title: Predicting shifts in generalization gradients with perceptrons.
abstract: Perceptron models have been used extensively to model perceptual learning and the effects of discrimination training on generalization, as well as to explore natural classification mechanisms. Here, we assess the ability of existing models to account for the time course of generalization shifts that occur when individuals learn to distinguish sounds. A set of simulations demonstrates that commonly used single-layer and multilayer perceptron networks do not predict transitory shifts in generalization over the course of training but that such dynamics can be accounted for when the output functions of these networks are modified to mimic the properties of cortical tuning curves. The simulations further suggest that prudent selection of stimuli and training criteria can allow for more precise predictions of learning-related shifts in generalization gradients in behavioral experiments. In particular, the simulations predict that individuals will show maximal peak shift after different numbers of trials, that easier contrasts will lead to slower development of shifted peaks, and that whether generalization shifts persist or dissipate will depend on which stimulus dimensions individuals use to distinguish stimuli and how those dimensions are neurally encoded.
authors: Department of Psychology, Park Hall, University at Buffalo, The State University of New York, Buffalo, NY 14260, USA. mgw@buffalo.edu
authors: Matthew G
authors: MG
authors: Wisniewski
authors: Milen L
authors: ML
authors: Radell
authors: Lauren M
authors: LM
authors: Guillette
authors: Christopher B
authors: CB
authors: Sturdy
authors: Eduardo
authors: E
authors: Mercado
mesh_terms: D001288:Attention; D003198:Computer Simulation; D004193:Discrimination Learning; D004192:Discrimination, Psychological; D005793:Generalization, Psychological; D006801:Humans; D008959:Models, Neurological; D016571:Neural Networks, Computer