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Satellite- vs. Verb-Framing Underpredicts Nonverbal Motion Categorization: Insights from a Large Language Sample and Simulations

In: Cognitive Semantics
Authors:
Guillermo Montero-Melis Centre for Research on Bilingualism, Stockholm University, SE-106 91 Stockholm, Sweden, montero-melis@biling.su.se

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Sonja Eisenbeiss University of Cologne, Germany & Macquarie University, Sydney, Australia, seisen@essex.ac.uk

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Bhuvana Narasimhan University of Colorado, bhuvana.narasimhan@colorado.edu

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Iraide Ibarretxe-Antuñano University of Zaragoza, Spain, iraide@unizar.es

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Sotaro Kita University of Warwick, England, S.Kita@warwick.ac.uk

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Anetta Kopecka French National Center for Scientific Research & University of Lyon, France, Anetta.Kopecka@univ-lyon2.fr

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Friederike Lüpke School of Oriental and African Studies, University of London, fl2@soas.ac.uk

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Tatiana Nikitina French National Center for Scientific Research tavnik@gmail.com

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Ilona Tragel University of Tartu, Estonia, & Beijing Foreign Studies University, China, ilona.tragel@ut.ee

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T. Florian Jaeger University of Rochester, New York, fjaeger@ur.rochester.edu

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Juergen Bohnemeyer University at Buffalo, New York, jb77@buffalo.edu

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Is motion cognition influenced by the large-scale typological patterns proposed in Talmy’s (2000) two-way distinction between verb-framed (V) and satellite-framed (S) languages? Previous studies investigating this question have been limited to comparing two or three languages at a time and have come to conflicting results. We present the largest cross-linguistic study on this question to date, drawing on data from nineteen genealogically diverse languages, all investigated in the same behavioral paradigm and using the same stimuli. After controlling for the different dependencies in the data by means of multilevel regression models, we find no evidence that S- vs. V-framing affects nonverbal categorization of motion events. At the same time, statistical simulations suggest that our study and previous work within the same behavioral paradigm suffer from insufficient statistical power. We discuss these findings in the light of the great variability between participants, which suggests flexibility in motion representation. Furthermore, we discuss the importance of accounting for language variability, something which can only be achieved with large cross-linguistic samples.

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