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) enh ancing 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 learn ing 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.The world is full of things that vary in their similarity and interconfusability.O rganisms must somehow resolve this confusion, sorting and acting upon things adaptively. It might be important, for example, to learn which kinds of mushrooms are poisonous and which are safe to eat, minimising the confusion between them (Greco, Cangelosi & Harnad 1997).