| Abstract: | This article presents a connectionist model of correlation‐based categorization by 10‐month‐old infants (Younger, 1985). Simple autoencoder networks were exposed to the same stimuli used to test 10‐month‐olds. The familiarization regime was kept as close as possible to that used with the infants. The performance of the model matched that of the infants. Both infants and networks used covariation information (when available) to segregate items into separate categories. The model provides a mechanistic account of category learning within a test session. It demonstrates how categorization arises as the product of an inextricable interaction between the subject (the infant) and the environment (the stimuli). The computational characteristics of both subject and environment must be considered in conjunction to understand the observed behaviors. |
