Shultz, T. R. (1999). Rule learning by habituation can be simulated in neural networks. Proceedings of the Twenty-first Annual Conference of the Cognitive Science Society (pp. 665-670). Mahwah, NJ: Erlbaum.
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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