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Classifiers in competition for categorization

Bibliographic Data

ID21819490
AuthorsLin Peng (0009-0003-1694-7522, Shangqiu Normal University), Sang-Gu Kang (0000-0002-9565-4773, Gangneung–Wonju National University, corresponding author)
Year2023
Volume15
Issue4
Pages815-833
Publication date2023-12-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueLanguage and Cognition (JOURNAL)
Journal identifiersISSN: 1866-9808 • E-ISSN: 1866-9859
PublisherCambridge University Press (CUP) (PUBLISHER)
DOI10.1017/langcog.2023.27
OpenAlexW4385420176
LanguageEN
References cited30

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

Categorization · Grammar · Linguistics · Mandarin Chinese · Natural language processing · Notice · Categorization, perception, and language · Computer Science · Language, Metaphor, and Cognition · Syntax, Semantics, Linguistic Variation · Artificial Intelligence

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Citation velocityhistorical
Highly citedNo

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