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Biased stochastic learning in computational model of category learning

By Toshihiko Matsuka

Abstract

Matsuka and Corter (2003b) presented evidence that people tend to utilize only the minimally necessary information for classification tasks. This approach for categorization was efficient and valid for the stimulus set used in the experiment, but might be considered a statistically or mathematically nonnormative approach. In the present paper, I hypothesized human category learning processes are biased toward simpler representation and/or conception rather than complex but normative ones. In particular, a few variants of “biased” learning algorithms are introduced and applied to Matsuka and Corter’s stochastic learning algorithm (2003a, 2004). The result of a simulation study showed that the biased learning models account for empirical results successfully

Year: 2008
OAI identifier: oai:CiteSeerX.psu:10.1.1.134.304
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