivate the other clausal members and thus specify the clausal
structure (Huttenlocher & Lui 1979; Pulman 1983:107; Engelkamp 1975, 1986;
Reyna 1987; Miller & Fellbaum 1991; Aitchison 1994:111; Persson 1995:96-97;
Wayland, Berndt & Sandson 1996; see also Pajunen 1999:14-15). Verbs are thus in
close interaction with many other lexical categories, such as nouns (see 3.2), prepositions (e.g., ‘to’), and adverbs (e.g., ‘out’; Persson 1995:111). In Finnish, which is
an agglutinative language, verbs are likely to influence the inflectional forms in
which the other, nominal members of the sentence occur (e.g., Pajunen 1999:16).
For example, the Finnish words in the sentence “The girl’s books fell off the table
onto the floor” are inflected in the following way:
40
Tytön
girl-GEN
kirjat
book-PL
putosivat pöydältä lattialle.
fell-PST-PL table-ABL floor-ALL
There is also evidence to indicate that at least some verbs tend to be closely
connected to other verbs. For example, Garrett (1992, see also Huttenlocher & Lui
1979) showed that normal speakers often substituted target verbs by verbs with an
opposite meaning (e.g., ‘start’ - ‘stop’), co-ordinate verbs from the same semantic
domain (e.g., ‘drink’ – ‘eat’, ‘watch’ – ‘listen’) that selected the same or semantically related arguments, or by functionally close verbs displaying an entailment of
the actions (e.g., ‘answer’ - ‘dial’, ‘eat’ - ‘cook’). These patterns have also been seen
in slips for other lexical categories, e.g., nouns (‘table’ for ‘chair’), adjectives (‘hot’
for ‘cold’), and prepositions (‘bottom’ for ‘top’), which demonstrate that lexical
items tend to be interconnected (Aitchison 1994:16-27, 85-88; Persson 1995:38,
123). Furthermore, information on aspect and tense, which relate situations to time,
is also closely associated to the semantic representation of verbs (see e.g., Persson
1995:104-107; see also Frawley 1992: chap. 7 and 8; Saeed 1997:114-124, and Cruse
2000:274-279). In conclusion, the data seem to demonstrate that lexical items tend
to be strongly interconnected (see Aitchison 1994:15).
Semantic roles
Verbs are conceptually-semantically dependent structures. For example, the verb
‘walk’ implies the presence of an entity performing the movement, the verb ‘give’,
on the other hand, involves by necessity three components – a giving part, a transferred entity, and a receiving part. These entities, part of the semantic structure of
verbs, are described in terms of arguments and semantic roles, also called thematic
roles (Huttenlocher & Lui 1979; Reyna 1987; Levelt 1989:90-94; Pinker 1989:165246; Aitchison 1994:117-121; Persson 1995:96-104; Collina, Marangolo & Tabossi
2001; for a theoretical view, see Frawley 1992:197-249; Saeed 1997:139-155; Pajunen
1999:23-37; Cruse 2000:281-284). When a verb is activated, its semantic roles are
activated as well, and a frame for a whole clause is given (Shapiro, Zurif & Grimshaw
1987; Persson 1995:96-97; Wayland et al. 1997).
Although some verbs may refer to concrete objects, and action can be encoded
in visual and sensory-motor form (Engelkamp 1975; Huttenlocher & Lui 1979;
Helstrup 1989; Pulvermüller 1999), semantic roles of verbs are pieces of knowledge
that represent gradually achieved and highly generalized information extracted from,
for example, motion, manner of movement, spatial configuration, causation, and the
different relations in which entities (e.g., noun-referent) act or are involved when
partaking in an event (Persson 1995:96-98; see 3.3.1). Semantic roles (e.g., Agent,
Patient, Mover, Source, Goal, Instrument, Theme, etc.) are a relatively restricted set,
which can be used and suited for several verbs (see McClelland & Kawamoto 1986;
Levelt 1989:90; see also Frawley 1002:197). Concerning their semantic role structure,
some verbs tend to be more complex and abstract than others (e.g., relational verbs
41
vs. verbs of involvement, Persson 1995:99; Jonkers & Bastiaanse 1996; Thompson,
Lange, Schneider & Shapiro 1997; Kemmerer & Tranel 2000; Collina et al. 2001;
see 3.3.2).
Generally speaking, semantic roles can be filled with a variety of lexical items
(e.g., ‘I’/’you’/’David’/’Helen’/’the child’/’the dog’ jumped). The specific meaning
of many verbs tends to impose semantic restrictions on the items in role positions.
In the example given above, only living entities of certain types have the ability to
jump, meaning that the semantic role function as the subject to the verb is filled with
an item representing a living creature with this particular ability. The role position
of some other verbs is closely connected to a restricted set of nouns (see the discussion in Persson 1995:96-104; Bastiaanse 1991; Jonkers & Bastiaanse 1996; Wayland
et al. 1996; see 3.3.2). Semantic roles are purely semantic entities that are sometimes not even formally but implicitly realized (Persson 1995:99). For example, in a
sentence “He ate”, the semantic role of Patient (specifying a passive participant who
experiences the effects of an action) is part of the verb’s semantic structure, as well
as ‘food’ representing the role of Theme (specifying the entity being eaten), which
is implicitly represented. Specific verbs, such as ‘hammer’ and ‘sweep’ implicitly
encode the semantic role Instrument (labelling a tool or an instrument used by an
agent in an event; Huttenlocher & Lui 1979; Persson 1995:99; cf. Behrend 1990;
Jonkers & Bastiaanse 1996; Kemmerer & Tranel 2000).
3.3.1 Categorization of actions
So far little is known about the principles and procedures of how actions are categorized, relative to the procedures of classifying objects (see 3.1.1). However, categorization of actions is likely to occur on the basis of semantic roles, which form the
contents of verbs (Persson 1995:99; Kersten & Billman 1997). Kersten and Billman
presented experimental findings on animated events supporting assumptions that
the principles of the correlational structure of semantic feature representation underlying object categorization and category learning may also apply to categorization of events (see 3.3.3). Their study showed that finding rich and systematic correlation among semantic features (e.g., agent path, state change, manner of motion,
and environment) caused better learning than when different features varied independently of one another (e.g., path and manner of motion were considered independent organizing principles; cf. Huttenlocher & Lui 1979; Persson 1995:101). A
rich correlational structure can be seen in the semantic representation of specific
verbs implying the use of an instrument, such as ‘hammer’ and ‘saw’, which specify
not only the use of a particular instrument but also particular actions and results. For
example, the verb ‘saw’ implies not only the use of a saw, but also a particular
physical motion associated to sawing, as well as the result of the affected object
being cut (Huttenlocher et al. 1983; Behrend 1990; Persson 1995:100; Kersten &
Billman 1997). According to Persson (1995:101), these verbs seem to combine more
than one semantic role.
42
Evidence to support the notion that semantic roles underlie category learning
and classification can be found from the studies of developmental psychology: the
most basic and developmentally earliest acquired form of activity is motion
(Huttenlocher et al. 1983; cf. Behrend 1990), which involves movement of the whole
body (e.g., ‘dance’) and movements of different parts of the body, such as hands and
legs (e.g., ‘applaud’, ‘kick’; Persson 1995:100; see also Tversky & Hemenway 1984;
Pulvermüller 1999; Pulvermüller et al., 2000, 2001). Encoding motion verbs tends
to have a strong perceptual basis by means of vision and sensory-motor experiences
(Miller & Johnson-Laird 1976:527; Engelkamp 1975, 1986; Engelkamp et al. 1989;
Huttenlocher et al. 1983; Persson 1995:77). Patterns of movement are acquired
through repetition and practice (Engelkamp 1986).
Subsequently, after children’s ability to note similarities in movements between themselves and others has developed, they are able to understand causality
first on the basis of their own sensory-motor self-experience (by 20 months of age)
and only later (at about 26 months) in other people’s movements and change of state
(Huttenlocher et al. 1983). Thus, fully mature categorization of action requires not
only information derived from an individual’s own sensory-motor experiences, but
also the knowledge to understand that there exists a parallel in the behavior and
intentions (causes) between oneself and others carrying out different types of action, as well as knowledge of goal-directed motives underlying actions (e.g., a child
realizes there is a similar motivation and patterns of movement between his or her
and another person’s actions when going to a sink to wash up; Huttenlocher et al.
1983; Persson 1995:102). Consequently, the semantic role of Mover specifying physical movement is likely to be among the first to organize information, followed later
by Agent that specifies causality of actions. The role of Instrument specifying the
instrument used in action causing a result or a change of state may also have a
crucial part in encoding and classifying information relatively early on in a child’s
development (Huttenlocher et al. 1983; Behrend 1990).
In the process of participatory interaction and while observing others taking
actions, children learn to interpret actions taking place in the socially and culturally
arranged world around them in terms of causal and spatial-temporal relations between
participating elements (e.g., actors, actions and objects) and relations between certain
actions leading to certain goals (Fivush & Slackman 1986; Nelson 1986; Slackman,
Hudson & Fivush 1986; Nelson 1996:5-8, 93-97). Daily routines (e.g., taking a
bath, going to bed, having lunch, etc.) and different types of episodes and events
(e.g., going to a birthday party or a restaurant) gradually develop into a generalized
event representation. In these representations, the structure of an event, as well as
objects and persons usually participating in the particular event, are translated into a
structure of a common event category (also called a schema or a script) containing
shared information and structural similarities (Fivush & Slackman 1986; Slackman
et al. 1986; Nelson 1996:96, 111, 154-155; see Schank & Abelson 1977; for a more
detailed description of scripts, see 3.3.4). Children at the age of three may have
well-developed event representations for familiar routine events (Fivush & Slackman
43
1986; Nelson & Gruendel 1986; Nelson 1996:17). The event representations change
with age and experience from simple to more complex and decontextualized scripts,
which may reflect a growing awareness of event categories and provide the basis for
more abstract temporal, causal, and taxonomic relationships (Slackman et al. 1986).
The generalized knowledge contained by a script may serve as a dynamic, creative,
and effective memory system in identification, encoding, and categorizing incoming
information, as well as in learning new things and organizing information in the
memory (Schank 1982:23-25, 80-90, 167-171; Rumelhart, Smolensky, McClelland
& Hinton 1986; Smolensky 1986; Persson 1995:62; Funnell 2001). Thus, taken that
the semantic roles underlie categorization of actions, highly generalized scripts imply
participation of a conglomerate of semantic roles in conjunction with each other
(Persson 1995:61-62, 101-102), and a very rich and systematic correlations between
them (Kersten & Billman 1997).
3.3.2 Subcategories and hierarchical structure of verbs
In psycholinguistically oriented approaches, the foundation of the verb distinction
is based on how the human semantic-perceptual system is able to extract information and encode the functional features contained in verbs from interaction with the
world (see 3.1). For example, the motor-functional information embodied in different types of verbs denoting, for example, various kinds of actions (e.g., ‘cut’, ‘walk’)
tends to originate in visual encoding (imaginary or observed actions) and sensorymotor experience (self-enactment; Engelkamp 1975, 1986; Engelkamp et al. 1989;
Huttenlocher et al. 1983; Persson 1995:77; Pulvermüller 1999; Pulvermüller et al.
2001; Cacciari & Levorato 2000; Vinson & Vigliocco 2002). However, all actionrelated associations do not involve the motor modality (Pulvermüller 1999). For
example, verbs denoting processes (e.g., ‘trickle’, ‘splinter’) seem to lack encoding
via motor activity (Engelkamp 1986; see Pajunen 1999:51-53), and verbs denoting
relationships between the participants of an event encode information that has no
direct link to perception, but is very abstract in nature (Williams & Canter 1987;
Persson 1995:101-102; Marshall, Chiat et al. 1996). In this thesis, most of the discussion concerns concrete actions.
Concrete action verbs can be defined as referring to intentional behaviors in
which an initiator carries out physical movements or motor programs alone or in
relation to other persons or objects (Huttenlocher et al. 1983; Engelkamp 1986; cf.
division of verbs into verbs of involvement and true relation by Persson 1995:96104). Action may be motivated by causality, for example, by causing change or
result (e.g., ‘open’), by making contact (e.g., ‘touch’, ‘hug’, ‘pet’), or by moving in
a particular way (e.g., ‘dance’, ‘jump’; Huttenlocher et al. 1983; Persson 1995:99100; cf. Behrend 1990). Actions, carried out as various physical movements, may
have a sequential and temporal structure (e.g., opening implies certain arm movements, such as lifting, pulling or pushing, as well as a certain amount of force, and
eating involves biting, chewing, and swallowing; Miller & Johnson-Laird 1976:549;
44
Huttenlocher et al. 1983; Engelkamp 1986; Persson 1995:100; Pulvermüller et al.
2001). The outcomes of movements can be realized, for example, as a change of
state, an initiator’s movement, the initiator’s contact with an object, the object’s
movement, a change in location, etc. (Huttenlocher et al. 1983; see also Kemmerer
& Tranel 2000). Recent neurophysiological studies by Pulvermüller et al. (2000,
2001) have provided evidence that actions carried out by different parts of the body,
such as legs/feet, arms/hands, and face/mouth, activate different regions in the motor cortex and at different rate implying that a psychologically plausible distinction
can thus be made between the semantic representation of verbs (e.g., ‘kick’, ‘lift’,
and ‘smile’; see also Warrington 1975; Warrington & McCarthy 1983, 1987;
Warrington & Shallice 1984; Tversky & Hemenway 1984; Allport 1985; Pulvermüller
1999; Pulvermüller et al. 1999).
Persson (1995:96-104, 196-197) presented a notion that a psychologically
plausible subdivision of verbs could be made between verbs symbolizing involvement of some entity in an event (e.g., ‘applaud’ or ‘jump’) and verbs symbolizing
relational structures (e.g., one loves somebody, or one gives something to somebody; see also Saffran, Schwartz & Marin 1980; Jonkers & Bastiaanse 1996, 1998;
Bastiaanse & Jonkers 1998; Thompson et al. 1997; Kemmerer & Tranel 2000). Verbs
of involvement tend to be concrete verbs containing one semantic role, Mover, that
acts out the particular movement implied by the functional features of actions (e.g.,
clapping of hands when applauding or moving of legs when walking; Persson
1995:77-79, 96-104). While being associated with concrete verbs denoting physical
involvement (cf. Agent that brings about a change), which requires movements of
the whole body or parts of the body, as well as visual perception, the semantic information conveyed by Mover is likely to contain perceptually inferred semantic features which are not directly derived from perception but built upon it, thus having a
perceptual back-up (Persson 1995:102; see also Pulvermüller 1999; Pulvermüller et
al. 2001). These verbs are also called actor-inherent verbs to emphasize the role of
the entity in action (Saffran et al. 1980; see also Huttenlocher et al. 1983).
Verbs containing a true relation, on the other hand, presuppose a semantic
structure of two or several roles. The meaning of such verbs (e.g., ‘chase’) contains
a synthesis of different roles implied by a particular verb. Thus, the meaning of
relational verbs cannot be inferred from the perceptual nature of a single entity in
the relation, but from the roles participating in conjunction to make up the semantic
content of the verb (Persson 1995:101-102). The division between verbs of physical
involvement and relational verbs was suggested as a plausible division also by the
study of Saffran et al. (1980) who observed that agrammatic aphasic subjects
expressed particular difficulty in accessing relational rather than actor-inherent verbs.
However, instead of showing any strength towards generability at the group level,
the study of Kemmerer and Tranel (2000; see also Jonkers & Bastiaanse 1996, 1998;
Thompson et al. 1997; Kim & Thompson 2000) indicated that the ability of some
brain-damaged subjects to retrieve action verbs with one vs. multiple roles varied
individually.
45
Another psychologically relevant distinction between verbs can be based on
specific verbs (e.g., ‘adopt’, ‘sweep’) and generic verbs (e.g., ‘make’, ‘put’;
Engelkamp 1975; Persson 1995:102-104; Kim & Thompson 2001; see also Breedin,
Saffran & Schwartz 1998 for “heavy” and “light” verbs). The meanings of some
specific verbs seem to consist of strong perceptual components, due to their close
associations with specific concrete nouns. Some of these verbs are closely related to
basic-level nouns (e.g., ‘sweep’ – ‘broom’), as a consequence of which their semantic representations contain semantic-perceptual features. Some other verbs are closely
related to more abstract superordinate nouns (e.g., ‘eat’ – ‘food’), the reason why
their semantic representations include perceptually inferred features (Engelkamp
1975; Huttenlocher & Lui 1979; Huttenlocher et al. 1983; Behrend 1990; Persson
1995:85,101-102; Jonkers & Bastiaanse 1996; Kerstin & Billman 1997). A noun
can be considered an aspect of the meaning of a particular verb. This is indicated,
for example, when a noun response is given to a verb in a free association task, and
that noun is a prototypic example of one of the arguments of that verb (e.g., ‘knife’
in response to ‘cut’; Huttenlocher & Lui 1979). The semantic-perceptual and perceptually inferred features of the nouns may function as a route to specify the verb
(Persson 1995:102-103, 208-209; cf. Engelkamp 1975; Davidoff & Masterson 1996).
One subset of verbs with a close association to certain nouns can be called instrument verbs because they designate the tool or the instrument being used, as well as
the action being performed or the result being accomplished by the action (e.g.,
‘sweep’, ‘hammer’, ‘mop’; Behrend 1990; Bastiaanse 1991; Jonkers & Bastiaanse
1996, 1998; Kemmerer & Tranel 2000; see 3.3.1).
The basic meaning of generic verbs (also called general purpose verbs), such
as ‘put’, ‘go’, ‘give’, ‘make’, ‘come’, is schematic in a sense that they denote a
certain relational structure of roles between entities participating in an action (e.g.,
‘come’) as physical movement between two poles which manifests the semantic
role structure of Mover - Source (specifying the point of origin) - Goal (specifying
the point of arrival). Unlike specific verbs, generic verbs are very frequent,
polysemous verbs (i.e., they have many meanings), and they can easily be modified
and shaded by the semantic content of items they occur with, such as fillers of the
semantic roles (Reyna 1987; Persson 1995:103; Berndt, Haendiges, Mitchum &
Sandson 1997; Breedin et al. 1998). Since they are flexible in meaning, generic
verbs have fewer contextual constraints than specific verbs and they can thus be
used in a broad range of situations, and they may allow for several interpretations.
Generic verbs contain purely endogenous information that lacks a direct connection
to perception (Persson 1995:102-103). These verbs also tend to be low in imageability
(Bird, Lambon Ralph et al. 2000). Empirical evidence has shown the relevancy of
the distinction between specific and generic verbs, for example, in studies in which
agrammatic speakers were found to produce specific (heavy) verbs easier than general (light) verbs by Persson (1995:209), Breedin et al. (1998) and Kim and Thompson (2001; cf. Berndt et al. 1997; Bastiaanse & Jonkers 1998; Kemmerer & Tranel
2000). Evidence supporting the division was also obtained from patients with AD
46
by Kim and Thompson (2001) who found that AD patients produced general verbs
better than specific verbs (see 9.4, 9.4.5, 10.1.3).
As with nouns, a graded structure (i.e., some verbs are considered better or
more typical examples of verb categories than others) can also be found at least
among some verbs. For example, to ‘murder’ can be considered as a better example
of ‘killing’ than ‘execute’, and ‘stride’ a better example of ‘walking’ than ‘limp’
(Pulman 1983:110-133; see also Coleman & Kay 1981; Ungerer & Schmid 1996:100,
191). In contrast to nouns, however, verbs do not seem to form a strict and neat
hierarchical structure with many levels of abstraction. Instead, most verbs have a
shallow, bushy organization (Miller & Fellbaum 1991; Pajunen 1998, 2001:60-61).
All superordinate nodes are not branched into more specific variations and there are
no well-defined levels of structure. In such a bushy structure, there typically are
only a couple of hierarchy levels and the verbs of a particular level are grouped in
several small, parallel groups. In very flat structures, such as the structure of cognition verbs (‘anticipate’, ‘think’, ‘intend’, ‘suppose’, and ‘know’), verbs do not form
a specific hierarchy but are grouped together as co-hyponyms (e.g., ‘think’, ‘consider’, ‘ponder’ and ‘suppose’, ‘suspect’, ‘imagine’, ‘hope’; Pajunen 2001:60-63).
The shallower the hierarchical structure, the weaker the representation of the verb
class (Pajunen 1999:20). The members of the same level of abstraction do not seem
to form a very firm network and there are few semantically similar verbs that can be
used as variations expressing certain actions or events (Pajunen 1998). On the other
hand, not all verbs can be grouped under a single top verb in a semantic field. Motion verbs, for example, tend to have two top nodes (‘move’, ‘make a movement’)
and (‘move’, ‘travel’, ‘displace’) and the verbs of possession can have three verbs as
top nodes (‘give’, ‘transfer’), (‘take’, ‘receive’), and (‘have’, ‘hold’; Miller &
Fellbaum 1991; Fellbaum 1998a:71-72, 81; see also Pulman 1983:107).
Some of the verbs, however, can form taxonomies that show a specific level
at which there are more verbs than at other levels, and where most of the verbs
cluster and have richer semantic relations to their superordinate verb than between
verbs on other levels in the same hierarchy (Miller & Fellbaum 1991; Fellbaum
1998a:80-81; cf. 3.1.2). For example, the taxonomy for speech act verbs may stem
from the highest level ‘communicate’ to the next lower level that contains relatively
few verbs, such as ‘talk’ and ‘write’. The verb ‘talk’, however, seems to have many
hyponyms, such as ‘babble’, ‘mumble’, ‘slur’, ‘murmur’, and ‘chatter’. The level
below the most richly lexicalized one has few verbs, which tend to be compounded
from their superordinate and a noun (such as ‘telecommunicate’). When descending
the verb hierarchy, semantic elaboration of different semantic components (e.g.,
manner, cause, speed, intensity, purpose, and volition) and sometimes nouns denoting, for example, instruments or materials, is needed (Pulman 1983:111-112; Miller
& Fellbaum 1991; Fellbaum 1998a:80). The variety of nouns that the verbs at a
given level can take as possible arguments diminishes, implying a function of the
increasing elaboration and meaning specificity of the verbs (Miller & Fellbaum 1991;
Fellbaum 1998a:80-81; Pajunen 2001:35; see also Pulman 1983:107-136; Ungerer
47
& Schmid 1996:102-103). For example, the semantic role of the verbs ‘communicate’ and ‘talk’ corresponding to the subject can be filled with either nouns referring
to figures, pictures, or humans, whereas the corresponding role of the verbs ‘fib’ or
‘perjure themselves’ is restricted to nouns denoting only human beings (Fellbaum
1998a:81). Thus, upper parts of the hierarchy tend to have more general meaning,
lower parts more specific meaning. In Finnish, verbs at the lowest level tend to be
very descriptive in nature (e.g., ‘hölkyttää’ / ‘jog’; Pajunen 1998, 2001:52-54; see
also Ungerer & Schmid 1996:101-104).
Miller and Fellbaum (1991; Fellbaum 1998a:79) merged the different kinds
of semantic dimensions that distinguished a verb hyponym from its superordinate
into a manner relation, called troponymy. Troponymy represents a particular kind of
entailment, where verb pairs are temporally coextensive and are related by manner
relation (Fellbaum 1998a:80). Thus, to ‘amble’ is to walk in a particular manner, to
‘hammer’ is to hit in a particular manner, and to ‘tape’ is to fasten in a particular
manner. Troponymy is the most frequently found relationship among verbs and a
very productive process for coining new words in a language (Miller & Fellbaum
1991).
3.3.3 Feature-based models as accounts of the semantic
representation of verbs
The account that word meanings can be decomposed into different types of semantic units can also be applied to the semantic representation of verbs. Some theories
maintain that there are certain basic actions (e.g., go, get) that can act as the core of
other verbs (e.g., ‘run’, ‘grab’; Breedin et al. 1998; see also Schank 1972). Some
other accounts share the view that the meaning of verbs denoting actions is composed of different semantic features (e.g., Miller & Johnson-Laird 1976: chap. 7,
esp. p. 525; Allport 1985; McClelland & Kawamoto 1986; Persson 1995:77-78, 85,
96-104; Marshall, Chiat et al. 1996; Bird, Howard et al. 2000; Pulvermüller 1999;
Pulvermüller et al. 2001). Bird, Howard et al. concluded from their simulation studies that the semantic representation of animate nouns, inanimate nouns, and action
verbs tended to form a continuum from more sensory (visual) and less functional
(motor) features to less sensory and more functional features in such a way that
there is a heavier weighting of sensory features for animate nouns designating living
entities, less sensory but more functional for inanimate nouns designating artifacts,
and more functional weighting for verbs designating concrete actions (see also
Marshall, Chiat et al. 1996; Marshall, Pring, Chiat & Robson 1996; Bird, Howard et
al. 2001; cf. Shapiro & Caramazza 2001a, b). A somewhat similar distinction was
made by Pulvermüller (1999; see also Pulvermüller et al. 2001; Vinson & Vigliocco
2002) who divided different words into “action” and “vision” words, according to
their type of featural composition and their topographical representation in the brain
rather than on the basis of their lexical categories (e.g., verbs referring to body movements were assigned as action words, concrete nouns such as animal names were
assigned as vision words, and names referring to tools were assigned as both).
48
Connectionist accounts share the view that functional information, symbolizing patterns of movement, is part of the semantic representation of concrete verbs
denoting physical involvement (e.g., ‘squeeze’, ‘jump’), which can be derived perceptually from multimodal stimuli (see 3.1, 3.2). However, in these accounts, it is
emphasized that visual and sensory-motor information are correlated. Subsequently,
combinations of (somato)sensory-motor information can be established to represent
these actions in the brain (Persson 1995:77-78; Pulvermüller 1999; Pulvermüller et
al. 2001). According to Persson (1995:79), functional features, however, do not stand
for the whole semantic representation of verbs denoting concrete action alone, but
as integrated with the semantic roles that specify the entities partaking in an action,
they form the structure of the semantic representation of these verbs. For example,
the functional features of concrete actions (e.g., the pattern of movements of legs
associated with running or the particular movements of one’s arm when sawing) and
the role of someone performing the action (Mover or Agent) are integrated in the
verbs ‘run’ and ‘saw’ (Persson 1995:99-100).
In connectionist modelling, such as the Parallel Distributed Lexical Processing (PDLP; see Persson 1995), the semantic roles are considered decomposed and
flexible structures, or features, that may be shared (distributed), overlapped, and
integrated with each other (see also McClelland & Kawamoto 1986; Persson
1995:101; Pulman 1983:109; Huttenlocher & Lui 1979; Behrend 1990; Miller &
Fellbaum 1991; Fellbaum 1998a:71-72, 81; Kersten & Billman 1997; Vinson &
Vigliocco 2002; 3.2, 3.3.1). This notion is not in agreement with the established
view, according to which semantic roles are semantic primitives, which lack internal structure, and which are mutually exclusive, meaning that only one semantic
role occupies one verb at a certain time (see the discussion in McClelland &
Kawamoto 1986 and Persson 1995:101; cf. Frawley 1992: chap. 5 and Saeed 1997:
chap. 6). Instead, it is emphasized that the human memory system allows for a flexible integration of features and that different routes can be utilized in processing a
semantic entity. For example, specific verbs (e.g., ‘sweep’) can be activated by
strongly established connections to certain basic-level or superordinate nouns (e.g.
‘broom’ - ‘sweep’; McClelland & Kawamoto 1986; Persson 1995:34, 78-79, 103;
see 3.3.2). Accordingly, semantic roles such as Agent (specifying an active participant and an instigator of an action) and Patient (specifying a passive participant who
experiences the effects of an action) can be simultaneously represented (McClelland
& Kawamoto 1986). For example, in the sentence “The boy moved” the boy is both
the instigator and the participant of the action. Respectively, Agent and Mover (specifying an entity carrying out any type of movement) can be simultaneously activated
but differently weighted in different verbs (Persson 1995:77, 100-101). For example,
in the concrete causative verb ‘cut’, the role of the Mover is considered less weighted
than the role of the Agent, whereas in the concrete, noncausative action verb ‘walk’,
the role of the Mover is highly emphasized. In relational verbs, semantic roles work
in conjunction with each other making up the content of the verbs (see 3.3.2).
49
3.3.4 Scripts as the account of semantic
representation of verbs
Script theories seem to provide a broad semantic framework for explaining the
semantic representation of objects and actions, the complex relationship between
the participants, and the spatial, temporal, and socio-cultural circumstances presented
in an event (Schank & Abelson 1977:36-68; Schank 1982; Nelson 1986; Fivush
1987; Grafman et al. 1991; Nelson 1996; see 3.1.1). Although there may yet be little
neuropsychological evidence to support the existence of scripts as knowledge
representation (Funnell 2001; cf. Weingartner, Grafman, Boutelle, Kaye & Martin
1983; Grafman et al. 1991), plausible findings from cognitive and developmental
psychology supporting their existence can be found (e.g., Bower, Black & Turner
1979; Galambos 1983; Fivush 1987; Nelson 1996). A script (also called a schema, a
scene, a frame, and an activity) is a high-level, usually highly generalized and thus
very abstract, knowledge structure of a rather typical or stereotyped episode (e.g.,
going to the doctor, washing a car, or eating at a restaurant), which does not correspond
to, or have a basis in, any single episode as such but is formed gradually of pieces of
knowledge comprising the most prototypical or regular parts of recurring episodes
(Schank & Abelson 1977:41; Graesser 1978; Bower et al. 1979; Galambos 1983;
Nelson 1986; Persson 1995:62; Funnell 2001). A person may learn about scripts by
personal experience and by reading about them, by seeing them done, or by being
told about them by others (Schank & Abelson 1977:36-46; Bower et al. 1979; Schank
1982:3-12, 23-24; Galambos 1983; Fivush & Slackman 1986; Fivush 1987). Thus,
scripts contain both episodic (autobiographic) and semantic information (Fivush &
Slackman 1986; Hudson 1986; Nelson 1996:174-177; cf. Tulving 1972, 1983, 2000;
Schacter & Tulving 1994). Even very young children may have general, spatiallytemporally organized knowledge about routine and familiar events, such as eating
and going to bed (Fivush 1987; Nelson 1996:232-240; see also 3.1.2).
The internal structure of scripts appears to contain vertical and horizontal
connections between their components, similar to category representations (for a
comparison between categories and scripts, see Barsalou & Sewell 1985). The vertical connections are hierarchical part-whole relationships between higher level components and lower level components; for example, the restaurant script may involve
a component such as ‘ordering-food’ which serves as a superordinate to a subordinate component ‘customer-picks-up-a-menu’. The horizontal connections between
the parts of a script can be defined by structural characteristics, such as sequential
and temporal order between actions, causality, spatial relations, centrality, distinctiveness, and frequency of performing an action as a part of a script (Schank &
Abelson 1977:30-35, 38, 42-46; Graesser 1978; Bower et al. 1979; Galambos 1983;
Barsalou & Sewell 1985; Fivush & Slackman 1986; Rifkin 1984 (in Lucariello &
Rifkin 1986); Nelson & Gruendel 1986; Slackman et al. 1986; Grafman et al. 1991;
Nakamura, Kleiber & Kim 1992; Nelson 1996:232-232). It has been indicated that
temporal properties and causal relationships in a script specify the order in which
50
certain actions take place; which actions occur early in the sequence, which happen
later (e.g., ordering a meal is temporally and causally related to getting and eating it;
Galambos 1983, Barsalou & Sewell 1985; Nakamura et al. 1992).
In scripts, actions seem also to be organized by centrality, meaning that some
actions seem to be more important to the performance of a script than others (e.g., it
is more essential for a script going on vacation to ‘decide-on-a-place’ than ‘stopthe-newspaper’; Galambos 1983; Nelson & Gruendel 1986; see also Barsalou &
Sewell 1985; Grafman et al. 1991; cf. 3.1.2). The centrality of an action may have an
influence on how easily it is activated for use and how easily a script can be comprehended (Galambos 1983). In scripts, the relationships between adjacent actions should
cause the encoding of one to prime the other. In addition, the prime should spread
beyond its adjacent actions to further actions, although the amount of priming should
decrease with distance (Barsalou & Sewell 1985; for schemata and sequential thought
processes in connectionist models, see e.g., Rumelhart, Smolensky et al. 1986). The
structural characteristics of a script can be defined by how distinctive an action is for
the script (Galambos 1983; Grafman et al. 1991; cf. 3.2). Some highly distinctive
actions tend to occur in only one particular script (e.g., ‘get-a-camera’ is specific for
the script of taking a photograph), whereas some other actions may be part of a
number of different scripts (e.g., ‘standing-in-a-line’ is part of various scripts, such
as shopping for groceries and going to the movies). Thus, these actions are low in
distinctiveness and do not seem to distinguish among various scripts. Actions of a
script can also be specified by how frequently they are included when a script is
performed. Some actions in a script are almost always performed (‘get-a-towel’ in
the script of washing your hair), while some others may be less often included in a
script (e.g., ‘get-a-tripod’ in taking a photograph).
4 Spoken word production
In normal speech, about two to five words per second are retrieved from the mental
lexicon that contains tens of thousands items (Levelt 1989:199; Levelt et al. 1999;
Niemi & Laine 1994). Retrieval of words is thus a high-speed, fairly automatic and
effortless process (Levelt 1989:2, 20-22), as far as frequent and familiar words are
concerned (Persson 1995:32; Harley 1998; Astell & Harley 1998). Infrequent and
less familiar words, as well as deficits in the language system, tend to reduce the
degree of automatic processing, and may require attentional control in the form of,
for example, a more conscious search for particular words (Persson 1995:32; Astell
& Harley 1996). Controlled processing is always needed when the speaker intentionally chooses his or her perspective to compose a message including any relevant
information (Levelt 1989:20; 1999a; Levelt et al. 1999). Automatic processing can
run simultaneously at multiple levels in parallel, whereas operations, in which controlled processing is needed, seem to take place in a serial order (Levelt 1989:2, 2022, 28; Persson 1995:32-33, 48-50).
Although word production is a high-speed process, the rate of errors of lexical selection is merely in the range of one or two per thousand (Garnham, Shillcock,
Brown, Mill & Cutler 1981; Levelt et al. 1999; Levelt 2001). Among normal speakers, the rate of errors is likely to increase due to attentional lapses and under high
and stressful processing demands (Levelt 1989:487). According to Dell (1986), the
semantic-lexical organization, in which semantically and phonologically related items
are easily activated, seems to some extent to be prone to speech errors. The system
probably tolerates errors because of its productive and flexible nature in terms of
allowing novel combinations of items, through which new words can be coined in a
language. Word production is sustained by a system of cortical and subcortical regions, primarily across the left hemisphere of the brain (for a review on the neural
architecture underlying word production, see Price, Indefrey & Van Turennout 1999).
4.1 Theories of spoken word production
Generally speaking, word production follows the following pattern (Levelt et al.
1999a; Price et al. 1999). First, the speaker has to decide on what kind of information to express, whereby he or she selects the semantically most appropriate lexical
52
Spoken word production
concept (i.e., concept for which there exists a word in the target language; e.g.,
‘table’) among several other semantically related and activated alternatives (e.g.,
‘furniture’, ‘chair’). Subsequently, the lexical item’s syntactic properties (e.g., grammatical class and, in some languages, gender) and morpho-phonological word form
(/teibl/) are encoded, followed by syllabification with associated phonemes (/tei//bl/). Finally, the chosen phonemes are translated into a phonetic plan and articulated. During and after the process, the system provides the speaker feedback (internal speech) to monitor the output for corrections.
Spoken word production can be approached from several perspectives that
have their roots in different research traditions (see the review in Levelt 1999a;
Price et al. 1999; Nickels 2001). Among the most current models of naming, there
are chronometric models that account for distributions of reaction times in word
production (e.g., Levelt 1989, 1999a, b, 2001; Levelt et al. 1991b, 1999; Roelofs
1992), as well as models that account for the distributions of spontaneous or induced speech errors (e.g., Stemberger 1985; Dell 1986; Dell & O’Seaghdha 1991,
1992; Martin, Dell, Saffran & Schwartz 1994; Persson 1995; Dell, Schwartz, Martin, Saffran & Gagnon 1997; Foygel & Dell 2000). The models of both traditions
explain the knowledge domain as a network of associative links, and the same underlying processes of word production in terms of activation spreading in the network. Both accounts hold that two stages, lexical selection and phonological encoding, are the crucial phases of successful word production. Despite the similarities,
the approaches seem to differ in many details, such as the relationship between
meaning and form retrieval and the architecture of the semantic representation.
However, the research traditions have begun to merge in recent years (Levelt 1999a).
According to the discrete two-stage word production model, developed by
Levelt et al. (see the reviews in Levelt 1999a, b, 2001; Levelt et al. 1999; see also
Roelofs 1992), the conceptual-semantic representations of words is described as
individual “wholes” which are non-decomposed chunks of information. The meaning of a word is defined by the strength and number of links there are between the
semantically related lexical concepts, such as super- and subordinates or co-ordinates of the same semantic category. The model allows bi-directional spreading of
activation at the conceptual-semantic stage among the competitive candidates of the
lexical concepts (e.g., ‘sheep’, ‘goat’, ‘llama’ or ‘choose (x, y)’, ‘elect (x, y)’, ’select
(x, y)’), and between the conceptual and the lemma stage, during which the grammatical information of the lexical concepts is specified (Levelt 1999a, b, 2001; Levelt
et al. 1999). However, there is only uni-directional, feed-forward spread of activation further down from the lemma level to the phonological level, during which the
selected item’s morpho-phonological form is established. Consequently, the word’s
phonological form does not contribute to the semantic specification of the word (see
the discussion in Persson 1995:51-54, 125; Laine & Martin 1996; Harley 1998;
Levelt 1999a).
53
There is evidence, however, to indicate that prior to the lexical selection,
feedback from phonological information flows to the level of semantic specification.
For example, Martin, Weisberg and Saffran (1989; see also Persson 1995:52-53,
127-130) showed that words sharing both semantic and phonological similarity were
vulnerable to substitutions. In the semantic fluency task, in which semantically related
words are to be named, words sharing both semantic and phonological features tend
to be produced (e.g., ‘pantteri’, ‘panda’, ‘puuma’/ ‘panther’, ‘panda’, ‘puma’ in Laine
1989:22 or ‘pomme’, ‘poir’ / ‘apple’, ‘pear’ in Roberts & Le Dorze 1994; see 5.3).
Furthermore, there is evidence to assume that semantic representations can be
decomposed into different types of features (e.g., Smith et al. 1974; Barsalou et al.
1982, 1983; Dell 1986). Taking these arguments into account, and considering a
theory’s possibilities to explain errors occurring during word production, the twostage interactive activation models appear to be architectures of choice in word
production studies, including the present study (see 10.1.3).
4.2 Two-stage interactive activation models
Similarly to the discrete two-stage model, the interactive, connectionist two-stage
word production models (e.g., Dell 1986; Dell & O’Seaghdha 1991, 1992; Martin et
al. 1994; Persson 1995; Dell et al. 1997; Foygel & Dell 2000), distinguishes meaning
vs. form retrieval in word production. However, the semantic representations of
words are decomposed into semantic features (see 3.2). The models are serial in the
sense that at onset, the semantic activation is followed by phonological activation,
and the output is likely to follow a certain order defined by the grammatical, morphophonological, and articulatory regularities. Above all, however, the models are
interactive, because the network allows a simultaneous and bi-directional excitatory
spread of activation in and across the levels during the word formation process. As a
consequence, the system allows interaction between semantic and phonological levels,
leading to an overlap of different functional stages during the word retrieval. The
crucial difference between the models concerns the extent of the activation between
the semantic and lexical level and the point of inhibition of the semantic competitors
(see the discussion in Persson 1995:127).
During the semantic feature encoding, as soon as fragments of the targetrelated information have become activated by an external stimulus (e.g., an object),
the semantic memory system starts encoding (Persson 1995:64-67). In a well working
system, the semantic features start exciting each other until a whole set of features
becomes simultaneously and quite automatically excited. According to the model of
Dell et al. (Dell 1986; Dell & O’Seaghdha 1991, 1992; Martin et al. 1994; Dell et al.
1997; Foygel & Dell 2000), activation from all the excited semantic features spreads
to the lexical (lemma) network to prime the target (e.g., ‘cat’), semantically related
nodes (e.g., ‘dog’), as well as semantically and phonologically related nodes (e.g.,
‘rat’), from which activation is fed back to the semantic network to reinforce the
54
activated semantic features. Activation also continues to spread forward to the
phonological network where both the primed and related segments and phonemes,
such as onsets /d/, /f/, /k/, /l/, /m/, and /r/, vowels /ae/, /o/, and codas /g/, /t/ are
triggered. Activation is fed back to the lexical network, which also receives
reverberating feedback from the activated semantic features, leading all the related
lexical nodes to be more or less activated (e.g., ‘fog’, ‘log’, ‘dog’, ‘cat’, ‘mat’, ‘rat’)
and the lexical selection to take place. At this point, the node with the strongest
activation gets selected, phonologically and phonetically encoded, and articulated
as a word.
In the Parallel Distributed Lexical Processing model (Persson 1995: 64-67,
125-130), the semantic structure of the target word becomes rapidly built-up and
disambiguated during the semantic feature encoding, requiring that the target structure gains most activation, and that the activation settles on the target, stops flickering, and ceases triggering features of less relevant non-targets, whereby the activation of the semantic competitors decays. Information from only the target semantic
entity spreads down to the morpho-phonological level of information, exciting the
word’s initial syllabic and segmential information. Subsequently, activation from
these early morpho-phonological probes (i.e., after the word onset) spreads back to
the semantic level, where the semantic feature encoding is then completed. Once the
entire semantic feature pattern has become selected, activation spreads back down
to the morpho-phonological level, when the rest of the morpho-phonological information is encoded and the word’s form is completed. Thus, the semantic information supports the encoding of the word’s final form and the successful word production. The purpose of early inhibition of non-targets is to avoid noise and to reduce
interference and errors in the system (see also Levelt 1999; Levelt et al. 1991, 1999).
Relative to other word production models, the advantage of multiple activation
at all three levels in the interactive models can be considered in their potential to
explain various types of speech errors occurring in and between the different phases
of word processing (Dell 1986; Martin et al. 1994; Schwartz, Saffran, Bloch & Dell
1994; Laine & Martin 1996; Dell, Burger & Svec 1997; Dell, Schwartz et al. 1997;
Foygel & Dell 2000; Persson 1995; see 10.1.3). On the other hand, the relevance of
the models that are based on speech error analysis in describing normal word
production, in which the error rate is very low, has been questioned by proponents
of, for example, chronometric models (Levelt et al. 1991a). A newer version of the
interactive word production model presented by Foygel and Dell (2000; see also
Dell, Schwartz et al. 1997) assumes that the semantic-lexical connections accounting
for the meaning component for one, and the lexical-phonological connections
accounting for the form component for the other, are different functional processes,
each of which can be damaged independently of each other, for example, in aphasia.
5 Semantic fluency performance in
elderly adults and Alzheimer’s patients
A generative naming task, also known as the verbal fluency task, is a commonly
used experimental method to investigate spoken word production. More specifically,
it is used to measure mental processing speed (Lafosse et al. 1997), the organization
and function of the lexical-semantic systems, as well as the ability of the speaker to
rapidly access these systems (Joanette & Goulet 1986; Huff 1988; Hodges & Patterson
1995). Verbal fluency has been explored, for example, in the developing (Lucariello,
Kyratzis & Nelson 1992; Cohen, Morgan, Vaughn, Riccio & Hall 1999; Riva, Nichelli
& Devoti 2000; Kaleva & Vanhala 2001) and ageing brain (Crossley, D’Arcy &
Rawson 1997; Troyer et al. 1997; Capitani, Laiacona & Barbarotto 1999), as well as
in psychiatric diseases, such as schizophrenia (Allen, Liddle & Frith 1993; Crowe
1996). The verbal fluency task has been a very popular method for investigating the
change in cognitive functioning in various neurological conditions, such as aphasia
(e.g., Roberts & Le Dorze 1994; Holappa & Nauha 2000), multiple sclerosis (MS;
Tröster et al. 1998), and amyotrophic lateral sclerosis (ALS; Abrahams, Leigh,
Harvey, Vythelingum, Grisé & Goldstein 2000). It is also commonly used in studying
the effects of some dementing diseases on cognitive processing, such as Parkinson’s
disease (PD), Huntington’s disease (HD; e.g., Randolph, Braun, Goldberg & Chase
1993; Tröster et al. 1998), and Alzheimer’s disease, which has been the main focus
of a wealth of studies (see 5.1). Fluency tasks are thus in wide linguistic and
neuropsychological use, both in clinical and experimental work (Capitani et al. 1999).
They are included in the standardized language assessment batteries as part of the
evaluation of the language production abilities, for example, the Boston Diagnostic
Aphasia Examination (BDAE; Goodglass & Kaplan 1972) and the Western Aphasia
Battery (WAB; Kertesz 1982), and incorporated into some dementia screening
batteries (e.g., Mattis 1976).
In the verbal fluency task, a person is asked to orally generate as many words
as possible according to a cue or specified rules in a limited time range, the period
most typically being 60 seconds. There are several kinds of verbal fluency tasks:
The phonemic (letter) fluency task involves words that begin with a specific letter,
such as ‘A’, ‘C’ ‘F’, ‘L’, ‘P’, or ‘S’ (e.g., Rosen 1980; Laine 1989:4-5, 20-21; Capitani,
56
Semantic fluency
Laiacona & Basso 1998). In the semantic fluency task, which is the focus of the
present study, subjects are asked to generate words that belong to a certain semantic
category, such as animals and vegetables (e.g., Diesfeldt 1985; Laine 1989:20-22;
Troyer et al. 1997; Capitani et al. 1999). The task may also be executed non-verbally
by drawing the category items (Mickanin, Grossman, Onishi, Auriacombe & Clark
1994). Furthermore, another form of the semantic fluency task, the supermarket
fluency task, involves items found in a supermarket (e.g., Martin & Fedio 1983;
Ober, Dronkers, Koss, Delis & Friedland 1986).
In the phonemic fluency task, phonological processing is investigated (Rosen
1980; Capitani et al. 1998), whereas the semantic fluency tasks are used to investigate the retrieval from, and the organization of, the semantic memory (Martin &
Fedio 1983; Ober et al. 1986; Chertkow & Bub 1990; Monsch et al. 1992; 1994;
Rosser & Hodges 1994; Roberts & Le Dorze 1994; Crowe 1998). In particular, the
semantic fluency task is widely used to reveal word finding problems and deteriorated cognitive processes associated with word retrieval (Ruff, Light, Parker & Levin
1997).
In comparison with other fluency tasks, the semantic fluency task has appeared
to be more sensitive than the phonemic fluency task with regard to revealing difficulty
in word finding and differentiating patients with AD from healthy elderly normal
control subjects (Monsch et al. 1992; Monsch et al. 1994; Weingartner, Kawas,
Rawlings & Shapiro 1993; Mickanin et al. 1994; Hodges & Patterson 1995; Goldstein
et al. 1996; Carew, Lamar, Cloud, Grossman & Libon 1997; Crossley et al. 1997).
However, opposite findings have been reported by Bayles, Trosset, Tomoeda,
Montgomery, and Wilson (1993) and Suhr and Jones (1998) who reported on the
AD patients’ worse phonemic fluency task performance relative to that on the semantic
fluency task. Furthermore, AD patients have been shown to perform better on the
supermarket fluency task for which words are available from several different
semantic categories (e.g., fruit, vegetables, pastry, dairy products, etc.; Monsch et
al.1992), as well as on a task tapping non-semantic drawing of designs (Mickanin et
al. 1994) than on the traditional semantic fluency task (e.g., animals). Differences in
performing various types of fluency tasks may be caused by the specific aspects of
language they are tapping (e.g., phonological vs. semantic utilization), and by different
neural substrates (e.g., verbal vs. nonverbal generation; Mickanin et al. 1994; Crossley
et al. 1997).
5.1 Word production during the semantic fluency task
In spontaneous speech, word production is guided by semantic and syntactic information contained by words, syntactic frame (e.g., a clause with different slots the
words fill in a certain order), as well as by contextual cues (Dell 1986). Picture
naming, on the other hand, is primarily supported by a picture or by a drawing of an
object or action. In retrieving a name for a picture, the very first stage of the naming
Semantic fluency
57
process is the computation of the visual representation of, for example, an object,
followed by categorization of the object as a car, table, etc., after which the information is subjected to syntactic, morphological, and phonological encoding (Levelt
1989:222-234; see chap. 4). The semantic fluency task is different in the sense that
a retrieval cue or a semantic probe is verbally given that defines the semantic category (e.g., vehicle, furniture) that needs to be first identified before word production can be initiated (e.g., Diesfeldt 1985; Laine 1989:22, Troyer et al. 1997; Capitani
et al. 1999).
The semantic fluency task is a multifactorial, complex task that involves several
psycho-linguistic components. Categorization (Troyer 2000; see 3.1), availability
of intact semantic representations, and the ability to break down a semantic category
into subordinate categories and category members (Diesfeldt 1985; Laine 1989:1819; Binetti et al. 1995; Pasquier, Lebert, Grymonprez & Petit 1995; see 3.1.1, 3.1.2)
are crucial for performing the task. Flexible functioning of the whole mental lexicon
is required in the form of semantic feature selection, lemma retrieval, and morphophonological encoding, as well as articulation, and self-monitoring (Laine 1989:5;
Price et al. 1999; see chap. 4). Performing the task is not constrained by syntactic
structure or discourse planning, as far as production of nouns is concerned, but a
semantically and/or phonologically guided search for appropriate responses is
required (Laine 1989:5, 14, 18, 22). For an optimal performance, the role of working
memory and executive functions, in particular, are underscored (Rosen 1980;
Diesfeldt 1985; Laine 1989:5; Chertkow & Bub 1990; Auriacombe et al. 1993; Rosen
& Engle 1997; Ruff et al. 1997; Abrahams et al. 2000; Cohen & Stanczak 2000;
Bayles 2003).
Performing the semantic fluency task requires the functioning of working
memory which provides temporary storage and processing of information necessary
for complex cognitive tasks (Baddeley, Logie, Bressi, Della Sala & Spinnler 1986;
Baddeley, Bressi, Della Sala, Logie & Spinnler 1991, Baddeley 1992; Gathercole &
Baddeley 1993:1-23; see also Morris 1994; Rosen & Engle; Zec et al. 1999; Abrahams
et al. 2000; Rende, Ramsberger & Miyake 2002; Bayles 2003). In particular, the
role of the central executive component of the working memory to rapidly initiate a
systematic, effective, and attentional search for subcategories and words through
the semantic memory and to flexibly change semantic subcategories seems to be
essential when performing the task (see Rosen 1980; Diesfeldt 1985; Ober et al.
1986; Laine 1985, 1989:5, 13-14, 18-19, 22-25; Chertkow & Bub 1990; Allen et al.
1993; Della Sala, Lorenzi, Spinner & Zuffi 1993; Randolph et al. 1993; Monsch et
al. 1994; Pasquier et al. 1995; Rosen & Engle 1997; Ruff et al. 1997; Troyer et al.
1997; Zec et al. 1999). Working memory is also responsible for monitoring the word
retrieval processes to avoid repeating previously retrieved words and breaking the
rules set for the fluency task (Diesfeldt 1985; Laine 1989:5; Auriacombe et al. 1993;
Della Sala et al. 1993; Pasquier et al. 1995; Rosen & Engle 1997; Ruff et al. 1997;
see also Levelt 1989:463-467, 1999a, b). Semantic fluency performance may also
58
Semantic fluency
be influenced by nonverbal factors, such as imagery and ability to generate visual
images (Diesfeldt 1985; Chertkow & Bub 1990; Mickanin et al. 1994; Rende et al.
2002; see also Baddeley et al. 1986, 1992; Gathercole & Baddeley 1993:17-22).
Word production and factors affecting semantic fluency performance
Traditionally, semantic fluency tasks are analyzed by counting the sum of correct
responses for each semantic category and by comparing the scores among different
subjects (Roberts & Le Dorze 1994; Crowe 1998). Evidence on the semantic fluency performance of healthy elderly adults representing different cultures or languages (see Table 4) revealed a relatively wide range in the number of the words that
were produced for different categories. For example, in the semantic fluency task
with naming words for the category of animals (for 60 seconds), which is the most
often studied semantic category, on the average 14.1 words were produced by an
Italian-speaking group of subjects (Binetti et al. 1995), 14.2 and 17.8 words by two
Canadian English groups (Crossley et al. 1997; Troyer et al. 1997), 19.3 words by
an American English group (Chertkow & Bub 1990; see also Hodges et al. 1992),
22.4 words by a Finnish group (Kontiola et al. 1990), and 26 words by a French
group (Pasquier et al. 1995), the subjects’ mean age being between 65 and 74 years.
