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.