Multiplex lexical networks reveal patterns in early word acquisition in children
Massimo Stella, Nicole M. Beckage, Markus Brede

TL;DR
This study models the early vocabulary development of children using a multiplex lexical network that integrates multiple types of linguistic relationships, revealing that combined network topology better predicts word acquisition order than individual relationships.
Contribution
It introduces a multiplex network approach to model the multi-relational structure of the mental lexicon, improving predictions of early word acquisition patterns.
Findings
Multiplex network topology predicts age of word acquisition more accurately than single-layer models.
Different network layers contribute variably over the course of lexical development.
Free associations become the dominant factor in word learning after approximately 23 months.
Abstract
Network models of language have provided a way of linking cognitive processes to the structure and connectivity of language. However, one shortcoming of current approaches is focusing on only one type of linguistic relationship at a time, missing the complex multi-relational nature of language. In this work, we overcome this limitation by modelling the mental lexicon of English-speaking toddlers as a multiplex lexical network, i.e. a multi-layered network where N=529 words/nodes are connected according to four types of relationships: (i) free associations, (ii) feature sharing, (iii) co-occurrence, and (iv) phonological similarity. We provide analysis of the topology of the resulting multiplex and then proceed to evaluate single layers as well as the full multiplex structure on their ability to predict empirically observed age of acquisition data of English speaking toddlers. We find…
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