Performance on the semantic fluency task also shows great heterogeneity
among AD patients (see Table 4). There exists a number of studies indicating that
AD patients in different phases of the disease generated significantly fewer words in
the semantic fluency task than normal control subjects across several semantic categories (e.g., Rosen 1980; Appell et al. 1982; Ober et al. 1986; Tröster, Salmon,
McCullough & Butters 1989; Mickanin et al. 1994; Binetti et al.1995; Crossley et
al. 1997; cf. Fischer, Gatterer, Marterer & Danielczyk 1988). In the animal fluency
task, a marked impairment in the ability of AD patients to generate exemplars for
that category was discovered. Chertkow and Bub (1990) noticed that AD patients
listed overall only 40% as many animal names as the controls, the proportion being
51% in the study by Cronin-Golomb et al. (1992).
Studies in which the animal fluency task was employed and the severity of
dementia was assessed with the MMSE (see Table 4), significantly more words
were produced by normal control subjects (e.g., 14.1 words in Binetti et al. 1995,
17.5 words in Hodges & Patterson 1995, and 17.7 words in Carew et al. 1997)
relative to patients with minimal dementia (14.0 words in Hodges & Patterson 1995),
and patients with mild dementia (6.7 words in Carew et al. 1997, 8.3 words in Binetti
et al. 1995, and 9.5 words in Hodges & Patterson 1995). Respectively, the performance
of the moderately demented subjects (4.5 words in Hodges & Patterson 1995), and
the severely demented subjects (3.5 words in Binetti et al. 1995) were also
significantly poorer than that of healthy elderly control subjects. It was also reported
that AD patients with very mild dementia tended to perform better than mildly
demented subjects, who in turn produced significantly more words than moderately
(Mickanin et al. 1994; Hodges & Patterson 1995; Crossley et al. 1997) and severely
Semantic fluency
59
demented AD subjects (Binetti et al. 1995). In the study of Randolph et al. (1993),
the outcome of the AD patients was poorer even in the cued version of the task in
which retrieval cues in terms of subcategories (e.g., pets, farm animals, jungle and
water animals) were provided.
It was pointed out in some studies that common words were retrieved before
uncommon ones by most of the normal control subjects and AD patients, and that
they tended to be recalled early in the retrieval period (Rosen 1980; Diesfeldt 1985;
Ober et al. 1986). It was also shown that, on average, AD patients produced words in
a significantly higher level of prototypicality (Beatty et al. 2000) and mean lexical
frequency (Weingartner et al. 1993; Binetti et al. 1995) than normal control subjects
during the course of the whole semantic fluency task. Some other studies, however,
failed to observe such differences (Ober et al. 1986; Chan et al. 1993; Goldstein et
al. 1996; see also Binetti et al. 1995).
Rosen (1980; see also Butters, Granholm, Salmon, Grant & Wolfe 1987) found
that, in the animal fluency task (60 s), healthy control subjects, as well as mildly and
moderately-to-severely demented AD subjects, retrieved most of the words during
the first 15-second interval. The normal control subjects produced more words than
the mild and the severe AD groups during the first interval, as did the mildly demented
AD patients relative to the moderately-to-severely demented AD patients. During
the second interval, the only difference in the number of words emerged between
the normal controls and the moderately-to-severely demented AD subjects. In a similar
setting, Ober et al. (1986) found that the rate of production of correct responses
differed among the groups, the asymptote occurring earliest for the moderate-to
severe AD subjects, then for the mild AD subjects, and last for the healthy controls.
The semantic fluency task was shown be significantly affected by advancing
age among normal control subjects (Tröster et al. 1989; Crossley et al. 1997; Troyer
et al. 1997; Troyer 2000; Capitani et al. 1999). Older participants were found to
produce fewer words than younger participants (Butters et al. 1987; Tomer et Levin
1993; Tröster et al. 1989; Troyer et al. 1997; Troyer 2000). In the animal fluency
task, Troyer et al. (1997) observed that both younger and older participants formed
clusters consisting of two semantically related words belonging to the same subcategory of animals (e.g., ‘dog’, ‘wolf’), but the older subjects were able to generate
fewer animal names and to activate fewer semantic dimensions for subcategories in
the animal category, implying a less effective retrieval strategy among the older participants. Among AD patients, age failed to explain reductions in verbal fluency (Huff
et al.1986; Bayles et al. 1993; Mickanin et al. 1994; Crossley et al. 1997). In a one
year follow-up study conducted by Hodges et al. (1990), it was observed that the
rapid decline in the AD patients’ performance on measures of semantic memory could
not be explained by ageing because age-matched normal controls showed no decline
in fluency performance over the course of the study. Rather than advancing age,
neurogenerative effects of AD were assumed to cause the decline in semantic fluency
(Huff et al.1986; Bayles et al. 1993; Mickanin et al. 1994; Crossley et al. 1997).
animals
supermarket
items
a. clothes
Rosen 1980
Martin & Fedio 1983
Diesfeldt 1985
(Dutch)
vehicles,
vegetables,
tools,
clothes +
a. colors
b. animals
c. towns
d. fruit
supermarket
items
a. animals
b. vehicles
c. fruit
d. body parts
e. tools
f. furniture
g. clothing
Huff et al. 1986
Hart et al. 1988
Tröster et al. 1989
Chertkow & Bub 1990
b. fruit
3 min +
Semantic
category
Study
(language, other than
English)
10
20
12
18
24
11
10
73.0
70.4 (6.23)
69.8 (6.2)
62.0
83.0 (4.1)
61.5
83.6
NC
NC
n
mean age (SD)
MMSE
a. 16.1
b. 12.0
c. 12.0
d. 21.2
e. 13.2
f. 15.1
g. 18.7
19.35 (0.8)
a. 13.1 (2.1)
b. 8.0 (3.3)
c. 19.9 (5.0)
d. 14.5 (3.8)
52.6 (9.6)
b. 16.9 (4.7)
a. 25.4 (6.7)
22.7
11.3
NC
mean number
of words (SD)
10
MMSE
17.3
miAD 20
moAD 20
15
15
miAD 12
moAD 10
seAD 1
66
moAD 44
seAD 22
14
miAD 10
mo-seAD 10
AD
n
severity of
dementia#
MMSE
76.3
72.2 (8.1)
70.6 (6.7)
68.3 (4.0)
65.3
80.5 (6.4)
58.2
85.2
84.8
AD
mean age (SD)
a. 5.7 *
b. 5.0 *
c. 5.3 *
d. 8.8 *
e. 4.2 *
f. 3.1 *
g. 6.5 *
12.55 (3.97) *
8.95 (3.07) *
a. 7.1 (2.7) *
b. 7.3 (2.9) *
c. 4.7 (2.6) *
d. 6.2 (1.9) *
23.9 (16.0) *
b. 4.2 (2.6)*
a. 5.5 (4.0)*
11.4 *
8.4 *
1.7 *
AD
mean number of
words (SD)
semantic impairment (loss) and semantic
search, affected by attention problems,
motivation and inefficient strategies, no visual
deficits
disruption in the structure of semantic
knowledge
impaired access to lexical information
loss of specific information required to
distinguish among members of a category,
associated with difficulty in naming
-impaired knowledge of more specific attributes
of concepts / retrieval
-weakened thought and search strategy
-impaired short-term memory capacity
-slowness of search through long-term store
-increased arousal level
specific disruption in the organization of
semantic knowledge (loss or access) and
relative preservation of broader categorical
information
impaired retrieval
Hypothesized cause of the impairment
Table 4. Studies on the semantic fluency in normal control subjects (NC) and patients with Alzheimer’s disease (AD)
60
Semantic fluency
a. animals
b. birds
c. water
creatures
d. dogs
e. houshold items
f. vehicles
g. musical instr.
h. boats
a. animals,
fruit, and
vegetables
3 min +
Hodges et al. 1992
Monsch et al. 1992
Monsch et al. 1994
a. living
categories
(animals, birds,
water creatures) +
Rosser & Hodges 1994
animals,
fruit/
vegetables
3 min +
b. man made
categories
(household
objects, vehicles,
musical
instruments)
animals
Chan et al. 1993
b. supermarket
items
animals
Kontiola et al. 1990
(Finnish)
Table 4 (continued)
72.3 (5.8)
65.3 (9.1)
44
25
24
70.0 (10.6)
69 (8.4)
70.0 (6.2)
53
71.2 (7.9)
MMSE
28.8 (1.1)
26
86
48.9 (8.4)
b. 55.1 (8.5)
a. 57.4 (12.7)
19.25 (5.4)
b. 22.8 (4.7)
a. 48.4 (9.8)
a. 19.7 (4.5)
b. 14.1 (4.5)
c. 13.0 (3.6)
d. 10.2 (4.1)
e. 19.8 (5.7)
f. 13.9 (4.8)
g. 14.0 (3.8)
h. 11.6 (3.4)
22.41 (5.5)
44
10
46
89
MMSE
18.0 (5.0)
22
33
71.5 (6.5)
67.2 (9.9)
71 (7.7)
72.1 (6.6)
69.5 (5.4)
70.0 (10.4)
21.6 (8.2) *
b. 32.5 (9.6) *
a. 31.1 (6.1) *
10.7 (4.37) *
b. 8.4 (4.9) *
a. 16.2 (9.6) *
a. 9.9 (4.1) *
b. 5.4 (2.8) *
c. 4.4 (2.6) *
d. 3.2 (2.0) *
e. 9.1 (3.8) *
f. 6.9 (3.0) *
g. 6.5 (2.6) *
h. 4.4 (2.9) *
9.5 (4.4) *
Table 4 continues
breakdown in the structure of the semantic
knowledge
breakdown of semantic knowledge
breakdown in the structure of semantic
knowledge
deterioration of the semantic knowledge system
semantic breakdown caused by storage
degradation; differential impairment across the
hierarchy of semantic knowledge (preserved
superordinate knowledge but impaired lower
level knowledge)
general intellectual impairment and memory
disorder
Semantic fluency
61
animals
Binetti et al. 1995
(Italian)
e. househ. items
f. vehicles
g. musical instr.
h. boats
Hodges & Patterson 1995 a. animals
b. birds
c. water animals
d. dogs
Semantic
category
Study
(language, other than
English)
Table 4 (continued)
24
MMSE
29.1 (1.0)
35
MMSE
28.0 (1.8)
NC
n
69.7 (7.8)
67.4 (7.3)
NC
mean age
(SD)
e. 17.4 (3.4)
f. 11.7 (2.6)
g. 15.3 (3.4)
h. 10.8 (3.3)
a. 17.5 (3.9)
b. 15.9 (4.5)
c. 14.0 (6.7)
d. 10.8 (3.4)
14.1 (5.4)
NC
mean number
of words (SD)
e. 10.0 (4.5) *
f. 6.9 (2.7) *
g. 7.1 (3.9) *
h. 4.4 (3.2) *
e. 3.3 (2.9) *
f. 3.1 (1.9) *
g. 2.1 (2.4) *
h. 1.4 (1.8) *
moAD
a. 4.5 (3.5) *
b. 3.1 (2.8) *
c. 1.3 (1.8) *
d. 1.2 (1.3) *
miAD
63.4 (8.8)
moAD 18
MMSE
10.0 (4.6)
a. 9.5 (4.3) *
b. 6.7 (3.6) *
c. 4.9 (4.6) *
d. 4.7 (2.2) *
e. 11.1 (4.1) *
f. 8.2 (3.9) *
g. 8.0 (1.7) *
h. 6.3 (2.9) *
67.0 (8.3)
miAD 17
MMSE
20.9 (1.7)
a. 14.0 (4.1) *
b. 7.9 (3.1) *
c. 6.7 (3.9) *
d. 5.8 (2.2) *
3.5 (2.4) *
8.3 (3.6) *
AD
mean number of
words (SD)
minAD
72.2 (7.2)
71.3 (9.8)
seAD 30
MMSE
12.3 (2.6)
minAD 17
MMSE
25.6 (1.8)
69.7 (7.8)
AD
mean age
(SD)
miAD 40
MMSE
21.4 (2.7)
AD
n
severity of
dementia #
task difficulty, a central semantic deficit
damage to subordinate categories /
degradation of semantic knowledge
Hypothesized cause of the impairment
62
Semantic fluency
animals
food, 30s
vegetables, 30s +
animals
animals
animals,
fruit/vegetables
tools/kitchen
utensils +
animals
animals
Beatty et al. 1997
Monsch et al. 1997
(Swiss-German)
Crossley et al. 1997
(English and French)
Carew et al. 1997
Suhr & Jones 1998
Troyer, Moscovitch,
Winocur, Leach et al.
1998
Tröster et al. 1998
30
38
25
31
MMSE
28.8 (1.1)
635
50
MMSE
28.4 (1.4)
38
MMSE
28.7 (1.6)
10
70.8 (7.0)
73.8 (6.2)
67.9 (12.9)
17.8 (4.1)
17.9 (4.2)
50.9 (11.9)
17.7 (4.7)
13.8 (4.0)
78.8 (6.8)+
76.4 (6.6)
22.1 (5.0)
18.0 (4.9)
26
71.9 (7.2)
73.7 (8.7)
66.8 (8.1)
30
23
31
40
MMSE
22.2 (3.0)
154
miAD 46
moAD 108
50
MMSE
24.2 (3.1)
35
MMSE
18.5 (5.3)
10
MMSE
23.1 (5.2)
69.7 (5.9)
70.3 (8.4)
75.5 (5.6)
76.8 (5.9)
82.5 (5.9)+
72.4 (7.1)
76.5 (8.2)
68.2 (8.9)
7.6 (4.6) *
8.3 (4.2) *
30.1 (13.7) *
6.7 (2.5) *
9.5 (3.5) *
8.1 (3.5) *
13.6 (4.1) *
7.4 (4.7) *
17 *
inefficiency of access to lexical and semantic
memory stores
impoverished semantic memory or deficient
search processes within semantic memory
none
overall dissolution of semantic knowledge (loss
or degradation)
impaired semantic knowledge / limited capacity
store
impaired structures of semantic knowledge
degraded structure of the semantic memory;
deterioration of mechanisms that govern
initiation of search for appropriate
subcategories
dysfunction of one or several complex
processes (e.g., loss of semantic knowledge,
inadequate activation of the concept, reduced
retrieval, lack of systematic strategy, reduced
short-term memory capacity)
Note. MMSE = average scores on the Mini Mental State Examination (Folstein et al. 1975). +scores summed.
#
= severity of dementia in AD: minAD = minimal, miAD = mild, moAD = moderate, mo-seAD = moderate to severe, se = severe.
* AD patients performed significantly worse than control subjects.
animals
Pasquier et al. 1995
(French)
Table 4 (continued)
Semantic fluency
63
64
Semantic fluency
Education was found to be a sensitive factor in semantic fluency performance,
affecting the increase in performance the more educated the normal elderly subjects
were (Crossley et al. 1997; Capitani et al. 1999; Troyer 2000; cf. Ratcliff, Ganguli,
Chandra, Sharma, Belle, Seaberg & Pandav 1998 for the relatively high scores of
the uneducated and illiterate Haryanvi speakers). In AD, education did not covary
with semantic fluency performance (Rosen 1980; Crossley et al. 1997).
Monsch et al. (1992) reported that female participants fared significantly better
than male participants when generating words for the semantic fluency task in which
animals, fruit and vegetables were used as the semantic categories. The gender effect
was found both in the group of normal healthy elderly adults, as well as in the group
of AD patients. In the same vein, Capitani et al. (1999) found that gender had a
significant effect on the fluency performance but only in the categories of fruit and
tools. In their study, male participants produced significantly fewer words for fruit
but significantly more words for tools than female participants. Capitani et al.
explained that the gender effect could be due to male and female subjects’ different
experiences, habits, and occupation influencing the recall of words rather than either
gender having a larger semantic store per se. In contrast, Troyer (2000) found no
difference between male and female participants in the animal and supermarket
fluency performance. Likewise, Crossley et al. (1997) noticed that the semantic
fluency performance of AD patients on animals was gender-insensitive.
The performance of the AD patients on the semantic fluency task appears to
be significantly correlated with severity of dementia (Huff et al. 1986; Bayles et al.
1993; Mickanin et al. 1994; Hodges & Patterson 1995; Crossley et al. 1997), regardless of the methods used to evaluate severity of dementia and the semantic categories employed. Higher scores on the MMSE measuring the severity of dementia
were found to be associated with more correct responses in the task (Bayles et al.
1993; Mickanin et al. 1994; Crossley et al. 1997; cf. Shuttleworth & Huber 1988).
Hodges and Patterson (1995) demonstrated a significant difference in the semantic
fluency performance between AD patients with minimal (MMSE = 25.6), mild
(MMSE = 20.9), and moderate (MMSE = 10.0) dementia. However, their study also
indicated that AD patients with minimal and mild dementia were difficult to distinguish from each other when very specific semantic categories, such as birds, dogs,
vehicles, and musical instruments were used. Furthermore, Hodges and Patterson
(1995; see also Della Sala et al. 1993) found great heterogeneity in the performances
of the AD patients, especially in the mildest cases. A significant group difference in
the semantic fluency performance was found also between mild (MMSE = 23.7)
and moderate (MMSE = 17.5) AD patients (Mickanin et al. 1994).
Semantic fluency task has previously been mainly used to study the semantic
representations of nouns, whereas the semantic representations of verbs have so far
remained almost unexplored by this particular method. Two studies have employed
an action fluency task in PD patients with and without dementia, as well as normal
control subjects, who were asked to produce verbs under the instruction of “differ-
Semantic fluency
65
ent things people can do” (Piatt, Fields, Paolo, Koller & Tröster 1999; Piatt, Fields,
Paolo & Tröster 1999).
A variant of the semantic fluency task, however, has been used by asking AD
patients to generate script-like events to study their access to semantic memory and
its knowledge representation system, as well as its degradation (for a description of
scripts, see 3.3.4). Weingartner et al. (1983) reported that AD patients were impaired
both in the traditional category fluency task with four-footed animals and vegetables
and in sequencing activities of a complex event script. AD patients had difficulty in
arranging the subevents of a script of eating in a restaurant in an appropriate temporal
order. Their order errors tended to occur particularly on those events that were
temporally close to each other rather than far apart. When asked to produce activities
to the question “What are the things you would do after getting up in the morning?”,
AD patients generated significantly fewer and more frequent types of activities than
normal control subjects. Based on their findings, Weingartner et al. concluded that
AD patients’ access to semantic memory was impaired and that they, therefore, had
great difficulty in organizing and encoding semantic information (see chap. 6).
Grafman et al. (1991) replicated the study and confirmed the finding that some
activities produced by AD patients for the script ”all the things that you do when
you get up in the morning till you leave the house or have lunch” fell outside the
temporal boundaries of the script or were inappropriate (e.g., ‘going-shopping’).
Furthermore, some of the script events were inappropriately repeated. Grafman et
al. concluded that, either due to a structural degeneration or a processing problem,
AD patients presented a breakdown in script production.
The traditional scoring of the semantic fluency task, that is, counting the sum
of correct responses has been claimed to be insufficient to distinguish fluency
performance among different dementia groups (Troyer, Moscovitch, Winocur, Leach
et al. 1998) or focally lesioned patients with different lesion loci (Troyer, Moscovitch,
Winocur, Alexander & Stuss 1998), and it may not necessarily reveal whether subjects’
problems lie in the slowed production, the retrieval deficit, or the degradation of the
semantic memory (Raskin, Sliwinski & Bodor 1992; Della Sala et al. 1993; see
chap. 6). Merely looking at the number of correct responses in the semantic fluency
task may not be enough to highlight the semantic representations of the words or the
word retrieval processes (Roberts & Le Dorze 1994; Troyer et al. 1997). It is important
also to analyze and compare the qualitative aspects of semantic retrieval performance
among different subject groups, for example, the strategies of exploiting the semantic
field (Laine 1989:18-19, 22-23; Troyer et al. 1997; Troyer, Moscovitch, Winocur,
Alexander et al. 1998; Troyer, Moscovitch, Winocur, Leach et al. 1998), the rate of
responding (Rosen 1980; Laine 1989:22-24), the nature of correct responses in terms
of semantic associations (Allen et al. 1993; Binetti et al. 1995; Roberts & Le Dorze
1997), and the type of errors made during the task (Ober et al. 1986; Laine 1989:22;
Bayles et al. 1993; Binetti et al. 1995; Pasquier et al. 1995; Carew et al. 1997; Suhr
& Jones 1998; Tröster et al. 1998).
66
Semantic fluency
5.2 Clustering and switching
Performing the semantic fluency task does not occur at random, but in an organized
fashion (Laine 1989:18-19, 22-25). Successful retrieval of words belonging to different semantic categories depends on systematic and efficient retrieval strategies,
as well as on intactness of the information to be retrieved from the semantic memory
(Martin & Fedio 1983; Diesfeldt 1985; Ober et al. 1986; Butters et al. 1987; Laine
1989:5; Chertkow & Bub 1990; Hodges et al.1990; Monsch et al. 1992, 1994; Rosser
& Hodges 1994; Roberts & Le Dorze 1994; Hodges & Patterson 1995; Binetti et al.
1995; Pasquier et al. 1995). Although the semantic fluency task is widely used in the
assessment of language functions, there are only a few studies with a more thorough
description on the strategies involved during the word retrieval. However, knowledge concerning those strategies is limited to the production of nouns, mainly to the
category of animals and items available in a supermarket.
First, an access to the intact semantic memory or the semantic layer of the
mental lexicon is required. More specifically, access to the information corresponding to the semantic categories, as well as to the semantic features defining the category items, is necessary for performing the task (Schwartz et a. 1979; Rosen 1980;
Martin & Fedio 1983; Diesfeldt 1985; Chertkow & Bub 1990; Randolph et al. 1993;
Price et al. 1999; Troyer 2000). Second, a prototype (a typical item) or a very frequently occurring word of the category is likely to be first activated (e.g., ‘cat’ for
the category of animals), from which activation automatically spreads to closely
related semantic neighbours (e.g., ‘dog’; Rosen 1980; Diesfeldt 1985; Ober et al.
1986; Hodges et al. 1990; Rosser & Hodges 1994; Crowe 1998; see chap. 4). Subsequently, after using the most prototypical cases, a more active search of category
items is likely to be initiated, and an entrance into subcategories takes place (e.g.,
farm animals, birds, sea animals; Rosen 1980; Laine 1989:18; Crowe 1998). An
ability to generate an effective strategy, for example, an inner visualization of a
search set, such as a wardrobe or a dress shop for clothes, and a fruit desk or a
greengrocer shop for fruit, plays a role in retrieval and use of semantic information
(Diesfeldt 1985; Chertkow & Bub 1990; Randolph et al. 1993; Mickanin et al. 1994;
Capitani et al. 1999; Rende et al. 2002; see also Baddeley et al. 1986, 1992; Gathercole
& Baddeley 1993:17-22).). A systematic strategy reduces the likelihood of repetitions to emerge during the word retrieval (Diesfeldt 1985; Pasquier et al. 1995).
Gruenewald and Lockhead (1980) were among the first to introduce a twostage component model of the semantic fluency performance that was later
operationalized by other researchers (e.g., Laine 1989; Troyer et al. 1997; Mayr &
Kliegl 2000). For one, performing the task involves switching, that is, an effective
search for different semantic fields or subcategories and a change from one semantic subcategory to another. For the other, clustering includes production of words in
the subcategories once they are identified (Gruenewald & Lockhead 1980; Laine
1989:13-14, 18-19; Troyer et al. 1997). The search is not only for individual words
per se but also for a collection or cluster of words associatively or semantically
Semantic fluency
67
related to the particular semantic field (Gruenewald & Lockhead 1980; Rosen 1980;
Martin & Fedio 1983; Chan et al. 1993; Troyer et al. 1997).
According to Troyer et al. (1997; Troyer, Moscovitch, Winocur, Alexander et
al. 1998; Troyer 2000; see also Grunewald & Lockhead 1980; Beatty et al. 1997,
2000; Rich, Troyer, Bylsma & Brandt 1999), clustering and switching are dissociable
components of semantic fluency performance. They consider clustering to be semantic
processing related to the temporal lobe functioning, whereas switching involves
executive functions, such as flexibility in shifting from one subcategory to another
that is processed in the frontal regions of the brain. On the other hand, according to
Mayr and Kliegl (2000; see also Mayr 2002), the whole semantic fluency
performance, including clustering and switching, is predominantly semantic
processing and executive processes are likely to be accompanied in each act of word
retrieval, not just during between-cluster switches.
Clustering and switching determine the number of total words produced for
the task. Thus, larger cluster size would be associated with less switching, and vice
versa. Hence, an optimal fluency performance requires a balance between clustering
and switching (Laine 1989:14, 19; Troyer et al. 1997; Troyer 2000), although the
ability to produce clusters of semantically meaningful responses as such is
independent of the ability to search and shift from one cluster to another (Gruenewald
& Lockhead 1980; Carew et al. 1997). During the task, words are not produced
evenly in time but in spurts (Gruenewald & Lockhead 1980). Most of the words are
produced during the first 15 seconds, after which the word retrieval slows down and
more atypical and infrequently occurring words are retrieved (Rosen 1980; Ober et
al. 1986; Butters et al. 1987; Laine 1989:29; Crowe 1998). Generating words in a
certain subcategory should take place rather automatically and for as long as a
relatively fast pace can be maintained, after which a new search and a switch to
another subcategory should occur (Laine 1989:19). Switching from one subcategory
to another elicits longer pauses between produced words than retrieval in a
subcategory does (Gruenewald & Lockhead 1980, Laine 1989:23-24; Troyer et al.
1997; Troyer 2000; Carew et al. 1997). The study of Troyer et al. (1997) indicated
that younger Canadian English-speaking adults (mean age 22.3 years), when
producing approximately 21.8 (SD = 5.7) animal names, switched on average 10.6
(SD = 3.5) times between the subcategories and produced a mean of two words per
cluster, whereas the older adults with the same background (mean age 73.3 years)
produced approximately 17.8 (SD = 4.2) animal names, switched 8.5 (SD = 2.3)
times between the subcategories, and formed clusters of the same size as the younger
participants (see 5.1).
The way AD patients perform on the semantic fluency task seems to differ
from the way normal participants execute the task. AD patients were reported to
produce less and smaller clusters than normal control subjects (Rosen 1980; Martin
& Fedio 1983; Ober et al. 1986; Tröster et al. 1989; 1998; Binetti et al. 1995, Carew
et al. 1997, Troyer et al. 1998b; Beatty et al. 1997, 2000). In the supermarket fluency task, Martin and Fedio (1983), Ober et al. (1986), Tröster et al. (1989), and
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Semantic fluency
Beatty et al. (2000) found that those AD patients who produced the smallest number
of items also generated the fewest words per subcategory. Tröster et al. (1989; see
Tröster et al. 1998) indicated that both mild AD and moderate AD subjects clustered
less words in subcategories than normal control subjects, but, as expected, the performance of mild AD patients was less severely defected than that of moderate AD
patients. Ober et al. (1986) reported similar findings on the supermarket fluency
task between normal control subject group, the group of mild AD patients, and the
group of moderate-to-severe AD patients.
In the animal fluency task, Binetti et al. (1995) discovered that among Italianspeaking subjects, the total number of clusters was significantly decreased in the
group of moderately-to-severely demented AD patients, compared with the normal
control subjects and patients with mild AD. In their study, 86% of the normal controls,
58% of the mild AD group, and 24% of the severe AD group used the strategy of
clustering together semantically closely related words when executing the task.
However, all the subject groups formed clusters of equal size of approximately four
words. Contrasting findings have been reported by Troyer, Moscovitch. Winocur,
Leach et al. (1998), Tröster et al. (1998), and Beatty et al. (1997, 2000). They observed
that the cluster size differed significantly between the group of AD patients (between
one and two words) and normal control subjects (between two and three words).
In the supermarket fluency task, Beatty et al. (2000) reported that AD patients
switched less often between subcategories than normal control subjects. The findings obtained also from the animal fluency task indicated that AD patients switched
less often between animal subcategories than normal control subjects (Tröster 1998;
Troyer, Moscovitch, Winocur, Leach et al. 1998; Beatty 1997, 2000). For example,
Troyer et al. reported 5.1 (SD = 2.9) switches per 8.3 (SD = 4.2) animal labels in
their group of mild AD patients, whereas healthy control subjects switched 8.3
(SD = 2.4) times per 17.9 (SD = 4.2) words. Respectively, Tröster et al. reported 3.8
(SD = 2.9) switches per 7.6 (4.6) correct words in their AD group, and 7.6 (SD = 2.6)
switches per 17.8 (SD = 4.1) correct words in the group of normal control subjects.
5.3 Activation of different associations and semantic
dimensions
In addition to analyzing the productivity in the semantic fluency task, that is, the
subjects’ ability to systematically generate items in semantic subcategories in the
test category, the content of the responses can also be analyzed. The semantic representation of words can be operationalized according to the type and the degree to
which words are organized in the semantic space (Robert & Le Dorze 1994, 1997;
Binetti et al. 1995; Troyer et al. 1997). The number of subcategory labels may be
used to measure the extent to which subjects organize their responses around subgroups such as types of wild animals or farm animals (Laine 1989:24-25; Roberts &
Le Dorze 1994, 1997; Binetti et al. 1995). The size of clusters and the percentage of
words in clusters are measures of the ability to activate words in the semantic sub-
Semantic fluency
69
categories and the strength of associative links in the semantic-lexical network (Roberts & Le Dorze 1994, 1997; Troyer et al. 1997; Troyer, Moscovitch, Winocur,
Alexander et al. 1998; Troyer, Moscovitch, Winocur, Leach et al. 1998; Rich et al.
1999).
Associations between words have been defined by various criteria in different studies. Words belonging to the same semantic subgroup sharing physical features (e.g., ‘tiger’ – ‘lion’), functional characteristics (e.g., ‘bus’ – ‘train’), or items
that are frequently associated with each other by thematic relations (e.g., ‘cheese’ –
‘crackers’) may form an association (Laine 1989:22; Roberts & Le Dorze 1994;
Binetti et al. 1995; Troyer et al. 1997; Troyer, Moscovitch, Winocur, Alexander et
al. 1998; Troyer, Moscovitch, Winocur, Leach et al. 1998; see 3.2). Sometimes words
are generated by their phonological similarity (e.g., ‘pantteri’, ‘panda’, ‘puuma’/
‘panther’, ‘panda’, ‘puma’ in Laine 1989:22 or ‘pomme’, ‘poir’ / ‘apple’, ‘pear’ in
Roberts & Le Dorze 1994; see also Rosen 1980; Troyer et al. 1997; Kaleva & Vanhala
2001; see 4.1, 4.2) or in alphabetical order (Tröster et al. 1989). The criteria for the
number of words that form a plausible association rather than a random combination of words vary among the studies. In some studies, a string of two successively
generated words sharing semantic similarity was considered a minimum for a cluster of words (Troyer et al. 1997; Troyer, Moscovitch. Winocur, Alexander et al.
1998; Troyer, Moscovitch, Winocur, Leach et al. 1998; Beatty et al. 1997, 2000;
Tröster et al. 1998; Rich et al. 1999), whereas in some other studies, the cluster
needed to consist of at least three subsequent words (Laine 1989:22-23; Binetti et
al. 1995; Roberts & Le Dorze 1997; Holappa & Nauha 2000; Kaleva & Vanhala
2001).
Research indicates that normal controls tend to generate clusters of words
from more subcategories than AD patients (Martin & Fedio 1983; Ober et al. 1986;
Tröster et al. 1989; Binetti et al. 1995; Beatty et al. 2000). Tröster et al. (1989; see
also Beatty et al. 2000) observed that normal control subjects produced significantly
more subcategories of supermarket items (e.g., fruit, vegetables, meats, fish, bread,
dairy products, household items) than AD patients, but that there was no difference
in the number of categories between mild AD and moderate AD patients. In the
study of Binetti et al. (1995), normal control subjects showed a great variability in
the types of clusters and produced several different subcategories in the animal fluency task. AD patients were more likely to produce clusters that mainly involved
farm animals, whereas other types of clusters were produced only to a very limited
extent. Rosen’s (1980) study on the animal fluency task gives support to the findings
that subcategory entrance may be already impaired in mild AD, while retrieval of
prototypes is affected little or not at all. Furthermore, retrieval of prototypes was
likely to be impaired and entrance to the subcategories was hardly possible for moderately-to-severely demented AD patients.
Hodges et al. (1992) reported a difference in the number of words produced
from different hierarchical levels between normal control subjects and AD patients.
Both groups generated the greatest number of basic level words belonging to broader
70
Semantic fluency
superordinate categories (animals and household items), and both used fewer items
from the subcategory level of the hierarchy (types of boats and breeds of dogs; see
3.1.2). However, the AD patients retrieved only half of the words produced by the
normal control group for the superordinate categories and only a third of the words
for the subcategories. In the studies of Martin and Fedio (1983) and Tröster et al.
(1989), it was found that AD patients tended to give superordinates rather than names
of particular items of the subcategories. An overrepresentation of superordinates
characterized the performance of mild and moderate AD patients (Tröster et al. 1989;
see also Beatty et al. 2000), whereas the names of the categories were rarely used in
the group of normal control subjects (Martin & Fedio 1983).
In the study of Chan et al. (1993), cognitive maps were created from a pool of
words produced in a semantic fluency task, in which the semantic distance among
animal names was measured. For example, animals with high conjunction frequency
(e.g., ‘cat’ and ‘dog’, ‘lion’ and ‘tiger’, ‘zebra’ and ‘giraffe’) were considered closely
linked, whereas animals that had less in common (e.g., ‘bear’ and ‘cow’, ‘cat’ and
‘sheep’) were far apart on the map. The cognitive maps turned out dissimilar between
normal control subjects and AD patients. New but abnormal associations between
words were formed. For instance, AD patients’ dimensions of domesticity and size
of animals contained anomalies (e.g., ‘bear’ was considered a domestic animal, and
small animals were considered big, and vice versa). Further, associations between
closely related items (e.g., ‘cat’ and ‘dog’) were weaker. The authors concluded that
the general semantic structure appeared to be breaking down in AD, although scattered
intact clusters were still observed (see chap. 6.). The results reported by Chan et al.
implied a significant qualitative deterioration in the relative saliency of semantic
features and in the semantic space in the category of animals. Carew et al. (1997)
also reported findings on the change of strength of fine-grained semantic associations
between responses in the animal fluency task. In their study, AD patients did not
produce contiguous responses that consisted of very specific, distinctive features.
Instead of producing words sharing a very close perceptual relationship, they tended
to produce words that shared more general and thematic features, such as living
environment.
5.4 Error analysis
The qualitative analysis of the semantic fluency performance calls for a closer look
at the correct responses, as well as the errors that are produced during the task (Martin & Fedio 1983; Ober et al. 1986; Butters et al. 1987; Tröster et al. 1989; Crowe
1992; Bayles et al. 1993; Troyer et al. 1997; Beatty et al. 1997, 2000). The purpose
of the error analysis is to find patterns that can hint about the possible factors interfering the successful task performance (Joanette, Goulét & Le Dorze 1988). Participants that are less productive at the semantic fluency task are prone to both make
errors and produce fewer correct words (Roberts & Le Dorze 1994). The most frequent errors encountered in the task are intrusions (an inappropriate new response
Semantic fluency
71
outside the semantic category boundaries; e.g., ‘chair’ for vegetables) and
perseverations (recurrence of a previous response, e.g., ‘cat’, ‘dog’, ‘cat’). Also phonemic errors (responses that begin with the same sound), as well as generic errors
(response covering a very general category of response, e.g., ‘farm animals’) may
occur during the task but to a lesser extent than intrusions and perseverations (Ober
et al. 1986; Butters et al. 1987; Bayles et al. 1993). The performances of both normal control subjects and AD patients contain these errors (e.g., Ober et al. 1986;
Tröster et al. 1989; Rosser & Hodges 1994).
The erroneous responses of AD patients in the semantic fluency task have
been analysed in a number of studies. However, they appear to paint a somewhat
contradictory picture. For example, Ober et al. (1986) noticed that the performance
of normal control subjects, relative to mild and moderate-to-severe AD patients,
was characterized by a higher proportion of correct responses and a very low proportion of different types of errors in the fluency task employing animals and fruit.
The performance of the AD patients was characterized by significantly fewer correct responses and more intrusions, perseverations, and variants of the category items
(e.g., ‘kitten’ after having said ‘cat’).
On the other hand, the findings of Carew et al. (1997) on the animal fluency
task showed no differences in the number of perseverations, intrusions, non-specific
errors, or phonemic errors between the normal control group and the AD patients.
The number of errors made by individual participants remained low in their study.
In the same vein, Binetti et al. (1995; see also Fischer et al. 1988) found that in the
animal fluency task, the control subjects and the mild AD patients showed no phonemic errors, intrusions, or perseverative errors, and only a few instances of these
error types were observed in the moderate-to-severe AD group. Binetti et al. proposed that the type and number of errors in the task were not very informative variables in distinguishing the performance of controls from AD patients, especially in
the case of patients with mild dementia.
5.4.1 Intrusions
Intrusions, inappropriate items violating the category boundaries (e.g., ‘shoe’ for
furniture), seem to be very rare in the semantic fluency production of normal adults.
Suhr and Jones (1998) found that intrusions were non-existent among younger healthy
adults (mean age 44.1, SD = 10.1) and that their proportion was very low (0.9%,
SD = 1.9) among the errors made by older normal adults (mean age 67.9, SD = 12.9).
Similar findings have been reported by Diesfeldt (1985), Ober et al. (1986), Tröster
et al. (1989), and Binetti et al. (1995). However, the longitudinal study conducted by
Beatty et al. (2000) indicated that an increase in the proportion of intrusions took
place along with advancing dementia in some AD patients. Findings concerning the
occurrence of intrusions among the AD patients are somewhat mixed. Nevertheless,
most of the studies seem to report a relatively low number of intrusions compared to
other errors among the AD patients.
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Semantic fluency
In the fluency task naming vegetables and tools, Mickanin et al. (1994) found
that intrusions were more common among AD patients than among normal control
subjects. In their study, 9 of 22 AD patients, relative to 1 of 22 normal control subjects, made violations in their semantic fluency performance in 10% or more of
their total responses. However, most of the category violations were semantically
related to the target category and never violated the living/non-living nature of the
target category. In the supermarket fluency task, Tröster et al. (1989; see also Beatty
et al. 2000) discovered that the proportion of intrusions (i.e., items unobtainable in a
supermarket) was significantly higher in the moderate AD group (2.0%, SD = 5.0)
than in the group of older normal control subjects, who were free of category violations.
Contradictory findings were reported by Binetti et al. (1995) who found that
intrusions were absent among the Italian-speaking mild AD patients in the animal
fluency task, and that even moderate-to-severe AD patients produced only very few
intrusions. Similar results has been reported by Ober et al. (1986) who noticed that
only one of nine moderate-to-severe AD subjects gave two non-category responses
in the semantic fluency task employing supermarket items. Among the studies conducted with English-speaking subjects, Rosser and Hodges (1994) observed that in
the category fluency task with animals, birds and water creatures, household objects, vehicles, and musical instruments, the proportion of intrusions was not significantly different between a group of control subjects (0.7%) and a group of AD
patients (2.2%). In a similar vein, Suhr and Jones (1998) found no differences in the
proportion of intrusions between groups of normal control subjects (0.9%, SD =
1.9) and patients with AD (0.3%, SD = 1.2), when categories of animals, fruit, vegetables, tools, and kitchen utensils were used.
Diesfeldt (1985) also reported on the rarity of intrusions among Dutch-speaking
AD patients. Intrusions produced in the category of clothing consisted of accessories
with articles of clothing, such as ‘ornaments’ or ‘lady’s bag’. For the category of
fruit, sometimes vegetables were recalled, such as ‘potatoes’ or ‘carrots’. The
proportion of intrusions for clothing in the group of normal control subjects was
1.8%, the proportion being 1.9% in the AD group. For fruit, the proportion of
intrusions was 4.4% in the normal control group and 3.3% in the AD group.
Nevertheless, in spite of the emergence of intrusions in both subject groups, the
subjects seemed to adhere remarkably well to the search set and displayed a
preservation of the knowledge concerning superordinate categories and their
boundaries even in more advanced cases of AD. Diesfeldt’s findings thus implied
that at least in the categories of clothes and fruit, production of intrusions may to a
certain extent be a normal pattern among normal elderly control subjects, possibly
indicating some fuzziness of the category boundaries (see 9.2.6, 10.1.2, 10.1.3).
Semantic fluency
73
5.4.2 Perseverations
The number of perseverations (i.e., any continuation or recurrence of an earlier response) produced among healthy elderly adults in the animal fluency task has been
reported to be non-existent (Binetti et al. 1995) or very low (Carew et al. 1997). The
study of Ramage, Bayles, Helm-Estabrooks, and Cruz (1999) indicated that although
perseverations took place in 21 of their 60 normal healthy subjects (aged 20-35 and
60-75 years), a combined score of the semantic and phonemic fluency performance
yielded an average rate of perseverations as low as 1% of all the responses. Their
study did not show age or gender effects or their interaction on the frequency of
perseverations. The rate of perseverations reported in Gruenewald and Lockhead’s
(1980) study with young university students was about the same, 1.6% (range 0 –
7%) of the responses on words belonging to animals, birds, foods, or cold foods.
Similar findings were published by Suhr and Jones (1998; see also Butters et al.
1987; Bayles et al. 1993) who reported the rate of perseverations as being 1.1%
(SD = 1.5) for the younger healthy adults (mean age 44.1 years, SD = 10.4) and
1.4% (SD = 2.4) for the older healthy adults (mean age 67.9 years, SD = 12.9) when
producing animals, fruit/vegetables, and tools/kitchen utensils.
However, findings that advancing age may increase the frequency of
perseverations have been reported. Tröster et al. (1989) observed that elderly adults
(mean age 70.4 years, SD = 6.2) perseverated significantly more often (3.0% of the
responses, SD = 4.0) than middle-aged adults (1.0% of the responses, SD = 2.0;
mean age 50.8 years, SD = 8.6) in the semantic fluency task with supermarket items.
Perseverations in the fluency tasks among normal elderly people thus seem to be
rare but they may occur more often with increasing age. On the other hand,
Gruenewald and Lockhead (1980) considered perseverations as a normal phenomenon among the healthy adults in the fluency task and explained their occurrence by
the activation of overlapping semantic fields that causes the same words to activate
in different semantic contexts (see also Barsalou 1982, 1983; Persson 1995:30, 42).
Perseverations are a recognized sign of AD patients’ semantic fluency
performance (Bayles et al. 1993; Beatty et al. 1997, 2000) and other task performances
requiring lexical-semantic processing (e.g., Bayles, Tomoeda, Kaszniak, Stern &
Eagans 1985; Hodges et al. 1992; Lamar et al. 1997). However, studies on
perseveration in the semantic fluency task seem to give mixed findings about their
frequency of occurrence among patients with AD, as well as among healthy control
subjects. Rosser and Hodges (1994; see also Hodges et al. 1992) reported that the
rate of perseverations in AD patients (7.7%) across living (animals, birds, water
creatures) and non-living categories (household objects, vehicles, musical
instruments) was significantly higher than that of normal control subjects (1.4%). In
a similar vein, Suhr and Jones (1998) found that AD patients made a significantly
higher percentage (6.3%, SD= 9.1) of perseverative errors than healthy controls
(1.4%, SD = 2.4) when naming animals, fruit/vegetables, and tools/kitchen utensils.
Tröster et al. (1989; see also Beatty et al. 2000) noticed that both mild AD patients
74
Semantic fluency
(9.0%, SD=12.0) and moderate AD patients (7.0%, SD = 10.0) tended to perseverate
more than the older healthy control subjects (3.0%, SD = 0.04) in the supermarket
task. Moreover, Ober et al. (1986) found a similar pattern in the fluency task tapping
animal and fruit naming. However, perseverations have not been reported in all
studies. Butters et al. (1987) did not observe higher rates of perseverations in AD
patients than in normal control subjects in the animal fluency task. Also Carew et al.
(1997) study indicated that mild AD patients (MMSE = 22.2, SD = 3.0) made very
few perseverations when generating words for the category of animals.
With regard to the effects of dementia severity on the rate of perseverations,
the results seem to be somewhat contradictory. Ober et al. (1986) found no difference
in the proportion of perseverations between mild and moderate-to-severe AD patients
when they produced words belonging to animals and fruit. Similar findings were
reported in the study of Binetti et al. (1995) who observed that mild AD patients
(MMSE = 21.4, SD = 2.7) showed no perseverative behavior at all and that even
moderate-to-severe AD patients (MMSE = 12.3, SD = 2.6) produced only a few
perseverative responses when generating animal names. On the other hand, Ober et
al. (1986) noticed that mild AD patients tended to perseverate significantly less often
than moderate-to-severe AD patients in the semantic fluency task with supermarket
items. Using the same task, however, Tröster et al. (1989) found that the ratio of
perseverations to the total output was the same between patients with mild AD (9.0%,
SD = 12.0) and moderate AD patients (7%, SD = 10.0). In comparison to other
patient groups with dementia (e.g., HD and PD), several studies indicated that AD
patients produced significantly more perseverations over several semantic categories
(Bayles et al. 1993; Hodges et al. 1992; Rosser & Hodges 1994; Suhr & Jones
1998).
5.5 Performance in different semantic categories
The number of semantic categories used in the semantic fluency tasks varies among
studies from one to several different categories (see Table 4). In most cases, however,
the category of animals has been the category of choice. In the study of Chertkow
and Bub (1990), a wide variation appeared in the exemplars generated for eight
different semantic categories in the group of normal control subjects. Their average
performance varied between 12 and 21 items per category (mean 14.9 items per
category), while AD patients’ average production across the categories varied between
3 and 9 words (mean 5.5 items per category).
The nature of the semantic categories seems to have an effect on the semantic
fluency performance of the normal control subjects and AD patients (Rosen 1980;
Diesfeldt 1985; Bayles et al. 1989, 1993; Chertkow & Bub 1990; Capitani et al.
1999). Different semantic categories may have distinctive representations in the
cognitive system and in the brain (Caramazza and Shelton 1998; Moss et al. 2002;
see 9.2.6, 10.1.1). Some studies showed that normal control subjects produced significantly more words for the living than the man-made categories (Rosser & Hodges
Semantic fluency
75
1994; Binetti et al. 1999). For example, Binetti et al. indicated that healthy control
subjects generated more words for the category of animals than for the category of
tools. Nevertheless, also in the domain of living things differences were found by
Bayles et al. (1989) who observed that healthy elderly participants found the category of animals easier than the category of vegetables or fruit. There are some
reports on the dissociation between knowledge of animate and inanimate things in
the semantic fluency task also among AD patients, but these findings are somewhat
inconcistent (cf. Chertkow & Bub 1990; Gainotti et al. 1996; Moss et al. 2002).
However, Cronin-Golomb et al. (1992) and Rosser and Hodges (1994) indicated
that AD group generated fewer responses in the living categories than in the manmade categories, whereas the opposite was true for normal control subjects (see
Table 4).
The differences in the performances between the semantic categories described
above may also be explained by some categories being more familiar to the speakers
than others. People often tend to have a very subjective and imperfect knowledge of
categories and the category boundaries which may vary with the number of factors,
such as specialized knowledge and life experience (Aitchison 1994:39-50; Taylor
1994:72-75, 79, 242; Ungerer & Schmid 1996:14-20; Azuma et al. 1997; Roberts &
Le Dorze 1997). Furthermore, different categories tend to include a varying number
of members, some categories having more exemplars than others (Diesfeldt 1985;
Bayles et al. 1989; Chertkow & Bub 1990; Azuma et al. 1997; Crowe 1998; Binetti
et al. 1999). Diesfeldt (1985) and Crowe’s (1998) experiments showed that the larger
and more general a semantic category (e.g., animals or articles of clothing), the
more items were generated for the task, and the smaller and rarer a category (e.g.,
fruit and precious stones), the fewer items were produced. Nevertheless, Crowe found
a similar pattern in the production of large and small categories in terms of a significant decrease in both the number and the frequency of the words as a function of
time. Cronin-Golomb et al. (1992) noted that the pattern of performance across
several semantic categories was similar between healthy control subjects and AD
patients in the sense that those categories that elicited the fewest items (e.g., birds,
furniture, vehicles, and insects) and the most items (e.g., parts of human body, clothes,
animals, and vegetables) were the same for both subject groups. The study of
Chertkow and Bub (1990) seems to confirm this finding (see Table 4).
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Semantic fluency
6 Causes of the semantic impairment in
The nature and the cause of the impairment in the semantic system in AD have been
widely discussed and several different interpretations have been proposed to explain
the cause of the deficit (for a review, see Nebes 1989, 1992; see also Table 4). There
seem to be three main accounts for the cause of the semantic impairment. First, a
breakdown or a loss of information in the representations in the semantic system
may underlie the deficit (a storage deficit). Second, the deficit may be due to a
failure in the procedures called upon to retrieve and exploit the relatively well preserved semantic representations (an access deficit). Third, there are some researchers who account for a multifactorial deficit underlying the impaired semantic processing, in which both the degradation of semantic structure and the impaired retrieval of information contribute to the impairment. The storage versus access deficit has been hotly debated, not only in studies concerning AD, but also other neurological disorders (e.g., aphasia), and it is an ongoing controversy in the literature
(e.g., Shallice 1988:279-286; Caramazza et al. 1990; Chertkow, Bub & Caplan 1992;
Rapp & Caramazza 1993; Carew 1997; Crowe 1998; Hagoort 1998; see also Persson
1995:67-69).
6.1 Breakdown and loss of semantic structures
As far as the semantic fluency task is concerned, certain findings have been interpreted to possibly reflect a significant breakdown in the integrity of the structure of
the semantic memory or a loss of semantic information taking place in AD. For
example, smaller cluster size (Martin & Fedio 1983; Ober et al. 1986; Troyer,
Moscovitch, Winocur & Leach 1998), abnormal clusters, aberrant semantic distance
between the produced words (Chan et al. 1993; Carew et al. 1997), decrease in
switching between subcategories (Troyer, Moscovitch, Winocur & Leach 1998),
and smaller number of subcategories (Martin & Fedio 1983; Ober et al. 1986; Binetti
et al. 1995) have been considered signs of the semantic breakdown. Semantic category violations (e.g., Ober et al. 1986; Mickanin et al. 1994), decreased use of
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Causes of semantic impairment
words at the basic and subordinate levels, and increased use of superordinate category labels (Martin & Fedio 1983; Tröster et al. 1989; Hodges et al. 1992; Carew et
al. 1997; Beatty et al. 2000) were also interpreted as signs of semantic degradation.
Findings that retrieval cues did not aid word retrieval during a semantic fluency task
(Hodges et al. 1992; Randolph et al. 1993) and that both verbal and non-verbal
fluency tasks brought about an impaired performance among AD patients (Mickanin
et al. 1994) have also been interpreted along the same lines. Furthermore, support
for the breakdown hypothesis was provided by studies in which AD patients were
found to have greater difficulty in semantic than letter fluency tasks (Hodges et al.
1990; Monsch et al. 1992, 1994, 1997; Weingartner et al. 1993; Rosser & Hodges
1994; Mickanin et al. 1994; Goldstein et al. 1996; Crossley et al. 1997).
Breakdown of the semantic memory may occur early in the course of the
disease (Rosser & Hodges 1994; Monsch et al. 1997), and it is likely to be systematic
and progressive in nature (Hodges et al. 1990, 1992). It may involve a bottom-up
deterioration of the hierarchical structure of semantic knowledge, leading to an
appearance of the loss of items at the basic level and subordinate level categories
(e.g., type of boats and breed of dogs) and causing difficulty in any kind of semantic
functioning, such as naming. Meanwhile, the superordinate items (e.g., household
items and animals) may remain intact and their labels are available for naming (Martin
& Fedio 1983; Ober et al. 1986; Tröster et al. 1989; Chertkow & Bub 1990; Hodges
et al. 1990; 1992; Tippett et al. 1995). More specifically, the disruption may be
caused by a loss or a degradation of the weights and connections of the very specific
defining features (e.g., physical and functional) by which the meaning of the words
at the lower hierarchical levels are determined, identified, and distinguished from
semantically related items. Semantic information at the superordinate levels is likely
to be better preserved because it is general and more sparse, shared by all or most
members of the category, frequently used, and learned early (Warrington 1975; Martin
& Fedio 1983; Grober et al. 1985; Ober et al. 1986; Tröster et al. 1989; Chertkow &
Bub 1990; Chan et al. 1993; Monsch et al. 1992, 1994; Binetti et al. 1995; Tippett et
al. 1995; Goldstein et al. 1996; Carew et al. 1997; Harley 1998; Moss et al. 2002;
see also 3.1.2, 3.2). The loss or disruption of semantic features may lead to a loss of
some subcategories, the deactivation of which can be seen as an unsuccessful
performance in the word generation task in the form of limited number of different
types of clusters, such as production of solely farm animals in the animal fluency
task (Binetti et al. 1995; see also Allen et al. 1993) or difficulty in switching between
the subcategories (Troyer, Moscovitch, Winocur, Leach et al. 1998).
