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Stochastic learning algorithm for modeling human category learning

By Toshihiko Matsuka and James E. Corter

Abstract

learning use a gradient-based learning method, which assumes that locally-optimal changes are made to model parameters on each learning trial. This method tends to underpredict variability in individual-level cognitive processes. In addition many recent models of human category learning have been criticized for not being able to replicate rapid changes in categorization accuracy and attention processes observed in empirical studies. In this paper we introduce stochastic learning algorithms for NN models of human category learning and show that use of the algorithms can result in (a) rapid changes in accuracy and attention allocation, and (b) different learning trajectories and more realistic variability at the individual-level

Topics: cognitive modeling, radial basis function, stochastic optimization
Year: 2004
OAI identifier: oai:CiteSeerX.psu:10.1.1.308.959
Provided by: CiteSeerX
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