Two connectionist networks, DISLEX and DevLex-II, were used in this study to model the acquisition of lexical and grammatical aspect. Both models use multi-layered self-organizing feature maps, connected by asso- ciative links trained according to the Hebbian learning rule. Previous em- pirical research has identified a strong association between lexical aspect and grammatical aspect in child language, on the basis of which some re- searchers argue for innate semantic categories or prelinguistic predisposi- tions. Our simulations indicate that such an association can emerge from dynamic self-organization and Hebbian learning in connectionist networks, without the need of a priori assumptions about the structure of innate knowledge. Our modeling results further attest to the utility of self-organizing neural networks in the study of language acquisition.
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