Proceedings of the Annual Meeting of the Cognitive Science Society

In a classic experiment, Saffran, Aslin, and Newport (1996)used a headturn preference procedure to show that infants candiscriminate between familiar syllable sequences (“words”)and new syllable sequences (“non-words” and “part-words”).While several computational models have simulated aspects oftheir data and proposed that the learning of transitional prob-abilities could be mediated by neural-net or chunking mech-anisms, none have simulated the absolute values of infants’listening times in the different experimental conditions. In thispaper, we used CHREST, a model based on chunking, to sim-ulate these listening times. The model simulated the fact thatinfants listened longer to novel words (non-words and part-words) than familiar words. While the times observed with themodel were longer than those observed with infants, we makea novel finding with regard to phonological store trace decay.We also propose how to modify CHREST to produce data thatfits closer to the human data.