title: Early lexical development in a self-organizing neural network.
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.
authors.affiliation: University of Richmond, Richmond, VA 23173, USA. pli@richmond.edu
authors.forename: Ping
authors.initials: P
authors.lastname: Li
authors.forename: Igor
authors.initials: I
authors.lastname: Farkas
authors.forename: Brian
authors.initials: B
authors.lastname: MacWhinney
mesh_terms: D001185:Artificial Intelligence; D001245:Association Learning; D002648:Child; D003198:Computer Simulation; D006801:Humans; D016571:Neural Networks, Computer; D010700:Phonetics; D012660:Semantics; D049250:Speech Recognition Software