The breakdown theory has been supported by various types of findings on
other lexical-semantic tasks (see 2.4). One of the most remarkable findings in favor
of the breakdown hypothesis originates from studies in which item-to-item correspondence of errors or correlations between performances over various tasks and
different modalities have been found (Huff et al. 1986; Chertkow & Bub 1990; Hodges
et al. 1992; Mickanin et al. 1994; Hodges & Patterson 1995; Laine, Vuorinen et al.
1997). Furthermore, correlation between the poor confrontation naming and the poor
79
semantic fluency performances (Martin & Fedio 1983; Huff et al. 1986; Flicker et
al. 1987; Diesfeldt 1989; Chertkow & Bub 1990; Hodges et al. 1992; Randolph et
al. 1993), unhelpful semantic cueing (Chertkow & Bub 1990), and semantic naming
errors produced in both noun and verb confrontation naming tasks (Martin & Fedio
1983; Bayles & Tomoeda 1983; Bayles et al. 1990; Miller Sommers & Pierce 1990;
Hodges et al. 1991; Robinson et al. 1996; Astell & Harley 1998; Williamson et al.
1998) have been labeled as symptoms of the semantic breakdown. Confusion in
choosing the right target among the semantic foils in word-picture matching tasks
(Shuttleworth & Huber 1988; Hodges et al. 1992) and impaired sorting of pictures
into semantic categories (Hodges et al. 1992) have also been interpreted as semantic
degradation. More severe difficulty in naming animate than inanimate items has
been held as clear evidence of the semantic deficit (Gainotti et al. 1996). Moreover,
the findings that AD patients were impaired in ranking semantic features, generated
inappropriate features when defining words, and showed impaired determination of
relationships between semantic features, has been interpreted as supporing the hypothesis of the semantic memory breakdown in AD (Grober et al. 1985; Abeysinghe
et al. 1990; Hodges et al. 1992, 1996; Grossman, D’Esposito et al. 1996; Laatu et al.
1997; Laatu 1999; Laine, Vuorinen et al. 1997).
6.2 Impaired processing
The semantic fluency task is considered to be a rather complex task requiring a
directed search of the semantic memory. Clustering and switching are considered
necessary for the systematic search through the semantic categories and for the retrieval of words from various subcategories (Troyer, Moscovitch, Winocur, Leach et
al. 1998; see 5.1, 5.2). Therefore, reduction in the total output (Cronin-Golomb et
al. 1992; Troyer, Moscovitch, Winocur, Leach et al. 1998; Tröster et al. 1998), smaller
cluster size, and reduced switching (Troyer, Moscovitch, Winocur, Leach et al. 1998;
Tröster et al. 1998) have also been interpreted as signs of an impaired search and
processing of semantic information. Also the finding that the same semantic categories elicited the most vs. the fewest words in both the control subjects and the AD
patients led some researchers to hold that the organization of the semantic categories was intact in AD (Cronin-Golomb et al. 1992). Deficient search processes rather
than breakdown or loss of information in the semantic memory were considered a
more likely cause of the reduced semantic fluency performance in AD. However,
because AD patients tended to perform the phonemic fluency task significantly better than the semantic fluency task, the view that deteriorated search processes alone
would explain the impaired semantic fluency performance might not quite hold
(Troyer, Moscovitch, Winocur, Leach et al. 1998).
Support for the account that reduced processing of semantic information was
responsible for the impaired semantic performance was provided by data obtained
from other tasks measuring semantic memory functions. For example, AD patients
were observed to be aware of the category membership of the tested nouns (Smith,
80
Murdoch et al. 1989; Cronin-Golomb et al. 1992), and to be able to recognize both
nouns and verbs in a word-picture matching task (Diesfeldt 1985; White-Devine et
al. 1996). In fact, AD patients were observed to recognize pictures referring to nouns
that they were not able to name in the confrontation naming task (Smith, Murdoch
et al. 1989). As far as nouns are concerned, evidence was given to show that all or at
least part of their semantic feature knowledge was retained but that it was not available for appropriate use in AD (Grober et al. 1985; Bayles et al. 1990). Furthermore,
normal ranking of semantic features and category exemplars with different degrees
of typicality was shown by AD patients (Cronin-Golomb et al. 1992; Johnson et al.
1995). Moreover, a normal pattern of responding in some of the priming tasks was
interpreted as a sign of a normally functioning semantic memory system in AD
(Nebes et al. 1984, 1989; Nebes & Halligan 1996; Albert & Milberg 1989).
The difficulty in picture-based word retrieval may at least partly be caused by
a deficit in visual processing (Rochford 1971; Appell et al. 1982; Martin and Fedio
1983; Kirshner et al. 1984; Flicker et al. 1987; Shuttleworth & Huber 1988; Nicholas et al. 1996; Robinson et al. 1996; cf. Bayles & Tomoeda 1983; Huff et al. 1986;
Goldstein et al. 1992; Silveri & Leggio 1996; Laine, Vuorinen et al. 1997; Astell &
Harley 1998; see the discussion in Harley 1998). The errors in naming may also be
a consequence of a deficit at the levels of spoken word production (see chap. 4;
10.1.3). It was suggested that an impaired spread of activation among semantically
related items (Diesfeldt 1985; Nebes 1989) and an inability to use the semantic
features that distinguish related items from one another (Diesfeldt 1985) may lead
to difficulty in selecting amongst possible candidates (Bowles et al. 1987). Difficulty in the interactive connections between the semantic and the lexical level of
information during word production, difficulty in lemma retrieval, and an impaired
access to the phonological level of word production may also affect naming (Kirshner
et al. 1984; Huff 1988; Chertkow & Bub 1992; White-Devine et al. 1995; Nicholas
et al. 1996; Astell & Harley 1996, 1998; Harley 1998; see also Smith, Murdoch et
al. 1989). Evidence supporting the retrieval deficit arises also from studies indicating that AD patients had more difficulty with low-frequency words than high-frequency words (Kirshner et al. 1984; Shuttleworth & Huber 1988; Miller Sommers
& Pierce 1990; see 2.4.2) and that the naming performance of the AD patients was
facilitated by phonological cueing (Martin & Fedio 1983).
6.3 A multifactorial deficit
Several researchers seem to hold that a combination of the storage and access deficit
may underlie the semantic disorder in AD. In other words, the degradation of the
semantic memory and the impaired lexical-semantic retrieval may account for the
semantic difficulties found in AD (Schwartz et al. 1979; Martin & Fedio 1983;
Kirshner et al. 1984; Martin et al. 1985; Diesfeldt 1985; Ober et al. 1986; Huff,
Mack, Mahlmann & Greenberg 1988; Chertkow & Bub 1990; Monsch et al. 1994;
Mickanin et al. 1994; Laatu et al. 1996; White-Devine et al. 1995, 1996; Troyer,
81
Moscovitch, Winocur, Leach et al. 1998). Troyer and her colleagues assumed that
the smaller cluster size of the AD patients was caused by their disability to identify
semantic subcategories, and that the decreased switching reflected either the semantic impairment or the deficient search processes in the semantic memory (see also
Binetti et al. 1995). After all, as Auriacombe et al. (1993:188) speculated, “…it is
difficult to assert with confidence whether there is difficulty with lexical retrieval
during spontaneous category naming, however, because the characteristics of the
target mental domain that underlies the retrieval cannot be specified in detail”.
In addition to the possible causes mentioned above, several other complex
processes may be responsible for the reduction of word fluency in AD (Hodges et al.
1992; Pasquier et al. 1995). Findings reported in the literature, such as executive and
attentional problems (Diesfeldt 1985; Mickanin et al. 1994; Ober et al. 1986; Crossley
et al. 1997; Ruff et al. 1997; Rende et al. 2002), disability to generate imaginary
representations of a search set (e.g., wardrobe or fruit desk; Diesfeldt 1985), as well
as reduced processing speed, seem to refer to a deficit in the working memory system which was found to be present in AD (Baddeley et al. 1986, 1991; Morris 1994;
Bayles 2003; see also Diesfeldt 1985; Kopelman 1994; Mickanin et al. 1994; Pasquier
et al.1995; Ruff et al. 1997). Furthermore, limited general processing capacity (Huff
et al. 1986; Cronin-Golomb et al. 1992; Crossley et al. 1997), lack of motivation
(Diesfeldt 1985), and the difficulty of the task (Nebes et al. 1984; Nebes 1989;
Cronin-Golomb et al. 1992; Hodges & Patterson 1995) may also decrease the output of the AD patients in the semantic fluency task.
82
7 Aims of the study
Alzheimer’s disease is characterized by an impairment of multiple memory-related
systems, including deterioration of the language-specific semantic memory. The
semantic (category) fluency task has been used extensively as a method of studying
the semantic memory impairment in AD (e.g., Monsch et al. 1992; Binetti et al.
1995; Goldstein et al. 1996; Troyer, Moscovitch, Winocur, Leach et al. 1998). The
wide clinical use of the task is most probably based on its sensitivity to deficits and
its effectiveness in revealing information about a subject’s semantic processing and
word retrieval in a short time. In this task, participants are asked to generate words
belonging to different semantic categories, such as animals and supermarket items,
in a certain period of time (usually 60 seconds). According to previous studies, AD
patients do not perform the task in the way normal elderly subjects do. Their overall
production of words is lower and they seem unable to use an effective strategy to
generate semantically related words as clusters in the subcategories (e.g., farm animals: ‘cow’, ‘horse’, ‘sheep’, ‘pig’), or to switch among subcategories (e.g., from
farm animals to wild animals; Beatty et al. 1997, 2000; Troyer, Moscovitch, Winocur,
Leach et al. 1998). During the task, AD patients also tend to produce more errors
than healthy control subjects, such as perseverations and outside-category intrusions (e.g., Ober et al. 1986; Tröster et al. 1989; Mickanin et al. 1994; Beatty et
al.1997, 2000).
There are plenty of studies in which a semantic fluency task has been used as
a method of investigating the semantic memory functions in AD patients (see Table
4). However, detailed reports on how Finnish-speaking AD patients perform the
task are scarce. In general, studies seem to differ in terms of operationalizing the
task, some of them having used only one or two semantic categories to measure
word production or having used more general and fewer parameters than others.
Furthermore, in many studies, the theoretical background concerning the fundamentals for performing the task, for example, the semantic organization of words
and the processes responsible for word production seems to be somewhat vaguely
discussed. So far, very little is known about AD patients’ ability to generate verbs in
a fluency task in which different kinds of verb categories are used as semantic stimuli.
84
Aims of the study
The general aim of this dissertation is to provide information on the patterns
of how mildly and moderately demented Finnish-speaking AD patients utilize information in semantic memory in order to perform on the semantic fluency task in
which noun (object) and verb (action) categories are used as constraints for spontaneous word production. The objective of the study is to compare the performances
of mild and moderate AD patients with each other and with the performance of the
healthy elderly adults.
The specific research questions are:
1. How does the overall performance of mildly and moderately demented AD
patients compare to that of normal control subjects on the semantic fluency task
in which different noun categories are used to elicit word production? Can the
performance of the AD patients be characterized by a reduction in noun production and an impaired ability to use a strategic search for semantically related
nouns (i.e., to sample nouns in clusters and to switch between subcategories) as
stated in earlier studies? Are the performances of mildly and moderately demented AD patients different?
2. How does the content of the performance of mildly and moderately demented
AD patients compare to normal control subjects on the noun fluency task? Can
the performance of the AD patients be characterized by a tendency to violate
the boundaries of semantic categories (i.e., to produce intrusions) and to repeat
(i.e., to perseverate) previously produced nouns? Do mildly and moderately
demented AD patients show an inability to generate nouns from different semantic subcategories and a tendency to use more frequent and prototypical nouns,
as indicated in previous studies? Is there a difference between the performance
of mildly and moderately demented AD patients?
3. How does the overall performance of mildly and moderately demented AD
patients compare to that of normal control subjects on the semantic fluency task
in which different verb categories are used to elicit word production? Can the
performance of the AD patients be characterized by a reduction in verb production and an impaired ability to use a strategic search for semantically related
verbs (i.e., to sample verbs in clusters and to switch between subcategories), as
was previously found in their semantic fluency performance in noun categories? Are the performances of mildly and moderately demented AD patients
different?
4. How does the content of the performance of mildly and moderately demented
AD patients compare to normal control subjects on the verb fluency task? Can
the performance of the AD patients be characterized by a tendency to violate
the boundaries of semantic categories (i.e., to produce intrusions) and to repeat
(i.e., to perseverate) previously produced verbs? Do mildly and moderately
demented AD patients show an inability to generate verbs from different semantic
subcategories and a tendency to use more frequent and prototypical verbs? Does
the content of the performance on the verb fluency task differ between mildly
and moderately demented AD patients?
8 Method
8.1 Subjects
The subjects consisted of 20 mildly and 20 moderately demented AD patients and
30 healthy elderly control subjects (see Table 5). The AD patients came from the
Department of Neurology (Memory Clinic) of the Helsinki University Central
Hospital. The diagnosis of the probable AD was confirmed by neurological
examination, consistent with National Institute of Neurological and Communicative
Disease-Alzheimer’s Disease and Related Disorderes Association criteria (NINCDADRDA criteria; McKhann et al. 1984). The dementia severity was assessed using
the Mini-Mental State Examination (MMSE; Folstein et al. 1975). The Alzheimer’s
patients scoring 20-27 points of the total 30 points on the MMSE formed the group
of mildly demented subjects (miAD), and the patients scoring 12-19 points formed
the group of moderately demented subjects (moAD). The miAD group consisted of
12 female and 8 male participants whose mean age was 65.0 years and their mean
MMSE score was 23.5 points (see Table 5). The moAD group included 15 female
and 5 male subjects. Their mean age was 67.4 years and the mean MMSE score was
15.9 points. The miAD group had attended school for approximately 10.5 years and
the moAD group for 10.1 years.
The normal control subjects, matched for age and educational level to the AD
subjects, were 16 male and 14 female volunteers from the pool of participants of the
Helsinki Aging Brain Study which was started in 1989 with the aim of studying the
neurological and cognitive status of a random sample of persons aged 55, 60, 65, 70,
75, and 80 years of age living in the city of Helsinki (see Ylikoski 2000). All the
control subjects had previously undergone a complete neurological and neuropsychological examination. They did not have neurological or psychiatric diseases and
they did not suffer from alcohol or drug abuse or use medication that could have
affected their cognitive performance. The mean age of the control subjects was 66.7
years and they had attended school for approximately 9.7 years. The average score
of the control subjects on the MMSE was 28.9 points (see Table 5).
86
Method
There was no difference in the age, gender, or educational level among the
subject groups (see Table 5). The MMSE score of the NC group was significantly
higher than that of the miAD group (U = 0.5, p < .001) and the moAD group (U = 0.0,
p < .001). The MMSE scores differed significantly also between the miAD group
and the moAD group (U = 0.0, p < .001; for a more detailed description of the
statistical tests, see 8.2.7).
Examination of the AD patients began in 1994, first by conducting a pilot
study. The data collection was completed in 1997. The AD patients were tested
either at the Helsinki University Central Hospital or at their homes or care units,
depending on their transportation facilities. All the control subjects were tested at
the Helsinki University Central Hospital. All subjects were examined individually
in as quiet and undisturbed a setting as possible. All the participants or their caregivers
gave their informed consent. The study was approved by the Ethics Committee of
the Helsinki University Central Hospital.
8.2 Method
8.2.1 Procedure of the semantic fluency tasks
For the semantic fluency task, all subjects were given 60 seconds to generate as
many words as possible belonging to a given semantic category. Eight different
semantic categories were given in the following order: four noun categories (i.e.,
articles of clothing, vegetables, vehicles, animals) and four verb categories (i.e.,
preparing food, playing sports, construction, cleaning up). The reason these categories were chosen was to cover concrete, everyday categories of objects and actions.
The nominal categories represented both animate and inanimate objects categories
commonly used in other studies on semantic fluency performance (see Table 4).
In order to avoid confusions and misunderstandings, the subjects practiced
performing the task and produced examples for the categories of dishes (kitchen
utensils) and gardening. During the practice, subjects were encouraged by the
example of the examiner to produce the nominative singular forms for nouns (e.g.,
‘kuppi’ / ’cup’, ’lautanen’ / ’plate’, ’kulho’ / ’bowl’, etc.) and the first infinitive form
for the verbs (e.g., ‘kaivaa’ / ’dig’, ‘leikata’ / ’cut’, ‘istuttaa’ / ‘plant’, ‘kitkeä’ /
‘weed’, etc.), but no restrictions for the performance were given. The subjects were
given the following instructions for performing the noun fluency tasks: “Please name
as many nouns, that is, names of objects that belong to the category of XX (the name
of the semantic category) as possible in one minute. You can start now.” For the verb
production tasks, the instruction was as follows: “Please name as many verbs, that
is, names of actions that belong to the category of YY (the name of the semantic
category) as possible in one minute. You can start now.” The instruction was repeated
after 30 seconds to each subject if production of responses had ceased or the subject
appeared distracted. The task performance was timed by a stopwatch and all products
Method
87
Table 5. Demographic features of the subject groups
NC
(n = 30)
miAD
(n = 20)
moAD
(n = 20)
H
p-value
Female / Male
14 / 16
12 / 8
15 / 5
3.994 #
p = .136
Age
M (SD)
Mdn
66.7 (5.5)
66.0
65.0 (10.3)
64.5
67.4 (8.7)
64.5
0.962
p = .618
Education,
years
M (SD)
Mdn
9.7 (3.3)
9.0
10.5 (3.7)
9.0
10.1 (3.6)
10.0
0.380
p = .827
MMSE
M (SD)
Mdn
28.9 (0.9)
29.0
23.5 (2.0)
23.5 ***
15.9 (2.4)
60.893
17.0 *** ¤¤¤
p < .001
Note. H = values on the Kruskal-Wallis test, # = chi square test (see Ranta, Rita & Kouki
1991:136.143). Pair-wise comparisons computed using the Mann-Whitney U test:
*** = p < .001 when NC vs. miAD and NC vs. moAD, and ¤¤¤ = p < .001 when miAD vs.
moAD.
were written down by the examiner. The examinations were taped to guarantee a
verbatim transcription of the responses.
8.2.2 Analysis of the overall performance on the semantic
fluency tasks
In order to measure the overall productivity and the strategies of the performance of
the NC group, the miAD group, and the moAD group on the semantic fluency tasks,
and to compare their performance, the following parameters were used (see Table 6;
examples of the complete protocol are provided in conjunction with the results, see
also Appendix 4A-H).
The total number of words (i.e., the sum of nouns and verbs produced for
each category) was calculated, including repetitions and outside-category intrusions.
Correct words (i.e., correct, unique responses) were calculated separately.
The total number of clusters (i.e., groups of two or more successively
produced words belonging to the same semantic subcategory or sharing the first
phoneme (nouns only; see 8.2.3)) was calculated to measure the ability to use a
strategic search for words. For the clustering rules, see Table 6 and Appendix 1A
and 1B. The term subcategory was used to refer to any types of semantic groupings
found in each category.
88
Method
Table 6. Scoring of clustering and switching on the semantic fluency tasks
A sample of produced nouns
Clusters and switches
cat, dog, hen, cow, horse, sheep, pig, goat
lion, tiger
bird
hare, squirrel
elephant, giraffe, monkey
seal
CLUSTER farm animals
SWITCH and CLUSTER exotic animals
SWITCH
SWITCH and CLUSTER rodents
SWITCH and CLUSTER exotic animals
SWITCH
Total number of words: 17
Number of clusters: 4
Words in clusters: 15/17 (88.2%)
Number of switches: 5
Mean cluster size: 1.8
Counting the cluster size:
1 word: 2 * 0 = 0
2 words: 2 * 1 = 2
3 words: 1 * 2 = 2
8 words: 1 * 7 = 7
sum:
6
11
cluster size: 11/6 = 1.8
The mean cluster size was counted to measure the ability to access words in
the subcategories or phonemic clusters (for the latter, nouns only). The cluster size
for each category was counted by applying the protocol presented by Troyer et al.
(1997; Troyer 2000) and Rich et al. (1999; see Table 6). A single word was marked
with a cluster size of 0, two words with a cluster size of 1, three words with a cluster
size of 2, and so on, errors and repetitions included. First, the number of the clusters
of each cluster size was multiplied by the equivalent cluster size marker. Second, the
products of the multiplication were added together, and third, the sum was divided
by the total number of clusters, including single words. Successively repeated words
(‘jänis’, ‘jänis’, ‘jänis’ / ‘hare’, ‘hare’, ‘hare’ or ‘keittää’, ’keittää’, ‘keittää’ / ‘boil’,
‘boil’, ‘boil’) were not counted as a cluster.
The proportion of words in clusters was calculated to measure the coherence and efficiency of production. The formula for calculating the proportion was
((the number of the total words produced - the number of the single words) / the
number of the total words) * 100.
Number of switches (i.e., transitions between clusters), including single
words, was calculated to measure the ability to initiate a search for a new subcategory or the first phoneme shared by many category members (for the latter, nouns
only). Errors and repetitions were included, except for successively repeated words
that were scored as one transition.
Method
89
8.2.3 Clustering rules
The division of the clusters was based on the observation that, in addition to using
the semantic criteria to produce semantically closely related words together as a
cluster, subjects also seemed to produce chains of words that began with the same
phoneme (see also Laine 1989:22; Roberts & Le Dorze 1994; Kaleva & Vanhala
2001). Such phonemically similar strings of words appeared as separate clusters, as
well as embedded in a cluster of semantically very closely related items. In order to
find out whether phonemically similar words were used as a real strategy rather than
produced at random, the occurrences of such word chains were analysed in closer
detail. First, the appearance of all phonemic clusters in the real data was calculated.
Subsequently, the number of these phonemic clusters was compared to ones in a
randomized data set, in which the order of the word occurrence was randomized
with Excel for Windows 95, version 7.0.
For the noun categories, an average of 4.7 (SD = 2.6, Mdn = 4.0) strings of
words sharing phonemic similarity were produced in the real data, and 3.8 (SD = 2.3,
Mdn = 4.0) strings of words in the randomized data. The Wilcoxon matched-pair
signed ranks test (see Ranta, Rita & Kouki 1991:14-218; Howell 1997:652-656; see
8.2.7) revealed a statistically highly significant difference between the real and the
randomized data (Z = -3.281, p < .001). Based on this finding, clusters of nouns
were divided into three types of clusters to describe the nature of the strategic search
for words: The number of semantic clusters (words produced by using their
semantic relatedness as the criterion, such as farm animals, pets, and zoological
categories), the number of phonemic clusters (words generated by the resemblance
of the first phoneme, such as ‘kissa’, ‘kettu’, ‘karhu’, ‘koira’, ‘kani’ / ‘cat’, ‘fox’,
‘bear’, ‘dog’, ‘rabbit’), and the number of mixed clusters (semantic in nature;
combination of semantic and phonological strategy, e.g., ‘kissa’, ‘koira’, ‘kani’,
‘lammas’, ‘lehmä’, ‘hevonen’ / ‘cat’, ‘dog’, ‘rabbit’, ‘sheep’, ‘cow’, ‘horse’) were
recorded separately and counted as mixed clusters.
For the verb categories, only the successively produced verbs without
arguments, but sharing the first phoneme in common (e.g., ‘maalata’, ‘mitata’ / ‘paint’,
‘measure’) were counted for the analysis. The average length of phonemic strings in
the real data was 1.7 (SD = 1.6, Mdn = 1.5) and in the randomized data 1.5 (SD = 1.6,
Mdn = 1.0). Because the Wilcoxon matched-pair signed ranks test did not reveal a
statistically significant difference between the real and the randomized data
(Z = -1.519, p = .129), the strings of phonemically close words that were produced
for the verb categories were counted as semantic clusters only if they happened to
share a close semantic relationship.
The determination of the semantic clusters was done on the basis of a post
hoc analysis in which all produced words were registered and classified. A myriad
of semantic relations was introduced for all the eight categories. The cluster divisions are shown in Appendix 1A and 1B.
90
Method
In case two clusters overlapped, the overlapping items were counted in both
clusters (e.g., ‘kala’, ‘virtahepo’, ‘krokotiili’, ‘kenguru’, ‘tiikeri’, ‘leopardi’, ‘kirahvi’
/ ‘fish’, ‘hippopotamus’, ‘crocodile’, ‘kangaroo’, ‘tiger’, ‘leopard’, ‘giraffe’). In
the given example, the three first words formed a cluster of animals living in the
water. However, because the two last words of the cluster could also be classified as
exotic animals, they were considered to belong to two semantic clusters and two
different subcategories. Respectively, in a string of verbs ‘tehdä taikina’, ‘paistaa’,
‘keittää’, ‘grillata’ / ‘make dough’, ‘bake/’fry’, ‘boil’, ‘grill’, the verb ‘paistaa’
referred to both baking and frying and was thus considered part of the actions
belonging to baking and cooking.
Only the larger, common subcategory was used when smaller clusters (maximum size of three words) were embedded in a bigger cluster, or clusters overlapped,
but all items could correctly be counted as a common subcategory (e.g., ‘takki’,
‘ulsteri’, ‘turkki’, ‘hattu’, ‘pipo’, ‘myssy’, ‘kengät’, ‘saappaat’ / ‘coat’, ‘ulster’, ‘fur
coat’, ‘hat’, ‘knit hat’, ‘cap’, ‘shoes’, ‘boots’ = outdoor clothes or ‘hiihtää’, ‘luistella’,
‘juosta’, ‘lumilautailla’ / ‘ski cross-country’, ‘skate’, ‘run’, ‘snowboard’ = winter
sports).
8.2.4 Analysis of the contents of the responses on the semantic
fluency tasks
In order to analyze the quality of the performance, the intactness of the semantic
categories, and the semantic nature of the responses, the following parameters were
used:
The proportion of correct and unique words was counted to measure the
overall ability to activate and produce as many different but correct nouns and verbs
as possible belonging to the given category. Variations of the items (e.g., ‘lehmä’ /
‘cow’, ‘vasikka’ / ‘calf’ and ‘sonni’ / ‘bull’) were not counted if produced by the
same subject but compound words (e.g., ‘rekka-auto’ / ‘trailer truck’, ‘kilpa-auto’ /
‘racecar’, ‘pakettiauto’ / ‘van’) and superordinates (‘linnut’ / ‘birds’; ‘juokseminen’
/ ‘running’) were considered correct responses. Repetitions (perseverations) and
outside-category intrusions were excluded. The formula used for counting the proportion was (the total number of the words / the number of the correct and unique
words) * 100.
The following verb forms were accepted to refer to different types of action:
specific verbs (infinitive and inflected forms and collocations; e.g., ‘leikata’ / ‘cut,
‘juoksee’ / ‘[s/he] runs’, ‘hakata matot’ / ‘beat carpets’), verb phrases (e.g., ‘ensin
kaivetaan se alusta’ / ‘first the ground is dug’), deverbal forms (deverbal nouns, e.g.,
‘käveleminen’ / ‘walking’, ‘pesu’ / ‘wash’), and general (default) verbs and their
modifications such as ‘laittaa’ (‘make’, ‘do’, ‘put up’: ‘laittaa katto’ / ‘build the
roof’, ‘laittaa tapetit’ / ‘wallpaper’), ’panna’ (‘put’, ‘set’: ‘panna laastia’ / ‘put mortar’, ‘panna ikkunalasit’ / ‘glazing’), and ‘tehdä’ (‘do’, ‘make’: ‘tehdä huopakatto’ /
Method
91
‘lay a felt roof’). Because the verb fluency task seemed to elicit single nouns for the
verb categories, the number of these nouns was calculated. However, they were
considered intrusions (see below). The proportions of the different verb forms and
the separate nouns were calculated with the formula (the number of the verb forms
or nouns / the total number of the verbs) * 100.
The proportion of intrusions (i.e., outside-category words) was calculated
to measure the intactness (coherence) of the semantic categories. Semantic intrusions
(i.e., words from semantically related categories (‘omena’ / ‘apple’ produced for the
category of vegetables, ‘pelata bridgeä’ / ‘play bridge’ produced for the category of
playing sports), were counted separately from irrelevant intrusions (i.e., words
from unrelated categories (‘valokuva’ / ‘photograph’ produced for the category of
articles of clothing; ‘kiillottaa’ / ‘polish’ produced for the category of preparing
food). If an intrusion was repeated, it was counted as a perseveration. The formula
for the proportion of intrusions was (the number of the intrusions / the total number
of the the words) * 100.
As for the integrity measures of verb categories, the number of single nouns
(i.e., nouns produced without a verb) was also calculated. Semantically adequate
nouns related to the category were counted as semantic intrusions (e.g.,
‘makaronilaatikko’ / ‘macaroni casserole’ for the category of preparing food; ‘naula’
/ ‘nail’ for the category of construction; ‘rätti’ / ‘rag’ for the category of cleaning
up), and semantically unrelated nouns as irrelevant intrusions (e.g., ‘valokuva’ /
‘photograph’ for the category of playing sports).
The proportion of perseverations (i.e., repetition of a previously produced
word), was counted to measure the coherence and fluency of the word production.
Synonyms (e.g., ‘sukset’, ‘hiihtimet’ / ‘skis’ or ‘juosta aitajuoksua’, ‘aitoa’ / ‘hurdle’),
which were very few in number, were scored as repetitions because they seemed to
activate the same or almost the same concept. The formula for the proportion was
(the number of the perseverations / the number of the total words) * 100.
The number of different subcategories (i.e., semantic dimensions activated
for cluster production) was counted to measure the subjects’ ability to activate the
semantic space. The variety of different subcategories was taken into account by
displaying a distribution of the most often used criteria for semantic clustering (see
the cluster division criteria in Appendix 1A and 1B).
Prototypicality of words, as well as frequency ratings of words, were scored
in order to control for their effect on word production. Because norms for
prototypicality and frequency ratings of spoken Finnish were nonexistent, all the
words produced were controlled for prototypicality and frequency in two separate
post hoc sessions. In one of the sessions, 14 healthy adults (7 females and 7 males)
rated the prototypicality of the words given for the fluency tasks. In the other session,
another 14 healthy adults (7 females and 7 males) rated the frequency of the
occurrence of the words. The raters were recruited from the author’s circle of
acquaintances. In the group of the prototypicality raters, the mean age was 34.8
92
Method
years (SD = 4.5, Mdn = 37.0). Eight of the raters had a university degree and six
were undergraduate students or had a lower educational level. In the group of the
frequency raters, the mean age was 35.9 years (SD = 5.6, Mdn = 38.0). Nine of the
raters had a university degree and five were undergraduate students or had a lower
educational level. All the raters were native speakers of Finnish and none of them
reported any difficulty in their language skills, reading, or writing.
All the raters were given randomly ordered lists of words, adding up to 585
different nouns and 644 different forms for verbs that were produced for the semantic fluency tasks, and asked to indicate how good an example of a given category or
how frequent in everyday use they felt the words were on a 7-point scale. One indicated a very poor example of the category or a very infrequent word and seven
indicated a very good example or a very frequently used word. Four meant that the
word fit the given category moderately well or that the word was of moderate frequency. The other numbers indicated the intermediate judgments. For these ratings,
the method of Rosch (1975) was applied. On the basis of these ratings, a mean for
each word’s prototypicality and frequency of use was calculated. Examples of the
words with the highest, intermediate, and the lowest prototypicality and frequency
ratings are presented in Appendices 2 and 3. Afterwards, the prototypicality and the
frequency of the words generated by the NC group, the miAD group, and the moAD
group were calculated according to these ratings. Each noun and verb form was
scored, after which a mean score was calculated to represent the prototypicality and
the frequency of the items in each semantic category for each individual subject.
8.2.5 Inter-rater judgements
All protocols were scored twice by the author, the second scoring taking place one
year after the first scoring. Every seventh (n = 10) of the recordings and transcriptions was checked and verified by an independent judge. One third of the subjects’
responses (n = 23; nine subjects from the NC group, seven from the miAD group,
and seven from the moAD group) were rated for the clusterings, switches, p
rseverations, and intrusions by a linguist who acted as an independent judge. For t
is analysis, subjects’ productions were selected using random sampling so that all n
un and verb samples came from different participants. Inter-rater reliabilities with p
int-by-point agreement (PPA; Kazdin 1982:23-56) were calculated separately for n
uns and verbs. The formula for counting the PPA ratio was as follows: PPA = (agreements for the trial / disagreements for the trial)*100 (Kazdin 1982:54). After that,
all the cases with differing judgements were discussed and a ratio of discussion-toconsensus was calculated, as a result of which only the instances upon which an
agreement was made were accepted for the final analysis and scoring. The ratio of
the PPA for the nouns was as high as 89.1% and the ratio of the discussion-toconsensus reached 98.8%. For the verbs, the ratio of the PPA was 88.9% and the
discussion-to-consensus reached 99.7%.
Method
93
8.2.6 Control tasks
After the semantic fluency task was performed, all subjects performed a battery of
the following tasks to measure psycho-linguistic functioning, such as naming, comprehension, and recognition, as well as attention and short-term memory.
The following tasks were designed by the author to measure the overall semantic performance of the subjects. Some of the tasks required verbal responses to
the stimuli, while other tasks tapped non-verbal semantic performance. Each task
type was executed for each word class, first for the nouns and later for the verbs. In
these tasks, the following pool of words representing the semantic categories introduced in the semantic fluency task were used in different combinations: ‘pants’,
‘hat’, ‘shirt’, ‘coat’, ‘vest’, and ‘skirt’ for the category of clothes; ‘turnip’, ‘carrot’,
‘cabbage’, ‘pea’, ‘tomato’, and ‘cucumber’ for the category of vegetables; ‘plane’,
‘boat’, ‘train’, ‘car’, ‘bus’, and ‘bicycle’ for the category of vehicles; ‘horse’, ‘cat’,
‘cow’, ‘dog’, ‘pig’, and ‘sheep’ for the category of animals; ‘fry’, ‘bake’, ‘beat’,
‘grade’, ‘clean a fish’, and ‘peel’ for the category of preparing food; ‘run’, ‘jump’,
‘skate’, ‘cross-country skiing’, ‘swim’, and ‘stretch’ for the category of playing sports;
‘paint’, ‘saw’, ‘plane’, ‘drill’, ‘weld’, ‘hammer’ for the category of construction;
‘vacuum’, ‘wash a window’, ‘beat a carpet’, ‘dust’, ‘do dishes’, and ‘sweep’ for the
category of cleaning. The frequency and prototypicality of the words involved in the
tasks were not controlled. Originally, the tasks were not planned for the purpose of
serving as the control tasks for the semantic fluency tasks, which explains the difference in the number and the different combinations of the items among the tasks.
Picture naming tasks were conducted to measure the subjects’ confrontation naming abilities. Twenty photographs (5 from each noun and verb category)
were presented in a fixed order in each task. The subjects were given 20 seconds to
name each picture (see the BNT below). No cues were given to prompt naming.
Category recognition tasks were performed in order to measure the subjects’ non-verbal ability to differentiate between the four semantic noun categories
and the four semantic verb categories. During each task, subjects were shown four
series of examples from each of the four semantic categories and asked to point out
the picture representing the category named by the examiner. Each series consisted
of a different set of category members, one member from each category. The task
involved a total of 16 category responses (4 x 4).
Recognition tasks of in-category members were performed to measure the
subjects’ non-verbal ability to differentiate between nouns and verbs belonging to
the same semantic category. For each category, subjects were shown three sets of six
semantically related photographed objects and actions and asked to point out two
pictures per set according to the instruction given by the examiner. The task involved
recognition of 6 examples of each category, adding up to a total of 24 nouns and 24
verbs.
94
Method
Serial word naming tasks were executed to measure naming and recognition of semantically related category members belonging to the noun and verb categories. For each category, subjects were shown a series of six semantically related
photographs of objects and actions, and asked to name the items from left to right in
the given order. The task involved naming a total of 24 nouns and 24 verbs.
Card-sorting tasks were performed to measure the subjects’ non-verbal abilities to recognize and categorize semantically related pictures. In this task, subjects
were given a pile of 20 randomly ordered pictures of objects and actions belonging
to the semantic categories and asked to sort the cards out according to their semantic
similarity. A maximum of three minutes was given for subjects to complete each
card-sorting task. If the time limit was exceeded, subjects were asked to stop. The
scoring of the performance on each subtask was conducted as follows: a set of two
semantically related pictures occurring together earned one point, three pictures
earned two points, and four pictures earned three points, respectively. Five points
were given for a complete set of five correctly sorted pictures. A total of 20 points
was given for a correct performance on both sorting tasks.
To measure overall verbal functions, the Finnish test version of the Boston
Naming Test (BNT; Laine, Koivuselkä-Sallinen, Hänninen & Niemi 1997) and the
Token Test (short version; De Renzi & Faglioni 1978) were used. The Boston Naming Test has been widely used in assessing the word finding difficulties of normal
older population and demented patients (Lezak 1995:537-538). In the test, subjects
were presented with 60 black and white ink drawings of objects that they were
asked to name, each in 20 seconds. When necessary, semantic and/or phonemic
cues were provided to help word retrieval. The Token Test was assessed to measure
auditory language comprehension. Originally, the test was designed to assess language comprehension difficulties among mild aphasic subjects (De Renzi & Vignolo
1962), but it has also been used to measure language comprehension skills also in
dementia, including AD (e.g., Swihart, Panisset, Becker, Beyer & Boller 1989;
Tomoeda, Bayles, Boone, Kaszniak & Slauson 1990). The test consists of 20 big
and small tokens of squares and circles that come in five colors (red, yellow, green,
white, and black). The subjects are asked to show and manipulate the tokens according to a total of 36 verbal instructions given by the examiner.
In order to measure working (short-term) memory, the span of immediate
verbal recall, and attentional capacity, the Digit Span Test (forward) from the Finnish version of the Wechsler Memory Scale (Wechsler 1986; see also Lezak 1995:356360) was administered. In this task, subjects were asked to repeat series of digits in
the order they were given by the examiner.
8.2.7 Statistical analysis
Prior to the statistical comparison of groups, the scores of the parameters in the
semantic fluency task were examined for their distributions (Kolmogorov-Smirnov
test; see e.g., Ranta et al. 1991:150-154) and their homogenity of variance (Levene’s
Method
95
test; see e.g., Howell 1997:198-199, 321-322). Because the shape of the distribution
of very many variables described above deviated from the shape of the normal distribution and their variances were heterogeneous, nonparametric, distribution-free
tests were chosen for the statistical analyses of the data (Ranta et al. 1991:223-227,
316-319; Howell 1997:645-647). Mean (M) and median (Mdn) were chosen for the
measures of central tendency to describe the distribution of the data, and standard
deviation (SD) to describe the amount of variation in the data.
To assess whether a group difference existed in the performance among the
control group and the AD groups, a Kruskall-Wallis one-way analysis of variance
was computed (Ranta et al. 1991:322-325; Howell 1997:658-659). Post-hoc pairwise
comparisons between the control group and each AD group, as well as between the
miAD group and the moAD group were assessed using the Mann-Whitney U test
(Ranta et al. 1991:195-202; Howell 1997:652). For the statistical measures in the
subject groups, the Friedman test was used to compare more than three paired and
dependent variables. The post hoc pair-wise comparisons were calculated in order
to locate the differences in the sums of ranks computed by the Friedman test (Ranta
et al. 1991: 329-332). The Wilcoxon matched-pairs signed-ranks test was used to
assess the differences between two dependent variables in the subject groups (Ranta
et al. 1991:14-218; Howell 1997:652-656). The Spearman rank correlation coefficient was used to calculate the correlations between the correct responses on the
semantic fluency tasks and the scores of the control tasks (Ranta et al. 1991:437442; Howell 1997:289-290).
In all comparisons, an alpha level of 0.05 was used as the cutoff for the statistical significance. All the statistical data was processed using the program of Statistical Product and Service Solutions (SPSS 10.0 for Windows, 1999), except for the
post hoc comparisons of Friedman’s test which were calculated manually.
96
Method
9 Results
The results of this study are presented in six sections. In the first two sections, the
results concern the overall fluency performance and the content of the responses
given by the subjects in the noun fluency task, followed by the verb fluency results,
respectively. Each section is completed with a summary and a discussion of the
results. Later, the main results concerning the total noun and verb production are
summarized. Finally, results on the performance on the control tasks are presented
and related to the semantic fluency performance.
Samples of the semantic fluency performance selected from each subject group
and each semantic category, accompanied by full data analyses, are presented in
Appendix 4A-H. For the convenience of the reader, the data on the post hoc pairwise analysis between the different subject groups is collected in detail in Appendices 5, 6, and 7 instead of being presented in the text.
9.1 Overall performance on the noun fluency tasks
The overall performance across all four semantic categories, as well as the number
of words produced for individual semantic noun categories, was significantly different
among the groups (in all categories, p < .001; see Table 7). The post hoc pair-wise
comparisons between the NC group and the AD groups demonstrated a remarkable
decrease in word production in each category in the miAD group and the moAD
group (in each, p < .001). The most severe reduction in word production was found
in the moAD group, especially, who generated only half the number of words
produced by the NC group. Also in comparison to the miAD group, the moAD
group’s word production was significantly poorer (p < .01). The detailed description
of the post hoc pair-wise analyses in the noun production tasks can be found in
Appendix 5:1.
The number of words produced for the individual categories varied from 14
to 19 words in the NC group and from 9 to 13 words in the miAD group, whereas the
moAD group produced words in a more stable manner, 7 or 8 words per category.
The pair-wise comparisons between the groups revealed that, in comparison to the
98
Results
Table 7. Total number of words and number of correct nouns
NC
(n = 30)
miAD
(n = 20)
moAD
(n = 20)
M (SD)
Mdn
M (SD)
Mdn
M (SD)
Mdn
Clothes
16.8 (4.3)
16.5
14.1 (6.8)
12.5 *
Vegetables
13.6 (3.4)
13.5
Vehicles
Variable
H (df = 2)
p-value
7.8 (4.7)
7.5 *** ¤¤¤
28.119
p < .001
9.8 (4.4)
9.0 ***
7.0 (2.9)
7.0 *** ¤
28.935
p < .001
14.0 (3.0)
14.0
11.1 (5.8)
10.0 ***
7.6 (4.9)
7.0 *** ¤
25.487
p < .001
Animals
19.5 (4.7)
18.5
13.8 (6.3)
13.0 ***
8.2 (5.1)
7.5 *** ¤¤
33.966
p < .001
All categories
63.8 (11.7)
61.5
48.7 (20.5)
44.0 ***
30.6 (14.7)
30.5 *** ¤¤
36.969
p < .001
Clothes
15.7 (4.5)
15.5
12.2 (6.4)
11.5 **
5.9 (3.9)
5.0 *** ¤¤¤
33.592
p < .001
Vegetables
11.4 (3.2)
10.0
7.1 (3.6)
6.5 ***
4.5 (2.1)
4.0 *** ¤¤
39.292
p < .001
Vehicles
13.3 (2.8)
13.0
8.9 (5.1)
8.0 ***
4.9 (2.9)
4.0 *** ¤¤
40.683
p < .001
Animals
19.0 (4.7)
18.5
12.3 (5.7)
12.0 ***
6.3 (4.0)
5.5 *** ¤¤¤
44.023
p < .001
All categories
59.4 (11.5)
58.0
40.5 (18.7)
39.5 ***
21.5 (11.4)
44.806
21.0 *** ¤¤¤
p < .001
Total number
of words
Number of
correct nouns
Note. * = p < .05, ** = p < .01, *** = p < .001 when NC vs. miAD and NC vs. moAD, and
¤ = p < .05, ¤¤ = p < .01, ¤¤¤ p < .001 when miAD vs. moAD.
Results
99
NC group, significant reduction in word generation occurred in both AD groups,
and the moAD group’s performance was significantly poorer than that of the miAD
group across all semantic categories (see Appendix 5:1).
9.1.1 Number of correct nouns
Incorrect responses were found in all subject groups and in all semantic categories
(see Table 7 and 11). The difference in the number of all correctly produced nouns
between the three groups was highly significant (p < .001). There was a remarkable
reduction in the total number of correctly produced nouns in the miAD group and
the moAD group. The post hoc pair-wise analyses (see Appendix 5:2a) demonstrated that the NC group produced significantly more correct words in the task than
the miAD group and the moAD group (in both, p < .001). When the AD groups
were compared to each other, a significant difference was noticed in their performances, with the miAD group generating significantly more correct nouns in the
whole task than the moAD group (p < .001).
A similar pattern in the performance of the subject groups was found at the
level of individual semantic categories. A significant difference emerged among the
groups in the number of correct nouns throughout all categories (in each case,
p < .001). Compared to the NC group, a significant reduction in the number of correct responses was found in both the miAD and the moAD group, and the miAD
group generated significantly more correct words than the moAD group for all categories (see Appendix 5:2a).
According to the mean frequencies in all subject groups, most correct words
were produced for the category of animals, whereas the fewest correct responses
were generated for the category of vegetables (see Table 7). The Friedman test indicated that there was a significant difference in the number of correct words produced for the different categories in each subject group (NC group: χ2 = 56.8 (df = 3),
p < .001, miAD group: χ2 = 31.5 (df = 3), p < .001, and moAD group: χ2 = 10.1
(df = 3), p < .05). The post hoc pair-wise comparison revealed statistically significant differences in the number of correct responses between the semantic categories
in the NC group and the miAD group. In the moAD group, however, the pair-wise
analyses revealed no statistically significant differences between the categories, but
some of the differences in the sums of ranks approached the significant level (see
Appendix 5:2b).
There was no statistically significant difference in the number of correct words
produced for the whole task or the individual categories between the male and female
participants in the NC group, in which the distribution of gender was fairly equal
(16 men and 14 women; see Table 8).
100
Results
Table 8. Number of correct nouns produced by the male and
Male participants
(n = 16)
Female participants
(n = 14)
Category
M (SD)
Mdn
M (SD)
Mdn
Mann-Whitney
U test
Clothes
15.3 (4.4)
15.0
16.3 (4.7)
17.0
U = 94.5
p = .473, n.s.
Vegetables
10.4 (2.4)
10.0
12.5 (3.8)
13.0
U = 76.5
p = .142, n.s.
Vehicles
13.9 (2.4)
13.0
12.6 (3.1)
12.5
U = 85.5
p = .275, n.s.
Animals
18.7 (3.7)
18.0
19.4 (5.9)
18.5
U = 111.5
p = .984, n.s.
All categories
58.3 (9.2)
55.5
60.8 (13.9)
63.5
U = 102.0
p = .697, n.s.
Note. n.s. = non significant.
9.1.2 Clustering and switching
The subject groups indicated varying numbers of switches between the subcategories (e.g., from farm animals to birds) when performing the task (see Table 9). The
total score for switching indicated a significant overall difference among the groups
(p < .001). The post hoc analyses (see Appendix 5:3.) revealed that the NC group
was able to more easily shift from one subcategory to another than either the miAD
group or the moAD group, and that the miAD group was significantly better at
switching than the moAD group (in each case, p < .001).
When generating words in a category, the NC group switched five times on
average from one subcategory to another, the miAD group switched from three to
four times, while the moAD group was able to switch subcategories only twice in a
category. In all subject groups, the number of switches remained fairly constant
over the semantic categories. The post hoc pair-wise analysis revealed that the NC
group switched subcategories significantly more often than the miAD group in all
other categories but clothes (vegetables and animals p < .05, vehicles p < .001) and
significantly more often than the moAD group in all semantic categories (in each
category, p < .001). The miAD group showed a significantly better switching performance than the moAD group in all categories (clothes p < .001, vegetables and
animals p < .01, vehicles p < .05).
Results
101
Table 9. Clustering and switching in the noun fluency tasks
NC
(n = 30)
miAD
(n = 20)
moAD
(n = 20)
M (SD)
Mdn
M (SD)
Mdn
M (SD)
Mdn
H (df = 2)
p-value
Clothes
5.0 (1.9)
5.0
4.5 (1.9)
4.0
2.2 (1.9)
2.0 *** ¤¤¤
19.562
p < .001
Vegetables
5.3 (1.8)
5.0
4.2 (2.5)
3.5 *
2.2 (2.1)
2.0 *** ¤¤
23.274
p < .001
Vehicles
5.2 (1.3)
5.0
3.5 (1.9)
3.5 ***
2.7 (4.1)
1.5 *** ¤
23.653
p < .001
Animals
4.9 (1.9)
5.0
3.6 (2.2)
3.0 *
1.8 (1.7)
2.0 *** ¤¤
24.440
p < .001
All categories
20.4 (3.7)
20.0
15.7 (6.2)
14.5 ***
8.8 (7.1)
8.0 *** ¤¤¤
32.408
p < .001
Clothes
4.9 (1.7)
5.0
3.9 (1.9)
4.0 *
1.9 (1.4)
2.0 *** ¤¤¤
26.978
p < .001
Vegetables
4.1 (1.7)
5.0
2.9 (1.8)
3.0 **
1.7 (1.2)
2.0 *** ¤
23.581
p < .001
Vehicles
4.6 (1.4)
5.0
2.7 (1.3)
3.0 ***
1.6 (1.2)
1.5 *** ¤¤
36.464
p < .001
Animals
5.0 (1.7)
4.0
3.5 (1.7)
3.0 **
2.0 (1.4)
1.5 *** ¤¤
29.509
p < .001
All categories
18.6 (3.8)
19.0
13.0 (5.1)
12.5 ***
7.2 (4.0)
6.5 *** ¤¤¤
40.678
p < .001
Clothes
2.2 (1.0)
2.1
1.7 (0.9)
1.4 (*)
1.6 (1.6)
1.1 *
7.775
p < .05
Vegetables
1.4 (0.7)
1.3
1.1 (0.9)
0.9
1.6 (1.3)
1.5
3.214
p = .201, n.s.
Vehicles
1.5 (0.6)
1.5
1.7 (1.6)
1.5
1.6 (1.7)
1.0
0.820
p = .664, n.s.
Animals
2.8 (1.2)
2.8
2.3 (0.9)
2.6
2.6 (2.6)
1.8
3.367
p = .186, n.s.
All categories
1.8 (0.4)
1.7
1.5 (0.4)
1.4 **
1.5 (0.8)
1.4 *
8.430
p < .05
Clothes
93.0 (9.0)
94.0
85.8 (14.1)
89.2 *
73.5 (30.6)
85.4 **
8.341
p < .05
Vegetables
82.7 (16.3)
85.2
71.9 (25.9)
71.4
81.5 (21.6)
88.2
2.523
p = .283, n.s.
Vehicles
87.5 (14.0)
92.0
80.2 (21.5)
86.9
68.5 (38.1)
86.6
3.582
p = .167, n.s.
Animals
95.3 (5.6)
95.3
92.6 (9.8)
100.00
83.1 (29.7)
94.4
1.086
p = 582, n.s.
All categories
91.0 (4.6)
92.2
85.2 (6.7)
87.2 ***
78.5 (18.4)
81.9 **
12.950
p < .01
Variable
Number of switches
Number of clusters
Cluster size
Nouns in clusters (%)
Note. * = p < .05, ** = p < .01, *** = p < .001 when NC vs. miAD and NC vs. moAD, and ¤ =
p < .05, ¤¤ = p < .01, ¤¤¤ p < .001 when miAD vs. moAD. n.s. = non significant.
102
Results
The number of clusters of closely related nouns differed significantly among
the subject groups, both at the level of overall performance across the categories
(p < .001) and at the level of individual categories (in each category, p < .001; see
Table 9). The mean number of the clusters varied from 4 to 5 clusters in the NC
group and from 3 to 4 clusters in the miAD group. The moAD group produced
approximately two clusters for each semantic category. The total number of clusters
produced by the NC group was significantly higher than that produced by the two
AD groups (in each, p < .001), as was the number of clusters in all categories (see
Appendix 5:4). Furthermore, the miAD group produced significantly more clusters
than the moAD both when the number of all clusters (p < .001) and the number of
clusters in the single categories were analysed (clothes p < .001, vegetables p < .05,
vehicles, and animals p < .01).
The overall performance of the groups indicated a significant difference in
the cluster size (p < .05; see Table 9). When comparing the overall size of the noun
clusters, it was noticed that the NC group created significantly larger clusters than
the miAD group (p < .01) and the moAD group (p < .05), whereas the clusters
formed by the miAD group and the moAD group did not differ in size (see Appendix 5:5).
