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. One of the fundamental issues of cognitive science continues to revolve around which type of theoretical model better accounts for human cognition -- a symbolic rulebased account or a sub-symbolic neural network account. A recent study of infant habituation to expressions in an artificial language claims to have struck a damaging blow to the neural network approach (Marcus, Vijayan, Rao, & Vishton, 1999). The results of their study show that 7month-old infants attend longer to sentences with unfamiliar structures than to sentences with familiar structures. Because of certain features of their experimental design and their own unsuccessful neural network models, Marcus et al. conclude that neural networks cannot simulate these results and that infants possess a rule -learning capability unavailable to neural networks. A companion article suggests that rule learning is an innately provided capacity of the human mind, distinct from associative learning mechanisms like those in neural networks (Pinker, 1999). My paper presents neural network simulations of the key features of the Marcus et al. (1999) experiment, thus showing that their infant data do not uniquely support a rule-based account.