This study is aimed at detecting factors influencing perceptual feature creation. By teaching several new perceptual categories, we demonstrate the emergence of new internal representations. We focus on contrasting the role of two basic factors that govern feature creation: the informative value and the degree of parsimony of the feature set. Two methods of exploring the structure of internal features are developed using an artificial neural network. These methods were empirically implemented in two experiments, both demonstrating a preference for parsimonious internal representations, even at the expense of the informative value of the feature. Our results suggest that feature parsimony is maintained not only to optimize the resource management of the perceptual system but also to aid future category learning.
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