Depending on the category, all groups produced clusters consisting of two to
four words (i.e., cluster size between one and three). The category of vegetables
seemed to bring about the smallest clusters, while the animals evoked the largest
ones. At the level of individual semantic categories, however, the size of the clusters
among the subject groups differed significantly only in the category of clothes
(p < .05). The post hoc pair-wise analysis revealed that the moAD group formed
significantly smaller clusters for the category of clothes than the NC group (p < .01)
and that the difference in the cluster size between the NC group and the miAD
group approached the statistically significant level (p = .073). The cluster size difference between the miAD group and the moAD group was not significant.
Considering the coherence of the overall performance and the ability to use
the strategy of clustering words, a difference among the subject groups emerged in
the proportion of nouns in clusters (p < .01; see Table 9). The post hoc pair-wise
analyses (see Appendix 5:6) revealed that the NC group clustered more nouns than
the miAD group (p < .001) and the moAD group (p < .01), while the miAD group
and the moAD group clustered nouns to the same extent. In all groups and all semantic
categories, more than 70% of the words were clustered. However, for the category
of clothes, a difference in the performance among the groups emerged (p < .05).
The NC group clustered nouns more efficiently than the miAD group (p < .05) and
the moAD group (p < .01) but the difference between the two AD groups did not
reach the level of statistical significance.
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103
9.1.3 Summary of the results and discussion
The findings of this study indicated that relative to the NC group, the miAD group
and the moAD group showed a significant reduction in production of responses for
the semantic fluency task with noun categories, and that the miAD group was able
to produce significantly more responses for the categories than the moAD group.
The different semantic categories seemed to evoke different amounts of correct responses: in all subject groups, the category of animals provoked most words, whereas
the category of vegetables brought about the fewest responses. All in all, the performance of the subject groups indicated that the semantic categories elicited varying
numbers of responses in the NC group and the miAD group, whereas the moAD
group showed very little variation in the number of words produced across the categories. Although both AD groups produced significantly fewer clusters and switches
for the categories, they were able to generate nouns clustered together according to
some shared properties. It was noticed that the performance of all subject groups
contained erroneous responses, which is reported in more detail in 9.2.1 and discussed in 9.2.6.
Overall performance on the noun fluency tasks
When the number of correct and uniquely produced nouns was taken into account, a
significant reduction in the word production was found in both of the AD groups
relative to the NC group (see 9.1.1). The finding is consistent with several other
studies, such as Rosen (1980), Tröster et al. (1989; 1998), Binetti et al. (1995),
Chertkow and Bub (1990), Hodges and Patterson (1995), Crossley et al. (1997), and
Troyer, Moscovitch, Winocur, Leach et al. (1998; see Table 4). It was also noticed
that the moAD group produced significantly fewer correct nouns than the miAD
group, a finding in accordance with Bayles et al. (1993), Mickanin et al. (1994),
Crossley et al. (1997), and Hodges and Patterson (1995).
Unfortunately, as can be seen in Table 4, the studies cannot be directly compared to each other, for the following methodological reasons. The sizes of the subject groups differ, as do the tests used to assess the degree of dementia. The cut-off
scores to indicate the stage of dementia also vary. Furthermore, in many studies, AD
patients of different degrees of dementia were often considered one group or only
patients with mild AD were involved in the studies. The semantic categories chosen
for the studies are often dissimilar. In some studies, only one semantic category was
used to tap the fluency performance, while in others data was collected from several
semantic categories. Furthermore, instead of providing scores for individual semantic categories, the data in some cases was presented as a combined score over various categories. Unequal time limits (e.g., 60 or 90 s) were given for subjects to
perform the fluency task. Finally, some studies tend not to provide the reader with
sufficient basic frequency data, such as standard deviations.
Nevertheless, when comparing the results obtained from different studies on
the animal fluency task, which is the most often used form of the task, one can note
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Results
that the number of correctly produced words differs slightly among the studies (see
Table 4). The healthy control subjects of the present study produced a mean of 19.0
(SD = 4.7) correct words, which is nearly the same as the number of words produced by English-speaking healthy controls with approximately the same mean age
in the study of Chertkow and Bub (1990) and Chan et al. (1993). On the other hand,
the number of correct responses given by the control subjects in the present study
was greater than that of Italian-speaking subjects reported by Binetti et al. (1995)
and that of English-speaking subjects reported by Hodges and Patterson (1995).
However, French-speaking subjects in the study of Pasquier et al. (1995) produced
more correct responses than the subjects in the present study. The healthy controls
of the present study produced fewer correct words than their counterparts also in the
study of Kontiola et al. (1990), which is another study conducted with Finnishspeaking elderly subjects providing data, for example, on the fluency task.
When the performance of the patients with mild AD is compared to that from
studies in which MMSE was used in assessing the degree of dementia, the mean of
12.3 correct words (SD = 5.7) produced by the miAD group in the present study
seemed to be higher than that in the study of Binetti et al. (1995) and Hodges and
Patterson (1995), and lower than in the study of Pasquier et al. (1995; see Table 4).
There are very few comparable studies on patients with moderate AD. However, the
moAD group of the present study produced approximately the same number of correct words (M = 6.3, SD = 4.0) as their counterparts in the study of Chertkow and
Bub (1990), but more than those in the study of Hodges and Patterson (1995).
When comparing the results obtained in the present study to those reported
by Hodges and Patterson (1995; see also Chertkow & Bub 1990), it can be found
that not only the animal category, but also the category of vehicles provoked more
responses in all subject groups in the present study. The miAD group in the present
study produced slightly more correct responses than the patients with minimal and
mild degree of dementia in the study of Hodges and Patterson. The differences in
the mean age between the groups are not likely to explain the finding (see Huff et al.
1986; Bayles et al. 1993; Mickanin et al. 1994; Crossley et al. 1997). The method
used for assessing the degree of dementia of the AD patients may not contribute to
the differing performances either because the MMSE was used in both studies. A
possible reason for the differences between the studies may be the way the cut-off
points were set to indicate the degree of dementia. The cut-off point on the MMSE
to denote the degree of minimal dementia (25.6, SD=1.8) may be too low in the
study of Hodges and Patterson, as the MMSE scores in early AD usually range from
24 to 30 points and in mild AD from 18 to 26 points (Erkinjuntti, Rinne & Soininen
2001; Pirttilä & Erkinjuntti 2001; see Table 2). Alternatively, some of the patients of
the miAD group in the present study may have a very mild (minimal) degree of
dementia and, thus, high naming scores, and some of the AD patients in the Hodges
and Patterson study could be assessed as moderately demented, which could explain
their achieving lower scores on naming. The group of patients with moderate dementia
in the Hodges and Patterson study seems also to involve patients whose degree of
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105
dementia had already advanced to the severe stage of dementia, which may explain
the group’s lower number of correct words in the animal and vehicle fluency tasks,
relative to the scores of the moAD group in the present study.
Concerning the number of correct words, it is not easy to exactly pinpoint the
reasons underlying the differences among the studies. Partly, it may be a question of
the number of semantic categories used in the studies. If only one category had been
used, as was the case in the study of Kontiola et al. (1990) and Pasquier et al. (1995),
the subjects might not have tired of listing different words after another and thus
might have produced a high number of correct responses for the task. In this study,
as well as in the study of Hodges and Patterson (1995), several semantic categories
were involved implying that the subjects may have become fatigued during the course
of testing. In the latter study, the category of animals appeared as the first category
for the word production, whereas it was the last noun category introduced in this
study. Nevertheless, the scores of the present study were still higher than those in
the study of Hodges and Patterson between the groups of healthy elderly adults, the
groups of mildly demented AD patients, and the groups of moderately demented
AD patients.
Using only one semantic category in the task does not seem to fully explain
the better performance, reported by Binetti et al. (1995; cf. Crossley et al. 1997)
who used only the category of animals for the task and reported a strikingly poorer
number of correct animal words (M = 14.1, SD = 5.4) in the group of healthy control
subjects compared to other studies in which either one or more semantic categories
were administered. Differences in the languages and the cultural backgrounds of the
subjects in the studies may partly explain the variability. However, a high number of
correct responses was obtained by speakers with different backgrounds, that is, by
French-speaking subjects as shown in the study of Pasquier et al. (1995), by Finnishspeaking subjects as indicated in the study of Kontiola et al. (1990) and in the present
study, as well as by English-speaking subjects as reported by Hodges and Patterson
(1995). By contrast, Italian-speaking subjects in the study of Binetti et al. (1995) did
a lot worse than the French-speaking subjects.
The gender of the subjects has been considered an important factor in explaining the semantic fluency performance (e.g., Monsch et al. 1992; Capitani et al.
1999; see 5.1). For example, Monsch et al. found that both in the normal control
group and in the group of AD patients, female participants produced significantly
more correct responses than male participants for such categories as fruit, vegetables,
and animals. Capitani et al. showed that male subjects named significantly more
words denoting tools than female subjects. Unfortunately, because both the miAD
and the moAD group of the present study had more female than male subjects, the
effect of gender on their semantic fluency performance could not be accomplished
(see 9.1.1). However, the comparison between the male and female subjects in the
NC group indicated that gender did not play a role in the semantic fluency performance of the subjects in any of the semantic noun categories. The finding is consistent with that of Crossley et al. (1997) and Troyer (2000), who found no gender
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Results
effect on the animal fluency task, and inconsistent with the findings of Monsch et al.
(1992) and Capitani et al. (1999). Consequently, gender may not have played a significant role in the noun fluency performance in the AD groups.
Clustering and switching
Although the miAD group showed a significantly better ability to cluster nouns and
to switch between the subcategories than the moAD group, the performance of both
AD groups in clustering and switching was remarkably poorer than the NC group’s
performance (see 9.1.2). These findings accord with those presented by Martin and
Fedio (1983), Ober et al. (1986), Tröster et al. (1989, 1998), Binetti et al. (1995),
Carew et al. (1997), Beatty et al. (1997, 2000), and Troyer, Moscovitch, Winocur,
Leach et al. (1998).
Regardless of the reduction in the number of the words, and in the number of
switches and clusters, all subject groups produced clusters of similar size (from two
to four words) for all other categories but clothes, for which the moAD group produced
smaller clusters than the NC group. This finding supports the findings of Binetti et
al. (1995) who reported that the average cluster size in the animal fluency task was
equal (approximately four words) when comparing the performance of the normal
control subjects, the mild AD patients, and the moderate-to-severe AD patients. In
the present study, the average number of nouns within a cluster in the animal fluency
task approached four in the group of normal control subjects, three in the group of
mild AD patients, and four in the group of moderate AD patients (see Table 9).
Concerning cluster size, however, the finding is in contrast with that presented by
Troyer, Moscovitch, Winocur, Leach et al. (1998) and Tröster et al. (1998; see also
Beatty et al. 1997, 2000), who observed that the normal control subjects produced
significantly larger clusters (between two and three words) than the AD patients
(between one and two words) in the animal fluency task (see Table 10). It is worth
noting, however, that a general trend appeared in the present study indicating that
the average cluster size calculated over all semantic categories was significantly
smaller in each AD group than in the NC group.
The way the size of the cluster was calculated cannot explain the differences
between these studies. The study of Troyer, Moscovitch, Winocur, Leach et al. (1998)
and Tröster et al. (1998), as well as the present study, used a minimum of two subsequent words to form a cluster, ending up with contrasting findings (see Table 10).
On the other hand, Binetti et al. (1995) considered a minimum of three words in a
cluster and yet did not find a difference in the cluster size between the normal control group and the group of the AD patients. The difference cannot be well explained
by the size of the subject groups or the mean age of these subjects. The difference in
the mean age is only within a range of a few years among the studies, and age was
found to be an insensitive factor affecting the semantic fluency performance in AD
in several studies (Huff et al. 1986; Bayles et al. 1993; Mickanin et al. 1994; Crossley
et al. 1997; see 5.1). Furthermore, Troyer et al. (1997; see also Troyer 2000) did not
Results
107
Table 10. Data on the animal fluency task performed by normal control
subjects (NC) and Alzheimer’s patients (AD) in different studies
Troyer, Moscovitch,
Winocur, Leach et al.
(1998)
Tröster et al. (1998)
The present study
NC
AD
NC
AD
NC
miAD
moAD
Variable
M (SD)
M (SD)
M (SD)
M (SD)
M (SD)
Mdn
M (SD)
Mdn
M (SD)
Mdn
Number of
subjects
38
23
30
30
30
20
20
Age
73.8 (6.2) 70.3 (8.4)
70.8 (7.0) 69.7 (5.9)
66.7 (5.5) 65.0 (10.3) 67.4 (8.7)
66.0
64.5
64.5
Education
12.6 (2.7) 13.0 (3.3)
13.9 (2.4) 13.0 (1.7)
9.7 (3.3)
9.0
Degree of
dementia+
-
10.5 (3.7) 10.1 (3.6)
9.0
10.0
118.8 (13.1) 137.0 (4.3) 107.4 (13.4) 28.9 (0.9) 23.5 (2.0) 15.9 (2.4)
mild
moderate
29.0
23.5 *
17.0 * ¤
Number of correct 17.9 (4.2) 8.3 (4.2) *
words
17.8 (4.1) 7.6 (4.6) *
19.0 (4.7) 12.3 (5.7) 6.3 (4.0)
18.5
12.0 *
5.5 * ¤
Number of
switches
8.3 (2.4)
5.1 (2.9) *
7.6 (2.6)
3.8 (2.9) *
4.9 (1.9) 3.6 (2.2)
5.0
3.0 *
1.8 (1.7)
2.0 * ¤
Cluster size
1.1 (0.6)
0.6 (0.4) *
1.3 (0.5)
0.8 (0.6) *
2.8 (1.2) 2.3 (0.9)
2.8
2.6
2.6 (2.6)
1.8
Note. +For evaluating the degree of dementia, Dementia Rating Scale (DRS) was applied in the
study of Troyer, Moscovitch, Winocur, Leach et al. (1998) and Tröster et al. (1998), while Mini
Mental State Examination (MMSE) was used in the present study.
* = a statistical difference between the AD group and the NC group in different studies.
¤ = a statistical difference between the miAD group and the moAD group in
the present study.
find a difference in the cluster size in the animal category naming task between
younger (mean age 22.3 years, SD = 3.8) and older healthy adults (mean age 73.3,
SD = 6.5; see 5.1). Moreover, the differences between the performances of the subjects cannot be explained by the level of education, which is another factor found
not to affect the fluency performance in AD (Rosen 1980; Crossley et al. 1997; see
5.1). Besides, the subjects in the present study were the least educated but they
produced more words and larger clusters on average than their counterparts in the
other studies.
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Results
One way of explaining the differing findings may be the great number of
perseverations produced by the AD subjects in the present study and their possible
effect on the clustering performance in the AD groups (see 9.2.2, 9.2.6, 10.1.2, and
10.1.3). However, the studies are not comparable because Troyer, Moscovitch,
Winocur, Leach et al. (1998) and Tröster et al. (1998) did not provide a report on the
occurrence of perseverations in their AD groups. In the present study, all repetitions
of responses were included in the clusters, following the protocol of Troyer et al.
(1997). Another alternative explanation for the differing findings may be the use of
different methods in evaluating the severity of dementia and the criteria used in
forming the subject groups. In the study of Troyer, Moscovitch, Winocur, Leach et
al. (1998) and Tröster et al. (1998), dementia was rated mild using the Dementia
Rating Scale (Mattis 1988, in Troyer, Moscovitch, Winocur, Leach et al. 1998),
while in the study of Binetti et al. (1995) and the present study, the degree of dementia was evaluated using the MMSE (Folstein et al. 1975). Yet another possible factor
contributing to the contrasting findings may be the different statistical methods used
in the studies, this study having used non-parametric methods, while parametric
methods were applied in the study of Troyer, Moscovitch, Winocur, Leach et al.
(1998) and Tröster et al. (1998).
When the proportion of all clustered words was taken into account, the subject
groups showed a coherent pattern of performance by clustering more than 70% of
all nouns. In general, the tendency to cluster nouns was seemingly not disturbed by
the production of single words between the clusters in any subject group. However,
when producing words for clothes, the miAD and the moAD group indicated an
exceptional performance by clustering less and producing more single words than
the NC group. The tendency of the miAD group to perseverate words and the tendency
of the moAD group to produce words from outside the semantic category boundaries
for the category of clothes (see 9.2.2) may explain why the proportion of words in
clusters remained smaller in their groups than in the NC group. The high proportion
of words within clusters also implies that the production of nouns was guided by a
strategy of tying them together by some relation (see 9.1.2, 9.1.3, and 9.2.3).
In conclusion, the word production during the noun fluency task, when counted
as the number of total and correct words, switches, and clusters, was significantly
diminished in both AD groups, the moAD group faring significantly worse than the
miAD group. However, the performance of the AD groups appeared to be semantically as coherent as that of the NC group when the size of the clusters and the
proportion of the words in the clusters were taken into account.
Results
109
9.2 Analysis of the contents of the responses on the
noun fluency tasks
The qualitative analysis of the noun fluency task involved an error analysis in which
the proportion of correct words, intrusions, and perseverations in each subject group
and in each semantic category was determined. The strategies and the scope of
semantic space used by the subject groups, as well as the degree of prototypicality
and frequency of nouns produced for the task, were also analyzed.
9.2.1 Proportion of correct nouns
When considering the overall performance, the proportion of all correctly produced
nouns indicated a significant overall difference among the groups (p < .001; see
Table 11). The NC group produced more than 90% of the nouns correctly (i.e.,
unique words belonging to the given semantic category), the proportion of correct
responses being some 80% in the miAD group and 70% in the moAD group. The
post hoc pair-wise analyses showed that these differences were statistically significant (NC vs. miAD, p < .01; NC vs. moAD, p < .001; miAD vs. moAD, p < .05;
see Appendix 5:7).
In all subject groups, the average frequencies indicated that the category of
animals had the highest rate of correct responses, whereas the lowest rate was found
in the category of vegetables (see also 9.1.1). A significant group difference emerged
in the proportion of correct responses at the level of each semantic category (clothes,
vehicles, and animals p < .001; vegetables p < .05). The post hoc pair-wise analysis
indicated that the miAD group produced significantly more errors than the NC group
for the categories of clothes (p < .05), as well as for the categories of vehicles and
animals (in each, p < .01). When the NC group and the moAD group were compared, it was noticed that the moAD group produced more erroneous responses for
all categories (clothes, vehicles, and animals p < .001; vegetables p < .01). The
proportion of correct words in the categories remained the same between the AD
groups.
9.2.2 Proportion of intrusions and perseverations
Intrusions (i.e., words not belonging to the given semantic category) were
produced by all subject groups (p > .05), but their total number remained fairly low
in all groups (see Table 11). The performance of 17/30 subjects in the NC group, 12/
20 subjects in the miAD group, and 14/20 subjects in the moAD group contained
intrusions in at least one of the four semantic categories. According to the average
frequencies, most of the intrusions emerged in the categories of clothes and vegetables,
with the lowest rates observed in the category of animals. All subject groups seemed
to produce intrusions in a similar vein for the individual categories, except for the
category of clothes (p < .05), for which the moAD group generated significantly
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Results
Table 11. Proportion of correct words, intrusions, and perseverations
in the noun fluency tasks
NC
(n = 30)
miAD
(n = 20)
moAD
(n = 20)
M (SD)
Mdn
M (SD)
Mdn
M (SD)
Mdn
H (df = 2)
p-value
Clothes
94.0 (12.0)
100.0
86.1 (13.5)
89.4 *
72.5 (27.3)
83.8 ***
15.552
p < .001
Vegetables
85.3 (17.2)
90.9
73.4 (26.9)
73.2
66.3 (22.2)
69.1 **
8.142
p < .05
Vehicles
95.3 (6.1)
100.0
81.6 (17.7)
85.3 **
69.2 (28.7)
76.4 ***
16.629
p < .001
Animals
97.3 (3.8)
100.0
89.9 (10.8)
91.6 **
75.0 (27.4)
84.5 ***
15.074
p < .001
All categories
93.1 (6.9)
95.5
83.3 (13.7)
85.8 **
71.1 (18.2)
71.7 *** ¤
26.160
p < .001
Clothes
4.7 (10.7)
0.0
2.5 (6.1)
0.0
21.7 (29.7)
8.6 * ¤
8.849
p < .05
Vegetables
9.8 (16.3)
0.0
14.8 (26.7)
0.0
9.3 (18.2)
0.0
0.754
p = .686, n.s.
Vehicles
0.8 (2.1)
0.0
3.7 (9.6)
0.0
3.4 (9.4)
0.0
0.599
p = .741, n.s.
Animals
0.0 (0.0)
0.0
0.5 (1.6)
0.0
4.0 (12.0)
0.0
4.483
p = .106, n.s.
All categories
3.8 (4.9)
1.6
4.4 (6.6)
2.3
7.9 (7.7)
3.4
2.522
p = .283, n.s.
Clothes
1.3 (3.2)
0.0
11.4 (12.9)
9.8 ***
5.8 (9.5)
0.0
14.220
p < .001
Vegetables
4.9 (8.0)
0.0
11.8 (14.6)
6.0
24.4 (24.4)
23.6 **
9.863
p < .01
Vehicles
3.9 (6.4)
0.0
14.7 (16.2)
12.5 **
27.4 (26.8)
19.1 ***
14.208
p < .001
Animals
2.7 (3.8)
0.0
9.6 (10.9)
7.2 *
21.0 (21.0)
15.5 ***
14.401
p < .001
All categories
3.1 (3.7)
1.8
12.3 (10.9)
10.8 ***
21.0 (15.0)
16.5 *** ¤
27.678
p < .001
Variable
Correct nouns (%)
Intrusions (%)
Perseverations (%)
Note. * = p < .05, ** = p < .01, *** = p < .001 when NC vs. miAD and NC vs.
moAD, and ¤ = p < .05 when miAD vs. moAD. n.s. = non significant.
Results
111
Table 12. Number of semantically related and
unrelated intrusions produced in the noun fluency tasks
NC
(n = 30)
miAD
(n = 20)
moAD
(n = 20)
Type of intrusions
M (SD)
Mdn
M (SD)
Mdn
M (SD)
Mdn
Semantically
related intrusions
2.5 (3.3)
1.0
1.8 (2.7)
1.0
1.7 (2.1)
1.0
Semantically
unrelated
intrusions
0.03 (0.2)
0.0
0.3 (0.7)
0.0
0.5 (1.1)
0.0
Z-value+
p-value
-3.610
p < .001
-3.114
p < .01
-1.944
p < .05
Note. +Wilcoxon Signed Ranks Test: * = p < .05, ** = p < .01, *** = p <. 001
more intrusions than the NC group and the miAD group (in both cases, p < .05; see
Appendix 5:8).
Most of the intrusions were semantically related rather than unrelated to the
given category in the NC group (p < .001) and the miAD group ( p < .01), as well as
in the moAD group (p < .05; see Table 12). The intrusions produced for the category
of clothes consisted mostly of words referring to other pieces of fabric, such as
carpets, wall hangings, tablecloths, curtains, towels and bed linen. The outside-category words produced for the category of vegetables were in most cases different
kinds of berries, fruit, and grains. In the category of vehicles, toys, working machinery, and animals other than beasts of burden were classified as intrusions. As for the
category of animals, words without a specific referent (e.g., ‘buzzer’) were considered inappropriate words. Intrusions that had no semantic relationship to the given
category seemed to be words that were activated by the test situation and the place
of examination (e.g., ‘recorder’ referring to the tape-recorder on the table) or words
from outside the test context (e.g., ‘smoked fish’ for clothes).
Perseverations (i.e., repetition of previously produced words) were produced
in all subject groups and in each category (see Table 11). Perseverations were generated at least once by 22/30 subjects in the NC group, 18/20 subjects in the miAD
group, and 18/20 subjects in the moAD group. The average number of total
perseverations in the NC group was 3.1% (SD = 3.7) and their number increased both
in the miAD group (M = 12.3%, SD = 10.9) and in the moAD group (M = 21.0%, SD
= 15.0). The total number of perseverations over the categories indicated a significant
difference both among (p < .001) and between the subject groups (see Appendix 5:9).
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Results
There was a statistically significant difference in the tendency to perseverate
words among the different subject groups in all semantic categories (clothes, vehicles,
and animals p < .001; vegetables p < .01). The post hoc pair-wise analysis indicated
that the miAD group produced significantly more perseverations than the NC group
when naming items for the category of clothes (p < .001), vehicles (p < .01), and
animals (p < .05). The moAD group produced significantly more perseverations
than the NC group for the categories of vegetables (p < .01), vehicles, and animals
(in each, p < .001). The two AD groups’ rates of perseverations did not differ in any
single category. The number of perseverations seemed to fluctuate from one category
to another in each subject group. In the moAD group, only a few perseverations
were produced for the first category (i.e., clothes), after which their number increased
considerably during the second category (i.e., vegetables) and remained high for the
rest of the categories.
9.2.3 Clustering strategies
As shown in Table 13, in all subject groups more than 60% of the clusters were
produced using the pure semantic strategy (i.e., clusters formed on the basis of semantic relatedness without phonological resemblance between the words, e.g.,
‘leijona’, ‘tiikeri’ / ‘lion’, ‘tiger’), followed by the proportion of approximately 30%
of clusters formed applying the mixed strategy (i.e., clusters formed on the basis of
semantic and phonological relatedness between the words, e.g., ‘kissa’, ‘koira’, ‘kani’
/ ‘cat’, ‘dog’, ‘rabbit’ ), and some 5% of the clusters with the pure phonological
strategy (i.e., clusters formed on the basis of phonological resemblance between the
words, e.g., ‘sika’, ‘susi’ / ‘pig’, ‘wolf’). However, at the level of single semantic
categories, the ratios of different strategies used by the subject groups varied to
some extent. The mixed and the phonemic strategies were utilized to a similar extent
in all subject groups in all semantic categories. The extent of the use of the semantic
strategy among the subject groups was significantly different in the categories of
clothes and animals (in each category, p < .05).
The post hoc pair-wise analyses (see Appendix 5:10) indicated that the moAD
group produced significantly more clusters of clothes than the NC group (p < .01)
and the miAD group (p < .05) by applying the pure semantic strategy, whereas the
difference between the NC group and the miAD group was not significant. Unlike
the NC group and the miAD group, the moAD group tended not to utilize the phonemic resemblance between the words for the category of clothes. On the other
hand, the moAD group produced purely semantic clusters for the animal category
significantly less often than the NC group and the miAD group (in each, p < .05).
Again, the difference between the NC group and the miAD group in the use of the
semantic strategy did not reach statistical significance. The moAD group produced
more than 50% of the animal words using the mixed strategy that involved a combination of semantic and phonemic relatedness between the words within the clusters.
Results
113
Table 13. Clustering strategies in the noun fluency tasks
NC
(n = 30)
miAD
(n = 18)
moAD
(n = 15)
M (SD)
Mdn
M (SD)
Mdn
M (SD)
Mdn
H (df = 2) p-value
Clothes
74.9 (19.5)
77.5
74.2 (27.3)
80.0
90.7 (17.0)
100.00 ** ¤
7.007
p < .05
Vegetables
63.7 (28.3)
69.0
57.2 (37.3)
63.3
50.0 (45.0)
66.7
0.760
p = .684, n.s.
Vehicles
66.6 (23.0)
66.7
68.1 (35.5)
66.7
66.7 (41.2)
100.00
0.723
p = .697, n.s.
Animals
60.2 (16.5)
66.7
58.1 (25.5)
66.7
31.8 (37.5)
0.0 * ¤
6.240
p < .05
All categories
66.8 (8.5)
66.7
66.3 (16.2)
69.0
62.8 (17.2)
63.6
1.146
p = .564, n.s.
Clothes
20.0 (18.2)
16.7
20.1 (28.2)
7.1
9.3 (17.0)
0.0
3.915
p = .141, n.s.
Vegetables
28.1 (23.7)
25.0
33.9 (35.6)
26.7
46.7 (42.8)
33.3
1.514
p = .469, n.s.
Vehicles
29.4 (21.2)
25.0
30.1 (36.3)
29.2
30.0 (41.9)
0.0
0.996
p = .608, n.s.
Animals
31.3 (15.4)
33.3
38.7 (26.2)
33.3
58.8 (42.4)
50.0
4.313
p = .116, n.s.
All categories
26.1 (9.29)
24.3
27.8 (18.3)
16.1
32.1 (15.1)
32.1
1.859
p = .395, n.s.
Clothes
5.1 (9.8)
0.0
5.6 (13.1)
0.0
0.0 (0.0)
0.0
4.542
p = .103, n.s.
Vegetables
8.2 (11.7)
0.0
8.9 (13.5)
0.0
3.3 (12.9)
0.0
3.704
p = .157, n.s.
Vehicles
4.0 (8.3)
0.0
1.9 (7.9)
0.0
3.3 (12.9)
0.0
2.261
p = .323, n.s.
Animals
8.5 (11.3)
0.0
3.2 (7.6)
0.0
9.4 (16.9)
0.0
2.642
p = .267, n.s.
All categories
7.1 (5.3)
6.9
5.9 (5.7)
6.3
5.0 (10.6)
0.0
4.784
p = .091, n.s.
Strategy
Semantic strategy (%)
Mixed strategy (%)
Phonological strategy (%)
Note. * = p < .05, ** p < .01 when NC vs. moAD and ¤ = p < .05 when miAD
vs. moAD. n.s. = non significant.
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Results
9.2.4 Number and variety of different semantic subcategories
All four noun categories combined, the number of different subcategories
activated by the NC group was approximately 15, whereas the miAD group was
able to produce nouns from 10 and the moAD group from 5 different semantic
subgroups (see Table 14). The total number of the semantic subcategories distinguished the subject groups overall (p < .001), and the pairwise differences were
significant between all subject groups (between all, p < .001; see Appendix 5:11).
For each category, the NC group was able to produce clusters of nouns from
the mean of three to four different subcategories, but the variety of semantic subcategories narrowed down in the AD groups. The miAD was able to activate on average
two or three different semantic subcategories, while the moAD group produced words
from one or two subcategories. The number of semantic subcategories was significantly different among the subject groups in all semantic categories (p < .001). The
post hoc analysis revealed that in each semantic category, a significant decrease in
the number of subcategories took place in both AD groups compared to the NC
group, and a significantly greater decrease occurred in the moAD group relative to
the miAD group (see Appendix 5:11).
When investigating the variety of semantic subcategories, it was observed
that the distribution of the subcategories was rather similar between the NC group
and the miAD group across all semantic categories (see Figures 1-4). The moAD
group showed some reduction even in the use of the most common semantic subcategories. All subject groups tended to prefer clusters in which the words shared thematic properties (i.e., information of the contextual and spatial locations, and causal
and interactional relationships between the objects in a scene, as well as cultural
information) and functional properties (i.e., information concerning the manner and
the rules by which the objects move or interact with the environment or how humans
move when manipulating the objects) rather than combining words according to
their taxonomic relatedness (i.e., by means of the hierarchy of the class inclusion;
see 3.1.2 , 3.2). Physical features, such as part-whole analysis, were also used as a
criterion for clustering by the participants, especially when words were produced
for the category of clothes. Nevertheless, a lack of subcategories did not imply the
subjects’ inability to produce items belonging to a particular subcategory per se.
Rather, depending on the way clusters were formed, strings of subordinate nouns
were embedded within larger thematic clusters (e.g., ‘caps’, ‘ski suits’, ‘fur hats’,
‘hats’, ‘ulster’, ‘summer coat’, ‘jacket’ for outdoor clothes) and some of the subcategories were represented as a single word outside a cluster. The same words were
clustered by various semantic criteria by individual subjects (see Appendix 4A-4D).
When producing words for the category of clothes, outdoor (e.g., ‘coat’, ‘hat’,
‘gloves’) and indoor clothing (e.g., ‘socks’, ‘pants’, ‘skirt’, ‘blouse’) were the most
often produced subcategories in all subject groups (Figure 1; see also Appendix
4A). The third common subcategory for the NC group was different outfits (e.g.,
‘evening dress’, ‘national costume’), whereas underclothes (‘undershirt’, ‘bra’,
Results
115
Table 14. Number of different subcategories produced
for the semantic categories in the noun fluency tasks
NC
(n = 30)
miAD
(n = 20)
moAD
(n = 20)
Category
M (SD)
Mdn
M (SD)
Mdn
M (SD)
Mdn
H
(df=2)
p-value
Clothes
3.7 (1.2)
4.0
2.9 (1.3)
3.0 *
1.7 (1.1)
2.0 *** ¤¤
23.866
p < .001
Vegetables
3.2 (1.3)
3.0
2.1 (1.0)
2.0 **
1.4 (0.7)
1.0 *** ¤
23.482
p < .001
Vehicles
4.0 (1.1)
4.0
2.6 (1.4)
3.0 ***
1.5 (1.2)
1.0 *** ¤
32.347
p < .001
Animals
3.8 (1.0)
4.0
2.9 (1.2)
3.0 **
1.5 (1.1)
1.0 *** ¤¤¤
32.332
p < .001
All
categories
14.7 (2.7)
15.0
10.4 (3.5)
10.0 ***
6.1 (3.4)
5.0 *** ¤¤¤
43.130
p < .001
Note. * = p < .05, ** p < .01, *** = p < .001 when NC vs. miAD and NC vs. moAD,
and ¤ = p < .05, ¤¤ p < .01, ¤¤¤ = p < .001 when miAD vs. moAD.
‘pantihose’) were the third most often used subcategory in the group of miAD and
moAD patients. In the moAD group, coats (e.g., ‘trenchcoat’, ‘fur coat’, ‘raincoat’)
and footwear (e.g., ‘galoshes’, ‘boots’, ‘slippers’, ‘sports shoes’) were nonexistent
as a distinct subcategory.
The distribution of subcategories in the category of vegetables appeared somewhat dissimilar among the subject groups (Figure 2; see also Appendix 4B). When
producing clusters of vegetables, the three most often utilized subcategories were
the sprouts and greens (e.g., ‘cabbage’, ‘pumpkin’, ‘cauliflower’, ‘lettuce’), rootand tuberous vegetables (e.g., ‘potato’, ‘carrot’, ‘rutabaga’) and vegetables used for
salads (e.g., ‘tomato’, ‘cucumber’, ‘lettuce’) in the NC group and the miAD group.
The moAD group produced mostly words from the subcategories of sprouts and
greens and root- and tuberous vegetables. The subcategory of herbs (e.g., ‘dill’,
‘parsley’), onions (e.g., ‘onion’, ‘garlic’), or vegetables used for salads did not belong to the repertoire of subcategories in the moAD group.
116
Results
1.4
NC
Number of subcategories (M)
1.2
miAD
1.0
moAD
0.8
0.6
0.4
0.2
at
co
hi
en
’s
un
cl
de
ot
sh
rw
s
ng
s
irt
ea
r
ts
tfi
ou
om
w
ou
in
td
do
oo
or
rc
cl
lo
ot
th
he
s
es
0.0
Subcategories
Figure 1. The distribution and mean number of the most common
subcategories of clothes in different subject groups.
1.4
NC
1.2
miAD
1.0
moAD
0.8
0.6
0.4
0.2
es
rri
be
es
ic
sp
d
rb
s
an
ca
bb
ag
es
it
fru
he
ro
sp
ro
ut
s
gr an
ee d
ot
ns
an
d
ve tub
ge er
ta ou
bl s
es
us v
ed eg
fo eta
rs b
al les
ad
s
0.0
Subcategories
subcategories of vegetables in different subject groups.
Results
117
1.4
NC
1.2
miAD
1.0
moAD
0.8
0.6
0.4
0.2
m
ea
tra ns
ns of
po m
rta as
m
us
tio s
cl
n
epo
w
ve er
hi ed
on
-ro cles
ad
ve
hi
cl
es
bo
at
s
an
d
sh
ve
ip
hi
s
cl
es
on
w
he
el
s
ve
hi
in cle
w s
in u
te se
rti d
m
ve
e
hi
cl
es
on u
w sed
at
er
0.0
Subcategories
subcategories of vehicles in different subject groups.
1.4
NC
1.2
miAD
1.0
moAD
0.8
0.6
0.4
0.2
in
se
ct
s
ts
pe
s
rd
bi
ts
ro
de
n
s
al
im
w
ild
an
an
ot
ic
ex
fa
rm
an
i
im
m
al
al
s
s
0.0
Subcategories
subcategories of animals in different subject groups.
118
Results
When the subject groups produced clusters of vehicles, they most often
consisted of vehicles used in public transportation (‘bus’, ‘train’, ‘boat’, ‘plane’)
and road traffic (‘bus’, ‘car’, ‘bike’, ‘motorcycle’), as well as of vehicles operated
by muscle power (‘kickboard’, ‘kick sled’, ‘roller skates’, ‘a pair of roller skis’;
Figure 3; see also Appendix 4C). Both the miAD and the moAD group lacked the
subcategory of vehicles used during the winter (e.g., ‘kick sled’, ‘a pair of skis’,
‘snowmobile’, ‘reindeer sleigh’). Furthermore, the moAD group did not produce
the subcategory of vehicles used on the water (e.g., ‘ship’, ‘canoe’, ‘boat’, ‘ferry’).
However, a few items referring to winter vehicles (e.g., ‘a pair of skis’, ‘sleigh’) and
water vehicles (e.g., ‘rowboat’, ‘motorboat’) were produced but they were embedded
in other subcategories, such as vehicles operated by muscle power or vehicles referring
to boats, or produced as single words outside a cluster.
As for the category of animals, farm animals (e.g., ‘horse’, ‘cow’, ‘sheep’,
‘hen’, ‘dog’, ‘cat’), exotic or foreign animals (e.g., ‘whale’, ‘elephant’, ‘rhinoceros’), and wild animals (e.g., ‘hare’, ‘bear’, ‘wolf’, ‘fox’) were the three subcategories most often found in the NC and the miAD groups (Figure 4; see also Appendix 4D). In the group of moAD patients, the three most often used subcategories
consisted of farm animals, wild animals, and pets (e.g., ‘cat’, ‘dog’). For both the
miAD group and the moAD group, clusters of words denoting different types of fish
(e.g., ‘pike’, ‘zander’, ‘perch’, ‘roach’) were non-existent as separate subcategories,
unlike for the NC group.
9.2.5 Degree of prototypicality and frequency of the nouns
produced
With regard to the total number of nouns produced for all the semantic categories,
the degree of prototypicality did not differentiate between the subject groups (p > .05;
see Table 15 and Appendix 2). The post hoc pair-wise analyses indicated that the
words referring to vehicles were significantly more prototypical in the miAD group
(p < .05) and the moAD group (p < .05) than in the NC group (see Appendix 5:12).
For all other categories, the AD groups produced nouns with an equal degree of
prototypicality as the NC group. The level of prototypicality of the words produced
by the AD groups remained the same in all the semantic categories.
The study indicated a significant difference among the subject groups in the
overall frequency of nouns produced (p < .05; see Table 15 and Appendix 3), the
moAD producing more commonly occurring words than the NC group (p < .01; see
Appendix 5:13). A similar trend was found in the miAD group, as the difference
between the miAD group and the NC group in the frequency ratings approached
statistical significance (p < .06). The AD groups produced words with the same
frequency of occurrence.
At the level of individual semantic categories, words with the equal frequency
of occurrence were produced for the category of clothes by all subject groups but
with significantly different frequencies for vegetables and animals (in each, p < .05),
Results
119
Table 15. Degree of prototypicality and frequency of the nouns produced
NC
(n = 30)
miAD
(n = 20)
moAD
(n = 20)
M (SD)
Mdn
M (SD)
Mdn
M (SD)
Mdn
H
(df = 2)
p-value
Clothes
5.49 (.46)
5.51
5.41(.36)
5.41
5.12 (1.82)
5.70
1.218
p = 544, n.s.
Vegetables
5.71 (.28)
5.74
5.68 (.32)
5.62
5.63 (.20)
5.64
3.538
p = .170, n.s.
Vehicles
5.63 (.43)
5.65
5.98 (.51)
6.01 *
6.7 (.74)
6.06 *
8.603
p < .05
Animals
6.50 (.22)
6.60
6.60 (.31)
6.68
6.21 (1.49)
6.57
1.729
p = .421, n.s.
All nouns
5.84 (0.19)
5.86
5.91 (0.26)
5.95
5.76 (0.59)
5.89
1.692
p = .429, n.s.
Clothes
4.73 (.31)
4.67
4.78 (.33)
4.79
4.48 (1.62)
4.90
2.256
p = .324, n.s.
Vegetables
4.56 (.27)
4.58
4.46 (1.15)
4.57
4.93 (.45)
4.91 **
7.433
p < .05
Vehicles
4.63 (.30)
4.65
4.96 (.50)
5.00 *
5.14 (.59)
5.12 ***
14.117
p < .001
Animals
4.64 (.27)
4.54
4.86 (.39)
4.90 *
4.64 (1.23)
5.03 (*)
6.513
p < .05
All nouns
4.89 (0.18)
4.90
5.00 (0.35)
5.06 (*)
5.09 (0.35)
5.07 **
8.777
p < .05
Variable
Degree of
prototypicality
of nouns
Degree of
frequency of
nouns
Note. Judgements were made on a 7-point scale: 1 = a very poor example of a category / a very
infrequent word, 7 = a very good example of a category / a very frequent word.
(*) almost statistically significant, * = p < .05, ** = p < .01, *** p < .001 when NC vs. miAD
and NC vs. moAD. n.s. = non significant.
120
Results
as well as for vehicles (p < .001). The post hoc pair-wise analyses revealed that,
compared to the NC group, the miAD group produced more frequent words for the
categories of vehicles and animals (in each, p < .05). The moAD group produced
more frequent words than the NC group for the categories of vegetables (p < .01)
and vehicles (p < .001). The frequencies of the words in the category of animals
between the NC and the moAD group were nearly significant (p < .06). The AD
groups produced words with the same frequency level in all semantic categories.
The analysis of the contents of the responses showed that each subject group,
including the normal control subjects, occasionally erred when producing nouns for
the semantic categories. Errors were not made by just a few individuals in each
group but by many of them (see 9.2.2). Compared to the performance of the NC
group, the proportion of erroneous responses in the miAD group was significantly
higher in all categories except vegetables, whereas the moAD group made significantly more errors in all semantic categories. The finding of a remarkable increase
in errors in the performance of the AD patients supports the findings of Ober et al.
(1986) and Bayles et al. (1993) on the animal and fruit fluency task. The finding
stands in contrast to the results of Binetti et al. (1995) and Carew et al. (1997), who
reported that practically no errors occurred in the performance of healthy control
subjects and mild and moderate-to-severe AD patients on the animal fluency task. In
the present study, the proportion of errors did not differentiate between the miAD
and the moAD group in any individual category, which is in accordance with the
finding of Binetti et al. (1995). However, the total proportion of correct responses
indicated that, throughout the whole task, the moAD group was significantly more
likely to make errors than the miAD group. The present finding is consistent with
the study of Bayles et al. (1993) who also discovered a relationship between the
severity of dementia and the increase in the overall error rate.
In this study, the errors were divided further into intrusions and perseverations.
Such phonological errors, which would have made the interpretation of the target
word impossible, were non-existent, and minor phonological changes in the output
were not counted as errors. Very few intrusions were produced by all the subject
groups, whereas the number of perseveration was significantly higher in the miAD
and the moAD group than in the control group. Furthermore, the ability of the patients
in the miAD and the moAD group to generate words from a varying set of semantic
dimensions was significantly reduced relative to the subjects in the NC group.
Intrusions
Even though intrusions were made by many individual subjects, the proportion of intrusions remained very low in all subject groups and most of the intrusions
were semantically related to the given category (see 9.2.2). Only in the category of
clothes did the moAD group produce more nouns outside the category boundaries
Results
121
than either the NC group or the miAD group. The finding that very few intrusions
emerged in the fluency performance of both healthy adults and AD patients is in
accordance with the results reported by Diesfeldt (1985), Rosser and Hodges (1994),
Binetti et al. (1995), Carew et al. (1997), as well as Suhr and Jones (1998). These
studies employed several different semantic categories (see Table 4). The findings
of the present study, concerning the number of intrusions made by the moAD group
for the category of clothes, also partly support those discovered by Ober et al. (1986)
who reported higher proportions of intrusions among the mild and the moderate-tosevere AD patients relative to healthy control subjects in the combined fluency scores
from animal and fruit fluency tasks. The present study also supports the finding of
Tröster et al. (1989) and Beatty et al. (1997, 2000) who noticed that AD patients
generated a higher proportion of intrusions in the supermarket fluency task than the
control subjects. Tröster et al. also pointed out that intrusions were more typical of
the performance in the group of moderately demented AD patients, which is in accordance with the present study.
The finding implies that, depending on the category, both mildly and moderately demented AD patients tended to produce nouns according to the instruction
and to stay within the limits of the category boundaries. This indicates relatively
intact categorization processes in terms of successful convergence and disambiguation of appropriate semantic features corresponding to the given categories, which
is a prerequisite for the production of category members in the semantic fluency
task (see 3.1, 3.1.1, 3.1.2, and 3.2). Consequently, knowledge of category membership may have remained relatively intact for the AD patients, which lends support to
the findings of Martin and Fedio (1983), Hodges et al. (1992), and Chertkow and
Bub (1990), among others. Nevertheless, the intrusions produced by the moAD group
for the category of clothes may have implications for assuming that the activation in
the semantic memory does not always work well for patients with moderate AD (see
Persson 1995:125-130, 132-135, 177-182; Astell & Harley 1996; Dell, Schwartz et
al. 1997; see 10.1.3). This finding, on the other hand, also supports the ones made
by Grossman et al. (1996), Laatu et al. (1997), and Laine, Vuorinen et al. (1997)
who observed that AD patients had difficulty in apprehending and identifying
superordinate category knowledge (see 2.4.1).
Once off-category words were produced in this study, they mostly originated
from semantically very closely related categories, such as accessories (e.g.,
‘suspenders’) and linen for the category of clothes, berries, and fruit for the category
of vegetables, and machines and tools for the category of vehicles. The semantically
related intrusions produced for the category of animals were more general nouns
without a specific referent, such as ‘buzzer’ referring to the sound of some flying
insect(s) or ‘the snake of Eden’. The number of semantically unrelated errors was
very low in all subject groups.
The occurrence of intrusions can be explained in relation to the functioning
of the semantic memory and/or the process of the semantic layer of the mental lexicon
during spoken word production (see chap. 4). During word production, the activation
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Results
first spreads along the semantic network and simultaneously triggers features of
both the target and the semantically related items, the activation of which may cause
noise in the system. Due to this background activation, incorrect features may have
higher activation levels than correct ones, or their decay rate may be increased, as a
consequence of which they are more likely to get selected and thus replace the correct
targets (Dell 1986; Persson 1995:35-39, 181; Foygel & Dell 2000). Alternatively,
damage to or loss of the target features, or their connections to other features, may
cause semantically related features to become more easily activated and selected for
further processing (Hinton & Sejnowski 1986; Persson 1995:80-84, 132-135; Devlin
et al. 1998; see 10.1.3). Selection of more general names for objects may reflect an
impaired spreading of activation which cannot pick out the subordinate items by
integrating their fine-grained semantic information, but settles for a more general
target on the superordinate level which has a sparser set of features. Alternatively,
the activation may settle for a nominal substitution that describes one strongly shared
perceptual characteristic, for example, the sound of the target (see 3.2, and chap. 6;
see also Barsalou 1982; Persson 1995:178).
The present study lends support to the notion that due to a similar semanticpragmatic structure, some but not all of the category boundaries may appear fuzzy
or to some extent overlapping even in healthy elderly adults (see e.g., Rosch et al.
1976; Rosch 1978; Lakoff 1987a, b; Diesfeldt 1985; Chertkow & Bub 1990; Aitchison
1994:39-50; see 3.1.1). For example, some of the machines and tools (e.g., ‘roller’)
may have many shared features with vehicles (e.g., the big size, the ability to move,
and the use for example on the road) but relatively few distinguishing features (Moss
et al. 2002; see 3.2). Similarly, vegetables and fruit share semantic features denoting
their physical appearance (e.g., colour, shape), function, and use (e.g., to grow, to
nourish), as well as thematic information in terms of a common context of appearance in the environment (e.g., a garden, a greenhouse, a grocery store, or a market
place), but they have very few distinguishing features, which may make them difficult to differentiate at the point of word selection.
Chertkow and Bub (1990) reported that the healthy elderly control subjects,
as well as AD patients, erred in differentiating fruit from vegetables. Nevertheless,
the study of Vinson and Vigliocco (2002) showed that undergraduate students made
a clear distinction between fruit and vegetables. On the other hand, Capitani et al.
(1999) reported gender and age differences among the healthy control subjects in
the fluency performance, in which female participants produced more fruit than
male participants, and younger participants produced more words than older
participants. In the present study, however, gender was not found to affect word
production in any of the noun categories among the normal control subjects (see
also Crossley et al. 1997 and Troyer 2000). Consequently, the tendency of some of
the healthy elderly control subjects to produce intrusions might thus be explained
by their advancing age making them less specific about the feature disambiguation
of semantically related items, which can be shown as fuzziness at the borders of
Results
123
certain semantic categories. However, Bayles et al. (1993) did not find a significant
effect of age on the number or rate of intrusions in the animal or fruit fluency task.
As far as artefacts made of a piece of cloth are concerned, they tend to share
the physical structure in the form of the material (e.g., silk, cotton, wool), the fabric
(cloth made by weaving, knitting, etc.), and their colour. However, the functional
and thematic features, as well as some features denoting their physical appearance
(e.g., shape), commonly divide these items into distinguishable groups, such as
garments for the body (i.e., ‘clothes’ in English, ‘vaatteet’ in Finnish, e.g., ‘shirt’,
‘pants’, ‘coat’), things to decorate and protect the inside of the house (‘wall hanging’,
‘carpet’, ‘curtains’), and things to use for bedding (e.g., ‘blanket’, ‘sheets’) and
personal hygiene (e.g., ‘towel’). This study indicated that, to some extent, all subject
groups included semantically related items under the term ‘clothes’, which is
consistent with the finding of Diesfeldt (1985) who observed that the category of
clothes was prone to some fuzziness also among the healthy elderly adults.
The present study showed that in the category of clothes, the correct selection
of words in the moAD group might have been hampered by noise caused by semantically related items being simultaneously activated. The activation of features may
have been guided by the strongly shared physical properties (e.g., the material the
items were made of) instead of other features. Consequently, a changed or damaged
pattern of semantic feature activation and integration, leading to difficulty in specifying the category boundary of clothes, may have taken place in the moAD group
who produced significantly more incorrect words that denoted items semantically
related to clothing (see 10.1.3). Disambiguating between the different categories
made of cloth may be different from distinguishing other man-made categories in
the sense that disambiguation may be even more heavily dependent on the thematic
and functional features, which, in general, are likely to be more lightly weighted
and thus less discriminative than features denoting physical properties (see Persson
1995:82; Tyler and Moss 2001). It is also possible that the phonological form of the
word ‘vaatteet’ may have interfered with the feature selection. The selection of items
may have been based on the shared stem ‘vaatteet’, such as ‘liinavaatteet’ (‘linen’),
‘vuodevaatteet’ (‘bedclothes’), etc., as a consequence of which semantically related
words were produced (see 4.1, 5.3, and 10.1.3).
The findings of the present study imply that all semantic categories may not
be similarly exploitable during the semantic fluency task (see also 9.1.1). The semantic
characteristics of the category of animals are likely to be so clear cut and robust
against neural damage that semantically related intrusions hardly emerge, whereas
categories such as vegetables and clothes seem to be more vulnerable to category
violations, due to their few distinguishing features and their close relatedness to
other semantic categories in terms of shared features (see Moss et al. 2002; see also
10.1.3).
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Results
Perseverations
Concerning the production of perseverations, this study indicated that both AD groups
repeated previously produced words more excessively than the control group (see
9.2.2). Production of perseverations in both AD groups, however, did not form a
constant pattern across the semantic categories. Both AD groups produced more
perseverations than the NC group for the category of vehicles and the category of
animals. As far as the category of clothes was concerned, only the miAD group
perseverated significantly more often than the NC group, whereas in the category of
vegetables, it was the moAD group who perseverated significantly more frequently
than the NC group. The finding that the AD patients made more perseverative errors
than the normal control subjects supports the studies of Rosser and Hodges (1994),
Beatty et al. (1997, 2000), and Suhr and Jones (1998) who used several different
semantic categories such as supermarket items, animals, vehicles, musical instruments, fruit/vegetables, and tools/kitchen utensils (see Table 4). However, Butters et
al. (1987), Binetti et al. (1995) and Carew et al. (1997) reported that perseverations
seldom occurred among the normal control subjects or among the AD patients in the
animal fluency task. The present study also indicated that there was no difference
between the number of perseverations produced by the two AD groups with different degrees of dementia, which parallels the observations made by Tröster et al.
