berkes janos

ERP correlates of implicit probabilistic sequence learning
Andrea KÓBOR1, Ádám TAKÁCS2, Karolina JANACSEK2,3, Zsófia KARDOS3,4, Brigitta TÓTH4, Csenge TÖRÖK2,3, Zsófia ZAVECZ2,3,
Márk MOLNÁR3,2, Dezso NEMETH2,3
1: Brain Imaging Centre, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Budapest, Hungary
2: Institute of Psychology, Eötvös Loránd University, Budapest, Hungary
3: Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Budapest, Hungary
4: Department of Cognitive Science, Budapest University of Technology and Economics, Budapest, Hungary
Memory and
Language Lab
e-mail: [email protected] web: nemethlab.com
BACKGROUND & AIM
Behavioral correlates of statistical learning
in the Learning Phase and Testing Phase
P
r
2
1 2
3 4
P
r
1
1 2
3 4
Numbers denote arrow directions
P
r
3
1 2
3 4
P
r
3
1 2
3 4
Instruction:
4 Find the pattern of black arrows!
Structure:
3–r–4
Structure:
r–2–r
General skill improvements:
General increase in speed
3 – 1 – 4 (50%)
3 – 1 – 4 (12.5%)
never occurring
(always high)
3 – 1 – 1 (12.5%)
3 – 1 – 2 (12.5%)
3 – 1 – 3 (12.5%)
P
r
4
1 2
3 4
P
r
2
1 2
3 4
3–1–4
3–1–2
low frequency high frequency
triplet (P-r-P)
triplet (r-P-r)
High frequency
triplets
(62.5 % of all trials)
METHODS & DESIGN
 Participants: healthy young adults; N = 20 (age: M = 21 years,
SD = 1.88; 5 males, 15 females)
 Explicit version of the Alternating Serial Reaction Time
(ASRT) taskref 2 with arrow stimuli
 EEG recording: 64 Ag/AgCl electrodes, Synamps amplifier,
Neuroscan 4.5, 1000 Hz sampling rate, DC-70 Hz online filter;
0.5-30 Hz (48 dB/oct) bandpass with notch filter at 50 Hz as
offline filter
 ERP and RT analysis:
o ERPs and RTs time-locked to the onset of the stimulus
o Only correctly responded random trials were included
o -200 to 600 ms epochs
o N170: mean activity between 150-200 ms at Oz
o N2: mean activity between 200-350 ms at Fz
o P3: mean activity between 250-350 ms at POz
1← 2↑ 3↓ 4→
Low frequency
triplets
(37.5 % of all trials)
P
r
1
1 2
3 4
P
3–1–4
high frequency
triplet (r-P-r)
Low probability triplets
RT (ms)
Pattern (black) elements
alternated with random (red)
ones
High probability triplets
statistical learning = random low – random high
Because of the sequence structure, some runs of three
consecutive stimuli appear more frequently than others
→ high vs. low probability triplets
The ASRT task was administered in two
sessions:
 Learning Phase and Testing Phase
 Interference period: different repeating
sequence
RT difference (L – H) (ms)
 Probabilistic sequence learning (PSL)ref 1
o Extracts statistical regularities of the environment
o Crucial in perception, predictive processing, skill acquisition
 Different subprocesses of PSL at the behavioral levelref 2
o Explicit, implicit, and more general learning processes
 Aim: To investigate the temporal dynamics of pure statistical
learning using ERPs
ASRT task
blocks
Statistical learning:
The difference between the
RTs for high
and low probability triplets
blocks
RESULTS
General design: TYPE * PERIOD
ERP correlates of statistical learning
in the Learning Phase
* p < .05; ** p < .01
N2
Testing
Learning
380
-2.4
Random High
mean amplitude (µV)
Random Low
24-hour delay
370
RT (ms)
360
350
340
**
*
-1.8
-1.2
-0.6
0.0
330
Period 1
320
1
2
3
4
5
Period 2
Period 3
Main effect of TYPE:
F(1, 19) = 8.17, p = .010, ηp2 = .301
TYPE * PERIOD interaction:
F(2, 38) = 2.33, p = .111, ηp2 = .109
6
Periods
Learning Phase
Main effect of TYPE:
F(1, 19) = 43.20, p < .001, ηp2 = .695
Main effect of PERIOD:
F(2, 38) = 16.14, p < .001, ηp2 = .459
P3
3.5
mean amplitude (µV)
Testing Phase
TYPE * PERIOD interaction:
F(2, 38) = 5.52, p = .008, ηp2 = .225
Statistical learning in both Phases:
Differentiating between random high and
random low probability triplets
**
3.0
2.5
2.0
1.5
1.0
0.5
Period 1
Period 2
Period 3
TYPE * PERIOD interaction:
F(2, 38) = 3.47, p = .041, ηp2 = .154
p = .847
N170
16
-2.0
14
12
10
mean amplitude (µV)
24-hour delay
Memory score (ms)
18
8
Period 3
Period 4
Consolidation:
Retained statistical memory after 24 hours:
Similar performance at the end of the Learning
Phase and at the beginning of the Testing Phase
*
-1.6
-1.2
-0.8
-0.4
0.0
Period 1
Period 2
Period 3
TYPE * PERIOD interaction:
F(2, 38) = 7.62, p = .002, ηp2 = .286
DISCUSSION
1. Although the temporal regularity between non-adjacent trials was unknown to participants, pure statistical learning was found both at the behavioral and neural levels
o Lower probability stimuli → slower RT, enhanced ERP responses → higher cognitive load for unpredicted stimuliref 3
o Separable explicit and implicit learning processes: detection of mismatch with the implicit expectation of the subsequent stimulus (i.e., low probability triplet)ref 3,4,5
2. There might be a switch in the neurocognitive system underlying learning from top-down, more controlled processes to bottom-up, more automatic processes as implicit
statistical memory formation progresses
o While the visual N170 (cf. N170 for words) was sensitive to statistical regularities only at the later stage of learning, the frontal N2 and the P3 reflected the
discrimination and elaborative processing of statistical regularities, respectively, at the earlier stages of learning (first and middle periods)
REFERENCES
This research was supported by the KTIA NAP 13-2-2015-0002 (DN), Postdoctoral Fellowship of the Hungarian
Academy of Sciences (AK, BT), and János Bolyai Research Fellowship of the Hungarian Academy of Sciences (KJ).
1. Fiser, J., Berkes, P., Orban, G., & Lengyel, M. (2010). Statistically optimal perception and learning: From behavior to neural representations. Trends in Cognitive Sciences, 14(3), 119-130.
2. Nemeth, D., Janacsek, K., & Fiser, J. (2013). Age-dependent and coordinated shift in performance between implicit and explicit skill learning. Frontiers in Computational Neuroscience, 7, 147.
3. Koelsch, S., Busch, T., Jentschke, S., & Rohrmeier, M. (2016). Under the hood of statistical learning: A statistical MMN reflects the magnitude of transitional probabilities in auditory sequences. Scientific Reports, 6, 19741.
4. Eimer, M., Goschke, T., Schlaghecken, F., & Stürmer, B. (1996). Explicit and implicit learning of event sequences: evidence from event-related brain potentials. Journal of Experimental Psychology: Learning, Memory, and Cognition, 22(4), 970-987.
5. Fu, Q., Bin, G., Dienes, Z., Fu, X., & Gao, X. (2013). Learning without consciously knowing: evidence from event-related potentials in sequence learning. Consciousness and Cognition, 22(1), 22-34.