Neural network modeling of developmental effects in discrimination shifts

This paper presents neural network simulations of developmental phenomena i n 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 thoro ugh account is highlighted. Recent unsuccessful neural network simulations of shift learning are also reviewed. New simulations, using the cascade-cor relation 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 affec ts depth of learning, simulates developmental phenomena. It is suggested th at human developmental differences in shift learning arise from spontaneous overtraining by older participants, an interpretation consistent with the overtraining literature. (C) 1998 Academic Press.