(1989) on the supermarket fluency task.
In the present study, the proportion of perseverations of all the produced words
in the group of healthy control subjects varied between 1.3-4.9% across the semantic categories. In total, the proportion of all perseverations was 3.1% (SD = 1.8),
which seems to be consistent with the study of Tröster et al. (1989), who reported
the rate of perseverations among normal control subjects to be 3.0% (SD = 0.04) in
the supermarket fluency task, that is somewhat higher than the 1.4% (SD = 2.4)
reported in the study of Suhr and Jones (1998). The average proportion of
perseverations varied between 9.6-14.7% across the categories in the mild AD group
and between 5.8-27.4% in the moderate AD group. The total proportion of
perseverations was 12.3% (SD = 10.9) for the mild and 21.0% (SD = 15.0) for the
moderate AD group. The rates seem to be higher than AD patients’ average of 7.7%
in the Rosser and Hodges’s (1994) study and 6.3% (SD = 9.1) in the Suhr and Jones’s
(1998) study. The number of perseverations in the AD groups in the present study
was also higher than in the study of Tröster et al. (1989; see also Ober et al. 1986)
who observed that the rate of the perseverations in their mild AD group was 9.0%
(SD = 12.0) and 7.0% (SD = 10.0) in their moderate AD group.
All in all, this study showed that the word production system might not work
well in AD. Instead of activating the present or preparing to activate or prime the
future, the word production system of the AD patients seemed to “dwell on the past”
and it was not able to turn off the activation of the words already produced in the
fluency task (see Dell 1986; Dell, Burger et al. 1997). Generally, if the previously
activated words do not decay, the activation gets stuck on particular patterns and the
Results
125
system starts perseverating, consequently blocking the shifting from one processing
pattern to another and interfering with the activation and the selection of other words
(Dell 1986; Dell, Burger et al. 1997; Martin et al. 1994; Persson 1995:66; see the
discussion in Laine 1989:75-80). As discussed above, the tendency of the AD patients
to produce many perseverations can have an effect on the fluency performance that
can be manifested as an increase in the number of clusters and the size of clusters in
individual semantic categories. However, there was no general trend to support the
notion. The role of the perseverations in word production is further discussed in
9.4.5 and 10.1.2.
Strategies
The high proportion of words in clusters (see Table 9; see also 9.1.3) implies that
production of nouns was guided by a strategy of tying them together by some relation (semantic, phonological, or both). The subject groups tended to produce clusters using mostly the pure semantic strategy, that is, the activation of nouns was
most often guided by shared semantic properties without the contribution of a phonological component. However, phonological similarity between the words also
seemed to have an effect on word production. Some semantically related nouns
frequently produced by the subjects seemed to share some phonological features
(e.g., ‘kissa’, ‘koira’, ‘kukko’, ‘kana’ / ‘cat’, ‘dog’, ‘rooster’, ‘hen’ or ‘porkkana’,
‘peruna’, ‘punajuuri’ / ‘carrot’, ‘potato’, ‘beet’). The finding supports the study of
Laine (1989:22), Bayles et al. (1989), Roberts and Le Dorze (1994), as well as
Kaleva and Vanhala (2001). The pure phonemic strategy, in which the nouns did not
share a close semantic relatedness, other than the category membership (e.g., ‘kissa’,
‘kirppu’, ‘kala’ / ‘cat’, ‘flea’, ‘fish’), was used relatively little by all subject groups.
In other words, the performance of both the NC group and the AD groups during the
semantic fluency task was guided by a spread of activation to semantically and phonologically related items in the mental lexicon. The finding supports the two-stage
interactive activation models of word production (see 4.2).
Semantic subcategories
The present study indicated that the normal control subjects showed a more dynamic,
flexible, integrative, and creative retrieval of semantic information by being able to
activate more diverse semantic dimensions in production than either of the AD groups
(see 9.2.4). The finding that AD patients produced fewer subcategories than the NC
subjects is in accordance with the findings reported by Martin and Fedio (1983),
Ober et al. (1986), Tröster et al. (1989), Binetti et al. (1995), and Beatty et al. (2000).
Furthermore, a clear narrowing of the variety of semantic dimensions was found in
the moAD group relative to the miAD group. This finding supports the study of
Binetti et al. (1995) who observed that in the animal fluency task, mild AD patients
were able to generate category exemplars for farm animals, wild animals, and birds,
whereas moderate-to-severe AD patients produced words only for farm animals. In
126
Results
contrast, Tröster et al. (1989) did not find any difference between patients with mild
and moderate AD in their ability to generate subcategories in the supermarket fluency
task.
In all subject groups, the formation of clusters of nouns seemed primarly to
be based on functional and thematic features between nouns rather than on physical
features or hierarchical relations of taxonomic subordination (e.g., strict zoological
or botanical relations), which were mainly used to guide word selection by the subjects in the NC group and to some extent by the subjects in the miAD group (see
Figures 1 - 4). The semantic dimensions used by the subjects in the moAD group
were restricted mainly to functional and thematic relations. Activating functional
and thematic features (e.g., contextual and spatial features) seemed to guide activation and selection of words in all categories: clothes to wear daily indoors and outdoors or on certain occasions, vegetables that tend to occur together in a salad or at
the dining table (e.g., ‘lettuce’, ‘cucumber’ and ‘tomato’) or that grow in, on the
surface, or above the soil, vehicles used by the public in an urban environment or on
the water, in the air, or on the roads, and animals to be found in the wilderness, at
home or in a foreign country, on a farm, in a zoo, etc. Temporal features, such as the
time of the year certain things are most likely to appear, were also used as the strategy to activate words for production. For example, clothes were clustered as winter
and summer clothes and vehicles as ones that could be used in the snow or on the
open water. Words denoting vehicles were at times also clustered according to the
manner or initiator of the movement, that is, whether transportation was carried out
by muscle power or by a motor. Physical features, such as part-whole analysis, were
used when clustering words mainly for clothes by specifying the part of the body
the item is used for (e.g., upper body, lower body, head, hands, feet). Other physical
features, such as color or size, were very rarely used as a criterion for the cluster
formation.
In the moAD group, the lack of taxonomic subordinate clusters (e.g., words
for different fish) may be an indication of a reduction of their ability to utilize the
semantic information of nouns based on the fine-grained semantic analysis of the
distinguishing features (e.g., physical features) among the items. This finding tentatively supports the study of Binetti et al. (1995) who suggested that a bottom-up
deterioration of the semantic features may take place in AD, which may lead to a loss
of, or a difficulty in disambiguating and integrating the defining features of words
and, subsequently, to a difficulty in naming items in a subcategory (see also Grober
et al. 1985; Hodges et al. 1992; Chan et al. 1993; Laine, Vuorinen et al. 1997; Laatu
et al. 1997; Laatu 1999; Moss et al. 2002; Whatmough & Chertkow 2002). On the
other hand, the ability to integrate information according to the functional and thematic features may be easier or better preserved in AD. The functional and thematic
information may allow cross-classification over different subcategories, and they may
thus be more salient, easier, and over-represented for the AD patients (see Huttenlocher
& Lui 1979; Barsalou 1982, 1983; Lucariello et al. 1992; Persson 1995:178; Nelson
1996:230-248; Tyler & Moss 2001; Moss et al. 2002; see also 3.1.2, and 3.2).
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127
Reduction in the flexible use of semantic information, however, does not necessarily imply a breakdown of the structure of semantic memory or an inability of
the AD patients to retrieve clusters of specific subordinate nouns. In the present
study, the words produced by some of the subjects even with moderate dementia
consisted of specific subordinate nouns which may have been either embedded in
certain functional or thematic clusters, or were produced as a “strictly” taxonomic
subordinate cluster (e.g., birds or fish), or as a single word. Hence, the thematic and
functional clusters outnumbering the others may reflect the methodological solution to combine smaller clusters into bigger ones (see 8.2.3), as a consequence of
which larger clusters may cover chains of words formed using various semantic
dimensions. Thus, one should attempt to break down these clusters in order to analyze the true performance of the subjects in different semantic dimensions (see Chan
et al. 1993; Carew et al. 1997).
Degree of prototypicality and frequency of the nouns produced
The qualitative analysis revealed that the subject groups tended to use words with
equal prototypicality (see 9.2.5). Differences emerged only in the category of vehicles, for which both AD groups produced more prototypical words than the NC
group. The present study thus only partially supports Beatty et al. (2000) who found
that on the supermarket fluency task, AD patients generated a greater proportion of
prototypical words than the normal control subjects (see also 5.1).
The study indicated that the mean frequency of nouns produced by the AD
groups was higher than that of the NC group in all other categories but the category
of clothes. The finding implies that the words used by the AD patients tended mainly
to be very commonly occurring words that were likely to have strongly connected
patterns of feature integration and a low threshold of activation, which made them
easily available for word production. The finding is consistent with Weingartner et
al. (1993) and Binetti et al. (1995). On the other hand, the finding contrasts those
made by Ober et al. (1986), Chan et al. (1993), and Goldstein et al. (1996) who did
not find a difference in the word frequencies between healthy control subjects and
patients with AD (see also 5.1).
On the basis of the present study, it can be concluded that the higher frequency
of nouns may have affected word production in the AD groups, whereas the degree
of prototypicality did not contribute to their semantic fluency performance. It can
also be speculated that the nature of the semantic category may have had some
impact on the frequency and prototypicality of the nouns produced by the AD groups.
For a more detailed discussion on the methodological issues, see 10.2.3.
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Results
9.3 Overall performance on the verb fluency tasks
The subject groups were statistically significantly different in their overall ability to
produce responses for the verb generation task (p < .001; see Table 16). During the
task, the NC group produced a total mean of 40 responses for the four semantic verb
categories, while the miAD group was able to generate 33 and the moAD group
only 19 responses. The post hoc pair-wise analyses revealed that, compared to the
NC group, a significant reduction in the word production emerged in the miAD
group (p < .01) and the moAD group (p < .001). The moAD group’s word production was significantly poorer than that of the miAD group (p < .01). More detailed
data on the post hoc pair-wise analyses concerning the total verb production can be
found in Appendix 6:1.
When generating words for the individual verb categories, the NC group’s
average performance varied between 8 and 12 responses, the miAD group’s between
7 and 9 responses, and the moAD group was able to give 4 to 5 responses per each
category (see Table 16). The difference in word production was significant among
the groups in all the semantic categories (in each category, p < .001). The miAD
group produced fewer responses than the NC group only for the categories of playing
sports and construction (in each, p < .01). The moAD group performed significantly
poorer than the NC group and the miAD group in all verb categories (see Appendix
6:1).
9.3.1 Number of correct verbs
Incorrect responses were found in all subject groups and in all semantic categories
(see Table 16; see also 9.4.1, 9.4.2, and 9.4.5). There was a significant difference
among the groups in the total number of all correctly produced verbs (p < .001). The
post hoc pair-wise analyses indicated a remarkable reduction in the total number of
correct verbs in the miAD group and the moAD group (in both, p < .001), compared
to the NC group (see Appendix 6:2a). On the whole, the miAD group performed
significantly better than the moAD group (p < .001).
A reduction of correct words in the AD groups was found also at the level of
individual categories. While the NC group produced 7 to 11 correct words per category, the miAD was able to produce about 6 to 7, and the moAD group about 2 to
3 correct verbs per category. A significant difference among the groups appeared in
all semantic categories (in each, p < .001). The post hoc analyses revealed significant differences in the number of correct words between the NC group and the
miAD group in all other categories but the category of cleaning (preparing food
p < .05, playing sports and construction p < .001). For each semantic category, the
moAD was able to generate significantly fewer correct verbs than the NC group and
the miAD group (see Appendix 6:2a).
According to the average frequencies, all subject groups produced most correct responses for the category of playing sports (see Table 16). The fewest correct
Results
129
Table 16. Total number of words and number of correct verbs
NC
(n = 30)
miAD
(n = 20)
moAD
(n = 20)
M (SD)
Mdn
M (SD)
Mdn
M (SD)
Mdn
H
(df = 2)
p-value
Preparing food
9.4 (2.9)
9.0
8.5 (5.2)
8.0
4.8 (3.7)
4.0 *** ¤
16.073
p < .001
Playing sports
11.4 (3.3)
11.5
8.5 (3.8)
7.0 **
5.0 (3.0)
4.0 *** ¤¤
29.005
p < .001
Construction
10.6 (3.1)
10.5
7.8 (4.9)
7.0 **
4.7 (3.6)
4.0 *** ¤
24.744
p < .001
Cleaning
8.6 (2.5)
8.0
8.1 (4.0)
8.0
4.3 (3.4)
4.0 *** ¤¤
21.858
p < .001
All categories
40.0 (8.7)
39.0
32.8 (16.8)
30.0 **
18.8 (11.5)
15.5 *** ¤¤
28.250
p < .001
Preparing food
8.6 (2.7)
8.0
6.6 (4.7)
6.0 *
2.6 (2.2)
2.0 *** ¤¤¤
33.488
p < .001
Playing sports
10.9 (3.5)
11.0
6.9 (3.5)
6.0 ***
3.5 (2.9)
3.0 *** ¤¤
34.822
p < .001
Construction
9.1 (3.2)
9.0
5.6 (3.8)
5.0 ***
3.1 (2.8)
2.0 *** ¤
28.388
p < .001
Cleaning
7.5 (2.7)
7.0
6.5 (3.2)
6.0
3.5 (3.1)
2.5 *** ¤¤¤
23.925
p < .001
All categories
36.0 (8.8)
35.5
25.5 (13.9)
21.0 ***
12.6 (8.7)
9.5 *** ¤¤¤
36.118
p < .001
Total number
of words
Number of
correct verbs
Note. * = p < .05, ** = p < .01, *** = p < .001 when NC vs. miAD and NC vs. moAD,
and ¤ = p < .05, ¤¤ = p < .01, ¤¤¤ p < .001 when miAD vs. moAD.
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Results
Table 17. Number of correct verbs produced by the male and
Male participants Female participants
(n = 16)
(n = 14)
Category
M (SD)
Mdn
M (SD)
Mdn
Mann Whitney
U test
Preparing food
7.7 (1.7)
8.0
9.6 (3.3)
9.0
U = 81.5
p = .208, n.s.
Playing sports
11.1 (3.4)
12.0
10.6 (3.6)
11.0
U = 97.0
p = .552, n.s.
Construction
10.2 (2.9)
10.5
7.8 (3.2)
7.5
U = 63.0
p < .05 *
Cleaning
6.3 (1.1)
6.0
8.9 (3.4)
8.5
U = 47.0
p < .01 **
All categories
35.3 (7.2)
35.0
36.9 (10.6)
36.5
U = 100.5
p = .637, n.s.
Note: * = p < .05, ** = p < .01. n.s. = non significant.
responses were produced for the category of cleaning in the NC group, for the category of construction in the miAD group, and for the category of preparing food in
the moAD group. The Friedman test indicated that there was a statistically significant difference among the number of correct responses produced for the different
semantic categories only in the NC group (χ2 = 20.7 (df = 3), p < .001). The post hoc
pair-wise comparisons revealed a statistically significant difference in the number
of correct responses between the category of playing sports and the category of
cleaning (see Appendix 6:2b).
There was a statistically significant difference between the male and female
participants in the NC group in the number of correct words produced for the category of construction (p < .05) and the category of cleaning (p < .01; see Table 17).
For the former, the male participants produced significantly more responses than
the female participants, whereas for the latter, it was the female participants who
produced significantly more correct verbs than the male participants. However, there
was no statistically significant difference in the average mean number of correct
verbs produced for all the four verb categories.
Results
131
9.3.2 Clustering and switching
A significant group difference emerged in the overall switching performance among
the subject groups (p < .001; see Table 18). The NC group was remarkably more
flexible at changing the subcategories during word production than the miAD group
and the moAD group (in both, p < .001; see Appendix 6:3). Moreover, the miAD
group was able to switch between the subcategories more often then the moAD
group (p < .01).
The NC group switched subcategories about 4 to 5 times in each category, the
miAD group from 2 to 4 times, and the moAD group were able to shift only once or
twice in a category. The miAD group switched significantly less frequently than the
NC group when producing verbs for the categories of playing sports and construction (in both, p < .001). In any category, the switching performance of the moAD
group was significantly poorer than that of the NC group (preparing food, playing
sports, and construction p < .001, and cleaning p < .01) and the miAD (in all categories, p < .05).
The NC group produced, on average, 10 clusters during the whole action
fluency task, the miAD group 8, and the moAD group only 4 clusters (see Table 18).
The total number of all clusters differed significantly among the subject groups (p <
.001), as well as between the groups (NC vs. miAD, p < .01; NC vs. moAD and
miAD vs. moAD, p < .001; see Appendix 6:4).
The NC group produced from 2 to 3 clusters per verb category, the miAD
group approximately 2, and the moAD group only 1 cluster for each category. Compared to the NC group, the miAD group produced significantly fewer clusters for
the categories of playing sports and construction (in each, p < .05). The difference
in the number of clusters produced by the groups for the category of preparing food
was almost statistically significant (p = .078), whereas in the category of cleaning
the number of clusters did not differentiate between the two groups. The moAD
group formed significantly fewer clusters than the NC group and the miAD group
for all categories (see Appendix 6:4).
As far as the total performance was concerned, it was noticed that all subject
groups produced clusters with the size of approximately 1, implying that the clusters contained mainly two semantically related verbs (see Table 18). At the level of
individual verb categories, the NC group and the miAD group were able to produce
verbs in clusters the size of which varied between 1 and 2 (i.e., containing 2 or 3
words). In the moAD group, the cluster size was smaller than 1 in all other categories but the category of construction. A group difference in the cluster size was
found for the category of preparing food (p < .05), for which both the NC group and
the miAD group formed clusters of the same size but significantly larger than the
moAD group (NC vs. moAD, p < .01; miAD vs. moAD, p < .05; see Appendix 6:5).
The cluster size differed among the groups also in the category of cleaning (p < .05),
with the NC group producing significantly larger clusters than the moAD group
(p < .01). The size of the clusters did not differ among the subject groups in the
categories of playing sports and construction.
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Results
Table 18. Clustering and switching in the verb fluency tasks
Category
NC
(n = 30)
miAD
(n = 20)
moAD
(n = 20)
M (SD)
Mdn
M (SD)
Mdn
M (SD)
Mdn
H (df = 2)
p-value
3.9 (1.5)
4.0
5.8 (2.6)
5.0
3.9 (1.6)
4.0
3.4 (2.1)
4.0
17.0 (4.8)
16.5
3.3 (2.7)
3.0
3.3 (2.0)
3.5 ***
2.3 (2.7)
2.0 ***
3.4 (2.3)
3.5
12.2 (7.3)
11.5 ***
2.0 (2.4)
1.5 *** ¤
1.9 (1.6)
2.0 *** ¤
1.1 (1.4)
1.0 *** ¤
1.7 (2.2)
1.0 ** ¤
6.6 (5.9)
6.5 *** ¤¤
12.177
p < .01
29.810
p < .001
30.638
p < .001
9.168
p < .05
28.000
p < .001
2.6 (0.9)
2.0
2.9 (1.3)
3.0
2.7 (1.2)
3.0
2.1 (1.1)
2.0
10.2 (2.6)
10.0
2.1 (1.4)
2.0 (*)
2.0 (1.5)
1.5 *
2.0 (1.5)
2.0 *
1.8 (1.1)
2.0
7.8 (4.4)
7.0 **
0.7 (0.9)
0.5 *** ¤¤¤
1.1 (1.1)
1.0 *** ¤
1.0 (1.0)
1.0 *** ¤
1.1 (1.2)
1.0 *** ¤
3.9 (2.7)
3.0 *** ¤¤¤
27.884
p < .001
19.271
p < .001
20.059
p < .001
12.978
p < .001
32.118
p < .001
1.0
0.8
0.8
0.6
1.3
1.2
1.6
1.0
0.9
0.9
1.0
1.0
1.1
0.8
1.6
1.3
1.3
0.7
1.0
0.8
0.9 (1.7)
0.1 ** ¤
0.6 (0.8)
0.4
1.7 (2.3)
1.0
0.7 (1.0)
0.3 **
0.9 (1.0)
0.6
8.614
p < .05
4.648
p = .098, n.s.
1.417
p = .492, n.s.
6.759
p < .05
4.772
p = .092, n.s.
7.421
p < .05
3.186
p = .203, n.s.
4.314
p = .116, n.s.
3.258
p = .763, n.s.
5.011
p = .082, n.s.
Switches
Preparing food
Playing sports
Construction
Cleaning
All categories
Number of clusters
Preparing food
Playing sports
Construction
Cleaning
All categories
Cluster size
Preparing food
Playing sports
Construction
Cleaning
All categories
(0.5)
(0.6)
(0.8)
(1.6)
(0.3)
(0.8)
(1.1)
(1.7)
(1.5)
(0.6)
Verbs in clusters (%)
Preparing food
Playing sports
Construction
Cleaning
All categories
74.3
70.7
64.7
66.7
62.8
62.0
74.0
80.0
72.9
72.7
(16.7)
(22.9)
(14.8)
(25.4)
(9.7)
69.1
73.2
69.9
68.3
72.2
72.1
63.1
71.4
71.4
72.3
(29.4)
(24.0)
(19.4)
(31.3)
(14.7)
42.7 (38.6)
41.7 ** ¤
48.8 (36.1)
61.9
69.6 (31.4)
66.7
57.1 (36.2)
58.3
60.1 (24.3)
59.7
and ¤ = p < .05, ¤¤ = p < .01, ¤¤¤ p < .001 when miAD vs. moAD. n.s. = non significant.
Results
133
When the proportion of clustered verbs was taken into account, the difference among the groups in the overall clustering performance across the four categories approached statistical significance (p = .082; see Table 18). On average, the NC
group and the miAD group clustered 70% of all verbs. In the moAD group, the
proportion of clustered verbs was approximately 60%. There was no significant
difference among the subject groups in the extent of clustering verbs in the single
categories of playing sports, construction, and cleaning. Instead, the groups differed
in the category of preparing food (p < .05), for which the NC and the miAD group
clustered words to a similar extent but significantly more often than the moAD group
(NC vs. moAD, p < .01; miAD vs. moAD, p < .05; see Appendix 6:6).
The present study provided new information about how verbs were produced in the
verb fluency tasks by normal control subjects, as well as by AD patients. Relative to
the NC group, a significant reduction in the total number of responses and the total
number of correct verbs was found in both AD groups. The moAD group showed a
remarkable reduction in verb production, also when compared to the miAD group.
Although the strategic use of clustering and switching was limited in number in
both AD groups, they indicated a semantically coherent pattern of verb activation.
Overall performance on the verb fluency tasks
The total number of correct responses produced by the NC group for the different
categories varied between 7 and 11 verbs. A significant difference appeared in the
number of correct verbs generated for the semantic categories by the NC group.
Most of them were produced for the category of playing sports, while the least
responses were found in the category of cleaning (see 9.3.1). Such a variation in the
semantic fluency performance between the categories was not found in the AD groups,
both of which tended to generate correct words evenly for the different semantic
categories.
The verb production of the NC group seemed to be poorer than that of elderly
control subjects in the studies of Piatt, Fields, Paolo, Koller, and Tröster (1999) and
Piatt, Fields, Paolo, and Tröster (1999). In the former study, the healthy subject
group (mean age 71.4 years, SD = 6.5) produced on average 17 (SD = 4.7) verbs and
in the latter study (mean age of the subjects 72.9, SD = 7.5) approximately 15
(SD = 4.3) verbs for the verb fluency task (60 s). It is worth noting, however, that
none of the tasks used by Piatt et al. was semantically restricted but the subjects
were asked to generate as many single verbs as possible denoting different things
people do. Given this constraint, the task used in their study was likely to activate
words from a number of various semantic fields, whereas the categories given in the
present study were restricted, which may explain the difference between the studies.
134
Results
Different from the performance on the noun categories (see 9.1.1, 9.1.3), the
semantic fluency performance of the NC group in some of the categories appeared
to depend on the gender of the subjects: the female participants produced significantly more correct verbs for the category of cleaning than the male participants,
whereas the opposite was true for the category of construction. The finding may be
explained by the fact that some types of actions may be more familiar to females
while some others are more associated with males, due to different life experiences,
habits, occupations, etc. (see Monsch et al. 1992; Aitchison 1994:39-50; Taylor
1995:72-75, 79, 242; Ungerer & Schmid 1996:14-20; Azuma et al. 1997; Roberts &
Le Dorze 1997; Capitani et al. 1999; see 5.1 and 5.5). However, when the average of
the total output over all the verb categories was considered, the male and female
subjects produced correct verbs to a similar extent (for nouns, see Crossley et al.
1997; Troyer et al. 2000). The gender effect was not controlled for the AD patients
because of the unequal number of the male and female subjects in each AD group.
Nevertheless, based on the finding obtained from the NC group, there may not be a
general trend for the female participants to fare better on the verb fluency tasks than
the male participants.
Compared to the NC group, the miAD group showed a significant reduction
in verb production only in two of the categories, playing sports and construction, but
the moAD group produced significantly fewer words across all the semantic verb
categories. Their performance in each semantic category was significantly poorer
also when compared to the miAD group. The decreased verb production in the AD
groups is in accordance with those obtained in studies involving the script generation task (a variation of the verb fluency task) conducted by Weingartner et al. (1983)
and Grafman et al. (1991). In their studies, subjects were asked to produce a script
of actions, which was likely to take place in a certain temporal-causal order (e.g.,
going to a restaurant; see 3.3.4, 5.1). Reduced verb production among AD patients
was also found in verb confrontation naming in the studies of Bowles et al. (1987),
Robinson et al. (1996), White-Devine et al. (1995, 1996), and Williamson et al.
(1998; see also 2.4.2). One should note here that the control tasks of the present
study, which included two confrontation naming tasks, indicated a significantly poorer
naming of verbs in the AD groups compared to the NC group (see 9.6.1, 9.6.4).
Clustering and switching
This study also suggested that a systematic strategy for performing the semantic
fluency task by producing various clusters of semantically related verbs and switching between semantic subcategories was, in general, less efficient in both AD groups
than in the NC group. However, at the level of individual categories, the miAD
group showed an ease of clustering and switching in two of the categories (preparing food and cleaning, see 9.3.2). The moAD group, in contrast, had significantly
more difficulty in clustering and switching than the other groups throughout all the
categories. The miAD group showed a better ability to cluster and switch than the
moAD group.
Results
135
The average cluster size appeared to be relatively small in all subject groups.
The clusters between the subject groups were of the same size in all categories but
two. For the categories of preparing food and cleaning, the moAD patients formed
significantly smaller clusters than the NC group. Relative to the miAD group, the
cluster size of the moAD group appeared to be smaller only in the category of cleaning. The number of perseverations made by the AD groups may have partly increased their cluster size (see Table 20, see also 9.4.2, 9.4.5, and 10.1.2). Nevertheless, even though the moAD group produced more perseverations for the category
of preparing food, their cluster size remained smaller than that of the NC group. The
small number of clusters and the small cluster size (approximately two words) generated by all subject groups for all semantic verb categories may be a reflection of
the shallow and bushy hierarchical organization of the verbs (see Miller & Fellbaum
1991; Pajunen 1998, 2001:60-61; see also 3.3.2). Instead of having a well-defined,
multi-layered structure the way many noun categories have, the superordinate verbs
typically branch out to a couple of hierarchy levels where there are not many semantically similar verbs that can be used as variations to express certain actions or events
(Pajunen 1998, 2001:60-63).
As far as the performance was measured as the proportion of semantically
related verbs in clusters, the subject groups performed almost identically across all
the semantic categories, with no less than 60% of the words clustered. The finding
implies that although the AD patients showed a remarkable decrease in the number
of verbs, clusters, and switches, their ability to use a coherent strategy to produce
verbs in a string on the basis of their close semantic relatedness was preserved. The
only exception was made by the moAD group who had problems in clustering together
verbs belonging to the category of preparing food. Instead of clustering verbs, they
tended to list single verbs with no close semantic relationship to each other except
for the category membership. They also tended to produce nouns that referred to
different dishes, which were counted as errors and were not included in clusters (see
9.4.2).
9.4 Analysis of the contents of the responses on the
verb fluency tasks
In the verb fluency task, the subjects referred to different types of actions with various forms (see Table 19). There was no difference among the groups in their use of
verb phrases or deverbal forms, whereas the extent to which general verbs and concrete nouns were used differentiated between the subject groups (in each, p < .05).
The groups tended to differ also in the production of specific verb forms (p < .058).
The NC group and the miAD group used mostly specific verb forms and
deverbal forms when generating words for the verb categories, whereas verb phrases,
general verbs, or single concrete nouns were relatively rarely used in these groups.
In the moAD group, the use of specific verbs tended to decrease significantly (p < .05;
136
Results
Table 19. Word forms produced in the verb fluency tasks
Word form
NC
(n = 30)
miAD
(n = 20)
moAD
(n = 20)
M (SD)
Mdn
M (SD)
Mdn
M (SD)
Mdn
H
(df = 2)
p-value
Specific verbs (%)
50.2 (37.3) 41.7 (36.8)
63.7
44.1
23.0 (28.2) 5.706
8.7 *
p < 058
Verb phrases (%)
14.0 (19.1) 13.6 (14.0)
3.5
8.3
13.0 (13.7) 0.009
11.0
p = .956, n.s.
Deverbal forms (%) 29.0 (33.8) 36.7 (37.2)
12.7
23.1
40.7 (29.8) 3.159
39.2
p = .206, n.s.
General verbs (%)
2.1 (5.1)
0.0
4.9 (9.2)
0.0
9.3 (11.8)
5.1 **
7.802
p < .05
Nouns in verb
categories (%)
4.7 (9.2)
0.0
3.1 (7.0)
0.0
14.0 (15.2) 6.489
13.5 * ¤
p < .05
¤ = p < .05, ¤¤ = p < .01, ¤¤¤ p < .001 when miAD vs. moAD. n.s. = non significant.
see Appendix 6:7) while the use of the other types of verb forms increased. The
moAD group produced significantly more general verb forms (e.g., ‘laittaa’, ‘panna’,
‘tehdä’ / ‘put’, ‘put’, ‘make’) than the NC group (p < .01), and they generated concrete nouns instead of verbs significantly more often than the NC group and the
miAD group (in both, p < .05).
9.4.1 Proportion of correct verbs
When performing the verb fluency task, all subject groups produced errors (see
Table 20). Comparison of the combined scores indicated an overall group difference
in the ratio of correctly produced verbs (p < .001). According to the post hoc pairwise analyses (see Appendix 6:8), the NC group produced significantly more correct responses to the verb fluency task than the miAD group (p < .01) and the moAD
group (p < .001). The difference in the overall performance of the miAD group and
the moAD group almost reached statistical significance (p < .07).
The mean percentage of the NC group’s correct verb production per category
varied between 86-95%, for the miAD group between 74-83%, and for the moAD
Results
137
Table 20. Proportion of correct words, intrusions,
and perseverations in the verb fluency tasks
NC
(n = 30)
miAD
(n = 20)
moAD
(n = 20)
M (SD)
Mdn
M (SD)
Mdn
M (SD)
Mdn
H
(df = 2)
p-value
Preparing food
92.4 (10.5)
96.7
77.6 (18.7)
81.5 **
59.3 (33.0)
61.1 ***
17.227
p < .001
Playing sports
94.4 (9.0)
100.00
82.1 (15.8)
85.4 ***
69.7 (32.5)
75.0 **
14.462
p < .001
Construction
86.2 (22.6)
100.00
74.0 (21.9)
72.7 *
65.9 (34.4)
75.0 *
7.022
p < .05
Cleaning
87.5 (16.4)
91.9
82.7 (17.2)
88.2
78.4 (24.7)
90.0
1.402
p = .496, n.s.
All categories
90.0 (9.9)
93.1
78.8 (14.3)
80.7 **
67.5 (21.0)
65.2 *** (¤)
18.370
p < .001
Preparing food
0.0
0.0
7.7 (16.2)
0.0 **
16.0 (26.4)
0.0 ***
12.561
p < .01
Playing sports
1.3 (6.9)
0.0
4.5 (7.4)
0.0 *
17.3 (25.9)
0.0 ***
13.710
p < .001
Construction
7.8 (17.8)
0.0
7.7 (17.6)
0.0
22.5 (37.3)
0.0
1.060
p = .589, n.s.
Cleaning
6.4 (15.0)
0.0
4.6 (12.2)
0.0
8.3 (17.4)
0.0
0.516
p = .773, n.s.
All categories
4.0 (8.9)
0.0
6.2 (8.9)
3.1
16.5 (15.1)
14.6 ** ¤
9.545
p < .01
Preparing food
7.6 (10.5)
3.3
14.7 (14.9)
13.4 (*)
24.7 (27.5)
16.7 **
8.250
p < .05
Playing sports
4.3 (6.7)
0.0
13.4 (12.9)
14.3 **
13.0 (25.2)
0.0
7.349
p < .05
Construction
6.0 (13.9)
0.0
18.3 (16.7)
19.1 **
11.6 (18.0)
0.0
9.731
p < .01
Cleaning
6.1 (7.3)
0.0
12.7 (14.6)
10.6
13.3 (21.9)
0.0
2.303
p = .316, n.s.
All categories
6.0 (5.5)
4.2
15.0 (10.7)
13.6 ***
16.0 (14.4)
12.5 **
17.260
p < .001
Variable
Correct verbs (%)
Intrusions (%)
Perseverations (%)
Note. (*) = almost statistically significant; * = p < .05, ** = p < .01, *** = p < .001 when NC
vs. miAD and NC vs. moAD, and ¤ = p < .05, ¤¤ = p < .01, ¤¤¤ p < .001 when miAD vs.
moAD. n.s. = non significant.
138
Results
group between 59-78%. A statistically significant difference emerged in the ratio of
correct verbs among the subject groups in all other categories but cleaning (preparing food and playing sports p < .001; construction p < .05). The post hoc pair-wise
analyses in these categories revealed that the NC group outnumbered the miAD and
the moAD group in the number of correct responses, but the ratio of correct verbs
was not significantly different between the miAD and the moAD group (see Appendix 6:8). However, the difference between the AD groups approached statistical
significance in the category of preparing food (p = .087).
9.4.2 Proportion of intrusions and perseverations
Some of the errors found on the verb fluency task were intrusions (i.e., verbs not
belonging to the given semantic category or nouns), which were observed in all
subject groups (see Table 20). Intrusions emerged in at least one semantic category
in the performance of 13/30 subjects in the NC group, 11/20 subjects in the miAD
group, and 14/20 subjects in the moAD group. The overall ratio of intrusions differed among the subject groups (p < .01), and the post hoc pair-wise analyses indicated that the moAD group produced significantly more intrusions than the NC
group (p < .01) and the miAD group (p < .05), while the NC group and the miAD
group produced intrusions to the same extent (see Appendix 6:9).
The NC group violated the category boundaries in all other categories but the
category of preparing food, whereas intrusions interfered in the performance of both
AD groups in all categories. Most of the intrusions emerged in the category of construction in all subject groups. The miAD group produced the fewest intrusions for
the category of playing sports and the moAD group for the category of cleaning.
There was a significant difference among the subject groups in the proportion of
intrusions in the categories of preparing food (p < .01) and playing sports (p <
.001). Both the miAD group and the moAD group generated significantly more
intrusions than the NC group in these categories (see Appendix 6:9), but the difference between the two AD groups in either category was non-significant.
Rather than being unrelated, intrusions were more frequently semantically
related to the given semantic category in the NC group and the moAD group (see
Table 21). The difference in the occurrence of related and unrelated intrusions was
statistically significant in each group (p < .01). In the miAD group, there was no
difference between the number of related and unrelated intrusions. Intrusions in the
category of preparing food consisted of things to prepare food of (e.g., ‘meat’) and
dishes to be prepared (e.g., ‘porridge’), kitchen utensils (e.g.,‘stove’), or outsidecategory words (e.g., ‘talk’). Intrusions in the category of playing sports were words
referring to sports equipment (e.g., ‘ball’), nouns referring to the location of playing
sports in (e.g., ‘gym’), semantically related actions (‘play bridge’), and outsidecategory intrusions (e.g., ‘crochet’). In the category of construction, tools (e.g., ‘hammer’) as well as things to be built (e.g., ‘the stairs’), things to be attached to a construction (e.g., ‘window’) and construction materials (e.g., ‘cement’) were consid-
Results
139
Table 21. Number of semantically related and unrelated intrusions
NC
(n = 30)
miAD
(n = 20)
moAD
(n = 20)
Type of intrusions
M (SD)
Md
M (SD)
Md
M (SD)
Md
Semantically related
intrusions
1.5 (2.9)
0.0
1.3 (2.1)
0.0
2.3 (2.4)
1.5
Semantically unrelated 0.03 (0.2)
intrusions
0.0
0.9 (1.5)
0.0
0.4 (0.8)
0.0
Z-value+
p-value
-.849
-2.655
p = .396, n.s. p < .01 **
-2.969
p < .01 **
Note. +Wilcoxon Signed Ranks Test: ** = p < .01. n.s. = non significant
ered intrusions. As for the category of cleaning, words referring to cleaning equipment (e.g., ‘rag’) and actions regarding decorating the inside of the house (e.g.,
‘decorate’), as well as semantically unrelated verbs (e.g., ‘sleep’) were counted as
intrusions.
All subject groups produced perseverations while generating verbs (see Table
20). The performance of 24/30 subjects in the NC group, 18/20 subjects in the miAD
group, and 17/20 subjects in the moAD group contained perseverations in at least in
one of the categories. Comparison of the total number of perseveration indicated a
significant difference among the subject groups (p < .001). Relative to the NC group,
significantly more perseverations were made in the miAD group (p < .001) and the
moAD group (p < .01), but there was no difference in the number of perseverations
between the two AD groups (see Appendix 6:10).
The NC group produced perseverations in a relatively stable manner across
the semantic categories, while the miAD and the moAD group showed more variation between the categories. There was a statistically significant difference among
the groups in the proportion of perseverations produced in all other categories but
the category of cleaning (preparing food and playing sports p < .05, construction
p < .01). According to the post hoc pair-wise analyses, the miAD group perseverated
significantly more often than the NC group when generating words for the category
of playing sports and construction (in each, p < .01). In the category of preparing
food, the difference in the number of perseverations between the two groups approached statistical significance (p = .07). Compared to the NC group, the moAD
group perseverated significantly more frequently only in the category of preparing
food (p < .01), while the number of perseverations remained statistically the same
140
Results
in the categories of playing sports and construction between these two subject groups.
There was no statistically significant difference between the miAD group and the
moAD group in producing perseverations for the categories of preparing food, playing sports or construction.
9.4.3 Number and variety of different semantic subcategories
The combined score indicating the number of different semantic subcategories from
which verbs were produced for the task differentiated the subject groups (p < .001;
see Table 22). Compared to the NC group, the miAD group (p < .01) and the moAD
group (p < .001) displayed a remarkable decrease in the use of subcategories, and
the miAD group produced clusters of verbs from significantly more dimensions
than the moAD group (p < .01; see Appendix 6:11).
For each semantic category, the NC group generated clusters from approximately two different subcategories, whereas the miAD group’s clustered words originated from one to two subcategories, and the moAD group clustered words using
only one subcategory. The number of semantic dimensions among the subject groups
was significantly different in all verb categories (preparing food, playing sports, and
construction p < .001, cleaning p < .01). The post hoc pair-wise tests indicated that
the NC group produced words from a significantly larger semantic scope than the
miAD and the moAD group in the category of preparing food, playing sports, and
construction (see Appendix 6:11). In the category of cleaning, the NC group generated more subcategories than the moAD group (p < .001), the difference between
the NC and the miAD group being non-significant. The miAD group outnumbered
the moAD group in the number of semantic dimensions in all other categories (preparing food p < .001; construction and cleaning p < .05) but playing sports.
The variety of semantic subcategories in the verb categories was dissimilar
between the subject groups. The NC group produced various types of semantic clusters, relative to the miAD group and the moAD group (Figures 5-8; see also Appendix 4E-4H). Characteristic of the NC group was that verbs were clustered according
to one main subcategory, followed by two to four other rather well represented subcategories for which verbs were divided in a more stable manner. In the miAD group,
verbs were mainly clustered according to two main subcategories, the rest remaining less represented. In the categories of construction and cleaning, the miAD group
lacked some of the semantic dimensions produced by the NC group. The moAD
group showed a remarkable reduction in the range of semantic activation and produced most of the verbs using mainly one semantic criterion for each category. For
the moAD group, more than one subcategory was very seldom represented for the
categories of preparing food, construction, and cleaning. However, for the category
of playing sports, all subject groups were able to activate several semantic subcategories.
When producing verbs for the category of preparing food, verbs denoting
cooking (e.g., ‘boil’, ‘fry’, ‘grill’) were most often produced in all subject groups,
Results
141
Table 22. Number of different subcategories produced
for the semantic categories in the verb fluency tasks
NC
(n = 30)
miAD
(n = 20)
moAD
(n = 20)
Category
M (SD)
Md
M (SD)
Md
M (SD)
Md
H
(df=2)
p-value
Preparing food
2.3 (0.9)
2.0
1.8 (1.0)
2.0 *
0.6 (0.8)
0.0 *** ¤¤¤
27.95
p < .001
Playing sports
2.5 (1.2)
2.0
1.7 (1.2)
1.0 **
1.1 (1.1)
1.0 ***
16.205
p < .001
Construction
2.5 (1.0)
2.5
1.7 (1.2)
1.5 *
1.0 (1.0)
1.0 *** ¤
19.018
p < .001
Cleaning
2.0 (1.0)
2.0
1.7 (1.0)
2.0
1.0 (1.0)
1.0 *** ¤
13.249
p < .01
All categories
9.2 (2.2)
9.0
6.8 (3.4)
5.5 **
3.7 (2.6)
3.0 *** ¤¤
30.568
p < .001
and ¤ = p < .05, ¤¤ = p < .01, ¤¤¤ p < .001 when miAD vs. moAD. n.s. = non significant.
followed by verbs referring to cleaning and preparing (e.g., ‘peel’, ‘wash’, ‘chop’)
and seasoning (e.g., ‘salt’, ‘spice’; Figure 5; see also Appendix 4E). Clusters of
specific verbs involving an instrument and referring to handling the ingredients (e.g.,
‘cut’, ‘mix’, ‘whip’) and baking (e.g., ‘bake’, ‘knead the dough’, ‘flour’) were produced by all other groups but the moAD group for whom they did not exist as a
dimension for eliciting clusters of verbs.
The distributions of the semantic subgategories in the category of playing
sports were more or less similar among the subject groups (Figure 6; see also Appendix 4F). In all groups, the most frequently used verbs were those referring to
different types of sports in which the use of feet/legs was foregrounded (e.g., ‘run’,
‘walk’, ‘jump’). The second most often used subcategory was verbs referring to
wrestling and boxing for the NC group, actions carried out in the water (e.g., ‘swim’,
‘butterfly’, ‘crawl’) for the miAD group, and types of games (‘play basketball’,
‘play football’) for the moAD group. Instrument verbs referring to winter sports
(e.g., ‘ski’, ‘skate’, ‘snowboard’, ‘ski downhill’, ‘cross-country skiing’) and actions
emphasizing the use of hands/arms (e.g., ‘throw’, ‘push’, ‘lift’) were also represented in all subject groups.
142
Results
1.4
NC
1.2
miAD
1.0
moAD
0.8
0.6
0.4
0.2
on
in
g
fo
od
ha
nd
se
lin
as
g
ba
ki
ng
ea
n
pr ing
ep a
ar nd
in
g
cl
co
ok
i
ng
0.0
Subcategories
subcategories of preparing food in different subject groups.
1.4
NC
1.2
miAD
1.0
moAD
0.8
0.6
0.4
0.2
em
p sp
ha ha orts
nd sis w
s on ith
an u th
d si e
ar ng
m
s
es
m
ga
rts
po
te
rs
in
w
sp
o
em rts
ph wit
us a h
in sis the
g
bo
le on
xi
gs
ng
an
d
w
re
st
lin
g
0.0
Subcategories
subcategories of playing sports in different subject groups.
Results
143
1.4
NC
1.2
miAD
1.0
moAD
0.8
0.6
0.4
0.2
e han
su d
rfa ling
ce
s
k
or
w
c
si
ba
w
or
ki
ng
of
th
a
th
e
bu fab
ild ric
in
g
k
or
kw
w
on
oo
br
ic
dw
or
ki
ng
0.0
Subcategories
subcategories of construction in different subject groups.
1.4
NC
1.2
miAD
1.0
moAD
0.8
0.6
0.4
0.2
lin kin
en g
an car
d eo
ca f
rp the
et
s
ta
cl
ea
n
g
pr
ep
ar
in
of
c
s
to
le
an
in
g
ai
rin
g
w
ay
cl
ea
ni
n
g
th
e
flo
or
0.0
Subcategories
subcategories of cleaning up in different subject groups.
144
Results
In the category of construction, verbs describing the ways of handling wood
(e.g., ‘carve’, ‘saw’) were the ones most often used in all subject groups (Figure 7;
see also Appendix 4G). Verbs connected with masonry (e.g., ‘carry bricks’, ‘mix the
mortar’, ‘mason’, ‘plaster’, ‘pour’) were the second most often used dimension in
the NC group, but they were rather seldom produced as a cluster in the AD groups.
Verbs referring to various types of action taking place in the foundation (e.g., ‘dig’,
‘lay the foundation’, ‘cover with boards’, ‘cast’, ‘tear down the boarding’) and on
other parts of a building (e.g., ‘cover [with roofing], ‘insulate’, ‘cover with boards’)
were rather well represented in the cluster production of the NC group and the miAD
group, whereas the moAD group produced clusters of verbs only for actions taking
place during the laying of the foundation. Respectively, the miAD group lacked
clusters of verbs referring to actions done on the surface of the walls (e.g., ‘paint’,
‘tile’, ‘wallpaper’).
The distributions of the most common semantic subcategories differed among
the subject groups also in the category of cleaning (Figure 8; see also Appendix 4H).
In all subject groups, clusters mostly involved verbs that referred to cleaning the
floors (e.g., ‘vacuum’, ‘sweep’, ‘wipe’, ‘polish’, ‘wash’, ‘scrub’). In the NC group
and the miAD group, the second best represented subcategory was verbs denoting
airing (e.g., ‘shake’, ‘beat’, ‘air’), a subcategory that did not exist as a source of
cluster production for the moAD group. Verbs referring to different ways of cleaning (‘scrub’, wash’, ‘wipe’) and taking care of the linen (e.g., ‘do the bed’, ‘change
the sheets’) elicited some clusters of verbs in all subject groups. Clusters of verbs
denoting getting ready to clean (e.g., ‘pour water into a bucket’, ‘get a vacuumcleaner’) existed only for the NC group.
9.4.4 Degree of prototypicality and frequency of the verbs
produced
There was no difference in the degree of prototypicality among the subject groups in
any single verb category, not even when the analysis was based on the total number
of verbs produced (Table 23). A statistically significant difference in the overall
verb frequency among the groups was evident (p < .05). The post hoc pair-wise
analyses revealed that the miAD group produced more frequent verbs than the moAD
group (p < .05), whereas the frequencies of the verbs between the other groups
remained the same (see Appendix 6:12). At the level of individual categories, all
subject groups produced verbs at the same level of frequency.
The responses given for the verb fluency task consisted of five types of word forms:
specific verb forms, phrasal structures, deverbal forms, and general verbs combined
with different argument structures. Furthermore, single concrete nouns were also
produced in all subject groups. The NC group and the miAD group gave similar
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Table 23. Degree of prototypicality and frequency of the verbs produced
Variable
NC
(n = 30)
miAD
(n = 20)
moAD
(n = 20)
M (SD)
Mdn
M (SD)
Mdn
M (SD)
Mdn
H
p-value
(df = 2)
Degree of prototypicality
of verbs
Preparing food
5.13 (0.67) 5.12 (1.00) 4.35 (2.66) 0.532
5.28
5.16
5.55
p = .767, n.s.
Playing sports
5.55 (0.52) 5.43 (0.52) 4.69 (2.27) 1.295
5.69
5.26
5.62
p = .523, n.s.
Construction
4.99 (0.54) 4.78 (0.79) 4.50 (1.72) 1.598
5.13
4.74
4.89
p = .450, n.s.
Cleaning
5.91 (0.41) 5.81 (0.60) 5.22 (1.98) 0.179
6.01
5.85
5.95
p = .914, n.s.
All verbs
5.28 (0.26) 5.23 (0.42) 4.61 (1.1)
5.26
5.25
4.75
3.412
p = .182, n.s.
4.68 (0.32) 4.90 (0.67) 4.20 (2.42) 3.218
4.68
4.78
5.03
p = .200, n.s.
Playing sports
4.56 (0.36) 4.59 (0.48) 4.05 (1.86) 0.082
4.56
4.59
4.79
p = .960, n.s.
Construction
4.00 (0.29) 4.18 (0.56) 3.84 (1.49) 3.658
3.97
4.32
4.14
p = .161, n.s.
Cleaning
4.50 (0.38) 4.62 (0.43) 3.91 (1.42) 4.829
4.58
4.76
4.29
p = .089, n.s.
All verbs
4.44 (0.23) 4.60 (0.36) 3.96 (0.89) 5.828
4.43
4.51
4.16 ¤
p < .05
Degree of frequency
of verbs
Preparing food
Note. Judgements were made on a 7-point scale: 1 = a very poor example of a category / a very
infrequent word, 7 = a very good example of a catefory / a very frequent word. ¤ = p < .05
when miAD vs. moAD. n.s. = non significant.
types of responses for the task, whereas the performance of the moAD group consisted of semantically more general responses.
Most of the errors in the NC group and the miAD group were perseverations.
In the moAD group, one half of the errors consisted of intrusions and the other of
perseverations. It is worth noting that in each subject group, including the NC group,
errors were not made by just a few participants, but a majority of them generated
errors at least once during the task (see also 9.4.2, 9.4.5). Compared to the NC
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group, both AD groups made significantly more errors in general and in all other
individual categories but the category of cleaning. The error analysis was sensitive
enough to differentiate between the two AD groups only at the level of overall performance. The qualitative analysis revealed a remarkable narrowing of the semantic
scope of subcategories from which to generate verbs for the task in the AD groups.
There was no difference between the groups in the degree of prototypicality of verbs,
but the frequency ratings showed that, in general, the moAD group used verbs with
significantly lower frequency of occurrence than the miAD group.
Word forms
The NC group and the miAD group seemed to name actions in a similar
manner, most of the responses consisting of specific verbs and deverbal forms of
verbs (see 9.4). The moAD group, however, showed a tendency to lose the degree of
specificity when generating verbs for the task, favoring more general verbs and single
concrete nouns to specific verbs. Thus, the responses given for the verb fluency task
did not follow the examples given by the examiner for the practice category, that is,
to use single verbs in the first infinitive (e.g., ‘kaivaa’ / ‘dig’, ‘istuttaa’ / ‘plant’).
Although sentence-like phrases were not introduced or encouraged for production during the practice phase, some of the subjects in each group produced
verbs which were embedded in phrasal structures. The tendency to produce whole
phrases may reflect the conceptual-semantic dependency of the verbs on other words
and the verbs’ central position which relates to nouns as their arguments and thus
motivates the whole clausal structure for production (see Huttenlocher & Lui 1979;
Engelkamp 1975; Reyna 1987; Shapiro et al. 1987; Aitchison 1994:111; Persson
1995:96-97; Wayland et al. 1996; Pajunen 1999:14-15; see 3.3). In some confrontation naming tasks, such a response pattern may have been interpreted as a
circumcolutory or a descriptive error that described a perceptual or a functional
feature of the target (e.g., Robinson et al. 1996; White-Devine et al. 1996). In this
study, however, the phrasal structures were considered appropriate responses because they designated the whole frame for the action, including the verb and its
arguments, which can be considered part of the meaning structure of the verb (see
9.4.1).
The use of deverbal forms of verbs is likely to display a normal pattern of
nominalization common to any language, which does not essentially modify the
conceptual-semantic content of the verb, as stated by Langacker (1991:25) and Vinson
and Vigliocco (2002). Nominalization may contribute as a shift in the profile of the
action by changing the focus from the process of the action to its nominal entity, for
example, to an internal subject (e.g., ‘blender’), an internal object (e.g., ’choice’),
an instrument (e.g., ‘probe’), a product (e.g., ‘painting’), or a location (e.g., ‘lounge’),
to a single episode of a process (e.g., ‘make a throw’), or to an atemporal abstraction
of the relationships between the participants of a process (e.g., ‘walking’; Langacker
1991:22-28; see also Saffran et al. 1980; Leino 1999:80, 89). Consequently, a noun-
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147
like profile may have been taken for verbs and deverbal forms were thus listed in a
noun-like manner as a continued pattern of response strategy, originating from the
preceding noun fluency task.
