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Classifiers in competition for categorization

Published online by Cambridge University Press:  31 July 2023

Lin Peng Open the ORCID record for Lin Peng [Opens in a new window]  and
Sang-Gu Kang Open the ORCID record for Sang-Gu Kang [Opens in a new window]
Lin Peng
Affiliation:
Department of Foreign Languages, Shangqiu Normal University, Shangqiu, China
Sang-Gu Kang*
Affiliation:
Department of English Language and Literature, Gangneung-Wonju National University, Gangneung, Korea
*
Corresponding author: Sang-Gu Kang; Email: kangsg39@gmail.com
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Abstract

This research probed how classifiers marking an object’s membership in the grammar of classifier languages like Mandarin Chinese and Korean may influence their speakers to categorize objects differently compared to speakers of non-classifier languages like English. Surveys in multiple-choice format were given to native speakers of the three languages. Analysis of the results demonstrated that significant proportions of Mandarin Chinese and Korean speakers behaved differently from English speakers due to the classifier-based strategy influencing classifier language speakers’ categorization. Adopting the Competition Model, we suggest that among the various categorizing strategies available to language users, the one with the greatest strength at the moment of performing the task wins the categorization competition. Classifiers that are grammaticalized in classifier languages may be providing their speakers with a powerful cognitive tool to notice diverse characteristics shared between objects, which is usually unavailable to non-classifier languages. Therefore, the strength of classifier-based strategy in the minds of classifier language speakers is strong enough to win some of the categorization competitions, which guides them to make different categorizing decisions from non-classifier language users.

Keywords

categorizationclassifierscategorizing strategiesCompetition Model
Type
Article
Information
Language and Cognition , Volume 15 , Issue 4 , December 2023 , pp. 815 - 833
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Copyright
© The Author(s), 2023. Published by Cambridge University Press

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