R. Pevtzow & S. Harnad
Abstract In innate Categorical Perception (CP) (e.g., colour perception), similarity space is "warped," with regions of increased within-category similarity (compression) and regions of reduced between-category similarity (separation) enhancing the category boundaries and making categorisation reliable and all-or-none rather than graded. We show that category learning can likewise warp similarity space, resolving uncertainty near category boundaries. Two Hard and two Easy texture learning tasks were compared: As predicted, there were fewer successful Learners with the Hard task, and only the successful Learners of the Hard task exhibited CP. In a second experiment, the Easy task was made Hard by making the corrective feedback during learning only 90% reliable; this too generated CP. The results are discussed in relation to supervised, unsupervised and dual-mode models of category learning and representation.