The use of nominalizations, which does not require the use of the whole rolestructure of the verb, has been interpreted as a way of agrammatic speakers of English to ease their verb production (Saffran et al. 1980). Possibly, the increasing
number of nominalizations in the AD groups may be an indication of a need to ease
the demands of the naming task, especially in the moAD group. However, the difference between the NC group and the moAD group did not reach the level of statistical significance in the proportion of deverbal forms. Therefore, the role of the
nominalizations as a compensatory manner to ease word production in AD remains
speculative in the present study.
The tendency of the moderate AD patients to express different types of action
by forming structures containing more general verbs (e.g., ‘panna’, ‘laittaa’, ‘tehdä’
/ ‘put’, ‘make’, ‘do’) rather than specific verbs supports the study of Kim and Thompson (2001) who reported on the tendency of AD patients to use generic verb
forms rather than more complex verbs in the confrontation naming task. For the AD
patients, generic verbs, although they are relational and lacking a direct connection
to perception, may be easier to retrieve than specific verbs because generic verbs are
very high in frequency, polysemous, easy to modify semantically, and flexible to
use in different contextual situations (see Reyna 1987; Persson 1995:103; Berndt et
al. 1997; Breedin et al. 1998; Kim & Thompson 2001; see also 3.3.1). Specific
verbs, on the other hand, are rich in functional and semantic role information. For
example, verbs like ‘bake’, ‘mix’, and ‘sweep’ that were produced for the task consist of a conglomerate of semantic features that can be simultaneously activated.
They involve such semantic roles as Mover to encode the physical movements of
the body or a part of the body, Agent to encode the entity to bring about a change,
Instrument to encode the involvement of a tool or an instrument with which to carry
out the action, Patient to encode the entity being acted upon, and Result encoding
the outcome of the action (Persson 1995:99-103; cf. Levelt 1989:90-94; see 3.3, and
3.3.3).
The present study supports the finding of Persson (1995:101) who claimed
that some of the semantic roles might overlap and constrain the effects and the interpretation of each other. As noted by her, the role of Mover may be more emphasized
than Agent in verbs referring to different types of motion (e.g., ‘walk’), whereas
Agent may be more fore-grounded in verbs encoding causative action than Mover
(e.g., ‘cut’). The data of the present study may imply that the roles of Patient and
Result may be overlapping in certain verbs. For example, ‘bake’ implicitly contains
the underlying element of Patient, the entity being acted upon (i.e., the batter), and
Result, the entity being changed during the action (i.e., the cake). Another aspect
that may make specific verbs more difficult for the AD patients to retrieve is that,
relative to generic verbs, a flexible modification of specific verbs is not possible
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because they are closely integrated with specific nouns (e.g., ‘sweep’ entails ‘broom’;
see Engelkamp 1975; Huttenlocher & Lui 1979; Huttenlocher et al. 1983; Persson
1995:102-193).
It can be assumed that the occurrence of general purpose verbs in the performance of the moderately demented AD patients may be a sign of the activation to
settle for information that is sparser in terms of semantic features which is thus
semantically more available or less of an effort for them to be integrated and produced for an output than the information contained by specific verbs (see 3.3.1,
10.1.3). Thus, the selection of semantically related nouns for production may be
explained by AD patients’ difficulty in integrating the semantic information of specific verbs, whereby nouns corresponding to the semantic roles of the verb structure
may have become more activated and selected for the production. For example,
instead of a specific cleaning verb (e.g., ‘wipe’), the subject may produce the Instrument (e.g., ‘rag’) designating the thing with which the action is carried out, which
usually is not specified when the verb is produced but is indirectly implied by the
verb (see Behrend 1990; Persson 1995:99). For a further discussion on the appearance of nouns among verb responses, see below under Intrusions and 10.1.3.
The pattern of producing general verbs and nouns that are part of the semantic role structure suggests that the system tries to compensate for the non-availability of the more specific features by semantically more available features in AD. This
bears certain similarities to the findings concerning the noun representation changes
in AD patients, with a better availability of the general, superordinate knowledge
relative to the more specific knowledge at the lower levels of the semantic hierarchy,
which has been documented by a number of authors (e.g., Warrington 1975; Schwartz
et al. 1979; Martin & Fedio 1983; Martin et al. 1985; Huff et al. 1986; Shuttleworth
& Huber 1988; Chertkow et al. 1989; Chertkow & Bub 1990; Hodges et al. 1992,
Hodges & Patterson 1995; Tippett et al. 1995; see 9.6). Thus, it seems as if a reduction in semantic specificity takes place in AD, especially at its moderate stage. For
those patients, naming an action seems to be dependent more on the generic verbs
and concrete nouns than on the specific verb forms.
Intrusions
The total number of intrusions (i.e., verbs outside the category boundaries and single
nouns) was significantly higher in the moAD group than in the other groups (see
9.4.2). In the NC group and the moAD group, most of the intrusions tended to be
verbs and nouns semantically related to the given category, whereas the miAD group
produced both semantically related and unrelated intrusions to the same extent.
Intrusions were produced selectively for the individual semantic categories by the
NC group, whereas both AD groups tended to violate category boundaries. Compared
to the NC group, the number of intrusions in the AD groups was significantly higher
in the categories of preparing food and playing sports. As discussed earlier, most of
the intrusions consisted of semantically related concrete nouns, such as dishes to be
eaten and sports equipment. Concrete single nouns also appeared in other semantic
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categories. Common to all categories, most of the nouns were names of different
objects involved in the action. More specifically, they seemed to represent the semantic
roles encapsulated in the verbs (see 3.3). For example, the semantic role Instrument
was often used to designate things the action was carried out with (e.g., ‘hammer’,
‘rag’). The role Patient was involved in carrying the information of an entity to be
acted upon or to be changed by the action (e.g., ‘meat’, ‘cement’). Result was used
to designate the result or the product of the action (‘porridge’, ‘stairs’), and Location
the site of the action (e.g., ‘stove’, ‘gym’).
The finding that syntagmatic errors occurred in AD is in contrast to the study
of Gewirth et al. (1984) who claimed that in the word association task, demented
subjects were as sensitive to the grammatical class of the stimulus word as normal
control subjects and that the frequency of syntagmatic responses was not likely to
change significantly with increasing severity of dementia. In the present study,
syntagmatic errors did not occur while words were produced for the noun fluency
task. However, violations of the grammatical class of verbs were made, particularly
in the moAD group. The finding may imply that the grammatical class of verbs is
more vulnerable to a breakdown in AD or that naming actions is even more dependent on other word classes, especially nouns, for these patients than for normal
elderly subjects. However, although most of the subjects of the dementia group in
the study of Gewirth et al. had AD, the group involved other subjects with miscellaneous types of dementia (e.g., patients with multi-infarct dementia), which is why a
straightforward comparison between the studies may be questionable.
The deficit underlying the emergence of intrusions may involve the incorrect
features having a higher level of activation or an increased decay rate whereby they
are more likely to be selected for further processing (see 9.2.6, 10.1.2, 10.1.3). Also
the process behind the production of semantically related concrete nouns instead of
verbs may imply an impaired semantic feature integration of verbs. In order for a
selection of an item to take place successfully, a convergence of a whole set of
semantic features is required. Especially in the case of the subjects with moderate
AD, the semantic system, after finding the encoding of the functional features and
the semantic roles of a verb difficult or impossible, may compensate the unsuccessful feature integration by settling the spread of activation on perceptually richer,
more transparent, more static, and more available features belonging to the nominal
component of the verb’s semantic structure (e.g., ‘rag’; see Persson 1995:26, 84-86,
92-104, 146; Guasti 2002:81). Consequently, the nouns produced for the verb categories may have acted as substitutions for the specific verbs. The finding that semantically related concrete nouns occurred in verb categories supports the notion
that verbs are semantically dependent structures and that the semantic representation of specific verbs includes nouns (Persson 1995:96-104; see 3.3 and 3.3.3). However, putting aside theoretical speculations about the process underlying the intrusions, it may also be possible that the occurrence of nouns in the verb categories
may be an indication of the subjects having forgotten the task instruction (see Astell
& Harley 2002).
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The errors made by the AD patients in the semantic fluency task may bear a
certain similarity to the semantically related and unrelated errors, as well as to the
descriptive and part-whole naming errors reported to occur in verb confrontation
naming tasks (White-Devine et al. 1996; Robinson et al. 1996; Williamson et al.
1998; see 2.4.2). These errors may imply difficulty in the integration of semantic
features (see 10.1.3).
Perseverations
Perseverations were produced in all subject groups for all semantic categories (see
9.4.2). Overall, significantly more perseverations were produced in the miAD group
and the moAD group than in the NC group. Surprisingly, the number of perseverations
did not differentiate between the AD groups when the combined score was taken
into account. Compared to the NC group, the category of preparing food seemed to
provoke significantly more repetitions than other semantic categories in both the
miAD group and the moAD group. Furthermore, the miAD group perseverated significantly more often than the NC group in the categories of playing sports and
construction, while the moAD group perseverated to the same extent as the NC
group. One of the reasons for the moAD group producing fewer perseverations may
be their tendency to generate more intrusions than others, at least for some categories.
Similar to the AD patients’ performance on the noun fluency task, their performance on the verb fluency task gives reason to assume that their word production
system may not work well because perseverations tend to interfere with the activation of new responses (see 9.2.6, 10.1.2, 10.1.3). It can be speculated that the fewer
number of perseverations in the moAD group relative to the miAD group may partly
be due to semantically irrelevant features (i.e., intrusions) gaining even more activation than previously activated appropriate features. Another possibility may be that
the self-monitoring system may function better for the miAD group than for the
moAD group which prevents the miAD group from violating the semantic and grammatical category but not from selecting the previously activated patterns of features
(see Diesfeldt 1985; Della Sala et al. 1993; Pasquier et al. 1995; see also Baddeley
et al. 1986, 1991; Levelt 1989:463-467, 1999a, b; Rosen & Engle 1997).
In earlier reports on the AD patients’ performance on the confrontation naming tasks with verbs, perseverations were not reported to take place (see e.g., Bowles
et al. 1987; White-Devine et al. 1995, 1996; Robinson et al. 1996; Williamson et al.
1998). Thus, semantic fluency tasks may involve at least partly different semanticcognitive functions from confrontation naming tasks (see 9.6.1, 9.6.3, 9.6.4).
Semantic subcategories
The present study indicated that the normal control subjects were better able
to activate varying subcategories to evoke verb production and thus showed a more
dynamic, flexible, integrative, and creative use of the semantic information con-
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151
tained by different types of concrete verbs than the patients with mild and moderate
AD did (see 9.2.4, 9.2.6). The category of cleaning notwithstanding, a remarkable
decrease in the number of available semantic subcategories was found in the miAD
group. The most obvious narrowing of the dimensions was found in the moAD
group that produced significantly fewer subcategories than the NC group throughout all verb categories and less than the miAD group for all other categories but the
category of playing sports. Hence, the moAD group showed the most restricted
potential for activating new semantic subcategories from which to draw verbs for
production.
Typical of the clusters in the categories of preparing food, construction, and
cleaning was the appearance of verbs denoting agent-initiated, goal-directed action
causing a change of state and/or a result as an outcome of the action, for example,
verbs denoting different ways (i.e., troponyms) of cooking (‘boil’, ‘fry’, ‘grill’),
bricklaying (e.g., ‘mason’, ‘plaster’, ‘pour’), and airing carpets (‘shake’, ‘beat’, ‘air’).
Characteristic for the category of playing sports was the occurrence of verbs with a
reference to motion involving the whole body (e.g., ‘wrestle’, ‘box’), body parts
(e.g., ‘run’, ‘walk’, ‘jump’ and ‘throw’, ‘push’), and games involving more than one
participant (e.g., ‘play basketball’, ‘play ice-hockey’). Common to all semantic categories, some of the clusters consisted of verbs the semantic content of which contained a concrete noun (e.g., a tool or an instrument) as a part of the verb structure,
such as verbs denoting diminishing and handling the ingredients when preparing
food (e.g., ‘chop, ‘mix’, ‘whip’), actions of sports (e.g., ‘skate’, ‘snowboard’, ‘downhill skiing’, ‘cross-country skiing’), as well as verbs referring to handling wood
(e.g., ‘carve’, ‘saw’, ‘paint’) and cleaning the floor (e.g., ‘vacuum’, ‘sweep’, ‘polish’, ‘wash’, ‘scrub’). Furthermore, thematic information, such as temporal-contextual information, was encoded in the clusters (e.g., types of winter sports and particular targets or phases of construction a house). Common to all subject groups,
clusters of verbs were also produced by ordering the actions in a sequential, temporal order in which they are likely to take place when preparing food, construction, or
cleaning a house. For the category of playing sports, these script-like listings of
actions were rarely given. In sum, the verb clusters were formed using thematicfunctional, troponymic, and temporal-causal relationship between verbs (see 3.3).
Degree of prototypicality and frequency of the verbs produced
The statistical analysis indicated that the subject groups produced words of
the same degree of prototypicality for all four verb categories. However, it is worth
noting that, throughout all the semantic categories, the ratings of the verbs indicated
the highest prototypicality for the NC group and the lowest for the moAD group. As
far as the frequencies of the verbs produced are concerned, all the subject groups
produced verbs with the same level of frequency for all individual verb categories,
but the overall frequency score indicated that the miAD group produced more words
of higher mean frequency than the moAD group.
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These findings are in contrast to the finding obtained from the prototypicality
and frequency ratings on the noun fluency task (see 9.2.5, 9.2.6; cf. Ober et al. 1986;
Chan et al. 1993; Weingartner et al. 1993; Binetti et al. 1995; Goldstein et al. 1996;
Beatty et al. 2000). It is possible that, because the number of verbs produced by the
moAD group remained very small, individual performances were likely to gain more
weight and thus they may have skewed the outcome of the verb frequency ratings
towards more seldom occurring verbs. For a further discussion on the methodology,
see 10.2.2.
9.5 Summary of the overall semantic fluency
performance
Comparing the overall semantic fluency performance of the miAD group and
the moAD group to the performance of the NC group on the noun and verb categories, it can be summed up that some of the parameters chosen for the study appeared
to be more sensitive in differentiating between the subject groups than others. As
indicated in Table 24, the parameters seemed to work better to differentiate between
the subject groups in the noun than in the verb fluency tasks.
As far as the noun categories are concerned, almost all parameters differentiated the miAD group and the moAD group from the NC group. Furthermore, the
critical parameters were the same in these comparisons. This implies that relative to
the performance of the healthy normal elderly adults, the semantic fluency performance of the mild and the moderate AD patients on the four noun categories was
significantly impaired. Both AD groups produced nouns that were fewer in number,
more frequent, and from a smaller number of semantic subcategories than the nouns
produced by the NC group. Moreover, the switching and clustering performance of
the AD groups was significantly poorer and the AD patients produced more errors,
mainly perseverations, for the task. In the AD groups, the role of the perseverations
may have an influence on the clustering performance in individual categories (see
9.2.2, 9.2.6). The parameters showing no significant difference between the groups
imply that intrusions rarely took place and that the responses given for the task were
of an equal level of prototypicality in all subject groups.
Comparison between the miAD group and the moAD group indicated that
the miAD group was significantly better than the moAD group at generating more
total and correct responses, switches, clusters, and semantic subcategories for the
noun categories. The overall clustering performance, however, indicated that although the number of clusters was significantly smaller in the moAD group, the
cluster size and proportion of clustered nouns of the groups were similar. Both groups
seemed to use nouns at the same level of prototypicality and frequency. Concerning
the error analysis, the moAD group produced significantly more perseverations than
the miAD group. The number of intrusions remained the same in all three groups.
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Table 24. Summary of the comparison of the semantic fluency
performance between the subject groups
NC
vs. miAD
NC
vs. moAD
miAD
vs. moAD
NC
vs. miAD
NC
vs. moAD
miAD
vs. moAD
Variable
Nouns
Nouns
Nouns
Verbs
Verbs
Verbs
Total number
of words
***
***
**
**
***
**
Number of
correct words
***
***
***
***
***
***
Number of
switches
***
***
***
***
***
**
Number of
clusters
***
***
***
**
***
***
Cluster size
**
*
n.s.
n.s.
n.s.
n.s.
Words in clusters
***
**
n.s.
n.s.
n.s.
n.s.
Degree of
prototypicality of
words
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
Frequency of
words
(*)
**
n.s.
n.s.
n.s.
*
Proportion of
correct words
**
***
*
**
***
(*)
Propotion of
intrusions
n.s.
n.s.
n.s.
n.s.
**
*
Propotion of
perseverations
***
***
*
***
**
n.s.
Number of
semantic
subcategories
***
***
***
**
***
**
Note. The comparisons are based on the combined average scores.
(*) = almost statistically significant, * = p < .05, ** = p < .01, *** = p < .001.
n.s. = non significant
Concerning the semantic fluency performance on the verb categories, the
overall performance of the AD groups was significantly impaired relative to the NC
group. The NC group produced significantly more total and correct responses,
switches, and clusters on a larger scale of semantic dimensions than the miAD group
and the moAD group. However, the cluster size and the proportion of clustered
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verbs remained the same between the subject groups. The AD groups also produced
verbs as prototypical and frequent as the NC group. The error analysis indicated that
both AD groups made significantly more errors than the NC group. Both AD groups
committed more perseverations than the NC group, but only the moAD group violated the boundaries of the semantic and the grammatical categories by producing
significantly more intrusions than the NC group. The tendency of the AD groups to
perseverate responses may cruicially affect the cluster formation and the overall
performance on the tasks (see 9.4.2, 9.4.5).
The miAD group produced significantly more total and correct responses from
a wider variety of semantic dimensions, as well as more switches and clusters for
the verb categories than the moAD group. Nevertheless, the size of the verb clusters
and the proportion of clustered verbs was similar in these two groups. Furthermore,
the verbs appeared to be of the same level of prototypicality but the verbs produced
by the moAD group were of lower frequency than those produced by the miAD
group. The error analysis indicated that the moAD group produced more intrusions
than the miAD group, but these two groups did not differ in the number of
perseverations produced for the verb categories.
To sum up, the most sensitive parameters to differentiate the performance of
the different subject groups in the present study appeared to be the number of total
and correct responses, the number of clusters and switches, and the proportion of
correct words, as well as the number of different semantic subcategories employed.
As far as the error analysis is concerned, the proportion of perseverations seems also
to be a sensitive parameter, especially when measuring semantic fluency performance on nouns and the performance differences between the NC group and the AD
groups.
9.6 Performance on the control tasks
The control tasks chosen for the present study consisted of tasks for which the participants were asked to give verbal responses (naming) and non-verbal responses
(pointing to or sorting the targets).
9.6.1 Performance on the control tasks requiring verbal
responses
The overall group differences were significant in the semantic tasks requiring verbal
responses and in the test tapping the functioning of the working (short-term) memory
(p < .001, see Table 25). The miAD group performed significantly worse than the
NC group on all tasks requiring the naming of verbs (p < .001; see Appendix 7). The
miAD group named fewer picture objects in the BNT and in the serial naming task
than the NC group (p < .001), but the groups did not differ in naming single photographed objects originating from the four semantic categories used in the semantic
fluency task. Compared to the NC group and the miAD group, the moAD group
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155
Table 25. Performance of the subject groups on
the control tasks requiring verbal responses
NC
miAD
moAD
Control task
M (SD)
Mdn
M (SD)
Mdn
M (SD)
Mdn
H (df = 2)
p-value
BNT
53.8 (3.7)
55.0
42.2 (12.3)
54.5 ***
33.8 (10.8)
34.0 *** ¤
37.259
p < .001
Naming, nouns
19.8 (0.5)
20.0
19.0 (3.1)
20.0
17.4 (2.9)
18.0 *** ¤
15.170
p < .001
Naming, verbs
18.3 (1.8)
19.0
16.3 (3.5)
17.0 ***
13.0 (3.5)
34.477
14.0 *** ¤¤¤
p < .001
Serial naming, nouns
24.0 (0.2)
24.0
22.8 (3.5)
24.0 ***
21.3 (3.7)
23.0 *** ¤
23.278
p < .001
Serial naming, verbs
23.9 (0.3)
24.0
22.1 (3.0)
23.5 ***
18.3 (4.1)
19.5 ***¤¤¤
41.383
p < .001
Digit span forward+
5.2 (1.7)
5.0
3.8 (1.4)
4.0 **
2.9 (1.1)
3.0 *** ¤
25.904
p < .001
Note. BNT = Boston Naming Test (Laine, Koivuselkä-Sallinen et al. 1997). * = p < .05,
** = p < .01, *** = p < .001 when NC vs. miAD and NC vs. moAD, and ¤ = p < .05,
¤¤¤ = p < .001 when miAD vs. moAD. +interpreted with a licensed psychologist.
performed significantly worse on every task (p < .01 or smaller). Relative to the NC
group, the functioning of the short-term memory was significantly limited in the
miAD group (p < .01) and the moAD group (p < .001), and the short-term memory
span appeared to be significantly shorter in the moAD group than the miAD group
(p < .05).
9.6.2 Performance on the control tasks requiring non-verbal
responses
Overall group differences were statistically significant in all tasks requiring nonverbal responses (p < .01 or smaller; Table 26). Relative to the NC group, the Token
Test tapping comprehension and interpretation of verbal instructions was performed
significantly worse by the miAD group and the moAD group (both, p < .001; see
Appendix 7). The miAD group did not show signs of deteriorated recognition of
categories or category exemplars, either of nouns or verbs. However, the miAD
group failed to sort the cards of objects and actions according to their category
membership (p < .001). The moAD group performed significantly poorer than the
NC group on all semantic tasks (p < .05 or smaller), and worse than the miAD group
on all other tasks (p < .05 or smaller) but the tasks of category recognition.
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Results
Table 26. Performance of the subject groups on
the control tasks requiring non-verbal responses
NC
miAD
moAD
Control task
M (SD)
Mdn
M (SD)
Mdn
M (SD)
Mdn
H (df = 2)
p-value
Token-test
33.7 (2.1)
34.0
30.1 (3.7)
30.8 ***
21.1 (6.5)
24.0 *** ¤¤¤
38.349
p < .001
Category recognition,
nouns
16.0 (0.0)
16.0
16.0 (0.0)
16.0
15.4 (3.0)
16.0 *
9.746
p < .01
Category recognition,
verbs
16.0 (0.0)
16.0
15.8 (0.7)
16.0
15.0 (3.6)
16.0 **
11.278
p < .01
In-category recognition,
nouns
23.9 (0.3)
24.0
23.6 (1.6)
24.0
22.8 (2.3)
23.5 *** ¤
15.487
p < .001
In-category recognition,
verbs
23.8 (0.5)
24.0
22.9 (2.5)
24.0
18.9 (4.9)
21.0 *** ¤¤¤
37.786
p < .001
Card sorting, nouns
19.8 (0.5)
20.0
18.6 (1.6)
19.0 ***
13.9 (5.5)
15.0 *** ¤¤¤
36.130
p < .001
Card sorting, verbs
19.7 (0.8)
20.0
16.3 (3.5)
18.0 ***
10.0 (5.3)
10.5 *** ¤¤¤
47.594
p < .001
¤ = p < .05, ¤¤¤ = p < .001 when miAD vs. moAD.
9.6.3 Correlations among scores on the semantic tasks
The number of correct nouns in the NC group correlated only with the Token Test
(p < .01; see Table 27). In the miAD group, positive correlations were found between
correct nouns and the BNT, the Token Test, and the naming of category related
nouns (all, p < .01). For the moAD group, there was a high positive correlation
between the number of correct nouns and almost all verbal and non-verbal tasks:
Token Test and naming of category related nouns (p < .01), serial naming of nouns,
category recognition, and in-category item recognition (all, p < .05). In the moAD
group, the number of correct nouns also correlated highly with the digit span test
(p < .05).
The number of correct verbs was correlated in the NC group only with the incategory recognition of verbs (p < .05). For the miAD group, a significant positive
correlation existed between the number of correct verbs and the main linguistic
tests, the BNT (p < .01) and the Token Test (p < .05), as well as the digit span test
(p < .01). For the moAD group, the correct verbs correlated highly with the Token
Test (p < .01), the digit span test (p < .05), the task of serial naming of verbs (p < .05),
the category recognition task (p < .05), and the card-sorting task (p < .05).
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157
Table 27. Spearman rank-order correlation coefficients (ρ) between the
correct responses of the semantic fluency tasks and
the control tasks in the subject groups
Noun fluency task
Noun fluency task
ρ
ρ
NC
miAD
(n = 30) (n = 20)
moAD
(n = 20)
NC
miAD
(n = 30) (n = 20)
moAD
(n = 20)
BNT
.244
.615**
.395
.259
.685**
.255
Naming category
related items
.026
.631**
.595**
.173
.148
.248
Serial naming
.183
.304
.547*
.186
.205
.503*
Digit span forward
.246
.379
.538*
.301
.480*
.537*
Token-test
.506**
.568**
.622**
.340*
.450*
.611**
Category
recognition
(-)
(-)
.504*
(-)
-.023
.585**
In-category
recognition
-.008
.302
.474*
.439*
.322
.357
Card sorting
.081
.115
.227
-.205
.122
.532*
Control task
Verbal tasks
Non-verbal tasks
Note. n = 30 (NC): ρ > .339, p < .05 = *; ρ > .437, p < .01 = **. n = 20 (miAD, moAD):
ρ > .444, p < .05 = *; ρ > .561, p < .01 = **. (-) = no variation.
9.6.4 Discussion on the semantic tasks
The present study indicated that both AD groups had difficulty in tasks requiring
verbal and non-verbal semantic processing and working memory functioning. The
miAD group showed selectively impaired semantic abilities. They performed as
well as the NC group on tasks that required recognition of noun and verb categories
and category members, as well as naming of very familiar category-related photographed objects (i.e., clothes, vegetables, vehicles, and animals). However, their
performance deteriorated on the BNT and the Token Test. They also fared worse
than the NC group when naming single verbs and series of semantically related
nouns and verbs that required simultaneous processing of various types of semanti-
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Results
cally related information. Relative to the NC group, the moAD group had remarkable difficulty in all types of semantic processing, including the easiest tasks. Comparing the performance between the AD groups, the miAD group fared significantly
better only in the category recognition tasks. The findings of the present study give
support to Hodges and Patterson (1995), Huff (1988), Mickanin et al. (1994), and
Laine, Vuorinen et al. (1997) who observed that the semantic impairment in AD
could be revealed by using both verbal and non-verbal tasks.
On the basis of the control tasks, it can be concluded that both AD groups
displayed impaired naming of concrete nouns and verbs. The finding is consistent
with many other studies (e.g., Kirshner et al. 1984; Kertesz et al. 1986; Bowles et al.
1987; Nebes 1989; Chertkow & Bub 1990; Nicholas et al. 1996; Robinson et al.
1996; White-Devine et al. 1996; Laine, Vuorinen et al. 1997; Cappa et al. 1998;
Tröster et al. 1998; Williamson et al. 1998; Kim & Thompson 2001; see 2.4.2).
Naming of concrete objects may be better preserved in the miAD group than in the
moAD group, whereas naming of concrete actions appeared to be impaired in both
groups. Moreover, subjects in the moAD group tended to suffer from significantly
more severe naming difficulties than subjects in the miAD group. Worsening of the
naming difficulties thus seems to be associated with the severity of dementia, a
finding in accordance with several others (e.g., Bayles & Tomoeda 1983; SkeltonRobinson & Jones 1984; Bowles et al. 1987; Shuttleworth & Huber 1988; Smith et
al. 1989; Bayles et al. 1990; Hodges & Patterson 1995).
The present study suggests that the information needed for recognizing semantic categories and in-category members of both nouns and verbs was likely to be
intact in the miAD group but impaired in the moAD group. The finding opposes the
general notion according to which superordinate information is likely to be relatively preserved in AD (e.g., Warrington 1975; Schwartz et al. 1979; Martin & Fedio
1983; Martin et al. 1985; Huff et al. 1986; Chertkow et al. 1989; Chertkow & Bub
1990; Tippett et al. 1995). There are other recent studies that have also indicated
deteriorated information processing at the superordinate level in AD (Grossman,
D’Esposito et al. 1996; Laatu et al. 1997; Laine, Vuorinen et al. 1997; see also
Hodges et al. 1992; see 2.4.1). On the other hand, the finding that the more specific
information needed for disambiguating category co-ordinates may be impaired in
AD partly supports many previous studies (e.g., Warrington 1975; Schwartz et al.
1979; Martin & Fedio 1983; Martin et al. 1985; Huff et al. 1986; Chertkow et al.
1989; Chertkow & Bub 1990; Tippett et al. 1995; see 2.4.1).
On the basis of the present study, however, it can be assumed that the impaired semantic processing at the super- and subordinate level may not involve all
AD patients but is likely to be restricted to the moderate and later phases of the
disease. In this sense, the present study supports the finding of Hodges and Patterson
(1995) who indicated that the deterioration of processing category information progressed with advancing dementia. Nevertheless, indicated by the performance on
the Token Test, comprehension of more complex language was impaired in both AD
groups, a finding consistent with earlier studies (Swihart et al. 1989; Tomoeda et al.
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159
1990). It should be borne in mind, however, that the Token Test might not just measure linguistic abilities (i.e., semantic and syntactic knowledge), but it probably involves other cognitive functions, such as working memory, attention, visuospatial,
and praxis abilities that may be affected as well in AD and, therefore, contribute to
the linguistic processing (see Rochon, Waters & Caplan 1994).
The variety of performance found in the subject groups on the different tasks
may be explained by some of the tasks being semantic-cognitively more demanding
than others. For example, in the category recognition and the card-sorting tasks,
which were meant to measure non-verbal category identification, the miAD group
performed like the NC group on the former but considerably worse on the latter. The
recognition tasks required identification of only one item from a set of unrelated
items, whereas the card-sorting tasks required active and simultaneous processing
of semantically related and unrelated items, as well as self-initiated executive functioning. A reduction in naming was not found in the miAD group when naming of
single, concrete objects related to the four semantic categories was involved. However, when they were asked to name the same items as a set of closely related category members, which required rapid and flexible functioning of the whole mental
lexicon, a remarkable reduction in their noun production took place.
The finding that different experimental tasks may impose differing cognitive
demands supports the notions discussed widely by Nebes (1989, 1992; see also
Nebes et al. 1984, 1989; Bayles 2003). Nebes et al. noticed that AD patients were
rather successful at tasks in which the semantic information was heavily constrained
and guided by the task and the stimuli (e.g., in the priming conditions, see 2.4.1). At
the same time, they were likely to have difficulty in tasks that required intentional
search or manipulation of the semantic memory or making conscious decisions and
judgements about semantic information (e.g., fluency tasks). Such semantic processing makes heavy demands on the capacity of the working memory (e.g., executive and attentional functions), which is known to deteriorate in AD (e.g., Diesfeldt
1985; Baddeley et al. 1986, 1991; Ober et al. 1986; Bayles et al. 1989; Bayles 2003;
Nebes 1989; Chertkow & Bub 1990; Morris 1994; Pasquier et al. 1995; Rosen &
Engle 1997). Consequently, a deficit in working memory functions, indicated by the
significantly weakened performance on the digit span task in both AD groups, is
likely to have had an effect on performing some of the control tasks (e.g., the Token
Test) and the fluency task. For example, the emergence of perseverations may be
explained as an impaired ability to monitor the output and to remember the previously produced words (see Rochon et al. 1994; Rosen & Engle 1997).
Concerning the correlations between the correct responses on the fluency tasks
and the control tasks, it was found that both the noun and the verb fluency performance of all subject groups correlated significantly with the Token Test. The finding
is in line with Ober et al. (1986) and Swihart et al. (1989), as far as production of
nouns is concerned. The significant correlation between the tasks may reflect the
multi-faceted nature of the tasks, each of them requiring complex linguistic and
non-linguistic processes (Rosen 1980; Diesfeldt 1985; Laine 1989:5; Chertkow &
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Results
Bub 1990; Auriacombe et al. 1993; Rochon et al. 1994; Ruff et al. 1997; Cohen &
Stanczak 2000). The correlation between the semantic fluency tasks and the BNT
being significant only in the miAD group for both nouns and verbs remains somewhat puzzling, considering that a correlation between those tasks has previously
been explained by both of them involving semantic retrieval performance (Ober et
al. 1986; Randolph et al. 1993; cf. Bayles et al. 1989). However, these tasks may
involve partly different semantic-cognitive processes, which may explain the lack
of correlation between the tasks in the NC group.
Although all correlations did not seem to form a regular and easily interpreted
pattern, it was discovered that in the miAD group fluency performance on the noun
categories was highly correlated with the verbal tasks, whereas in the moAD group,
a significant association was found with tasks requiring both verbal and non-verbal
responses. Somewhat similar relationships existed for each AD group between the
verb fluency task and the control tasks tapping verb processing. The correlation
between the fluency task and the digit span in both AD groups may indicate that
their fluency performances were also influenced by impaired working memory
function, as discussed above.
On the basis of the fluency tasks, the control tasks, and the correlations between them, it can be concluded that semantic processing was impaired in both AD
groups and that the impairment was likely to show a progressing pattern: In the
miAD group, the impairment could be revealed by cognitively more demanding
tasks, such as semantic fluency and naming tasks emphasizing verbal output, whereas
in the moAD group, the semantic impairment could be indicated even by easier
tasks, such as recognition tasks. However, it should be borne in mind that also nonlinguistic factors, such as deficit in working memory functions, probably had an
impact on the semantic performance of the AD patients. Finally, on the basis of the
fact that the miAD group performed significantly worse than the NC group on a
greater number of tasks requiring the processing of verbs relative to those of nouns,
it can be presumed that in AD, the ability to process semantic information contained
by different verbs may be impaired earlier than information contained by concrete
nouns. In other words, the semantic features making up verbs may be more vulnerable to damage caused by AD than the semantic features contained by concrete
nouns.
10 General discussion
The semantic fluency task has been considered an easy and useful task for clinicians
to assess spoken word production and the organization and functioning of the lexical-semantic system in different subject groups (see chap. 5). The semantic fluency
task, in fact, is very complex and attempts have been made to operationalize the task
and reveal the underlying semantic-cognitive processes (i.e., clustering and switching) required for performing the task. However, the task is also related to very fundamental cognitive processes, such as categorization and word production, but very
little attention has been paid to clarifying these processes in conjunction with the
semantic fluency task. Furthermore, the way semantic information is represented
and processed in semantic memory, which is one of the most fundamental aspects of
the task, has very seldom been looked into in detail. The present study was an attempt to discuss these elements and shed light on how they may be related to performing the semantic fluency task with different types of semantic categories and
grammatical classes.
In addition to providing detailed information about how the semantic fluency
task was performed by healthy elderly adults and AD patients with mild and moderate dementia on different noun and verb categories, the findings of the present study
provided empirical support to some of the theoretical issues discussed above. The
multiple and varying responses given for the semantic fluency tasks by individual
participants in the present study may reflect the multifaceted principles of categorization, which underlies any semantic processes (see 3.1). As humans are able to use
different processes to grasp the similarities and differences of objects and actions
around them, semantic memory is likely to be built up of a vast amount of different
types of encoded information of these entities and their relations. Physical and functional similarities, as well as thematic and temporal-causal relations underlying the
cluster formation in the semantic fluency tasks, may reflect the way different entities and actions are categorized and related to each other in semantic memory.
The ways clusters are formed may also be taken as support for the notion that
the information contained by semantic memory can be described in terms of semantic features and semantic roles that are connected to each other, and that can very
162
General discussion
flexibly be processed for different purposes of the language (see 3.2, 3.3.3). The
processes taking place during the semantic fluency performance, such as integrating
the semantically related information in the semantic memory and preparing it for
spoken word production, can be well understood in light of the current connectionist
interactive models (see 4.2). Furthermore, the errors taking place during the semantic fluency performance can be explained by those theories.
10.1 Semantic fluency performance in mild and
moderate Alzheimer’s disease
The general aim of the present study was to find out how mildly and moderately
demented AD patients performed the semantic fluency task with noun and verb
categories. The more specific objective of the study was to reveal AD patients’ word
production strategies in the tasks, and to analyze the content of their responses via
errors (i.e., intrusions and perseverations), types of semantic subcategories employed,
and the average frequency and prototypicality of the words.
10.1.1 Decreased semantic fluency performance
The findings of the present study indicated that the ability of the AD patients to
dynamically and flexibly process and use the semantic information corresponding
to a semantic category, to integrate related information as clusters of semantically or
phonologically similar words, and to switch between different semantic dimensions
to guarantee optimal word production, was reduced compared to the healthy elderly
adults. The findings confirm those of several previous studies (Martin & Fedio 1983;
Ober et al. 1986; Tröster et al. 1989, 1998; Binetti et al. 1995; Carew et al. 1997;
Troyer, Moscovitch, Winocur, Leach et al. 1998). Unlike the earlier studies, the
present study broadened the scope of the semantic fluency task and involved a detailed
analysis to cover the performance not only on the category of animals, but also on
other semantic categories of concrete nouns (i.e., clothes, vegetables, vehicles) and
verbs (i.e., verbs denoting the actions of preparing food, playing sports, construction,
and cleaning up). Although it was found that the AD patients produced significantly
fewer responses than the normal control subjects, the performance of the subjects
was, to some extent, affected by the nature of the semantic category.
The present study provided information about how the severity of the dementia
affected semantic fluency performance. It was found that, some semantic categories
notwithstanding, the miAD patients were significantly poorer than the subject in the
NC group but better than the moAD patients at processing the semantic-lexical
information and producing words, clusters, and switches for both types of tasks.
The finding that the fluency performance was affected by the severity of dementia is
in accordance with earlier studies (e.g., Huff et al. 1986; Bayles et al. 1993; Hodges
& Patterson 1995; Crossley et al. 1997).
General discussion
163
Despite the marked reduction in word production in both AD groups, it was
discovered that the performance of all subject groups was semantically relatively
coherent. The clusters produced for the tasks tended to be of equal size and the
proportion of clustered words appeared to be similar in the subject groups. As far as
clustering of nouns was concerned, the subject groups similarly utilized the semantic and phonological relatedness between the items for cluster formation. Furthermore, category violations were not produced regularly but in individual categories,
mainly for verbs. Concerning cluster size, the finding of the present study supports
that of Binetti et al. (1995) and opposes those presented by Beatty et al. (1997,
2000), Tröster et al. (1998), and Troyer, Moscovitch, Winocur, Leach et al. (1998)
who claimed that the cluster size of the AD patients was significantly smaller than
that of the healthy elderly adults. The prominent role of perseveration found in the
performance of the AD patients in the present study, however, may explain the discrepancy between the findings and partly question the relevance of the method and
the internal validity of these studies (see 10.1.2).
The analyses of the contents of the clusters revealed that the AD groups showed
a significantly reduced range of semantic subcategories for both noun and verb production. In the noun categories, the most relevant semantic dimensions for disambiguating the subpatterns of features for the cluster formation consisted mainly of
thematic and functional features, that is, features denoting contextual, spatial, and
temporal information of the items, as well as their functional purpose. Physical features denoting the appearance (e.g., parts, colour, or size) of objects or strictly taxonomic relations between words (e.g., birds, fish) emerged relatively rarely, mainly
in the NC group and the miAD group. Thematic and functional features seemed to
allow a cross-classification of different lexical items over the semantic subcategories, which may explain their high frequency of occurrence among the subject groups
(e.g., Barsalou 1982, 1983; Lucariello & Rifkin 1986; Lucariello et al. 1992; Nelson
1996:232-248; see 3.1.1).
Relating object concepts on the basis of their physical features, that, in general, tends to require delicate differentiation between the feature patterns, may be
more difficult for the AD patients than relating items according to common thematic
and functional features. Physical (perceptual) features have been found to be more
vulnerable to brain damage than functional features (Tyler et al. 2000), which may
also contribute to the cluster formation in the semantic fluency task in AD. Although
thematic features are supposed to be more lightly weighted and less discriminative
and thus less effective as retrieval cues than functional and physical features, they
may commonly fit several different lexical items and make cross-classification of
items possible (see McClelland, Rumelhart et al. 1986; Persson 1995:79-80, 82; see
3.2). The present study indicated that the thematic features served well as integrating cues for cluster formation for all subject groups (see 9.2.4, 9.2.6). In fact, some
of the thematic features may be very heavily weighted and well resistant to changes
in semantic memory, because they are learnt first and early in childhood as the basis
for category formation (Lucariello et al. 1992; see also Nelson 1996:232-248), origi-
164
General discussion
nate from emotional and autobiographical experiences, and are culturally shared by
many (Persson 1995:79; Funnell 2001).
As far as the verb fluency task is concerned, the most foregrounded semantic
roles that were used to activate semantic subgategories, and to guide activation further
to more specific feature patterns, involved Instrument (denoting tools or instruments
with which the action was carried out), Result (denoting the outcome or the result of
the action), and Location (denoting spatial configurations of the action; see 9.4.3,
9.4.5). In the category of playing sports, the roles of Mover (denoting the entity carrying out any type of movement), as well as form-functional features denoting the part
of the body doing the action, played an important role in disambiguating verbs for
production. Sports verbs were also clustered on the basis of thematic features denoting temporal (seasonal) information implied by the verb. Sports verbs notwithstanding, verbs were also produced by applying the temporal-causal, sequential order (i.e.,
scripts, see 3.3.4), reflecting the order in which actions are likely to take place in the
real world. Yet another way to cluster verbs was to integrate the semantic information
sharing the manner in which actions are carried out (i.e., troponyms; Miller & Fellbaum
1991; Fellbaum 1998a:79; Marshall, Chiat et al. 1996; see 3.3.2).
The analysis of the variety and distribution of the most common semantic
subcategories showed that the performance of the AD patients lacked some of the
semantic dimensions expressed by the control subjects, especially those that denoted
different phases or parts of a script (see 9.4.3). Furthermore, the moderate AD patients
produced fewer clusters of verbs denoting actions carried out with specific
instruments. Instead, they tended to compensate for the specific verb forms by naming
the semantic roles (e.g., Instrument) corresponding to the basic-level noun implied
by the verb or by using general purpose verbs (e.g., ‘do’, ‘put’, ‘make’).
Activation of words and different semantic dimensions for the verb categories seemed to be more limited than during the noun fluency task, which may reflect
the smaller number of verbs than nouns in Finnish (Saukkonen et al. 1979:9-11) and
a shallower semantic structure of the verb categories compared to the semantically
rich organizational structure of the noun categories (Miller & Fellbaum 1991; Pajunen
1998; 2001:60-63). On the other hand, similar to some nouns, some of the verbs
(e.g., ‘rinse’, ‘cut’, ‘lift’) appeared to cross over the boundaries of the semantic
categories and to occur in different semantic contexts (e.g., ‘rinse’ was associated
with the verbs of preparing food and cleaning up, ’cut’ with the verbs of preparing
food and construction, and ‘lift’ with the verbs of playing sports and construction).
Furthermore, the data of the present study seemed to support the notions that verbs
may contain more than one semantic role (e.g., Mover and Agent in ‘walk’ and
‘cut’) and that verbs have strong connections to certain basic-level nouns that can be
considered part of their semantic structure (see Engelkamp 1975; Huttenlocher &
Lui 1979; Huttenlocher et al. 1983; Behrend 1990; Persson 1995:100, 102-104,
208-209; Jonkers & Bastiaanse 1996; Kersten & Billman 1997; see 3.3.2, 9.4.5).
The data also showed that verbs were connected to each other by their close temporal-causal relationships (see 3.3.4, 9.4.5).
General discussion
165
10.1.2 Errors as indicators of impaired semantic memory
functioning
The AD groups produced significantly more errors than the NC group, with the
miAD group exhibiting a more accurate performance than the moAD group. The
errors consisted mainly of semantically related intrusions and perseverations. The
semantic categories seemed to have an impact on the accuracy of word production,
some categories provoking more errors than others. However, despite the category
of clothes, the error analysis appeared insufficiently sensitive to differentiate the AD
groups from each other at the level of individual noun and verb categories, a finding
parallel to that made by Ober et al. (1986) and Binetti et al. (1995).
In general, the total number of intrusions in the noun categories remained
relatively low, which is consistent with previous findings (Diesfeldt 1985; Rosser &
Hodges 1994; Binetti et al. 1995, Carew et al. 1997; Suhr & Jones 1998). It was
discovered that only in the category of clothes did a higher proportion of intrusions
emerge and they were related to more advanced dementia, which is in accordance
with earlier studies (Diesfeldt 1985; Ober et al. 1986; Tröster et al. 1989; Beatty et
al. 2000) and adds to the discussion about the nature of the semantic categories
affecting the semantic performance in AD (Moss et al. 2002). Moreover, the present
study indicated that intrusions could appear also in verb categories. Among those
intrusions were concrete nouns referring to tools and instruments needed for different actions, which implied the occurrence of grammatical category violations in AD
(i.e., syntagmatic errors), a finding in contrast with the results of Gewirth et al.
(1984). One reason for the occurrence of intrusions during semantic fluency performance may be an unsuccessful feature integration in the semantic memory (see
9.2.6, 9.4.5, and 10.1.3).
In particular, the present study revealed that very many perseverations were
produced in both AD groups for both types of tasks. Even though the emergence of
perseverations is well documented in some earlier studies (e.g., Tröster et al. 1989;
Rosser & Hodges 1994; Suhr & Jones 1998), the nature and role of perseverations
in the fluency performance of the AD patients has not been widely studied or discussed. The present study, the data of which originated from several semantic noun
and verb categories, gives reason to assume that perseverations are a prominent
feature of the semantic fluency performance among both mildly and moderately
demented AD patients. Perseverations may, at least partly, be taken as a cause which
diminishes the overall production of words, prevents the subject from forming clusters and easily changing the subcategories and dimensions to activate new words for
the output, and affects the cluster size (see 9.2.2, 9.2.6, 9.4.2, 9.4.5, 9.5; see also
Dell 1986; Laine 1989:75-80; Dell, Burger et al. 1997; Martin et al. 1994; Persson
1995:66). In other words, the variables, with which productivity, efficiency, and
coherence of the word production are measured, may be affected by perseverations.
In the present study, even though the subjects had to perform eight different
semantic fluency tasks in a row, thus involving a heavy loading of the cognitive-
166
General discussion
linguistic capacity, the proportion of perseverations did not accumulate in the last
semantic categories (cf. Sandson & Albert 1987). The time pressure in the fluency
tasks may have had an impact on the performance, as a consequence of which an
adequate spread and decay of activation in the lexicon did not occur. Thus, previously produced words may have remained activated because they were not turned
off properly by the self-inhibition mechanism, and the new items did not receive
enough activation from the higher representations to compete successfully (see Dell
1986; Dell, Burger et al. 1997; cf. Schwartz et al. 1994). In a more relaxed setting, in
which more time had been given for the activation to spread, the system might have
edited out some of the perseverations.
It can also be speculated that in AD perseverations may have appeared as a
sign of an impoverished active vocabulary, due to semantic degradation or loss of
semantic information, leading the subject to activate and repeat the same words and
clusters over and over again (see 6.1). On the other hand, repetitions may have been
an attempt to activate new routes or semantic dimensions to facilitate the retrieval of
new words, as proposed also by Gruenewald and Lockhead (1980; see also Persson
1995:33-35). However, the present study, as well as that of Binetti et al. (1995),
showed that activation of different semantic subcategories was difficult for the AD
patients. Consequently, word retrieval from different semantic subcategories might
have been hindered rather than facilitated by the increased number of perseverations
in the AD patients.
Generally speaking, perseverations may be of different kinds (e.g., Liepmann
1905:115-127; Luria 1965; Hudson 1968; Helmic & Berg 1976; Buckingham,
Whitaker & Whitaker 1979; Sandson & Albert 1984, 1987; Bayles et al. 1985, 1993;
Albert & Sandson 1986; Vilkki 1989; Hotz & Helm-Estabrooks 1995a, b; Ramage
et al. 1999). They may be produced when the failure to access a target in the lexicon
triggers the activation of preceding targets that have been strongly primed due to
related meaning and re-exited by spreading activation, thus raising the likelihood of
their being retrieved. Factors such as reduced behavior regulation and planning (Luria
1965; Daigneault, Braun & Whitaker 1992), inability to change the mental set
(Shindler, Caplan & Hier 1984), defective attention (Ober et al. 1986; Albert &
Sandson 1986; Sandson & Albert 1987; Hotz & Helm-Estabrooks 1995b), poor
self-monitoring skills (Luria 1965; Shindler et al. 1984; Albert & Sandson 1986),
and impaired inhibition of memory traces, and suppression of incorrect responses
(Hudson 1968; Buckingham et al. 1979; Shindler et al. 1984; Sandson & Albert
1984; Bayles et al. 1985; Hotz & Helm-Estabrooks 1995b) have been claimed to
trigger perseverations. Damaged motor mechanism (Liepmann 1905; Luria 1965),
general slowing of functions (Buckingham et al. 1979; see also Dell 1986), and
paucity of ideas (Bayles et al. 1985) may also bring about perseverations. These
symptoms seem to be familiar clinical findings of AD (see 2.2), and factors associated with performance on the semantic fluency task (5.1). Taken together, these
disorders may refer to a dysfunction of working memory and its components which
is found in AD patients (Baddeley et al. 1986, 1991; Baddeley 1992; Gathercole &
General discussion
167
Baddeley 1993), and which may also contribute to their linguistic abilities and, consequently, the occurrence of perseverations (see Bayles 2003).
The dysfunctions mentioned are related to changes in neural function that take
place in AD and other neurological diseases (e.g., Luria 1965; Lees & Smith 1983;
Sandson & Albert 1987; Hotz & Helm-Estabrooks 1995b). The role of the frontal
lobe lesions in the emergence of perseverations has been underscored by several authors
(Luria 1965; Sandson & Albert 1987; Vilkki 1989; Shindler et al. 1984; Daigneault et
al. 1992; see also Hotz & Helm-Estabrooks 1995a), and it has also been related to
fluency performance in the form of decreased switching (Troyer, Moscovitch, Winocur,
Alexander et al. 1998). Lesions to other regions of the brain, including left temporal
and parietal areas, have also been associated with the appearance of perseverations
(Buckingham et al. 1979; Albert & Sandson 1984; Sandson & Albert 1987; Vilkki
1989; see also Hotz & Helm-Estabrooks 1995a), also in AD (Bayles et al. 1985). The
participation of temporal lobes is likely to be vital for clustering during the semantic
fluency task (Troyer, Moscovitch, Winocur, Alexander et al. 1998; Pihlajamäki et al.
2000). As far as the neuropathology of AD is concerned, the typical cortical lesion
sites include the (pre)frontal and the temporo-parietal region, even in the mild stage
of the disease (Braak & Braak 1991, 1996; Pirttilä & Erkinjuntti 2001; see 2.1, 2.3).
Hence, more research on the occurrence of perseverations and their interaction with
other variables of the fluency task, as well as other tasks measuring linguistic and
non-linguistic skills, is needed before plausible conclusions can be made regarding
their contribution to the word fluency performance.
10.1.3 Causes of the semantic impairment in Alzheimer’s disease
The poor performance of the AD patients on the semantic fluency task and other
tasks measuring the functioning of the semantic memory may be caused by
deteriorated perceptual functions, loss of the semantic information, impaired access
to the semantic information, or a combination of the last two causes (see chap. 6).
The accounts of access and/or storage disorder, however, have been criticized for
being too vague not only in dementia research, but also in aphasia research (see the
discussion in Warrington 1975; Warrington & McCarthy 1983; Shallice 1988:274286; Rapp & Caramazza 1993; Persson 1995:43-44, 67-68; Harley 1998). There is a
continuing controversy with regard to the criteria for a storage vs. access deficit.
Furthermore, conclusions regarding the possible causes of the impairment may have
been drawn on the basis of symptoms rather than taking a stand on the underlying
semantic representation, its format and structure. This may also apply to some of the
studies on the semantic fluency performance in AD, which seem to have concentrated
on describing the overt behavior of the subjects in the task without taking an explicit
position on the nature and functioning of the semantic memory or the semantic layer
of the mental lexicon. Moreover, the findings obtained from the semantic fluency
performance of the AD patients have not often been discussed in relation to word
production theories.
