Neural network modeling of developmental effects in discrimination shifts.
This paper presents neural network simulations of developmental phenomena in discrimination shifts. The discrimination shift literature is reviewed in order to identify the empirical regularities. Leading theoretical accounts of the development of shift learning are reviewed, and the lack of a thorough account is highlighted. Recent unsuccessful neural network simulations of shift learning are also reviewed. New simulations, using the cascade-correlation algorithm, show that networks can capture the regularities of the discrimination shift literature better than existing psychological theories. Manipulation of the amount of training that networks receive, which affects depth of learning, simulates developmental phenomena. It is suggested that human developmental differences in shift learning arise from spontaneous overtraining by older participants, an interpretation consistent with the overtraining literature.
Sirois
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McGill University, Montréal, Quebec, Canada. sirois@psych.mcgill.ca
Shultz
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D000328:Adult; D000367:Age Factors; D000704:Analysis of Variance; D002648:Child; D002657:Child Development; D002675:Child, Preschool; D004193:Discrimination Learning; D006801:Humans; D008960:Models, Psychological; D016571:Neural Networks, Computer; D010056:Overlearning