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A Neural Network Model of the Effect of Prior Experience with Regularities onSubsequent Category Learning
Abstract
A popular dual systems theory of category learning argues thatthe structure of categories in perceptual space determines themechanisms that drive learning. However, less attention hasbeen paid to the nature of the perceptual dimensions definingthe categories. Researchers typically assume that there is adirect, linear relationship between experimenter-definedphysical input dimensions and learners’ psychologicaldimensions, but this assumption is not always warranted.Through a set of simulations, we demonstrate that, based on thenature of prior experience, the psychological representations ofexperimenter-defined dimensions can place drastic constraintson category learning. We compare the model’s behavior toseveral human studies and make conclusions regarding thenature of the psychological representations of the dimensionsin those studies. These simulations support the conclusion thatthe nature of psychological representations is a critical aspectto understanding the mechanisms that drive category learning.
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