“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= 0.6212 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= 0.465 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= 0.465 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. 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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