168
General discussion
An alternative approach to the access-storage issue is provided by connectionist
models, according to which the semantic representation corresponds to dynamic
and flexible patterns of activation across a conglomerate of different semantic
microfeatures, which are triggered by the content of the input and processed quite
automatically without a separate access-mechanism. The models also provide a direct access to the syntactic and phonological features of the item(s) in the form of
spreading activation (e.g., Dell 1986; Dell & O’Seaghda 1991, 1992; McClelland,
Rumelhart et al. 1986; Farah & McClelland 1991; Martin et al. 1994; Persson
1995:66-67; Dell, Schwartz et al. 1997; Foygel & Dell 2000; see 4.2). Nevertheless,
the way the subjects produced responses (i.e., clusters and switches) on the semantic fluency tasks in the present study indicated that some sort of retrieval strategies
were needed to perform the task, some of which probably required intentional search
of semantic information while some others may have taken place quite automatically as a result of the activation spreading in the semantic network (see also Diesfeldt
1985; Laine 1989:5; Allen et al. 1993; Monsch et al. 1994; Binetti et al. 1995; Pasquier
et al. 1995; Rosen & Engle 1997; Mayr & Kliegl 2000).
As discussed earlier, some of the connectionist models assume that damage
to the semantic microfeatures and/or the connections, noise-induction in the system,
and changes in the rate at which activated information decays (Hinton et al. 1986;
Hinton & Sejnowski 1986; Dell 1986; Smolensky 1986; Farah & McClelland 1991;
Martin et al. 1994; Gonnerman et al. 1997; Devlin et al. 1998; Harley 1998; see
9.2.6, 9.4.5) may interfere with the performance on the semantic tasks. The notion is
compatible with the finding that AD causes widespread damage to the brain and a
loss of neurons (see 2.1), in that microfeatures are thought to work in a neuron-like
fashion (e.g., Gonnerman et al. 1997; Harley 1998; Moss et al. 2002). Damage to
semantic microfeatures may lead to a heterogeneous degradation in performance in
AD, including naming, matching, and feature decisions (Harley 1998; Moss et al.
2002). Nevertheless, as far as word production is concerned, damaged or lost
microfeatures may not affect only the functioning at the semantic level of the mental
lexicon but also involve functioning at the lemma (lexical) and the phonological
level, due to the interconnectivity between the levels (Harley 1998). Consequently,
the performance of the subjects should not only reflect the functioning of the semantic memory or the semantic layer of the mental lexicon, but also the whole word
production system. However, the interpretation of the loci of errors seems to depend
on the theories.
According to the two stage interactive model of word production introduced
by Dell and his colleagues (see 4.2), semantic substitutions (i.e., semantically related
intrusions produced for both noun and verb categories) are likely to occur during the
lemma access while syntactic category violations (i.e., the occurrence of basic-level
nouns among the verbs) probably take place during phonological access, because of
its indifference to the information concerning the grammatical categories (Dell,
Schwartz et al. 1997; see also Astell & Harley 1998; cf. Persson 1995:125-127,
Bird, Howard et al. 2000). On the other hand, Persson (1995:125-130, 132-135,
General discussion
169
177-182) speculated that damage to the semantic layer of the mental lexicon was
most likely to be responsible for substitutions, although the post-semantic morphophonological processing may also have an effect on misguiding item selection.
The reduced number of words produced in the AD groups may be interpreted
as omissions resulting from suppression of connections between the semantic features or a total failure of activating and integrating semantic and phonological features corresponding to a lexical item (see Rumelhart, Hinton et al. 1986; Persson
1995:133-134, 159, 181; Laine & Martin 1996). Perseverations produced particularly by the AD patients may take place at the lemma level and their occurrence can
be interpreted as a change in the rate at which activated information decays, whereby
the prolonged activation of a previous item interferes with the selection of upcoming information (Dell 1986; Dell, Burger et al. 1997; cf. Foygel & Dell 2000; see
9.2.6, 9.4.5). Unrelated, irrelevant words, which were also produced but in a very
low number by all subject groups in the present study, are supposed to occur due to
activation from non-related or distant connections to the target during lemma access
and/or during phonological access when the correct word, which was selected at the
lemma level, is substituted by another word at the phonological level (Dell, Schwartz
et al. 1997).
The occurrence of intrusions in the noun categories may also reflect the greater
vulnerability of some categories, or more specifically, some types of semantic feature
patterns, to damage in the semantic system. It has been suggested that due to the
constellations of different types of semantic features, feature correlations, and
distinctive features in the semantic categories, the semantic categories may be
differently sensitive to the damage in the semantic network (see Gonnerman et al.
1997; Devlin et al. 1998; Garrard et al. 2001; Tyler et al. 2001; Tyler & Moss 2001;
Moss et al. 2002; McRae & Cree 2002; Whatmough et al. 2003; see also Tversky &
Hemenway 1984; Persson 1995:80-81). The more strongly inter-correlated features
the items share with each other, the more robust they seem to be against damage
because they provide enough mutual activation for appropriate features to be selected.
The more weakly correlated features, the less mutual activation there is in the system,
and the more vulnerable features are to damage.
The present finding that very few category violations were made in the category of animals and vehicles while a number of violations took place at the border
of the category of vegetables (e.g., fruits and berries) and, in particular, the category
of clothes (e.g., bed linen) partly supports the notion that categories tend to be differently affected to damage in AD (see Gainotti et al. 1996; Gonnerman et al. 1997;
Devlin et al. 1998; Moss et al. 2002; Whatmough et al. 2003). However, category
violations in the categories of vegetables and clothes were also found among the
normal control subjects, raising issues such as the type of features that make the
critical correlations upon which the members of the categories can be distinguished,
the fuzziness of the category boundaries in general, and the role of aging, gender,
education, and life experiences as contributors to the semantic representation of
category-specific information (see e.g., Labov 1973; Rosch 1975, 1978; Smith &
170
General discussion
Medin 1981:22-60; Medin & Smith 1984; Barsalou 1982, 1983; McClelland &
Kawamoto 1986:278; Lakoff 1987a; Tröster et al. 1989; Aitchison 1994:39-41;
Crossley et al. 1997; Hampton 1998; Capitani et al. 1999; see 3.1.1).
Equivalent to nouns, verbs and verb categories are also likely to consist of
different constellations of semantic features and their correlations (Huttenlocher et
al. 1983; Behrend 1990; Persson 1995:96-104; Kersten & Billman 1997; see 3.3.2,
3.3.3), but connectionist simulations and empirical studies with AD patients on the
consequences of damage to the verbs semantics are, to my knowledge, so far lacking.
However, applying the notions presented by Persson (1995:96-104; 145-146, 149;
see also Reyna 1987; Robinson et al. 1996; Bird, Howard et al. 2000), it can be
speculated that verbs may be more vulnerable to damage than nouns, because verbs,
in general, consist of a restricted set of semantic features which are perceptually
inferred or language-dependent (endogenous) information rather than perceptually
transparent (sensory, exogenous) information, and their semantic structure is
dependent on other items across different grammatical classes (e.g., basic-level nouns;
see 3.1, 3.3). Consequently, damage to the features and connections may complicate
the integration of widely spread information contained by verbs in AD patients.
Because verbs may have less shared, less densely intercorrelated features, and fewer
distinctive features, they may be less robust against the effects of the damage in AD
than nouns. Evidence for this was provided by the moAD group producing
syntagmatic errors for the verb fluency task and both AD groups performing worse
on the control tasks requiring processing of verbs relative to nouns. It can also be
concluded that the semantic representation of verbs may be affected earlier during
the course of AD than that of concrete nouns.
As a consequence of a more sparse semantic structure, verbs tend to be lower
in imageability (i.e., more difficult to produce a mental image for a word) than
nouns and, therefore, they tend to be slower and more difficult to produce (see Bird,
Howard et al. 2000). Moreover, imagining verbs is likely to be more difficult than
imagining nouns because forming an image of verbs requires the integration of
dynamic sequences of actions, whereas objects are static and thus better suited to
imagery encoding (Engelkamp et al. 1989; see also Reyna 1987; Helstrup 1989;
Persson 1995:161; Fung et al. 2001). Given that semantic fluency performance is
influenced by imagery (Diesfeldt 1985; Chertkow & Bub 1990; Mickanin et al.
1994), the reduced semantic structure of verbs, as well as nouns, may lead to poorer
skills in forming mental images for word production in AD. The difference between
the semantic representation of nouns and verbs has also been explained by their
being processed by differently distributed neural systems and their being stored in
anatomically distinct locations (e.g., Damasio & Tranel 1993; Martin et al. 1995;
Koenig & Lehmann 1996; Perani et al. 1999; Tranel, Adolphs, Damasio & Damasio
2001; cf. Pulvermüller et al. 1999; Bird, Howard et al. 2000; Tyler et al. 2001),
which may also have an impact on the difference in the semantic fluency performance
on nouns and verbs in AD.
General discussion
171
With regard to both nouns and verbs, there may be a tendency among the AD
patients to replace more specific, distinctive, semantic information by more general
information that could be shared by many other lexical items. On the basis of the
content of the clusters, it seemed as if the moAD group used more thematic relations
to form semantic clusters rather than combining semantically related words according to their structural or functional similarity. Furthermore, the tendency of the moAD
group to use less specific verbs and to favour more general and all-purpose verbs
can be interpreted as a trend towards more general information (see 3.3.2, 9.4, 9.4.5).
These findings tentatively lend support to the notions suggesting that distinctive
semantic features in particular may be damaged or lost in AD (Diesfeldt 1985; Harley
1998; Moss et al. 2002), but not until the moderate stage of the disease. Credence to
the notion may be given by the significantly poorer performance of the moAD group
on all control tasks. However, as discussed earlier, the errors made by AD patients
may imply that they might not only have difficulties in processing semantic information, but also the grammatical and phonological information of words.
In addition to the impaired semantic representation of nouns and verbs and
impaired word production, deterioration of other cognitive functions may be yet
another plausible factor explaining the quantitative and qualitative changes in the
performance of AD patients on the semantic tasks relative to the normal control
subjects. Impaired working memory and, especially, executive functions affecting
planning, initiating, and monitoring one’s performance, cognitive flexibility to shift
mental sets, and so on, are likely to have a negative impact on the performance of
the AD patients, including the semantic fluency task (Diesfeldt 1985; Chertkow &
Bub 1990; Kopelman 1994; Morris 1994; Rosen & Engle 1997; Troyer, Moscovitch,
Winocur, Leach et al. 1998; cf. Binetti et al. 1996). On the other hand, Astell and
Harley (2002) suggested that it is due to a failure in metalinguistic skills that AD
patients fail to keep in mind the demands of a particular task, and regulate and
monitor the retrieval and organization of the relevant material. However, the relation
between the executive processes and the functioning of the semantic memory and
the relation of the metalinguistic processes to the linguistic processes seem to be
unclear and require further study (Kopelman 1994; Astell & Harley 2002).
10.2 Methodological considerations of the study
10.2.1 Subjects
The medical background of the subjects participating in the present study was carefully checked because all AD patients were recruited from the Department of Neurology of the Helsinki University Central Hospital and the healthy elderly control
subjects had previously undergone a thorough examination to rule out any neurological symptoms (see chap. 2, 8.1). However, all subjects voluntarily participated
in the study, which may bias the selection towards fitter, more capable, and more
motivated subjects and thus restrict the generalizability of the study.
172
General discussion
The sizes of the subject groups in this study were small but they represented
the average, or even larger than average, size used in other semantic fluency studies
(see Table 4). Although a statistical difference in the ratio of male/female subjects
did not emerge among the subject groups, both AD groups had more female
participants, and thus gender may have had some biasing effect on the semantic
fluency performance (see Monsch et al. 1992; cf. Capitani et al. 1999; Hebert et al.
2000; Troyer 2000; see also 5.1). However, the interpretation should be taken with
caution because, as indicated by the male and female participants in the NC group,
the semantic fluency performance was affected by gender only in two of the eight
semantic categories, the male participants faring better on the category of construction
and the female participants on the category of cleaning. Furthermore, no general
gender effect was found in the total noun or verb production of the NC group (see
9.1.1, 9.3.1). In the future, larger samples, groups with an equal number of
participants, and groups with an equal number of male and female subjects would
be optimal for a more reliable comparison of semantic fluency performance.
In the present study, the mental status of all the participants was assessed and
the division of the AD patients into the miAD group and the moAD group was made
using the Mini Mental State Examination (Folstein et al. 1975), which has been
found to be a suitable method for assessing the severity of dementia (Rantakrans
1996:22-24). The division into mild and moderate AD groups was well founded
taking into consideration the statistically highly significant differences between the
subject groups, and the tendency of the higher scores on the MMSE to be associated
with a better fluency performance across the semantic categories. Similar findings
on the relationship between the scores on the MMSE and the fluency task have been
reported in several other studies (e.g., Bayles et al. 1993; Mickanin et al. 1994;
Hodges & Patterson 1995; Crossley et al. 1997). It should be kept in mind, however,
that a short test like the MMSE does not cover the whole scope of cognitive and
functional capacity of the participants. Furthermore, although the MMSE was used
for grouping the subjects according to their mental status, a great heterogeneity in
the fluency performance across different parameters in the subject groups was found
(see also Della Sala et al. 1993; Hodges & Patterson 1995). The heterogeneity in the
fluency performance may be partly caused by such factors as different occupations
and expertise in particular fields, as well as autobiographical experiences and habits
of the subjects, which are likely to affect the structure of semantic representation
(e.g., Rosch et al. 1976; Barsalou 1992; Aitchison 1994:39-50; Taylor 1994:72-75,
79, 242; Ungerer & Schmid 1996:14-20; Azuma et al. 1997; Capitani et al. 1999;
see 5.5). It should also be taken into account that fatigue and test anxiety may have
obscured the performance of some subjects (Roberts & Le Dorze 1994).
General discussion
173
10.2.2 Considerations of the semantic fluency task
The most common measure derived from the semantic fluency task is the number of
correct responses generated for the task, which, however, has proved insufficient to
uncover the processes and strategies exploited by the subjects to perform the task, to
highlight the semantic relationships between the words, or to reveal the types of
errors made by subjects during the task (Ober et al. 1986; Laine 1989:18-19, 22-23;
Allen et al. 1993; Bayles et al. 1993; Roberts & Le Dorze 1994, 1997; Binetti et al.
1995; Pasquier et al. 1995; Carew et al. 1997; Troyer et al. 1997; Troyer, Moscovitch,
Winocur, Alexander et al. 1998; Troyer, Moscovitch, Winocur, Leach et al. 1998;
Troyer 2000; Suhr & Jones 1998; Tröster et al. 1998). Attempts to operationalize
and to describe the multifactorial semantic fluency performance have been made
(e.g., Gruenewald & Lockhead 1980; Laine 1989; Troyer et al. 1997), but the precise
nature of this task remains unclear and complex to explain probably because of the
many overlapping processes involved (Chertkow & Bub 1990; Troyer et al. 1997;
Troyer, Moscovitch, Winocur, Leach et al. 1998; Mayr & Kliegl 2000). There appears
to be no agreement among the researchers on the nature of the parameters or on the
most influential measures to best cover fluency performance (e.g., Binetti et al. 1995;
Troyer et al. 1997; Troyer, Moscovitch, Winocur, Alexander et al. 1998; Troyer,
Moscovitch, Winocur, Leach et al. 1998; Tröster 1998; Rich et al. 1999; Mayr 2002).
Choosing the best possible parameters to describe the performance on the
semantic fluency task is not very easy because parameters tend to depend on each
other (see Laine 1989:14; Beatty et al. 1997; Troyer et al. 1997; Mayr 2002). However, the parameters used for example in the studies of Troyer et al. (1997; Troyer,
Moscovitch, Winocur, Leach et al. 1998; Troyer 2000) and Tröster et al. (1998; e.g.,
the number of correct words produced for the task, the number of switches, and the
mean cluster size) do not seem to be sufficient to highlight all the important aspects,
such as the semantics, of the fluency performance. In addition to these parameters,
the total number of words produced for the tasks should be given in order to compare the difference between the total and the correct output. Reporting the number
of clusters should also be included because it makes possible a more transparent
comparison between the switching and clustering performance of the subjects. An
analysis of switching alone is not informative enough because it includes both clusters of words and single words that, as such, do not reveal the true clustering of
words along some semantic or associative relation. Moreover, the mean cluster size
may not be enough to cover the clustering performance or to reveal the coherence of
the behavior in the task because it can be given on the basis of only one single
cluster. Reporting the mean cluster size does not provide enough information about
the overall tendency of the subject to cluster words as does reporting the proportion
of clustered words.
Nevertheless, taking into account the number of clusters alone does not
differentiate between repeated subcategories and those used only once because
subjects may keep producing words from the same subcategories over and over
again (see also Laine 1989:25). Therefore, counting the number of different
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General discussion
subcategories from which clusters are formed may better reflect the ability of the
subject to use the multifaceted information contained by the semantic memory.
Finally, a more detailed error analysis, although it did not turn out to be sensitive
enough to differentiate the subject groups in all semantic categories in the present
study, should be provided. Taking into account the types and the proportion of errors
produced by subjects may highlight the processes of word production and the
robustness of the semantic categories involved in the task.
Instead of considering clustering and switching as two separate processes,
the former involving semantic processes mediated by the temporal lobe and the
latter involving the executive processes associated to the frontal lobe (see Troyer et
al. 1997; Troyer, Moscovitch, Winocur, Alexander et al. 1998; Troyer, Moscovitch,
Winocur, Leach et al 1998; Troyer 2000; Tröster et al. 1998), the notion that both
clustering and switching are part of the semantic processing is put forth by Mayr
and Kligel (2000). They claimed that the semantic process involved the betweencluster and the in-cluster retrieval of words in the form of relatively automatic spreading of activation in the semantic network and that the executive control was likely to
accompany each act of word retrieval in the form of, for example, updating the
current search criterion and stopping or initiating retrieval processes (see also Ober
et al. 1986; Bayles et al. 1989; Mayr 2002). Switching may thus involve an ability of
the semantic system to activate and flexibly integrate patterns of features corresponding to different subcategories or semantic dimensions in the semantic network. As a consequence, a new feature set and its strong connections to other features, which correspond to the semantically related category co-ordinates or items
belonging thematically or functionally together, are activated and elaborated for
further processing in the mental lexicon and the articulatory apparatus. Overtly, the
process can be viewed as the subject producing a cycle of clusters of semantically
related words and switches to semantic subcategories for other sets of related words.
In the present study, the mean cluster size and the proportion of words in the
clusters were calculated in order to measure the activation, as well as the coherence
and efficiency, of the production of semantically related words (cf. Beatty et al.
1997, 2000; Troyer et al. 1997, Troyer, Moscovitch, Winocur, Leach et al. 1998;
Tröster et al. 1998). As stated earlier, the cluster size and the proportion of words
produced in clusters did not differ between the subject groups in very many categories (see 9.1.2, 9.3.2). Mainly, the differences were located between the NC group
and the moAD group in the category of clothes, preparing food, and cleaning. Consequently, depending on the semantic category, the finding could be interpreted as
the AD groups being as capable of activating items sharing highly inter-correlated
features in semantic memory and having semantically as coherent and as efficient a
process as the NC group in their semantic feature disambiguation and integration.
Should this finding imply an intact and normal functioning of the semantic memory
in AD, as suggested by Tröster et al. (1998) who claimed that a reduced cluster size
was a sign either of impaired lexical and semantic memory or a difficulty of access
to the memory stores? Taking into consideration the increased number of
General discussion
175
perseverations in the performance of both the miAD and the moAD group throughout the categories, it may be possible that perseverations have an enlarging effect on
the clustering performance (i.e., number of clusters, size of clusters, and proportion
of clustered words). Thus, a straightforward interpretation of the integrity of or the
ease of functioning of semantic memory in light of these parameters should not be
taken before the location of the perseverations (i.e., in or between the clusters) is
first controlled. Counting the perseverations as part of a cluster may, in general,
jeopardize the internal validity of the study and obscure the results and interpretation of the performance of the AD patients. A separate registering of intact clusters
or clusters from which perseverations have been ruled out and clusters containing
perseverations would make the comparison between the performance of the AD
patients and the normal control subjects more plausible.
If the number of different subcategories exploited during the task was not
registered, the reduced number of clusters and the relatively high proportion of clustered words found in both AD groups could be interpreted as normal but slowed
performance on the semantic fluency task (see Laine 1989:25). However, the number of subcategories and semantic fields appeared to be a sensitive variable because
it differentiated the subject groups and gave reasons to assume that the lexical-semantic processing did not work well in either AD group. Relative to the NC group,
both AD groups produced words from significantly fewer subcategories for all noun
categories and for all but one verb category, and a remarkable reduction of subcategory exploitation was found in the moAD group. Consequently, registering only
the number of clusters or the proportion of clustered words may not be enough to
account for semantic fluency performance, because the subjects may repeat the same
few semantic subcategories and even the same clusters of items during the task. In
order to control the re-occurrence of the same subcategories and words, as well as to
control the variety in activating the semantic space, the number of different subcategories should be involved as one parameter.
The clustering rules applied according to those described by Troyer et al.
(1997; Troyer 2000; see also Rich et al. 1999) appeared to be somewhat unspecific
(see also Kaleva & Vanhala 2001). For example, Troyer et al. did not provide information on how large the size of a small cluster was supposed to be in order for it to
be embedded together with another small cluster. In the present study, small clusters
of the maximum size of three words were embedded if a common denominator was
found. Applying the clustering rules to find how verbs were produced for the task
was at times complicated because verbs were produced in a script-like manner, the
temporo-causal information guiding the production. Moreover, verbs were not only
produced as one-word responses by the subjects, which were encouraged during the
rehearsal category, but also as a variety of word forms, including phrases. All in all,
to avoid too subjective decisions about ambiguous clusters and responses, it was
necessary to have a sample of the data analyzed by an independent rater. Afterwards, the analyses were collected and all the ambiguous points discussed.
176
General discussion
Using the absolute number of responses (e.g., the number of switches, clusters, and subcategories) instead of proportional scores to analyze the verbal fluency
performance of the AD patients, and to compare the performances between different subject groups, may be questionable because any reduction in the total number
of words generated in a category is likely to be associated with a reduced number of
any of the parameters dependent on the total output (see e.g., Laine 1989:25; Binetti
et al. 1995; Troyer et al. 1997; Troyer, Moscovitch, Winocur, Alexander et al. 1998;
Troyer, Moscovitch, Winocur, Leach et al. 1998; Troyer 2000; Tröster et al. 1998).
However, proportional scores may be uninformative and partly misleading because
subjects with different numbers of total words and other parameters may come up
with the same proportional scores, as stated also by Troyer, Moscovitch, Winocur,
Alexander et al. (1998; see also Troyer 2000) and Rich et al. (1999). For example, a
subject with a low total of 8 words and 4 switches in the animal fluency task would
have a proportional score of 50% for the switches as would another subject with a
high total of 16 words and 8 switches (Rich et al. 1999). Thus, the proportional
scores being equal, they may hide information about the variation in the performances between the subject groups. Support for the decision to use the raw scores to
describe the fluency performance can also be found in the study by Tröster et al.
(1998), who attempted to analyze switching in proportion to total word output but
came up with a conclusion that the proportional measure of switching did not give
an appropriate picture of the fluency performance, because the subjects performed
the task in different ways. For example, of the subjects with an equal number of
words, those who employ the strategy of clustering necessarily make fewer switches
than those who do not cluster words during the task.
To enable comparison between the performances in different semantic categories for one, and to ensure the registering of major tendencies in the fluency
performance for the other, the present study provided the fluency data both as separated for individual semantic categories and as averaged across the different categories. A combined score over the different semantic categories may reveal larger trends
in the overall performance and offer more stability and reliability than the scores
provided by a single category (Monsch et al. 1992, 1994, 1997). Providing only
averaged results across the categories, however, may mask category-specific information, as discovered in the study of Capitani et al. (1999), in which the gender of
the subjects was found to affect the naming of fruit and tools, with females faring
better on the former and males on the latter.
The data of the present study also indicated that specific categories differently affected the semantic fluency performance (see 9.1.1, 9.3.1). Presenting the
fluency scores at the level of individual semantic categories is recommended not
only to prevent the masking of information, but also because the categories tend to
be different in their semantic structure, difficulty, size, and familiarity (e.g., Diesfeldt
1985; Ober et al. 1986; Hart et al. 1988; Bayles et al. 1989, 1993; Chertkow & Bub
1990; Hodges et al. 1992; Azuma et al. 1997; Crowe et al. 1998; Capitani et al.
1999; Mayr & Kliegl 2000; Moss et al. 2002; see 5.5). Furthermore, the categories
General discussion
177
may be differently sensitive to the cognitive deficits in AD (e.g., Chertkow & Bub
1990; Silveri et al. 1991; Gonnerman et al. 1997; Moss et al. 2002). Consequently, if
findings on the semantic fluency performance of the AD patients are based only on
one semantic category (e.g., animals) they may lead to misleading conclusions about
the effects of AD on the semantic memory and language (cf. Azuma et al. 1997).
10.2.3 Limitations of the study
The conclusions drawn in this study are limited by several factors that should be
kept in mind when generalizing the results. First of all, the theoretical notions and
the experimental decisions taken, as well as the conclusions drawn in this study,
concerned concrete nouns and verbs. Second, the study focused on discussing the
semantics of the fluency task, leaving the role of working memory and executive
functioning aside, although they have been found to affect semantic processing in
AD (e.g., Bayles 2003).
In the present study, more attention should have been paid to the selection and
the order of presentation of the semantic categories. The verb categories should
have a priori been controlled for gender insensitivity and selected to provide as wide
a variety of different verb types as possible. In this study, goal-oriented verbs denoting results of an action were foregrounded. A category with a broad variety of verbs
(e.g., what kind of hobbies do people have) might have replaced one of the three
goal-oriented categories. The categories of preparing food and cleaning up could
have been combined as a single category, household activities, whereby responses
given for the category might have been more in number and from a wider range of
activities (e.g., activities belonging to doing laundry, rearranging furniture, etc.).
Similarly, the noun categories should also have been selected paying more attention
to their semantic structure: either the category of clothes or the category of vehicles
could have been replaced by a category with a richer structure of physical-functional features (e.g., tools and kitchen utensils) in order to determine whether possible damage to the distinguishing features found in AD could be reflected in the
patients’ semantic fluency performance.
The fixed order in which the semantic categories were presented to the subjects may have had an effect on the efficiency of performance towards the end of the
task thus favoring the performance on the noun categories over the verb categories
(see Hart et al. 1988). Therefore, it is recommended that in future studies, the order
of the categories be systematically varied. The categories should have been controlled for difficulty and for the difficulty of noun vs. verb production (see Hart et al.
1988; Bayles et al. 1993; Azuma et al. 1997; Mayr & Kliegl 2000). Furthermore, to
make comparison between the different retrieval modes (semantic and phonological) and to draw more reliable conclusions about the functioning of the different
levels of the mental lexicon during word production, the phonemic fluency task
should have been included in the study.
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General discussion
Because prototypicality and frequency ratings of the words were made by a
small number of volunteers, conclusions about the effects of these factors on subjects’ word production should be taken with caution. Unfortunately, frequency
corpuses of spoken language were not available in Finnish, and the existing corpuses
were based on newspaper texts (e.g., Saukkonen et al. 1979; Laine & Virtanen 1999)
and thus not appropriate for the use of the present purposes. Furthermore, they did
not contain all the words produced by the subjects in this study. In order to have an
objective control of the word frequencies in the future, a sample of words could be
evaluated using the ratings of the corpuses of written Finnish.
Considering the complex cognitive-linguistic processes involved in the
semantic fluency task, the great variability between the performances of different
subjects, and the very poor performance of some of the moderately demented subjects
in this study, it is reasonable to think about the relevance of the task in experimental
and clinical use. Although performing the task provides much information about
how effectively and flexibly semantic information is processed and retrieved from
semantic memory and how fluently words are produced, factors having an influence
on performance seem to be many, intermingled, and difficult to interpret. Furthermore,
the ability of a subject to perform the task may be sensitive to his or her mental
agility and mood, which may sometimes cause an individual to fail in the task. In
order to get a more reliable picture of the functioning of semantic memory by using
the semantic fluency task, the task ought to be carried out by the same subjects,
frequently, and regularly.
The semantic fluency task should not be used alone to study semantic processing. In order to increase the reliability of the task, other tasks that also measure
semantic functions should be employed. As discussed earlier, the outcome of the
control tasks in the present study indicated that AD patients performed some of the
tasks better than others. Nevertheless, they also indicated that the semantic functions of the AD patients were impaired compared to the healthy elders, and that
semantic impairment was most obvious in the group of moderately demented AD
patients. However, even though a number of other tasks were involved as control
tasks that measured the overall semantic performance of the subjects in the present
study, a more detailed qualitative analysis on the subjects’ performance on the control tasks would still be needed. Furthermore, the performance of the subjects on the
semantic tasks should be assessed in relation to common neuropsychological tests,
especially those measuring different memory functions.
Finally, the present study, to the best of my knowledge, was the first attempt
to broaden the use of the semantic fluency task and to investigate how the semantic
information contained by verbs was utilized during word production by healthy elderly adults and patients with AD. Therefore, a replication of the study is required to
validate the findings.
General discussion
179
10.3 Clinical implications
One of the starting points of this study was the need to find out more about the
semantic factors underlying the semantic fluency task which is very commonly used
in the clinical and experimental assessments of speech pathologists, among others.
For the purpose of studying the linguistic functions of semantic memory and the
speech production in patients with neural dysfunction, the theoretical issues discussed throughout the study provided an insight into the complex semantic structure
of both concrete nouns and verbs, semantic representation being the prerequisite for
naming also in the fluency tasks. This study provided information about how verbs
may be represented semantically and about how they are produced in the semantic
fluency task by healthy elderly adults and AD patients with different degrees of
dementia. Furthermore, a wider analysis of the semantic fluency task was applied,
including different semantic categories from two grammatical classes. An attempt
was also made to integrate the semantics of the task and the current word production
models.
Semantic processing by healthy individual subjects can be very dynamic and
flexible, resulting in a variety of combinations of words during the semantic fluency
task. The present study gave guidelines for the quantitative and qualitative performance of the healthy elderly controls over several semantic categories that can later
be used as the basis for comparing and assessing the performance of other subjects
(e.g., subjects with different types of dementia or aphasia). The present study also
provided information about the reduction and changes in the fluency performance
among patients with mild and moderate AD, which was the other main objective of
the study. The error analyses notwithstanding, the findings concerning the overall
fluency performance (i.e., number of correct words, switches, and clusters) can be
of use when staging the severity of dementia in AD.
The study sketched a detailed method of analyzing the semantic fluency
performance with both nouns and verbs. As pointed out before, the analysis should
not be restricted to just counting total and correct responses, but it should also include
the switching and clustering performance of the subjects. The number of different
semantic subcategories used for the cluster formation, in particular, may be a sensitive
parameter to reveal the subjects’ ability to use the vast amount of information
contained by the semantic memory. In the present study, it was among the few
parameters which was powerful enough to differentiate the performance of the control
subjects and the patients with mild and moderate AD from each other on both noun
and verb categories. Furthermore, although the error analysis did not distinguish
between the subject groups, it may give an insight into the nature and the location of
the deficits in the functioning of the mental lexicon.
Considering the differences between semantic categories, it is recommended
that more than one semantic category is used when the fluency task is applied to
investigate the semantic memory of AD patients (see also Bayles et al. 1993). Therefore, the categories should be chosen with care. As far as nouns are concerned,
180
General discussion
categories should be selected from both living and non-living domains, keeping in
mind the differences in their semantic composition (see 3.1.2, 3.2). In order to control for hesitations or semantic degradation at the borders of the different categories,
a category with a low probability of semantic intrusion (e.g., animals) and a category with a higher probability of semantic intrusion (e.g., vegetables, fruit, or clothes)
may be included.
Taking into account that semantic memory consists not just of information
for concrete nouns and/or objects, it is recommended that the semantic fluency task
be extended to cover other grammatical classes, such as verbs. Concerning the
selection of verb categories, categories providing verbs that denote different types
of action are worth considering (e.g., categories providing instrument verbs, goaldirected verbs, and motion verbs). For the reasons described previously, it is also
recommended that the performance of the subjects be examined by each semantic
category at a time, rather than averaging the performance by combining the test
scores over several semantic categories. A summative score can be used to reveal
larger trends in semantic functioning, with the reservation that significant information
may be obscured (see the discussion in Capitani et al 1999; cf., Huff et al. 1986;
Fischer et al. 1988; Diesfeldt 1989; Monsch et al. 1992, 1994, 1997; Bayles et al.
1993; Rosser & Hodges 1994; Suhr & Jones 1998).
Because the semantic fluency task involves several cognitive processes and
because the parameters derived from the task tend to be inter-dependent, other tasks
measuring semantic memory and other cognitive functions should be included in
the studies (see Chertkow & Bub 1990; Monsch et al. 1992). Conclusions about the
semantic fluency performance should be drawn in conjunction with the observations obtainable from other semantic tasks, such as confrontation naming, definition, and recognition tasks.
10.4 Implications for further study
In order to provide more reliable findings on the semantic fluency performance of
healthy control subjects and AD patients with mild and moderate dementia, the limitations of the present study discussed earlier translate into essential needs for further
study (see 10.2.3).
First of all, the role of perseverations produced for the semantic fluency performance requires further investigation. In order to draw conclusions about the efficiency of the functioning of the semantic memory and the coherence of the performance during the task, the exact location of perseverations in the output of normal
controls and AD subjects should be examined. More specifically, the emergence of
the perseverations in and between clusters should be checked. Should more
perseverations be found in clusters in AD groups than in the group of normal control
subjects, it may explain why the cluster size remained the same among the subject
groups in many semantic categories. Furthermore, the role of perseveration in the
control tasks (e.g., confrontation naming task, serial naming task) needs to be taken
General discussion
181
into consideration in more detail. In general, it would be very important to find out
if there is a difference in the nature of the perseverations produced by AD subjects
compared to ones made by healthy control subjects (see Sandson & Albert 1984,
1987; Albert & Sandson 1986; Bayles et al. 1993; Ramage et al. 1999). It would
also be informative to know if there is a tendency for the moderate AD patients to
produce more deviant perseverations relative to the mild AD patients. In the future,
the emergence of perseverations in the semantic fluency task should be related to
the performance in certain neuropsychological tests measuring executive processes
(e.g., the Trail Making Test and the Stroop Test; see Lezak 1995:373-376, 381-384).
The data of the present study provides an opportunity to deepen the qualitative
analysis of the semantics of the responses given during the fluency tasks, as well as
the control tasks, and to compare the findings among the subject groups. Because of
the great variability in the performance in each subject group, the semantic fluency
performance should be investigated at the level of the individual subject to get a
profile of an individual’s performance over several categories. In order to get a more
profound profile of an individual’s semantic performance, case studies can be
extended to also cover the performance of the subject on different control tasks.
In order to find out more about the nature of the associations between the
noun responses, a closer look at the cluster formation is required. The embedded
clusters should be broken down in order to better investigate the emergence of pure
taxonomic and physical-functional associations vs. thematic associations in the subject groups. As far as verbs are concerned, a closer look at the responses given for
the verb categories, especially the composition of the semantic roles contained by
these verbs, would shed light on the semantic representation of verbs and add knowledge about the changes of the structure of the semantic memory that are likely to
take place in AD. The occurrence of different verbs (e.g., instrument vs. non-instrument verbs) would provide further information about the types of verbs that are
prone to emerge in the semantic fluency task. In the future, in order to get an insight
into the strategies exploited for cluster formation, subjects could be asked to specify
the way they produced the words during the tasks. Furthermore, the differences
found in the performance of the male and female participants in the NC group in
some of the verb categories gives reason to examine the effects of gender on the
verb fluency performance of AD patients.
Further investigation of the data collected for the present study provides an
opportunity to participate in two ongoing discussions. Firstly, in order to examine
the presence of the category-specific disorder that has been found to differently
affect the semantic representations of living and non-living categories in AD, the
data obtained from the semantic fluency tasks and the control tasks can be divided
into living (i.e., vegetables and animals) and man-made entities (i.e., clothes and
vehicles; see, e.g., Bayles et al. 1989; Chertkow & Bub 1990; Crown-Golomb et al.
1992; Rosser & Hodges 1994; Binetti et al. 1995; Gainotti et al. 1996; Moss et al.
2002; see also 3.1.2, 3.2, 5.5). Secondly, it is possible to analyze in more detail how
nouns vs. verbs are processed in the subject groups in the different tasks. More
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General discussion
specifically, investigations can be expanded to uncover the patterns in which words
are produced for inanimate categories vs. verb categories, both of which typically
contain functional information (see Bird, Howard et al. 2000, 2001; Bird, Lambon
Ralph et al. 2000; Shapiro & Caramazza 2001a, b; see also 3.3.3).
The present study has also brought up several new aspects of the semantic
fluency task that would be interesting and valuable to investigate. New data with
larger groups of participants and a more balanced distribution of gender could be
collected. A new set of categories could be used for which the factors such as varying size, familiarity, and semantic differences between the different noun and verb
categories were better controlled (see Hart et al. 1988; Bayles et al. 1993; Azuma et
al. 1997; Mayr & Kliegl 2000). In order to better control for the occurrence of intrusions, semantically closely related categories that are likely to provide category violations could be employed (e.g., vegetables vs. fruit or tools vs. kitchen utensils or
vehicles). Moreover, AD patients’ behavior in the semantic fluency tasks could be
contrasted with other types of fluency tasks. For example, the phonemic and the
supermarket fluency task could be included, as well as tasks measuring production
of adjectives (e.g., states of mind), abstract words (e.g., occupations), names (e.g.,
celebrities), and non-verbal images (see Mickanin et al. 1994).
Further, it would be of interest to examine the fluency performance of those
AD patients who have deteriorated language skills and those whose language abilities
are better preserved, using both noun and verb fluency tasks (cf., Beatty et al. 2000).
In order to investigate the effects of different dementing processes on the semantic
representation of nouns and verbs, the semantic fluency performances of the AD
patients could also be compared with patients having other types of dementia (e.g.,
fronto-temporal dementia, vascular dementia, etc.). A longitudinal study between
the subject groups would reveal if there exists a systematic pattern of decline in the
semantic information in the different dementing diseases. Moreover, having monovs. bilingual (e.g., Finnish vs. Finnish-Swedish) AD patients perform the task could
shed light upon the effects of AD on different languages, as well as on the nature of
the semantic associations produced for the task.
The scope of the investigation could be broadened to cover the performance
of normal elderly subjects and AD patients on the variation of the semantic fluency
task in which subjects are asked to produce different types of action that belong to a
particular script (e.g., going to the doctor or making coffee; see Weingartner et al.
1983; Grafman et al. 1991; 3.3.4, 5.1). It would be worthwhile to carry out a semantic analysis of the errors taking place and the strategies used to perform the task by
normal elderly subjects and by AD patients, and to compare these errors and strategies to those found in the traditional semantic fluency tasks. Because both nouns
and verbs may be produced for such a script and because they can be considered
appropriate responses (see Lucariello & Rifkin 1986; Fivush 1987; Nelson 1996:231248), the effects of AD on the occurrence of those grammatical classes could be
examined in more detail in a script-like framework.
11 Conclusions
On the basis of the present study, the following conclusions can be made:
1. During the semantic fluency task with the noun categories (superordinates) as
the category constraint, the miAD and the moDA groups showed a significant
reduction in total and correct word production relative to the NC group. Both AD
groups also showed a remarkable reduction in the extent to which clustering and
switching were used as the strategy to prompt noun production. The severity of
dementia significantly affected the overall semantic fluency performance, which
was indicated by the miAD group producing a greater total number of nouns and
more correct nouns, as well as more clusters and switches, than the moAD group.
Despite the smaller number of clusters produced in the AD groups, the size of the
clusters and the proportion of all clustered nouns tended to remain the same
among the subject groups. The finding might imply that the exploitation of the
subcategories, once activated, and the tendency to produce nouns according to
their shared properties worked equally well and coherently in the subject groups.
Nevertheless, the emergence of the perseverations in the fluency performance of
the AD groups is likely to affect the interpretation of the finding (see conclusions
2 and 6).
2. The integrity of the semantic categories appeared to be relatively intact in all
subject groups, indicated by very few category violations. Only in the category
of clothes did the moAD group show a significant increase in the number of
intrusions that may imply an altered or deficient pattern of semantic feature activation and integration. Instead, perseverations, the inability to deactivate previously produced responses, seemed to be a prominent feature characterizing the
performance of both AD groups in virtually every semantic category. However,
the error analysis did not appear very sensitive in differentiating between the
miAD group and the moAD group in individual semantic categories. Only the
combined proportion of perseverations produced across all semantic categories
differentiated the two AD groups from each other.
184
Conclusions
When forming clusters, all subject groups produced nouns not only according to
their semantic associations, but also using their phonological relatedness. The
miAD group tended to form clusters from a significantly more restricted set of
semantic subcategories than the NC grorup, but from a significantly wider range
than the moAD group. For the NC group and the miAD group, formation of the
semantic clusters was based on physical, functional, and thematic features shared
by nouns, whereas the clusters produced in the moAD group were mainly formed
on the basis of less specific (physical) but more robust functional and thematic
relatedness between nouns.
Overall, both AD groups tended to use more frequently occurring nouns than the
NC group, whereas the degree of prototypicality of the responses between the
AD groups and the NC group remained the same in all other categories but
vehicles. The degree of frequency and prototypicality of nouns did not differentiate
between the two AD groups.
3. The miAD group produced a significantly smaller number of words than the NC
group in the semantic fluency task with verb categories. Nevertheless, the reduction
in verb production did not occur in all semantic categories. Once they activated
the semantic space, the NC group and the miAD group clustered verbs and
switched between the semantic dimensions in some categories to an equal extent,
while in some others, the number of clusters and switches was reduced in the
miAD group. Relative to the NC group and the miAD group, the moAD group
was significantly impaired in the ability to generate verbs, clusters of verbs, and
switches in all semantic categories.
Performance of the miAD and the NC group were semantically as coherent and
efficient in each semantic verb category, as far as the cluster size and the proportion of all clustered verbs were concerned. Instead, the moAD group showed a
significantly less coherent and less effective pattern of verb production than the
NC group and the miAD group, but only in some of the verb categories. However, the appearance of perseverations in the fluency performance of the AD
groups affects the interpretation concerning the AD groups’ coherent and efficient semantic fluency performance (see conclusions 4 and 6).
4. The responses given for the verb categories in all subject groups not only included
specific verbs, but also nominalized verb forms, general verbs, phrasal structures,
and noun responses. The NC group and the miAD group did not seem to differ in
their responses, whereas the moAD group tended to produce fewer specific verbs
and more general all-purpose verbs and nouns than the NC group. The finding
was interpreted as an impaired activation of specific semantic features of verbs,
a tendency to process the semantic information of verbs at a more general level,
and as a sign of the dependency of verbs on other parts of speech.
Both AD groups showed signs of an altered and deficient semantic feature
activation by producing significantly more intrusions, mainly concrete nouns,
than the NC group. Both AD groups also showed a tendency to get stuck on
Conclusions
185
previous responses by producing significantly more perseverations than the NC
group. However, intrusions and perseverations did not seem to characterize the
performance of the AD groups in all semantic categories. Relative to the miAD
group, the moAD group produced significantly more intrusions indicating a more
severe change or damage to the semantic feature activation of verbs. The number
of perseverations remained the same between the AD groups.
The variety of subcategories from which verbs were produced tended to be significantly narrower in both AD groups than in the NC group. The severity of
dementia appeared to have affected the semantic range with the miAD group
being better able to activate a wider scope of semantic subcategories than the
moAD group. The clusters formed by the NC group consisted of verbs denoting
different kinds of motion, change of state and/or result of an action which could
be carried out, for example, by different manners of acting, by using the whole
body or different parts of the body, and by using various instruments. Verbs were
also produced in a script-like manner in which the temporal-causal order of the
actions was brought to the fore. The clusters formed by the miAD group and the
moAD group tended to lack some parts of the scripts that were covered by the
NC group. The moAD group also lacked specific clusters of instrument verbs
denoting specific tools or instruments with which the action can be carried out.
The subject groups were able to produce verbs with an equal degree of
prototypicality and frequency.
5. The nature of the noun and verb categories, probably owing to their different
semantic feature constellations, tended to have an effect on the semantic fluency
performance of the AD groups. Some categories were more prone than others to
eliciting fewer responses and more errors (intrusions and perseverations) in the
AD groups than in the NC group. Therefore, conclusions about the deteriorated
semantic fluency performance in AD should not be based on a single semantic
category only.
6. Perseverations are likely to have affected the ability of the AD patients to perform
the semantic fluency task. The tendency to get stuck on the previous responses
may have prevented the AD patients from activating new responses for the tasks.
Perseverations may also have inflated factors with which efficiency and semantic
coherence of word production were measured (i.e., the cluster size and the
proportion of clustered words). Therefore, conclusions about the effectiveness
and coherence of the clustering performance of the AD groups should be made
with caution until the location of perseverations (i.e., between or in the clusters)
is checked and the size of the intact clusters (i.e., clusters without perseverations)
is compared between the NC group and the AD groups.
7. Characteristic of the performance of the two AD groups on both noun and verb
fluency tasks was the reduction in the number of responses in general and the
reduction of very specific responses. The tendency towards using more general
responses was notable in the moAD group. This finding may imply deteriorated
186
Conclusions
or damaged feature integration at the very specific level of semantic features of
nouns and verbs.
8. Overall, the performance of the AD patients on the semantic fluency task reflected
a semantically rather coherent but less specific, effective, and flexible functioning
of the semantic memory or the semantic layer of the mental lexicon. However,
the emergence of intrusions and perseverations in some of the categories may
not only be interpreted as an impaired spread of activation and feature integration
at the semantic level, but also as an impaired functioning of other levels of the
mental lexicon (e.g., lemma level) participating in the process of word production.
9. Based on the significantly worse performance on the semantic fluency tasks and
on most of the control tasks used for examining semantic processing, it can be
concluded that semantic processing of both nouns and verbs was impaired in
each AD group, more severely in the moAD group than in the miAD group.
However, it should be borne in mind that the impaired working memory and
executive function common to AD patients may also have played a crucial role
during the semantic fluency performance, as well as while carrying out other
tasks requiring semantic processing.
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Appendix 1
225
Appendix 1A. Cluster division for the noun categories
Clothes
Accessories: belt, bow tie, handkerchief, suspenders, tie.
Coats: bathrobe, blazer, cardigan, cloak, coat, fur coat, jacket, leather coat, leather
jacket, parka, rain cape, raincoat, suit coat, trench coat, Windbreaker, winter coat.
Footwear: ankle boots, boots, cleats, galoshes, inside shoes, outdoor shoes, riding
boots, rubber boots, sandals, shoes, ski boots, slippers, sneakers, tennis shoes, winter
boots, winter footwear.
Gloves: gloves, mittens, muff, wool gloves.
Headwear: cap, fur hat, hat, scarf.
Indoor clothes: ankle socks, apron, blazer, blouse, bow tie, cardigan, dress, dress
shirt, jeans, knee socks, pantihose, pants, shirt, shoes, skirt, socks, suit, suit coat,
suit pants, sweater, sweater vest, tie, t-shirt, top, undershirt, vest, wool skirt, wool
socks, wool sweater.
Men’s clothing: bow tie, dress coat, long johns, pants, suit, tie, tuxedo.
Outdoors clothes: cap, cardigan, cloak, coat, felt hat, footwear, fur coat, fur hat,
gloves, hat, leather coat, leather jacket, mittens, muff, outdoor pants, overalls,
overcoat, parka, rain cape, raincoat, scarf, shoes, ski pants, ski socks, ski suit, slalom
pants, summer coat, sweater vest, sweat pants, swimsuit, trench coat, ulster,
Windbreaker, winter coat, wool gloves, wool pants, wool socks, wool sweater.
Outfits: ball gown, bikini, cocktail dress, diving suit, dress, evening dress, evening
gown, evening wear, jogging suit, kimono, military uniform, national costume,
pajamas, ski suit, sportswear, suit, swimsuit, tailcoat, tracksuit, tuxedo, wrestling
leotard.
Pants: jeans, long johns, pantihose, shorts, ski pants, sports pants, sports shorts, suit
pants, swim pants, underpants, wool pants.
Scarves: scarf, shawl.
Shirts: blouse, dress shirt, shirt, sports shirt, sweater, t-shirt, top, undershirt.
Skirts: dress, skirt, slip, wool skirt.
Socks: ankle socks, knee socks, wool socks.
Underwear: bathrobe, bra, corset, girdle, long johns, negligee, nightgown, nightie,
pajamas, pantihose, shirt, slip, underpants, undershirt.
Women’s clothing: bra, corset, dress, girdle, skirt.
Wool clothes: wool pants, wool scarf, wool socks, wool sweater.
226
Appendix1
Vegetables
Cabbages: broccoli, Brussels sprouts, cabbage, cauliflower, Chinese cabbage, Savoy
cabbage.
Color: green vegetables: cucumber, dill, lettuce, parsley, peas, spinach.
Herbs and spices: basil, chervil, chives, cilantro, cumin, dill, ginger, fennel,
marjoram, parsley, summer savory, tarragon, thyme.
Leguminous plants: beans, peas.
Onions: chives, garlic, leek, onion.
Root- and tuberous vegetables: beet, carrot, horseradish, Jerusalem artichoke,
parsnip, radish, rutabaga, sugar beet, turnip.
Sprouts and greens: artichoke, asparagus, broccoli, Brussels sprouts, cabbage,
cauliflower, celery, Chinese cabbage, chives, endive, garlic, iceberg lettuce, leek,
lettuce, onion, rhubarb, Savoy cabbage, spinach.
Vegetables used for salads: avocado, cucumber, lettuce, squash, tomato, zucchini.
Vehicles
Beasts of burden: dog, horse, horse and rider, horse cab, reindeer and sleigh.
Boats and ships: barge, boat, canoe, dinghy, motorboat, rowboat, sailboat, ship,
steamboat, submarine.
Cars: automobile, bus, police car, racecar, sports car, taxi, toy car, truck, van.
Means of mass transportation: airplane, bus, jet plane, passenger ship, ship, subway,
taxi, train, tram.
Muscle-powered vehicles: bicycle, boat, canoe, carriage, feet, ice skates, kickboard,
kick-sled, mountain bike, racing bicycle, roller skates, roller skis, rowboat, skateboard,
skis, sled, snowboard, tandem bicycle, tricycle, wheelchair.
On-road vehicles: bicycle, bus, car, convertible, jeep, kick sled, moped, motorcycle,
motorcycle and sidecar, police car, tandem bicycle, taxi, tractor, truck, van.
Vehicles in the air: airplane, glider, helicopter, hot air balloon, jet plane, jumbo jet,
parachute, rocket, sailplane, shuttle.
Vehicles on the rails: handcar, subway, train, tram.
Vehicles on wheels: bike, car, moped, motorcycle, roller skates, skateboard, taxi,
truck, van.
Vehicles used in wintertime: ice skates, kick-sled, reindeer and sleigh, skis,
snowboard, snowmobile.
Vehicles used on water: barge, boat, canoe, dinghy, ferry, motorboat, rowboat,
sailboard, sailboat, steamboat, submarine, water-skis.
Others: elevator, escalator.
Appendix 1
227
Animals
Beasts of burden: camel, donkey, horse, mule, ox.
Birds: bird of paradise, blackbird, black grouse, capercailzie, chaffinch, crane, crow,
eagle, emu, falcon, finch, goldeneye, goose, gull, hazelhen, magpie, mallard, ostrich,
parrot, peacock, pheasant, pied flycatcher, skylark, sparrow, starling, stork, swallow,
swan, tit, vulture, willow grouse, woodpecker.
Canine: dog, fox, hyena, wolf.
Deer: elk, reindeer.
Exotic, foreign animals: alligator, alpaca, bison, camel, caracal, chameleon, cheetah,
crocodile, donkey, elephant, emu, giraffe, gorilla, hippopotamus, hyena, impala,
jaguar, kangaroo, karakul, leopard, lion, llama, marmot, monkey, mule, ostrich, panda,
panther, parrot, polar bear, puma, rattlesnake, rhinoceros, sable, skunk, snake, tiger,
turtle, whale, wild boar, zebra.
Farm animals: bull, calf, cat, cow, dog, foal, goat, goose, hen, horse, kid, mare, ox,
pig, pony, rabbit, rooster, sheep, stallion.
Feline: cat, cheetah, cougar, jaguar, leopard, lion, lynx, panther, puma, tiger.
Fish: Baltic herring, burbot, gold-fish, herring, lavaret, perch, pike, roach, salmon,
vendace, zander.
Insects: ant, bedbug, butterfly, cockroach, dragonfly, flea, fly, louse, mosquito, spider,
wasp.
Invertebrates: worm, slug.
Pets: canary, cat, dog, goldfish, guinea pig, mouse, parrot, rabbit.
Reptiles: alligator, crocodile, frog, grass snake, lizard, newt, rattlesnake, snake, toad,
tortoise.
