A Neural Network Model of the Effect of Prior Experience with Regularities on Subsequent Category Learning

AbstractA popular dual systems theory of category learning argues that the structure of categories in perceptual space determines the mechanisms that drive learning. However, less attention has been paid to the nature of the perceptual dimensions defining the categories. Researchers typically assume that there is a direct, linear relationship between experimenter-defined physical input dimensions and learners’ psychological dimensions, but this assumption is not always warranted. Through a set of simulations, we demonstrate that, based on the nature of prior experience, the psychological representations of experimenter-defined dimensions can place drastic constraints on category learning. We compare the model’s behavior to several human studies and make conclusions regarding the nature of the psychological representations of the dimensions in those studies. These simulations support the conclusion that the nature of psychological representations is a critical aspect to understanding the mechanisms that drive category learning.

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