1. “Perceptual changes induced by Category Learning –an ERP study”
F. Pérez Gay Juárez1, H. Sabri2, D. Rivas3, M. Gregory4, N. Botero5, R. Morgan6, S. Harnad7
Departments of Neuroscience1, Cognitive Science4, 5, and Psychology6, McGill University. Laboratoire de Communication et Cognition, UQAM, 1, 2, 3, 4, 5, 6, 7.
Background
Results
Difficulty N
Age (mean)
M/F
%Learners
%Non Learners
%Borderlines
Trials to learn
Easy 25
25.14
9/12
77.3
18.1
4.6
100
Hard 21
22.64
10/15
33.3
47.6
19.1
273
After drawing learning curves, we divided our subjects in Learners and Non Learners according to the Learning Criterion. Those who reached the learned criterion but didn’t sustain the 80% were labeled as “Borderlines” We divided the EEG data:
before and after reaching learning criterion for the learners
First half vs. Second half in both borderlines and non-learners
Our research is on Categorical Perception (CP), a phenomenon in which our categories influence our perception, making members of the same category look more alike –compression- and members of different categories look more different –separation-.
The rainbow phenomenon is an example of inborn CP. Thanks to innate mechanisms in our retinas and brains, we perceive a continuum spectrum of light as discrete color bands.
ERP Grand Averages and scalp topography
Late (parietal and frontal) effects
Early (occipital) effect
Non - Learners
Learners
Learners
Non - Learners
Visible Light
The three kinds of cones in our retinas,
"tuned" to a distinct wavelength response each,
act as “innate category-detectors”
that generate compression and separation.
Wavelenght (in meters)
While inborn CP effects are well acknowledged, through our lives we learn new rules to sort and organize our knowledge of the world. Most of our categories are learned, and it makes sense to affirm that learning may induce changes in our perception (a top-down effect on perceptual processing).
This perception change may be related to neural networks in our brains detecting and highlighting the features that are relevant to sort things into newly learned categories. The question is: how and why?
Correlations
Similarity Judgements
Objectives and Methods
Separation (between-category pairs)
Compression (within-category pairs)
Learners
Non-Learners
Separation and Early ERP Effect
Separation effect and N1 peak
Separation effect and N1 amplitude
r= p= 0.004
To induce Learned Categorical Perception through:
A large set of unfamiliar visual stimuli with discrete features and increasing difficulty.
Training human participants to categorize the stimuli into two categories:
KALAMITES and LAKAMITES through a trial and error task.
-We found significant correlations between both the N1 amplitude and the N1 peak after learning and the separation effect. -No correlations were found between the early ERP effect and the SJ compression effect. -No correlations were found in the Non-Learners group.
r= p= 0.040
n= 14 Non-Learners
n= 21 Learners
Hard difficulty
LPC maximum and % of correct answers
Easy difficulty
LPC and correct answer percentage
3 co-varying (diagnostic) features 3 random features
6 co-varying (diagnostic) features
r= p= 0.040
-We didn’t find any correlation between the LPC and the changes in SJ scores. -We found a significant correlation between the LPC maximum voltage for after learning (and second half for non learners) and the % of correct answers.
We ran rANOVAS for both Compression and Separation effects (first vs second SJ scores) with GROUP as the between-subjects factor. Both compression (F(1, 39) = 4.083, p = .050) and separation (F(1, 39) = 6.340, p = .016) turned out significant.
Conclusions
References
Features present in Kalamites
Features present in Lakamites
Features present in Kalamites
Features present in Lakamites
Learning of a new category can be induced experimentally in a short lab task with unfamiliar stimuli of these characteristics.
Behaviorally, significant compression and separation exist for learners, separation being more important.
Non-learners show only a small separation effect (mere-exposure effect).
Electrophysiological correlates: N1. It could represent an top-down perceptual changes induced by learning. Evidence:
Reduction in Learners, absent in non-learners.
Occipital location.
Correlation with separation effect.
Pattern of in Borderlines is more similar to those of Learners.
5. The LPC rather corresponds for declarative memory processes and certainty of response. Evidence:
Augmentation in Learners, but not in Non-Learners nor in Borderlines.
No correlation with compression or separation effect.
Correlation with percentage of correct answers.
-Kay, P., & Kempton, W. (1984). What is the Sapir‐Whorf hypothesis?. American Anthropologist, 86(1),
-Categorical perception: The groundwork of cognition, Cambridge University Press, New York (1987) pp.
-Kaiser, P. K., & Boynton, R. M. (1996). Human color vision.
-Berlin B & Kay P (1969) Basic color terms: Their universality and evolution. University of California Press, BerkrleyRichler, J. J., & Palmeri, T. J. (2014, January 26). Visual category learning. Wiley Interdisciplinary Reviews: Cognitive Science. doi: /wcs.1268
-Harnad, Stevan (2005) To Cognize is to Categorize: Cognition is Categorization. In, Lefebvre, Claire and Cohen, Henri (eds.) Handbook of Categorization. Summer Institute in Cognitive Sciences on Categorisation, Elsevier.
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-Richler, J. J., & Palmeri, T. J. (2014, January 26). Visual category learning. Wiley Interdisciplinary Reviews: Cognitive Science. doi: /wcs.1268
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-Azizian, a, Freitas, a L., Watson, T. D., & Squires, N. K. (2006). Electrophysiological correlates of categorization: P300 amplitude as index of target similarity. Biological Psychology, 71(3), 278–88. doi: /j.biopsycho
-Gauthier, I., Tarr, M., & Bub, D. (2009). Perceptual expertise: Bridging brain and behavior. Oxford University Press.
-Folstein, J. R., Palmeri, T. J., & Gauthier, I. (2013). Category learning increases discriminability of relevant object dimensions in visual cortex. Cerebral Cortex, 23(4),
-Folstein, J. R., Palmeri, T. J., Van Gulick, A. E., & Gauthier, I. (2015). Category Learning Stretches Neural Representations in Visual Cortex. Current directions in psychological science, 24(1), 17-23
To assess the CP effects through:
Similarity Jugements changes before and after training.
Electrophysiological correlates of successful categorization.
1. Similarity Judgements - 40 pairs (20 KL, 10 KK, 10 LL) How similar are these textures from 1 to 9?
500 ms
1.25 s
750 ms
2. Categorization task (400 trials divided in 4 blocks)
3. Second Similarity Judgements