Early lexical development in a self-organizing neural network

Ping Li, Igor Farkas, Brian MacWhinney

    Research output: Contribution to journalArticlepeer-review

    165 Scopus citations


    In this paper we present a self-organizing neural network model of early lexical development called DevLex. The network consists of two self-organizing maps (a growing semantic map and a growing phonological map) that are connected via associative links trained by Hebbian learning. The model captures a number of important phenomena that occur in early lexical acquisition by children, as it allows for the representation of a dynamically changing linguistic environment in language learning. In our simulations, DevLex develops topographically organized representations for linguistic categories over time, models lexical confusion as a function of word density and semantic similarity, and shows age-of-acquisition effects in the course of learning a growing lexicon. These results match up with patterns from empirical research on lexical development, and have significant implications for models of language acquisition based on self-organizing neural networks.

    Original languageEnglish (US)
    Pages (from-to)1345-1362
    Number of pages18
    JournalNeural Networks
    Issue number8-9
    StatePublished - Oct 2004

    All Science Journal Classification (ASJC) codes

    • Cognitive Neuroscience
    • Artificial Intelligence


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