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Satellite-Vs. Verb-framing underpredicts nonverbal motion categorization insights from a large language sample and simulations

  • Guillermo Montero-Melis
  • , Sonja Eisenbeiss
  • , Bhuvana Narasimhan
  • , Iraide Ibarretxe-Antuñano
  • , Sotaro Kita
  • , Anetta Kopecka
  • , Friederike Lüpke
  • , Tatiana Nikitina
  • , Ilona Tragel
  • , T. Florian Jaeger
  • , Juergen Bohnemeyer
  • Stockholm University
  • University of Cologne
  • University of Colorado Boulder
  • University of Zaragoza
  • University of Warwick
  • CNRS
  • SOAS University of London
  • French National Center for Scientific Research
  • University of Tartu
  • University of Rochester

Research output: Contribution to journalReview articlepeer-review

20 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)36-61
Number of pages26
JournalCognitive Semantics
Volume3
Issue number1
DOIs
StatePublished - 2017

Keywords

  • Cross-linguistic comparison
  • Event categorization
  • Linguistic relativity
  • Motion events
  • Multilevel models
  • Power analysis
  • Semantic typology
  • Statistical simulations

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