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Computational Modeling of Word Learning Biases by using Known Words Meanings

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Please use this identifier to cite or link to this item:http://hdl.handle.net/2115/17140

Title: Computational Modeling of Word Learning Biases by using Known Words Meanings
Authors: Kurosaki, K. Browse this author
Omori, T. Browse this author
Keywords: connectionist model
cognitive process
word learning bias
novel noun generalization task
vocabulary spurt
Issue Date: 2005
Publisher: ACTA Press
Journal Title: Artificial Intelligence and Soft Computing ~ASC 2005~ (9/12/2005 - 9/14/2005 Benidorm, Spain)
Start Page: 201
End Page: 206
Abstract: In the acquisition of their early nouns, it is well-known that young children have a tendency to understand the mean ing of novel nouns based on the similarity of shape. This phenomenon is called “shape bias.” Though this bias is re markable in solid objects, it is reported that children over generalize and misapply the bias to non-solid objects. For this phenomenon, learning models using distributed repre sentation are proposed. But the computational mechanism behind such children’s behavior has not been clarified. In this paper we aim to clarify the more detailed computa tional mechanisms of these biases. Therefore, we explicitly define word meanings by a “word category neuron model” and propose a “nearest neighbor hypothesis” that represents a plausible mechanism for children’s cognitive processes. Then, from a computer simulation based on the novel noun generalization task of developmental psychology, we show that the proposed hypotheses can better explain the emer gence of word learning bias and deflection in children’s word learning.
Type: article (author version)
URI: http://hdl.handle.net/2115/17140
Appears in Collections:情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 黒崎 康介

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