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Children construct novel word meaning ad-hoc based on known words : Computational model of shape and material biases

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Please use this identifier to cite or link to this item:http://hdl.handle.net/2115/20565

Title: Children construct novel word meaning ad-hoc based on known words : Computational model of shape and material biases
Authors: Kurosaki, Kosuke Browse this author
Omori, Takashi Browse this author
Keywords: Connectionist model
Prototype model
Cognitive process
Fast mapping
Shape bias
Material bias
Overgeneralized shape bias
Issue Date: Jun-2007
Publisher: Elsevier
Journal Title: Cognitive Systems Research
Volume: 8
Issue: 2
Start Page: 110
End Page: 130
Publisher DOI: 10.1016/j.cogsys.2006.06.002
Abstract: Taking the stance that two well-known word learning biases (shape and material bias) are formed through learning (learned bias account, LBA), we illustrated a concrete computational mechanism with "ad-hoc meaning substitution (AMS)" hypothesis, and verified it by two computer simulations. AMS represents that when given a novel word and a corresponding instance, children create novel word meaning by using the known word meaning and the instance as an ad-hoc template. The AMS function enables fast mapping and vocabulary spurt. To describe the AMS process computationally, we introduced "word distributional prototype (WDP)," which is the explicit representation of word meaning with an inductive learning function. Simulation 1 revealed that when a network with WDP and AMS was given a biased vocabulary reflecting young children, it demonstrated shape, material, and overgeneralized shape biases. This result suggested that a triad of word meaning induction, ad-hoc meaning substitution, and early biased vocabulary is essential for the emergence of biases. Simulation 2 introduced the notion of maturity that denoted a degree of learning convergence for each word meaning, and then the network showed neither shape nor material bias during an early small vocabulary. This result indicated that the period at which each bias emerges is decided by maturity. Though AMS consists of simpler and valider mechanisms than those proposed in previous studies, it could reproduce behavior of shape and material biases and explain their emergence process clearly. These results suggest that phenomena concerning shape and material biases are explicable with a simple ad-hoc learning instead of meta-learning among LBA or innate language-specific ones.
Relation: http://www.sciencedirect.com/science/journal/13890417
Type: article (author version)
URI: http://hdl.handle.net/2115/20565
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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