Common Mechanisms in Infant and Adult Category Learning

Computational models of infant categorization often fail to elaborate the transitional mechanisms that allow infants to achieve adult performance. In this article, we apply a successful connectionist model of adult category learning to developmental data. The Supervised and Unsupervised Stratified Adaptive Incremental Network (SUSTAIN) model is able to account for the emergence of infants' sensitivity to correlated attributes (e.g., has wings and can fly). SUSTAIN offers 2 complimentary explanations of the developmental trend. One explanation centers on memory storage limitations, whereas the other focuses on limitations in perceptual systems. Both explanations parallel published findings concerning the cognitive and sensory limitations of infants. SUSTAIN's simulations suggest that conceptual development follows a continuous and smooth trajectory despite qualitative changes in behavior and that the mechanisms that underlie infant and adult categorization might not differ significantly.