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Correlation versus prediction in children's word learning: Cross-linguistic evidence and simulations

Published online by Cambridge University Press:  11 March 2014

Eliana Colunga,
Linda B. Smith and
Michael Gasser
Eliana Colunga*
Affiliation:
University of Coloradoat BoulderIndiana University
Linda B. Smith*
Affiliation:
University of Coloradoat BoulderIndiana University
Michael Gasser*
Affiliation:
University of Coloradoat BoulderIndiana University
*
Correspondence addresses: Eliana Colunga, Department of Psychology and Neuroscience, University of Colorado, Boulder, Colorado, 80309-0345, USA. E-mail: colunga@psych.colorado.edu.
Correspondence addresses: Linda B. Smith, Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana 47405-7007, USA. E-mail: smith4@indiana.edu.
Correspondence addresses: Michael Gasser, Computer Science Department, Indiana University, Bloomington, Indiana 47405-7104, USA. E-mail: gasser@indiana.edu.
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Abstract

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The ontological distinction between discrete individuated objects and continuous substances, and the way this distinction is expressed in different languages has been a fertile area for examining the relation between language and thought. In this paper we combine simulations and a cross-linguistic word learning task as a way to gain insight into the nature of the learning mechanisms involved in word learning. First, we look at the effect of the different correlational structures on novel generalizations with two kinds of learning tasks implemented in neural networks—prediction and correlation. Second, we look at English- and Spanish-speaking 2-3-year-olds' novel noun generalizations, and find that count/mass syntax has a stronger effect on Spanish- than on English-speaking children's novel noun generalizations, consistent with the predicting networks. The results suggest that it is not just the correlational structure of different linguistic cues that will determine how they are learned, but the specific learning mechanism and task in which they are involved.

Keywords

word learningcrosslinguisticmass/count syntaxneural networksprediction/correlation

Information

Type
Research Article
Information
Language and Cognition , Volume 1 , Issue 2 , June 2009, pp. 197 - 217
Copyright
Copyright © UK Cognitive Linguistics Association 2009

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