A liquid-state model of variability effects in learning nonadjacent dependencies
A liquid-state model of variability effects in learning nonadjacent dependencies. In L. Carlson, C. Hölscher, & T. Shipley (Eds.
), Proceedings of the 33rd Annual Conference of the Cognitive Science Society
(pp. 897-902). Austin, TX: Cognitive Science Society.
Language acquisition involves learning nonadjacent dependencies
that can obtain between words in a sentence. Several artificial
grammar learning studies have shown that the ability of adults and children to detect dependencies between A and B in frames AXB is influenced by the amount of variation in the X element. This paper presents a model of statistical learning which displays similar behavior on this task and generalizes in a human-like way. The model was also used to predict human behavior for increased distance and more variation in dependencies. We compare our model-based approach with the standard invariance account of the variability effect.