In this study we present a self‐organizing connectionist model of early lexical development.
We call this model DevLex‐II, based on the earlier DevLex model. DevLex‐II can simulate a …

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 …

Children tend to produce words earlier when they are connected to a variety of other words
along the phonological and semantic dimensions. Though these semantic and phonological …

How does a child rapidly acquire and develop a structured mental organization for the vast
number of words in the first years of life? How does a bilingual individual deal with the even …

There are an infinite number of possible word-to-world pairings. One way children could
learn words at an early stage is by computing statistical regularities across different …

Analyses of adult semantic networks suggest a learning mechanism involving preferential
attachment: A word is more likely to enter the lexicon the more connected the known words …

Early learned words are recognized and produced faster than later learned words. The
authors showed that such age of acquisition effects are a natural property of connectionist …

Recent accounts of language acquisition propose that the knowledge structures that
comprise language develop within a single, unified system that shares computational …

We explored how phonological network structure influences the age of words' first
appearance in children's (14–50 months) speech, using a large, longitudinal corpus of …

We present a neurocomputational model with self-organizing maps that accounts for the
emergence of taxonomic responding and fast mapping in early word learning, as well as a …