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An associative model of geometry learning: A modified choice rule.
Authors:Miller, Noam Y.   Shettleworth, Sara J.
Abstract:In a recent article, the authors (Miller & Shettleworth, 2007; see record 2007-09968-001) showed how the apparently exceptional features of behavior in geometry learning ("reorientation") experiments can be modeled by assuming that geometric and other features at given locations in an arena are learned competitively as in the Rescorla-Wagner model and that the probability of visiting a location is proportional to the total associative strength of cues at that location relative to that of all relevant locations. Reinforced or unreinforced visits to locations drive changes in associative strengths. Dawson, Kelly, Spetch, and Dupuis (2008; see record 2008-09669-009) have correctly pointed out that at parameter values outside the ranges the authors used to simulate a body of real experiments, our equation for choice probabilities can give impossible and/or wildly fluctuating results. Here, the authors show that a simple modification of the choice rule eliminates this problem while retaining the transparent way in which the model relates spatial choice to competitive associative learning of cue values. (PsycINFO Database Record (c) 2010 APA, all rights reserved)
Keywords:reorientation task   geometric cues   feature cues   artificial neural networks   operant choice   spatial learning   geometric module   Rescorla-Wagner model   associative learning   conditioned inhibition
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