Rodents: beaver, guinea pig, hamster, hare, hedgehog, mink, mole, mouse, muskrat,
rat, squirrel.
Water animals: alligator, beaver, crocodile, fish, frog, muskrat, newt, otter, seal,
toad, turtle, whale.
Wild mammals, found in Finland: Arctic fox, badger, bear, beaver, deer, elk, ermine,
fox, hare, hedgehog, lynx, mink, mole, mouse, muskrat, otter, pine marten, polecat,
raccoon dog, rat, reindeer, seal, weasel, wolf, wolverine.
228
Appendix1
Appendix 1B. Cluster division for the verb categories
Preparing food
Baking: add breadcrumbs, allow dough to rise, bake, beat, butter, dust with flour,
dust with sugar, foam, frost, knead dough, make dough, mix, pipe cream on cake,
put dough in mold, roll, season, simmer, strain.
Cleaning: clean, rinse, scrub, wash.
Cleaning and preparing: chop, clean, cut, cut into pieces, dice, fillet, grate, mash,
peel, pluck, reduce, rinse, scale and gut fish, skin, slice, split, wash.
Cooking: bake, blanch, boil, broil, brown, burn, cook, flame, frizzle, fry, gratiné,
grill, keep watch, roast, simmer, smoke, turn off/on the stove.
Diminishing: chop, cut, dice, mince, puree, reduce, slice.
Dining: clear the table, drink, eat, pour drinks, serve, set the table, wash the dishes.
Handling food: allow to cool, beat, clean, crumble, freeze, grate, grind, mash, melt,
mix, puree, scoop, skim, spoon, spread, strain, stir, thicken, thin, turn, warm up,
wash.
Pre-preparing: bring home, buy, get food, make the fire, put water in pot, shop,
turn on stove.
Preserving: can, freeze, preserve, salt.
Seasoning: marinate, salt, season, spice, taste.
Scripts: Listing actions in sequential order (e.g., rinse potatoes, peel potatoes, boil
potatoes).
Playing sports
Boxing and wrestling: box, go to the gym, lift weights, wrestle.
Dancing: dance, polka.
Flying: fly, glide.
Games: kickball, play baseball/golf/basketball/hockey/soccer/squash/tennis/
volleyball, play with a ball.
Gymnastics: aerobics, bend, do gymnastics, jump, lift, roll shoulders, swing arms,
turn head.
Horse racing: ride a horse, show jumping, steeplechase.
Jumping: high jump, jump, leap, long jump.
Motor sports: car racing, motor sports.
Playing sports with some equipment: bicycle, fence, skate, ski, snowboard.
Running: jog, run, run long distance, sprint.
Skating: ice-skate, figure skate.
Appendix 1
229
Sports with the emphasis on using hands/arms: hit, javelin, lift, lift weights, shotput, throw, throw a ball/discus/hammer.
Sports with the emphasis on using legs: cross-country running, cross-country
walking, high jump, hop on one foot, hurdles, jog, jump, jump rope, kick, kickball,
leap, long distance running, long jump, orienteering, relay race, run, run 100/400/
1000m, sprint, triple jump, walk.
Throwing: javelin, shot-put, throw a ball.
Water sports: butterfly, crawl, deep-water diving , dive, paddle, row, sail, surf,
swim, synchronized swimming.
Winter sports: cross-country skiing, downhill skiing, figure skate, skate, ski, ski
jump, slalom skiing, snowboard.
Construction
Basic work: beat, build a frame, build scaffolding, cast, dig, drain, fasten, lay the
foundation, lay pipes, level, mix concrete, pour cement, prepare the site, quarry,
sand the ground, set corner stones, tamp, till soil.
Brickwork: carry bricks, cast, concrete, even out the plaster, grind, joint, lay brick,
lay the foundation, masonry, mix concrete/mortar, plaster, pour concrete, rub down,
smooth out.
Diminishing: cut, saw, split.
Fastening : fasten, glue, seam.
Handling the surfaces: coat, cover, decorate, glue, grind, paint, panel, plaster, tile,
wallpaper.
Interior work: decorate, electrical work, furnish, install the fittings, lighting, make
the floor, paint, put up the cupboards, tile, wallpaper.
Preparing and supervising: apply for building permit, buy building materials, draft,
keep watch, order material/walls, pay building costs, plan, supply with materials,
survey (the building site).
Repairing a building: mend, putty, raise, repair, renovate, replace, take down, tear
down, trim, wreck.
Woodworking: beat, board up, carpentry, carry boards, carve, clean the wood, cut
the logs to length, drill, fit, fit the boards, grind, lath, measure, nail, paint, panel,
plane, sandpaper, saw, screw, shorten, stain, varnish.
Working on the fabric of a building: beat, board up, build the frame/the wall/the
roof/the stairs, carry materials, cover, drill, electrical work, fasten, fasten the windows,
glaze, install insulation, lift boards, make the floor, make the stone base, outdoor
painting, putty, raise the walls, screw, seal up, set up, set rain gutters, supply with
water, water and sewage work.
Scripts: listing actions in a temporal-causal, serial order (e.g., draft, apply for building
permit, survey (the building site), lay the foundation, build the frame, put up the
walls, build the roof, insulate, paint, wallpaper, furnish).
230
Appendix1
Cleaning up
Airing: air, air the apartment/bedding/carpets/linen, beat, beat the bedding/carpets,
dust, shake, shake a carpet.
Arranging: clean out the cupboards, organize the dishes, put the dishes in the
cupboard, take out the trash.
Cleaning the floor: air the carpets, beat the carpets, bring the carpets in, brush,
clean, clean the floor, clean the floor with a machine, dry, mop, polish, polish with a
polishing machine, put the carpets down, rinse, rub, scour, scrub, shake the carpets,
sweep, sweep the rubbish, use a vacuum-cleaner, vacuum, wash, wash the floor,
wax, wipe.
Cleaning up the other surfaces: clean, clean the doors, clean off spots, clean the
window frames, clean the windows, dry, dust, dust off the walls, polish, rinse, rub,
scour, scrub, wash, wash the cupboards, wash the walls, wipe, wipe the furniture,
wipe the tables.
Defrosting the refrigerator: arrange, defrost the refrigerator, dry the refrigerator,
put things in.
Preparing to clean: add the detergent, buy a brush/stuff for cleaning up, get the
washing water, pour water into a bucket.
Taking care of the cleaning equipment: clean the cleaning equipment, put the
cleaning equipment in places, take care of the cleaning equipment.
Taking care of the linen and carpets: air the bedding/carpets/linen, beat, beat the
bedding/carpets/clothes, change sheets, do laundry, dust the clothes, dry, get out the
linen, hang, hang on a line to dry, make the beds, shake the carpets, straighten, wash
the curtains.
Ways of cleaning: clean, disinfect, do the dishes, rinse, scour, scrub, wash, wash
the floor, wash with a brush, wipe.
Scripts: listing actions in a temporal-causal, serial order (e.g., air the linen, dust,
clean the floor, beat the carpets).
6-7 (highly prototypical)
blouse (pusero) 7.00, shirt (paita)
7.00, pants (pitkät housut) 6.93,
jeans (farkut) 6.79, coat (takki)
6.71, t-shirt (t-paita) 6.57, cardigan
(villatakki) 6.57, dress (mekko)
6.4, jacket (pikkutakki) 6.36
tomato (tomaatti)
7.00,
pepper (paprika) 6.79, cucumber
(kurkku) 6.57, iceberg lettuce
(jäävuorisalaatti) 6.43, lettuce
(lehtisalaatti) 6.36, cauliflower
(kukkakaali) 6.29, Chinese
cabbage (kiinankaali) 6.29,
cabbage (keräkaali) 6.14, broccoli
(parsakaali) 6.07, leek (purjosipuli)
6.00
train (juna) 6.93, bus (bussi) 6.93,
car (auto) 6.86, subway (metro)
6.86, bicycle (polkupyörä) 6.79,
plane (lentokone) 6.79, tram
(raitiovaunu) 6.71, ship (laiva)
6.57, motorcycle (moottoripyörä)
6.43, taxi (taksi) 6.36
Category
Clothes
Vegetables
Vehicles
truck (kuorma-auto) 5.93, jumbo
jet (jumbojetti) 5.79, steamboat
(höyrylaiva) 5.71, motorboat
(moottorivene) 5.64, sports
car (urheiluauto) 5.50, moped
(mopedi) 5.50, trailer truck (rekkaauto) 5.29, rowboat (soutuvene)
5.29, racing bike (kilpapyörä) 5.07,
sailboat (purjevene) 5.00
Brussels sprouts (ruusukaali) 5.86,
onion (sipuli) 5.71, potato (peruna)
5.64, carrot (porkkana) 5.64, bean
(papu) 5.64, rutabega (lanttu) 5.57,
spinach (pinaatti) 5.50, turnip
(nauris) 5.43, celery (lehtiselleri)
5.29, parsnip (palsternakka) 5.00
winter coat (talvitakki) 5.93, long
johns (kalsarit) 5.71, nightgown
(yöpaita) 5.64, underpants
(alushousut) 5.64, vest (liivi) 5.64,
bra (rintaliivit) 5.50, raincoat
(sadetakki) 5.29,
wool socks (villasukat) 5.14,
scarf (kaulahuivi) 5.14
5-6 (prototypical)
ferry (lossi) 4.93, sled (kelkka)
4.57, speedboat (pikavene) 4.50,
horse-drawn carriage (hevoskärryt)
4.43, canoe (kanootti) 4.36, race
car (kilpa-auto) 4.36
pumpkin (kurpitsa) 4.86, Savouy
cabbage (savoijinkaali) 4.86,
parsley (persilja) 4.71, radish
(retikka) 4.71, garlic (valkosipuli)
4.57, sugar beet (sokerijuurikas)
4.29, herbs (yrtit) 4.07
pantihose (sukkahousut) 4.93, wool
pants (villahousut) 4.86, socks
(sukat) 4.79, mittens (lapaset)
4.64, cap (lakki) 4.50, swim-suit
(uimapuku) 4.43, hat (hattu)
4.36, tie (kravatti) 4.29, headwear
(päähine) 4.14, sports shoes
(urheilukengät) 4.07
4-5 (intermediately prototypical)
reindeer and sleigh (poronpulkka)
3.00, feet (jalat) 2.64,
skates (luistimet) 2.64,
escalator (rullaportaat) 2.50, horse
and rider (ratsukko) 2.43, wood
sled (halkoreki) 2.36, parachute
(laskuvarjo) 2.29, shuttle (sukkula)
2.07
endive (endiivi) 2.71
suspenders (henkselit) 3.07,
boots (saappaat) 3.00, track
shoes (piikkarit) 3.00, ski boots
(hiihtokengät) 2.93, 2.50, diving
suit (sukelluspuku) 2.00
2-3 (not so prototypical)
Appendix 2. A sample of prototypicality ratings of the words produced for the semantic fluency tasks
Appendix 2
231
6-7 (highly prototypical)
boil (keittää) 6.64, make meatballs
(tehdä lihapullia) 6.57, fry (paistaa)
6.50, season (maustaa) 6.21, grill
(grillata) 6.14, simmer (hauduttaa)
6.14, cook (kypsentää) 6.07, smoke
(savustaa) 6.07
play soccer (pelata jalkapalloa)
6.93, ski (hiihtää) 6.86, run
(juosta) 6.79, swim (uida) 6.71, jog
(lenkkeillä) 6.64, play volleyball
(pelata lentopalloa) 6.57, throw
the javelin (heittää keihästä) 6.50,
wrestle (painia) 6.43, play baseball
(pelata pesäpalloa) 6.36, high jump
(hypätä korkeutta) 6.14
build a roof (rakentaa katto) 6.71,
masonry (muurata) 6.36, pour
foundation (valaa perusta) 6.29,
nail (naulata) 6.21, 6.14, build a
frame (rakentaa kehikko) 6.14,
panel (paneloida) 6.00
vacuum (imuroida) 7.00, sweep
(lakaista) 6.86, beat carpets
(piiskata matot) 6.79, mop (mopata)
6.71, dust (pyyhkiä pölyt) 6.64,
scrub (jynssätä) 6.36, take carpets
out (viedä matot ulos) 6.07
Categories
Preparing food
Playing sports
Construction
Cleaning up
Appendix 2 (continued)
brush (harjata) 5.86, wash (pestä)
5.71, brush (harjata) 5.43, air the linen
(tuulettaa liinavaatteet) 5.43, bring in
carpets (tuoda matot sisään) 5.36, beat
(piiskata) 5.36, arrange (järjestää) 5.29,
scrub (hangata) 5.14, air (tuulettaa) 5.00
cast (valaa) 5.93, hammer (vasaroida)
5.86, wallpaper (laittaa tapetit) 5.79, dig
the foundation (kaivaa pohja) 5.64, drill
(porata) 5.36, 5.29, screw (ruuvata) 5.29,
paint (maalata) 5.14, insulate (eristää)
5.07
cross-country running (maastojuoksu)
5.93, do gymnastics (voimistella) 5.86,
ride a bike (ajaa pyörällä) 5.79, crosscountry skiing (murtomaahiihto) 5.71,
figure skate (kaunoluistella) 5.64, slalom
skiing (pujotella) 5.50, throw the discus
(heittää kiekkoa) 5.29, shot-put (työntää
kuulaa) 5.43, sail (purjehtia) 5.36, throw
the hammer (heittää moukaria) 5.29
slice (suikaloida) 5.93, fillet (fileoida)
5.79, bake (leipoa) 5.79, roast (paahtaa)
5.64, stir (hämmentää) 5.50, beat
(vatkata) 5.50, mash (muussata) 5.36, heat
(kuumentaa) 5.29, mix (sekoittaa) 5.21,
peel (kuoria) 5.00
5-6 (prototypical)
wax (vahata) 4.93, polish (kiillottaa)
4.79, rinse (huuhdella) 4.57, change
bed sheets (vaihtaa lakanat) 4.57,
shake (kopistella) 4.36, wash dishes
(pestä astioita) 4.14, take care of
the cleaning equipment (huoltaa
siivousvälineet) 4.00
grind (hioa) 4.93, plane (höylätä) 4.86,
mix (sekoittaa) 4.64, mix the concrete
(sekoittaa betoni) 4.64, install the
fixtures (asentaa kalusteet) 4.50 pile
up (kasata) 4.14, repair (korjata) 4.43,
level (tasoittaa) 4.36, measure (mitata)
4.00
play ball (palloilla) 4.93, dance
(tanssia) 4.71, play golf (pelata
golfia) 4.64, walk (kävellä) 4.57, surf
(surffata) 4.50, car racing (ajaa autolla
kilpaa) 4.43, throw (heittää) 4.29,
jump (hypätä) 4.21, stretch (venytellä)
4.21, shoot (ampua) 4.00
season (höystää) 4.86, salt (suolata)
4.79, dice (palotella) 4.71, whisk
(vaahdottaa) 4.64, slice (viipaloida)
4.50, turn on the stove (laittaa
hella päälle) 4.43, thicken (saostaa)
4.36, cut (leikata) 4.07, warm up
(lämmittää) 4.00
4-5 (intermediately prototypical)
beat (hakata) 2.86, hang
(ripustaa) 2.36, embellish
(kaunistaa) 2.29, decorate
(koristella) 2.00
take down (purkaa) 2.93, split
(halkaista) 2.93, fasten (laittaa
kiinni) 2.86, cut (leikata) 2.79,
raise (korottaa) 2.71, carry
(kantaa) 2.57, shorten (lyhentää)
2.43, reduce (pienentää) 2.43,
decorate (koristaa) 2.36, clean
(puhdistaa) 2.00
dance the polka (mennä polkkaa)
2.93, hit (lyödä) 2.79, lift
(nostaa) 2.71, bend (taivuttaa)
2.71, do the butterfly stroke
(perhostella) 2.64, drive (ajaa)
2.36, swing oneʼs arms (heiluttaa
käsiä) 2.29, play (leikkiä) 2.21,
rotate (pyöriä) 2.21, swing
(keinua) 2.14
freeze (pakastaa) 3.00, preserve
(säilöä) 3.00, rinse (huuhdella)
2.93, spread (levittää) 2.93, lay
the table (kattaa) 2.93, pour
(kaataa) 2.57, spoon (lusikoida)
2.50, thin (ohentaa) 2.50, wash
(pestä) 2.29, clean (puhdistaa)
2.14
2-3 (not so prototypical)
232
Appendix 2
5-7 (very frequent)
coat (takki) 5.93, pants (housut) 5.86,
shirt (paita) 5.71, t-shirt (t-paita) 5.57,
pantihose (sukkahousut) 5.50, jeans
(farkut) 5.43, night gown (yöpaita)
5.29, wool sweater (villapaita) 5.14,
underpants (pikkuhousut) 5.07,
leather coat (nahkatakki) 5.00
potato (peruna) 6.00, cucumber
(kurkku) 5.71, carrot (porkkana),
5.36, salad (salaatti) 5.50, tomato
(tomaatti) 5.43, garlic (valkosipuli)
5.00
car (auto) 6.29, bicycle (polkupyörä)
6.00, train (juna) 5.79, bus (linjaauto) 5.64, walking (kävely) 5.57,
feet (jalat) 5.64, taxi (taksi) 5.43,
truck (kuorma-auto) 5.21, plane
(lentokone) 5.21, boat (vene) 5.00
dog (koira) 6.00, cow (lehmä) 5.93,
mosquito (hyttynen) 5.64, pig (sika)
5.57, horse (hevonen) 5.50, chicken
(kana) 5.43, fly (kärpänen) 5.36, ant
(muurahainen) 5.21, gull (lokki) 5.0,
moose (hirvi) 5.00
Category
Clothes
Vegetables
Vehicles
Animals
butterfly (perhonen) 4.86, Baltic
herring (silakka) 4.79, tiger (tiikeri)
4.50, eagle (kotka) 4.43, reindeer
(poro) 4.36, elephant (elefantti) 4.29,
rat (rotta) 4.21, fox (kettu) 4.14,
salmon (lohi) 4.07, bull (härkä) 4.00
tram (raitiovaunu) 4.71, motorcycle
(moottoripyörä) 4.64, horse
(hevonen) 4.57, trailer truck (rekkaauto) 4.57, skis (sukset) 4.43,
van (pakettiauto) 4.36, motorboat
(moottorivene) 4.29, skates
(luistimet) 4.29,
sailboat (purjevene) 4.00
pepper (paprika) 4.93, cabbage (kaali)
4.86, pea (herne) 4.79, cauliflower
(kukkakaali) 4.64, garlic (valkosipuli)
4.57, lettuce (lehtisalaatti) 4.57, beet
(punajuuri) 4.43, Chinese cabbage
(kiinankaali) 4.21, bean (papu) 4.07,
cabbage (keräkaali) 4.00
underwear (alusvaatteet) 4.93, gloves
(hanskat) 4.86, scarf (kaulahuivi)
4.79, skirt (hame) 4.71, cardigan
(villatakki) 4.64, suit (puku) 4.50, hat
(hattu) 4.36, dress (mekko) 4.29, long
johns (kalsarit) 4.14, tie (kravatti)
4.00
4-5 (intermediately frequent)
rabbit (kaniini) 3.93, swan (joutsen)
3.86, wolverine (ahma) 3.79, giraffe
(kirahvi) 3.71, lynx (ilves) 3.64,
flea (kirppu) 3.57, camel (kameli)
3.50, dragonfly (sudenkorento) 3.43,
leopard (leopardi) 3.29, alligator
(alligaattori) 3.14
sled (pulkka) 3.93, rowboat
(soutuvene) 3.86, passenger ship
(matkustajalaiva) 3.79, escalator
(rullaportaat) 3.64 , snowmobile
(moottorikelkka) 3.50, tricycle
(kolmipyörä) 3.43, police car
(poliisiauto) 3.36, moped (mopedi)
3.29, ferry (lossi) 3.14, roller skates
(rullaluistimet) 3.00
broccoli (parsakaali) 3.93, chives
(ruohosipuli) 3.86, cabbage
(valkokaali) 3.57, rutabaga (lanttu)
3.71, celery (lehtiselleri) 3.21, turnip
(nauris) 3.57, zucchini (kesäkurpitsa)
3.43, spinach (pinaatti) 3.36,
pumpkin (kurpitsa) 3.21, avocado
(avokado) 3.00
sports pants (urheiluhousut) 3.93, fur
coat (turkki) 3.79, blazer (bleiseri)
3.64, mittens (kintaat) 3.57, jacket
(jakku) 3.50, apron (esiliina) 3.36,
vest (liivi) 3.21, pajamas (yöpyjama)
3.14, rain cape (sadeviitta) 3.07,
headwear (päähine) 3.00
2-3 (not so frequent)
karakul (karakulla) 1.43, raccoon
dog (supikoira) 1.86
church trap (kirkkokiesit) 1.93
-
ulster (ulsteri) 1.93, kimono
(kimono) 1.86, corset
(kureliivi) 1.86, evening gown
(päivällispuku) 1.79
1-2 (rare)
Appendix 3. A sample of frequency ratings of the words produced for the semantic fluency tasks
Appendix 3
233
5-7 (very frequent)
boil (keittää) 6.00, wash (pestä)
5.71, fry (paistaa) 5.57, warm up
(lämmittää) 5.57, fetch food (hakea
ruoka) 5.57, heat (kuumentaa) 5.29,
clean (siivota) 5.29, do the shopping
(käydä kaupassa) 5.14, stir (sekoittaa)
5.14, do the dishes (tiskata) 5.14
run (juosta) 5.93, drive (ajaa) 5.93,
walk (kävellä) 5.79, play (pelata) 5.50,
lift (nostaa) 5.43, practice (harjoitella)
5.36, swim (uida) 5.29, sweat
(hikoilla) 5.21, throw (heittää) 5.07
build a roof (rakentaa katto) 6.71, pour
foundation (valaa perusta) 6.29, build
the frame (rakentaa kehikko) 6.14,
install doors and windows (laittaa ovia
ja ikkunoita) 6.00, repair (korjata)
5.50, cut (leikata) 5.29, measure
(mitata) 5.21, dig (kaivaa) 5.00
wash (pestä) 6.00
, do the dishes
(tiskata) 5.71, clean (puhdistaa) 5.64,
arrange (järjestää) 5.50, vacuum
(imuroida) 5.36, wipe (pyyhkiä) 5.29,
rinse (huuhdella) 5.21, brush (harjata)
5.00
Category
Preparing food
Playing sports
Construction
Cleaning up
dust (pyyhkiä pölyt) 4.79, sweep
(lakaista) 4.64, air (tuulettaa) 4.71,
clean the windows (pestä ikkunat) 4.43,
wipe the floor (pyyhkiä lattia) 4.29,
make the bed (pedata) 4.21, bring in
carpets (tuoda matot sisään) 4.14, keep
tidy (pitää järjestys) 4.07, wash the
floor 4.00, scrub (hangata) 4.00
saw (sahata) 4.93, hammer (vasaroida)
4.79, paint (maalata) 4.64, drill (porata)
4.57, furnish (kalustaa) 4.43, seal
(tiivistää) 4.36, level (tasoittaa) 4.29,
screw (ruuvata) 4.21, plane (höylätä)
4.14, lay brick (muurata) 4.00
jump (hypätä) 4.93, jog (lenkkeillä)
4.86, do gymnastics (voimistella)
4.71, dance (tanssia) 4.50, play ice
hockey (pelata jääkiekkoa) 4.29, stretch
(venytellä) 4.21, dive (sukeltaa) 4.14,
bend (taivuttaa) 4.07, lift weights
(nostaa painoja) 4.00
cook (kypsentää) 4.79, freeze
(pakastaa) 4.71, defrost (sulattaa) 4.59,
slice (viipaloida) 4.50, chop (pilkkoa)
4.43, stir (hämmentää) 4.29, grill
(grillata) 4.21, mash (muussata) 4.14,
slice (suikaloida) 4.07
4-5 (intermediately frequent)
1-2 (rare)
tear down (repiä pois) 2.93,
fill the foundation (täyttää
perustukset) 2.86, tamp
(juntata) 2.79
glide (liitolentää) 2.64,
rhythm skate (rytmiluistella)
2.00
beat carpets (tampata mattoja) 3.93,
decorate (koristella) 2.93,
hang (ripustaa) 3.79, wax (vahata)
polish (kirkastaa) 2.50
3.64, beat (hakata) 3.57, disinfect
(desinfioida) 3.43, mop (mopata)
3.36, scour (kuurata) 3.21, clean
walls (puhdistaa seinät) 3.14, arrange
the refrigerator (järjestää jääkaappi)
3.07, take care of cleaning equipment
(huoltaa siivousvälineet) 3.00
wallpaper (tapetoida) 3.93, caulk
(saumata) 3.86, split (halkaista)
3.79, carve (veistää) 3.64, plaster
(rapata) 3.57, cast (valaa) 3.43,
mix the concrete (sekoittaa betoni)
3.36, install rain gutters (asentaa
sadekouru) 3.14
wrestle (painia) 3.93, play
volleyball (pelata lentopalloa) 3.79,
box (nyrkkeillä) 3.64, relay race
(viestijuoksu) 3.57, paddle (meloa)
3.50, shot-put (työntää kuulaa) 3.43,
throw hammer (heittää moukaria)
3.36, do the pole vault (hypätä
seivästä) 3.29, bowl (keilailla) 3.21,
ski jump (hypätä mäkeä) 3.14
fillet (fileerata) 3.93, allow the dough flame (flambeerata) 2.07
raise (kohottaa taikina) 3.86, grind
(jauhaa) 3.79, strain (siivilöidä)
3.71, preserve (säilöä) 3.64, knead
(alustaa) 3.57, juice (mehustaa) 3.29,
frost (kuorruttaa) 3.21, 3.14, marinate
(marinoida) 3.07
2-3 (not so frequent)
234
Appendix 3
Appendix 4
235
Appendix 4A. Examples of the semantic fluency performance
given by a participant in each subject group : clothes
NC18
miAD43
moAD52
Finnish:
Finnish:
Finnish:
sukat/
kalsarit, aluspaita/
(aluspaita), päällyspaita, pusero,
villapaita/
(villapaita), takki, ulsteri, turkki,
pipo, myssy, kengät, saappaat/
tyyny, lakanat, täkki, peitto/
villapaita/
nenäliina/
rukkaset, hanskat, sormikkaat.
puserot, hameet, leningit/
takit, puserot, takit, hatut,
käsineet/
puserot, hameet/
takit, hatut, käsineet, huivit,
kaulahuivit.
kengät, sukat, housut/
hattu, myssy/
ääninauha/
jäljennys.
English:
English:
English:
socks
long johns, under-shirt/
(under-shirt), shirt, blouse, wool
sweater/
(wool sweater), coat, ulster, fur
coat, knit hat, cap, shoes, boots/
pillow, sheets, quilt, cover/
wool sweater/
handkerchief/
mittens, gloves, gloves/
blouses, skirts, dresses/
coats, blouses, coats, hats,
gloves/
blouses, skirts/
coats, hats, gloves, scarves,
neckerchiefs.
shoes, socks, pants/
hat, cap/
audio tape/
copy.
Words total: 22
Words total: 15
Words total: 7
No of switches (/): 7
No of clusters: 5
No of clusters: 4
No of clusters: 2
Mean cluster size: 2
Words in clusters: 19/22
Semantic strategy: 4/5
Mixed strategy: 1/5
Mixed strategy: -
Mixed strategy: -
Phonemic strategy: -
Correct words: 15/22
Correct words: 8/15
Correct words: 5/7
Intrusions: 5/22
Semantically related intr. 5
Semantically unrelated intr. 0
Intrusions: Semantically related intr. Semantically unrelated intr. -
Intrusions: 2/7
Semantically related intr. Semantically unrelated intr. -
Perseverations: 2/22
Perseverations: -
Semantic subcategories: 5
Mean prototypic. of words: 4.80
Mean frequency of words: 4.57
Note. Words in parentheses belong to two overlapping clusters.
236
Appendix 4
Appendix 4B. Examples of the semantic fluency performance
given by a participant in each subject group: vegetables
NC5
miAD31
moAD63
Finnish:
Finnish:
Finnish:
porkkana, nauris, lanttu, peruna/
keräkaali, kukkakaali,
brysselinkaali, kyssäkaali/
punajuuri/
sipuli, valkosipuli/
tomaatti/
herne.
porkkana, punajuuri/
sipuli/
peruna, papaija, punajuuri/
viinimarja, vadelma.
porkkana, lanttu, peruna, peruna/
kaali/
vehnä/
mansikatki istutettu.
English:
English:
English:
carrot, turnip, rutabaga, potato/
cabbage, cauliflower, Brussels
sprouts, kohlrabi/
beetroot/
onion, garlic/
tomato/
pea.
carrot, beetroot/
onion/
potato, papaya, beetroot/
currant, raspberry.
carrot, rutabaga, potato, potato/
cabbage/
wheat/
strawberries also planted.
Words total: 13
Words total: 8
Words total: 7
No of clusters: 3
No of clusters: 3
No of clusters: 1
Mean cluster size: 1
Words in clusters: 7
Mixed strategy: 1/3
Mixed strategy: 1/3
Mixed strategy: -
Phonemic strategy: 1/3
Correct words: 4/8
Correct words: 4/7
Intrusions: 3/8
Intrusions: 2/7
Semantically unrelated intr. -
Perseverations: -
Perseverations: 1/8
Perseverations: 1
Appendix 4
237
Appendix 4C. Examples of the semantic fluency performance
given by a participant in each subject group: vehicles
NC6
miAD37
moAD53
Finnish:
Finnish:
Finnish:
juna/
auto, polkupyörä/
lentokone, laiva/
(laiva), moottorivene, soutuvene/
(soutuvene), potkulauta/
juna/
kanootti, sukset, potkukelkka/
moottoripyörä/
raitiovaunu, metro.
auto, linja-auto, henkilöauto,
rekka-auto, autoja, linja-auto,
leikkiauto, lasten leikkiauto,
maitoauto.
auto, pyörä/
lentokone, lentokone/
pyörä, polkupyörä.
English:
English:
English:
train/
car, bicycle/
plane, ship/
(ship), motor-boat, row-boat/
(rowing boat), kick-board/
train/
canoe, skis, kick-board/
motor-cycle/
tram, subway.
car, bus, passenger car, trailer
truck, cars, bus, toy car,
childrenʼs toy car, milk van.
car, bike/
plane, plane/
bike, bicycle.
Words total: 15
Words total: 9
Words total: 6
No of switches (/): -
No of clusters: 6
No of clusters: 1
No of clusters: 1
Semantic strategy: -
Mixed strategy: 1/6
Mixed strategy: 1/1
Mixed strategy: -
Correct words: 5/9
Correct words: 3/6
Intrusions: 2/9
Perseverations: -
Perseverations: 2
Perseverations: 3/6
Note. Words in parentheses belong to two overlapping clusters.
238
Appendix 4
Appendix 4D. Examples of the semantic fluency performance
given by a participant in each subject group: animals
NC12
miAD46
moAD68
Finnish:
Finnish:
Finnish:
hiiri, rotta/
kissa, koira, kani, kili, hevonen,
lehmä, lammas/
jänis, kettu, susi, karhu/
leijona, seepra.
karhu, kettu, susi, naali, jänis,
orava/
hevonen, lammas, lehmä, vuohi/
sarvikuono, alligaattori,
krokotiili.
kissa, koira/
varis/
hiiri, rotta.
English:
English:
English:
mouse, rat/
cat, dog, rabbit, kid, horse,
cow, sheep/
hare, fox, wolf, bear/
lion, zebra.
bear, fox, wolf, arctic fox, hare,
squirrel/
horse, sheep, cow, goat/
rhinoceros, alligator, crocodile.
cat, dog/
crow/
mouse, rat.
Words total: 15
Words total: 13
Words total: 5
No of clusters: 4
No of clusters: 3
No of clusters: 2
Mixed strategy: 1
Mixed strategy: 2/3
Mixed strategy: 1/1
Correct words: 5/5
Perseverations: -
Perseverations: -
Perseverations: -
Appendix 4
239
Appendix 4E. Examples of the semantic fluency performance
given by a participant in each subject group: preparing food
NC15
miAD48
moAD60
Finnish:
Finnish:
Finnish:
paistaa, keittää, käristää, grillata/
maustaa, sekottaa, maistella/
kypsentää, höyryttää, kiehuttaa/
vispata/
saostaa, sulattaa/
keittämiset/
kuoria.
keittää, paistaa, keittämistä ja
paistamista/
syöminen siinä tarvitaan/
paistaa uunissa ja keittää.
keittäminen, paistaminen/
perunoitten kuoriminen/
puuron teko, mämmin teko/
lettujen paistaminen/
makaroonilaatikkoo,
makaroonilaatikkoo,
lihapullia, kaalikääryleitä,
kesäkeittoo, porkkanalaatikkoo,
lanttulaatikkoo/
kinkun valmistusta.
English:
fry, cook, crisp, grill/
season, stir, taste/
cook, steam, boil/
whip up/
thicken, melt/
cookings/
peel.
English:
cook, fry, cooking and frying/
eating is needed/
bake in the oven and cook.
English:
cooking, frying/
peeling potatoes/
making porridge, making Easter
pudding/
frying pancakes/
macaroni casserole, meat-balls,
cabbage rolls, summer soup,
carrot casserole, rutabaga
casserole/
preparing ham.
Words total: 15
Words total: 7
Words total: 14
No of clusters: 4
No of clusters: 2
No of clusters: 3
Correct words: 4/7
Correct words: 5/14
Intrusions: 6/14
Perseverations: 3/7
240
Appendix 4
Appendix 4F. Examples of the semantic fluency performance
given by a participant in each subject group: playing sports
NC19
miAD37
moAD55
Finnish:
Finnish:
Finnish:
juosta, kävellä, hypätä/
keilailla, pelata pesäpalloa/
uida, purjehtia, soutaa/
nyrkkeillä, painia/
hiihtää, mäkihypätä,
syöksylaskea/
autourheilu.
juosta/
työntää kuulaa/
hypätä korkeutta/
uida, sukeltaa, sukeltaa/
potkukelkkailla, luistella/
sukeltaa, syvyyssukellus.
juosta/
kylpeä/
potkia, hyppiä.
English:
English:
run, walk, jump/
bowl, play baseball/
swim, sail, row/
box, wrestle/
ski, ski jump, downhill skiing/
motorsports.
run/
shot put/
high jump/
swim, dive, dive/
kick-sled, skate/
dive, deep-water diving.
English:
run/
bath/
kick, jump.
Words total: 14
Words total: 10
Words total: 4
No of clusters: 5
No of clusters: 3
No of clusters: 1
Correct words: 8/10
Correct words: 3/4
Intrusions: 1/4
Semantically related intr. Semantically unrelated intr. 1
Perseverations: -
Perseverations: -
Appendix 4
241
Appendix 4G. Examples of the semantic fluency performance
given by a participant in each subject group: construction
NC3
MiAD41
moAD56
Finnish:
Finnish:
perustan kaivaminen, valu/
seinän pystytys, kattaminen/
maalaaminen, laatottaminen,
tapetoiminen/
kalustaminen.
Finnish:
höylää, hakkaa, höylää, hakkaa/
nostaa niitä tavaroita, nostaa,
nostaa/
nauloja tietysti hakataan sinne,
höylääminen.
English:
English:
English:
digging the foundation, pour/
putting up the walls, to roof/
painting, tiling, wallpapering/
furnishing.
plane, hit, plane, hit/
lift those things, lift, lift/
nails of course are hit there,
planing down.
boards, nails, a hammer, a saw,
an axe are needed/
hi---/
[he or she] paints.
Words total: 8
Words total: 9
Words total: 6
No of clusters: 3
No of clusters: 2
No of clusters: 1
Correct words: 8/8
Correct words: 3/9
Correct words: 1/6
Intrusions: 5/6
Perseverations: -
Perseverations: 6/9
Perseverations: -
siellä tarvitaan lautoja, nauloja,
vasaraa, sahaa, kirvestä/
hak---/
maalaa.
242
Appendix 4
Appendix 4H. Examples of the semantic fluency performance
given by a participant in each subject group: cleaning up
NC26
MiAD39
moAD65
Finnish:
Finnish:
Finnish:
pestä, imuroida, laasta, luututa/
tomuttaa, tampata/
pyyhkiä.
lakaista, luututa/
pölyjä pyyhkiä/
mattoja tampata/
pestä, huuhdella/
soittaminen/
verhoja vois laittaa vaikka ja
nuita koristeita tuonne seinälle tai
no kuuluuko se nyt sitte, tauluja
voi laittaa.
ämpäri, pesuvati/
pestä, pestä/
lakaista/
pe- niin pesu tuli jo/
lakaista/
kuurata/
siivota se on puhdistukseen/
ikkunan pesua, pestä ikkunoita.
English:
English:
English:
wash, vacuum, sweep,
wash the floor/
dust, beat/
wipe.
sweep, wash the floor/
dust/
beat the carpets/
wash, rinse/
playing/
curtains could be put up and the
decorations on the walls or does
it belong now there, pictures
could be put.
bucket, basin/
wash, wash/
sweep/
wa- yes washing came already/
sweep/
scrub/
to clean up it is to the cleaning/
washing windows, wash
windows.
Words total: 7
Words total: 10
Words total: 11
No of clusters: 2
No of clusters: 3
No of clusters: 1
Correct words: 7/7
Correct words: 6/10
Correct words: 5/11
Intrusions: 4/10
Intrusions: 2/11
Perseverations: -
Perseverations: -
Semantic subcategoires: 1
2b. Differences in the number of correct
words between different categories
2a. Number of correct nouns
U = 70.0, p < .001
U = 30.0, p < .001
U = 134.5, p < .001
U = 122.5, p < .001
U = 112.0, p < .001
Vehicles
Animals
All categories
U = 48.0, p < .001
U = 8.0, p < .001
U = 78.0, p < .001
U = 75.0, p < .001
Animals
All categories
|39.6 – 59.4|= 19.8 (*)
Appendix continues
n = 20, k = 4:
n = 20, k = 4:
(*) = p ≈ .05
(*) = p ≈ .05
21.3 < w4 < 21.59, p < .05 = * 21.3 < w4 < 21.59, p < .05 = *
|31.6 – 66.6| = 35*
n = 30, k = 4:
w4 ≥ 26.09, p < .05 = *
Vegetables – Animals |42.6 – 111.9| = 69.3*
|58.0 – 59.4| = 1.4, n.s.
|39.6 – 43.0| = 3.4, n.s.
|64.4 – 66.6|= 2.2, n.s.
|31.6 – 37.6| = 6, n.s.
|84.9 – 111.9| = 27.0*
Clothes – Animals
|58.0 – 39.6| = 18.4 (*)
|58.0 – 43.0| = 15.0, n.s.
|64.4 – 31.6| = 32.8*
Friedman’ s post hoc
pair-wise comparisons
moAD group
U = 60.5, p < .001
U = 68.5, p < .001
U = 90.5, p < .001
U = 100.0, p < .001
U = 68.5, p < .001
U = 85.5, p < .002
U = 93.0, p < .004
U = 114.5, p < .020
U = 127.0, p < .047
U = 79.0, p < .001
Mann-Whitney U test
moAD vs. miAD
|64.4 – 37.6| = 26.8*
Vegetables – Vehicles |42.6 – 60.6| = 18.0, n.s.
|84.9 – 42.6| = 42.3*
|84.9 – 60.6| = 24.3, n.s.
Clothes – Vegetables
miAD group
U = 8.5, p < .001
NC group
Clothes – Vehicles
Paired categories
U = 12.0, p < .001
U = 88.0, p < .001
Vehicles
U = 10.0, p < .001
U = 158.0, p < .01
U = 99.5, p < .001
Clothes
Vegetables
U = 28.0, p < .001
U = 22.5, p < .001
U = 40.0, p < .001
U = 179.0, p < .016
U = 137.0, p < .001
Clothes
1. Total number of words
NC vs. moAD
Mann-Whitney U test
NC vs. miAD
Mann-Whitney U test
Vegetables
Category
Variable
Appendix 5. Results of the post-hoc pair-wise analyses of the noun fluency tasks
Appendix 5
243
7. Proportion of correct nouns
6. Proportion of words in clusters
5. Cluster size
4. Number of clusters
U = 124.5, p < .001
All categories
U = 163.0, p < .004
U = 171.0, p < .007
U = 152.0, p < .003
Vehicles
Animals
All categories
U = 184.0, p < .015
U = 231.0, p = .160, n.s.
Clothes
Vegetables
U = 139.0, p < .001
All categories
U = 155.0, p < .004
U = 204.5, p < .05
Clothes
All categories
U = 104.0, p < .001
All categories
U = 209.5, p = .073, n.s
U = 145.0, p < .002
Animals
Clothes
U = 177.0, p < .013
U = 89.5, p < .001
Vegetables
Vehicles
U = 202.0, p < .049
U = 178.0, p < .014
Animals
Clothes
U = 182.0, p < .018
U = 123.5, p < .001
Vegetables
Vehicles
U = 59.0, p < .001
U = 133.0, p<.001
U = 118.5, p<.001
U = 155.0, p < .004
U = 119.5, p < .001
U = 159.0, p < .005
U = 166.0, p < .007
U = 200.5, p < .049
U = 171.5, p < .01
U = 11.5, p < .001
U = 48.0, p < .001
U = 33.5, p < .001
U = 64.5, p < .001
U = 54.0, p < .001
U = 47.0, p < .001
U = 61.0, p < .001
U = 92.5, p < .001
U = 70.0, p < .001
U = 91.5, p < .001
Mann-Whitney U test
U = 235.0, p = .192, n.s.
Mann-Whitney U test
Clothes
3. Number of switches
NC vs. moAD
NC vs. miAD
Category
Variable
U = 105.0, p < .009
U = 139.0, p = .102, n.s.
U = 157.0, p = .253, n.s.
U = 161.0, p = .301, n.s.
U = 137.5, p = .091, n.s.
U = 169.5, p = .409, n.s.
U = 164.5, p = .333, n.s.
U = 183.5, p = .659, n.s.
U = 154.5, p = .218, n.s.
U = 70.0, p < .001
U = 102.0, p < .007
U = 108.0, p < .010
U = 118.0, p < .021
U = 73.5, p < .001
U = 75.0, p < .001
U = 106.5, p < .010
U = 116.0, p < .022
U = 101.5, p < .006
U = 82.5, p < .001
Mann-Whitney U test
moAD vs. miAD
Appendix continues
244
Appendix 5
Note. n.s. = nonsignificant
U = 174.0, p < .013
U = 180.0, p < .017
U = 204.5, p < .059, n.s.
Vehicles
Animals
All categories
U = 183.0, p < .020
U = 285.0, p = .766, n.s.
Vehicles
Vegetables
U = 150.0, p < .002
Animals
12. Degree of prototypicality of nouns
U = 158.0, p < .004
U = 113.0, p < .001
Vegetables
Vehicles
U = 83.0, p < .001
U = 197.0, p < .037
All categories
Clothes
U = 255.5, p = .754, n.s.
Animals
U = 103.0, p <.001
U = 259.5, p =.821, n.s
All categories
Clothes
13. Degree of frequency of nouns
11. Number of different semantic subcategories
10. Strategies: semantic strategy
U = 180.0, p < .009
U = 181.0, p < .012
Vehicles
Animals
U = 224.0, p = .097, n.s.
Vegetables
U = 277.0, p = .543, n.s.
U = 133.0, p < .001
Clothes
U = 156.5, p < .004
U = 205.0, p = .060, n.s.
U = 122.5, p < .001
U = 155.5, p < 004
U = 174.5, p < .013
U = 41.5, p < .001
U = 42.5, p < .001
U = 78.0, p < .001
U = 67.5, p < .001
U = 8.0, p < .001
U = 126.0, p < .016
U = 118.5, p < .008
U = 71.0, p <.001
U = 133.0, p < .001
U = 136.0, p < .001
U = 157.5, p < .002
U = 232.0, p = .072, n.s.
U = 192.5, p < .015
Mann-Whitney U test
Mann-Whitney U test
Clothes
NC vs. moAD
NC vs. miAD
8. Proportion of intrusions
Category
9. Proportion of perseverations
Variable
U = 169.0, p = .402, n.s.
U = 189.0, p = .766, n.s.
U = 157.0, p = .253, n.s.
U = 138.5, p = .096, n.s.
U = 168.0, p = .398, n.s.
U = 82.0, p < .001
U = 116.0, p < .023
U = 121.0, p < .024
U = 94.5, p < .003
U = 75.5, p < .001
U = 82.0, p < .050
U = 83.5, p < .045
U = 125.5, p <.044
U = 139.0, p = .093, n.s.
U = 151.0, p = .175, n.s.
U = 144.5, p = .121, n.s.
U = 144.5, p = .108, n.s.
U = 119.0, p < .028
Mann-Whitney U test
moAD vs. miAD
Appendix 5
245
2b. Differences in the number of correct
words between different categories
2a. Number of correct verbs
U = 148.5, p < .003
Playing sports
U = 128.5, p < .001
| 96.6 – 78.6 | = 18.0, n.s.
| 78.6 – 53.1| = 25.5 (*)
Playing sports – Construction
Construction – Cleaning
n = 30, k = 4:
(*) = p ≈ .05
w4 = 26.09, p < .05 = *
| 72.0 – 78.6 | = 6.6, n.s.
| 72.0 – 53.1| = 18.9, n.s.
Preparing food – Construction
Preparing food – Cleaning
Preparing food – Playing sports | 72.0 – 96.6 | = 24.6 (*)
NC group
All categories
Paired categories
U = 132.5, p < .001
U = 228.0, p = .149, n.s.
Construction
Cleaning
U = 108.5, p < .001
Playing sports
U = 163.5, p < .007
U = 172.0, p < .011
All categories
Preparing food
U = 142.5, p < .002
U = 275.5, p = .625, n.s.
Construction
Cleaning
Mann-Whitney U test
U = 17.5, p < .001
U = 62.5, p < .001
U = 51.5, p < .001
U = 36.4, p < .001
U = 18.0, p < .001
U = 43.5, p < .001
U = 62.5, p < .001
U = 68.5, p < .001
U = 50.5, p < .001
U = 98.5, p < .001
Mann-Whitney U test
U = 239.5, p = .228, n.s.
Preparing food
1. Number of total words
NC vs. moAD
NC vs. miAD
Category
Variable
Appendix continues
U = 84.0, p < .001
U = 78.5, p < .001
U = 115.0, p < .021
U = 86.0, p < .002
U = 78.0, p < .001
U = 96.5, p < .005
U = 83.5, p < .002
U = 120.5, p < .030
U = 89.5, p < .003
U = 109.5, p < .014
Mann-Whitney U test
moAD vs. miAD
Appendix 6. Results of the post-hoc pair-wise analyses of the verb fluency tasks
246
Appendix 6
U = 288.0, p = .774, n.s.
U = 140.0, p < .001
U = 185.5, p < .018
U = 149.0, p < .003
Playing sports
Construction
All categories
U = 151.5, p < .002
Preparing food
Concrete nouns
8. Proportion of correct verbs
U = 246.5, p = .217, n.s.
General verbs
U = 298.5, p = .976, n.s.
U = 260.0, p = .427, n.s.
Preparing food
Specific verbs
6. Proportion of words in clusters
U = 294.0, p =.905, n.s.
U = 282.0, p =.721, n.s
Preparing food
U =162.0, p < .006
All categories
Cleaning
U = 192.0, p < .028
U = 249.0, p = .288, n.s.
Construction
Cleaning
U = 215.5; p = .078, n.s.
U = 180.5, p < .015
Preparing food
Playing sports
U = 133.0, p < .001
All categories
7. Word forms
5. Cluster size
4. Number of clusters
U = 102.0, p < .001
U = 294.5, p = .912, n.s.
Construction
Cleaning
U = 216.0, p = .091, n.s.
U = 128.5, p < .001
Preparing food
3. Number of switches
U = 106.5, p < .001
U = 177.0, p < .037
U = 144.5, p < .002
U = 105.5, p < .001
U = 200.0, p < .027
U = 175.0, p < .006
U = 180.0, p < .017
U = 165.5, p < .008
U = 171.0, p <.010
U = 154.0, p <.004
U = 29.0, p < .001
U = 127.0, p < .001
U = 86.5, p < .001
U = 93.0, p < .001
U = 49.5, p < .001
U = 57.5, p < .001
U = 158.5, p < .004
U = 58.5, p < .001
U = 46.0, p < .001
U = 132.5, p < .001
NC vs. moAD
Mann-Whitney U test
NC vs. miAD
Mann-Whitney U test
Playing sports
Category
Variable
U = 178.5, p < .065, n.s.
U = 170.5, p = .784, n.s.
U = 160.0, p = .411, n.s.
U = 121.5, p = .087, n.s.
U = 129.0, p < .033
U = 148.0, p = .137, n.s.
U = 148.0, p = .165
U = 126.0, p < .044
U = 143.0, p =.121, n.s.
U = 122.0, p <.032
U = 80.5, p < .001
U = 117.5, p < .018
U = 116.5, p < .018
U = 124.5, p < .033
U = 75.0, p < .001
U = 104.5, p < .01
U = 111.5, p < .015
U = 116.5, p < .020
U = 118.0, p < .024
U = 130.0, p < .050
Mann-Whitney U test
moAD vs. miAD
Appendix continues
Appendix 6
247
12. Degree of frequency of verbs
11. Number of different semantic subcategories
10. Proportion of perseverations
U = 228.0, p = .276, n.s
U = 155.5, p < .002
U = 109.0, p < .001
Construction
All categories
U = 232.0, p = .178, n.s.
U = 236.0, p = .180, n.s.
U = 151.0, p < .003
Cleaning
All categories
All categories
U = 130.0, p < .001
U = 173.0, p < .009
U = 180.0, p < .014
Playing sports
Construction
U = 224.5, p = .135, n.s.
U = 38.0, p < .001
U = 117.5, p < .001
U = 97., p < .001
U = 207.0, p < .05
Preparing food
U = 52.5, p < .001
U = 144.5, p < .002
U = 245.5, p = .356, n.s.
U = 211.0, p = .065, n.s.
U = 170.0, p < .005
U = 146.0, p < .006
U =160.5, p < .004
Preparing food
U = 244.5, p = .236, n.s.
All categories
U = 165.0, p < .001
U =160.5, p < .001
Playing sports
U = 225.0, p < .004
U = 223.0, p < .011
Preparing food
9. Proportion of intrusions
NC vs. moAD
Mann-Whitney U test
NC vs. miAD
Mann-Whitney U test
Playing sports
Category
Variable
U = 116.5, p < .024
U = 92.0, p < .003
U = 117.0, p < .017
U = 125.5, p < .034
U = 142.0, p = .097, n.s.
U = 75.5, p < .001
U = 186.5, p = .715, n.s.
U = 128.5, p = .114, n.s.
U = 144.5, p = .178, n.s.
U = 148.5, p = .349, n.s.
U = 125.5, p < .043
U = 152.5, p = .426, n.s.
U = 139.0, p = .102, n.s.
Mann-Whitney U test
moAD vs. miAD
248
Appendix 6
Appendix 7
249
Appendix 7. Results of the post-hoc pair-wise analyses of the control tasks
Test
NC vs. miAD
NC vs. moAD
miAD vs. moAD
Mann-Whitney U test
Mann-Whitney U test
Mann-Whitney U test
Boston Naming Test
U = 82.5, p < .001
U = 27.0, p < .001
U = 111.0, p < .015
Naming, nouns
U = 240.5, p = .132, n.s. U = 134.5, p < .001
U = 118.0, p < .026
Naming, verbs
U = 137.0, p < .001
U = 31.0, p < .001
U = 75.0, p < .001
Serial naming, nouns
U = 189.0, p < .001
U = 110.5, p < .001
U = 128.5, p < .052
Serial naming, verbs
U = 163.0, p < .001
U = 17.0, p < .001
U = 76.5, p < .001
Digit span forwards
U = 164.0, p < .005
U = 58.5, p < .001
U = 118.0, p < .026
Token test
U = 106.5, p < .001
U = 25.0, p < .001
U = 68.0, p < .001
Category recognition, nouns
U = 300.0, p = 1.0
U = 225.0, p < .014
U = 150.0, p = .183, n.s.
Category recognition, verbs
U = 270.0, p = .08
U = 195.0, p < .002
U = 147.5, p = .157, n.s.
Verbal tests
Non-verbal tests
In-category recognition, nouns U = 288.0, p = .613, n.s. U = 165.0, p < .001
U = 125.0, p < .043
In-category recognition, verbs
U = 253.5, p = .244, n.s. U = 12.5, p < .001
U = 57.0, p < .001
Card sorting, nouns
U = 161.5, p < .001
U = 38.5, p < .001
U = 77.5, p < .001
Card sorting, verbs
U = 98.0, p < .001
U = 2.0, p < .001
U = 64.0, p < .001

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