We present a neural network model of category learning that addresses the question of how rules for category membership are acquired. The architecture of the model comprises a set of statistical learning synapses and a set of rule-learning synapses, whose weights, crucially, emerge from the statistical network. The network is implemented with a neurobiologically plausible Hebbian learning mechanism. The statistical weights form category representations on the basis of perceptual similarity, whereas the rule weights gradually extract rules from the information contained in the statistical weights. These rules are weightings of individual features; weights are stronger for features that convey more information about category membership. The most significant contribution of this model is that it relies on a novel mechanism involving feeding noise through the system to generate these rules. We demonstrate that the model predicts a cognitive advantage in classifying perceptually ambiguous stimuli over a system that relies only on perceptual similarity. In addition, we simulate reaction times from an experiment by (Thibaut et al. Proc. 20th Annu. Conf. Cong. Sci. Soc., pg. 1055-1060, 1998) in which both perceptual (i.e., statistical) and rule based information are available for the classification of perceptual stimuli.
['Rosemary A. Cowell', 'Robert M. French']
Noise and the Emergence of Rules in Category Learning: A Connectionist Model