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What can Neighbourhood Density effects tell us about word learning? Insights from a connectionist model of vocabulary development*

Published online by Cambridge University Press:  17 February 2016

MARTIN TAKAC*
Affiliation:
Comenius University, Bratislava, Slovakia andUniversity of Otago, Dunedin, New Zealand
ALISTAIR KNOTT
Affiliation:
University of Otago, Dunedin, New Zealand
STEPHANIE STOKES
Affiliation:
University of Hong Kong, Hong Kong
*
Address for correspondence: Martin Takac, Comenius University – Centre for Cognitive Science, Mlynská dolina, Bratislava 84248, Slovakia. e-mail: takac@ii.fmph.uniba.sk

Abstract

In this paper, we investigate the effect of neighbourhood density (ND) on vocabulary size in a computational model of vocabulary development. A word has a high ND if there are many words phonologically similar to it. High ND words are more easily learned by infants of all abilities (e.g. Storkel, 2009; Stokes, 2014). We present a neural network model that learns general phonotactic patterns in the exposure language, as well as specific word forms and, crucially, mappings between word meanings and word forms. The network is faster at learning frequent words, and words containing high-probability phoneme sequences, as human word learners are, but, independently of this, the network is also faster at learning words with high ND, and, when its capacity is reduced, it learns high ND words in preference to other words, similarly to late talkers. We analyze the model and propose a novel explanation of the ND effect, in which word meanings play an important role in generating word-specific biases on general phonological trajectories. This explanation leads to a new prediction about the origin of the ND effect in infants.

Type
Articles
Copyright
Copyright © Cambridge University Press 2016 

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Footnotes

[*]

We are grateful to the Marsden fund of New Zealand for Grant 13-UOO-048 and Slovak VEGA agency for grant 1/0898/14 (Martin Takac). Big thanks to Jen Hay and Pat LaShell for the statistical analyses in this paper. We would also like to thank Igor Farkaš for valuable discussions on neural network issues. We also thank the anonymous reviewers of this paper for their numerous helpful comments.

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