Neural Network Simulation of Infant Familiarization to Artificial Sentences: Rule-Like Behavior Without Explicit Rules and Variables.

A fundamental issue in cognitive science is whether human cognitive processing is better explained by symbolic rules or by subsymbolic neural networks. A recent study of infant familiarization to sentences in an artificial language seems to have produced data that can only be explained by symbolic rule learning and not by unstructured neural networks (Marcus, Vijayan, Bandi Rao, & Vishton, 1999). Here we present successful unstructured neural network simulations of the infant data, showing that these data do not uniquely support a rule-based account. In contrast to other simulations of these data, these simulations cover more aspects of the data with fewer assumptions about prior knowledge and training, using a more realistic coding scheme based on sonority of phonemes. The networks show exponential decreases in attention to a repeated sentence pattern, more recovery to novel sentences inconsistent with the familiar pattern than to novel sentences consistent with the familiar pattern, occasional familiarity preferences, more recovery to consistent novel sentences than to familiarized sentences, and extrapolative generalization outside the range of the training patterns. A variety of predictions suggest the utility of the model in guiding future psychological work. The evidence, from these and other simulations, supports the view that unstructured neural networks can account for the existing infant data.