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Computational investigation of early child language acquisition using multimodal neural networks: a review of three models

Computational investigation of early child language acquisition using multimodal neural networks:... Current opinion suggests that language is a cognitive process in which different modalities such as perceptual entities, communicative intentions and speech are inextricably linked. As such, the process of child language acquisition is one in which the child learns to decipher this inextricability and to acquire language capabilities starting from gesturing, followed by language dominated by single word utterances, through to full-blown native language capability. In this paper I review three multimodal neural network models of early child language acquisition. Using these models, I show how computational modelling, in conjunction with the availability of empirical data, can contribute towards our understanding of child language acquisition. I conclude this paper by proposing a control theoretic approach towards modelling child language acquisition using neural networks. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Artificial Intelligence Review Springer Journals

Computational investigation of early child language acquisition using multimodal neural networks: a review of three models

Nyamapfene, Abel
Artificial Intelligence Review , Volume 31 (4) – Oct 24, 2009
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References (49)

Publisher
Springer Journals
Copyright
Copyright © 2009 by Springer Science+Business Media B.V.
Subject
Computer Science; Computer Science, general; Artificial Intelligence (incl. Robotics)
ISSN
0269-2821
eISSN
1573-7462
DOI
10.1007/s10462-009-9125-6
Publisher site
See Article on Publisher Site

Abstract

Current opinion suggests that language is a cognitive process in which different modalities such as perceptual entities, communicative intentions and speech are inextricably linked. As such, the process of child language acquisition is one in which the child learns to decipher this inextricability and to acquire language capabilities starting from gesturing, followed by language dominated by single word utterances, through to full-blown native language capability. In this paper I review three multimodal neural network models of early child language acquisition. Using these models, I show how computational modelling, in conjunction with the availability of empirical data, can contribute towards our understanding of child language acquisition. I conclude this paper by proposing a control theoretic approach towards modelling child language acquisition using neural networks.

Journal

Artificial Intelligence Review – Springer Journals

Published: Oct 24, 2009

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