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Open Access Publications from the University of California

Rule learning by Habituation can be Simulated in Neural Networks

Abstract

Contrary to a recent claim that neural network models are unable to account for data on infant habituation to artificial language sentences, the present simulations show successful coverage with cascade-correlation networks using analog encoding. The results demonstrate that a symbolic rule-based account is not required by the infant data.

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