Proceedings of the Annual Meeting of the Cognitive Science Society

Usage-based approaches suggest that language acquisition is a function of the statistical properties of the input. Wecompare predictions from neural network models with results of two elicited-production experiments on verb inflection withchildren in the morphologically complex languages Polish and Finnish. Three-layer neural networks were trained to produceperson/number-inflected present-tense verb forms in Polish and Finnish from phoneme representations of verb stems usingfrequency information from child-directed speech corpora. Simulated acquisition in both languages was affected by tokenfrequency and phonological neighbourhood density (PND) as well as an interaction such that low-frequency forms benefitedmore from PND than high-frequency forms. Suffix errors showed overgeneralisation and substitutions of low-frequency formswith higher-frequency forms. The model predictions are consistent with our empirical findings, except for the frequency XPND interaction. We discuss the experimental and simulated data and their implications.