Learned Categorical Perception (CP) occurs when the members of different categories come to look more dissimilar (between-category separation) and/or members of the same category come to look more similar (within-category compression) after a new category has been learned. To measure learned CP and its physiological correlates we compared dissimilarity judgments and Event Related Potentials (ERPs) before and after learning to sort multi-featured visual textures into two categories by trial and error with corrective feedback. With the same frequency of trials and feedback, about half the participants succeeded in learning the categories (learners: criterion 80\% accuracy) and the rest did not (non-learners). The lower the proportion of features distinguishing category members from non-members (i.e., greater difficulty), the fewer the learners and the more trials it took them to learn. At both lower and higher levels of difficulty, successful learners showed significant between-category separation in pairwise dissimilarity judgments after learning compared to before; their late parietal ERP positivity (LPC, usually interpreted as decisional) also increased and their occipital negativity (N1) (usually interpreted as perceptual) decreased. The LPC of learners increased with response accuracy whereas their N1 amplitude after learning increased with between-category separation  . Non-learners showed no significant changes in dissimilarity judgments, LPC or N1, within or between categories. This is behavioral and physiological evidence that category learning can alter perception. We sketch a neural net model for this effect.
Learned Categorical Perception (CP) occurs when the members of different categories come to look more dissimilar (between-category separation) and/or members of the same category come to look more similar (within-category compression) after a new category has been learned. To measure learned CP and its physiological correlates we compared dissimilarity judgments and Event Related Potentials (ERPs) before and after learning to sort multi-featured visual textures into two categories by trial and error with corrective feedback. With the same number of trials and feedback, about half the participants succeeded in learning the categories (learners: criterion 80\% accuracy) and the rest did not (non-learners). At both lower and higher levels of difficulty, successful learners showed significant between-category separation in pairwise dissimilarity judgments after learning compared to before; their late parietal ERP positivity (LPC, usually interpreted as decisional) also increased and their occipital negativity (N1) (usually interpreted as perceptual) decreased. LPC increased with response accuracy and N1 amplitude decreased with between-category separation for the Learners . Non-learners showed no significant changes in dissimilarity judgments, LPC or N1, within or between categories. This is behavioral and physiological evidence that category learning can alter perception. We sketch a neural net model for this effect.
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frequency
number
D
The lower the proportion of features distinguishing category members from non-members (i.e., greater difficulty), the fewer the learners and the more trials it took them to learn.
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The LPC of learners
LPC
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whereas their
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after learning increased
decreased
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for the Learners