Early lexical development in a self-organizing neural network

Ping Li, I. Farkas, B. MacWhinney

Research output: Journal article publicationConference articleAcademic researchpeer-review

146 Citations (Scopus)

Abstract

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. © 2004 Elsevier Ltd. All rights reserved.
Original languageEnglish
Pages (from-to)1345-1362
Number of pages18
JournalNeural Networks
Volume17
Issue number8-9
DOIs
Publication statusPublished - 1 Oct 2004
Externally publishedYes

Keywords

  • Language acquisition
  • Lexical development
  • Self-organizing neural network

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Artificial Intelligence

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