Shultz, T. R., & Bale, A. C. (2000). Infant familiarization to artificial sentences: Rule-like behavior without explicit rules and variables. Proceedings of the Twenty-second Annual Conference of the Cognitive Science Society (pp. 459-463). Mahwah, NJ: Erlbaum.



A recent study of infant familiarization to artificial sentences claimed to produce data that could only be explained by symbolic rule learning and not by unstructured neural networks. Here we present successful unstructured neural network simulations showing that these data do not uniquely support a rule-based account. In contrast to other neural network simulations, our simulations cover more aspects of the data with fewer assumptions using a more realistic coding scheme based on sonority of phonemes. Our networks show exponential decreases in attention to a repeated sentence pattern, more recovery to novel inconsistent sentences than to novel consistent sentences, some preference reversals, and extrapolation.


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