  In human cognition, the expansion of perceived between-category distances and
compression of within-category distances is known as categorical perception
(CP). There are several hypotheses about the causes of CP (e.g., language,
learning, evolution) but no functional model. Whether CP is essential to
categorisation or simply a by-product of it is not yet clear, but evidence is
accumulating that CP can be induced by category learning. We provide a model
for learning-induced CP as expansion and compression of distances in
hidden-unit space in neural nets. Basic conditions from which the current model
predicts CP are described, and clues as to how these conditions might
generalize to more complex kinds of categorization begin to emerge.
