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

in Cognitive Semantics
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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.

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

in Cognitive Semantics

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Figures

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    Example item. Left figure: ROLL UP target; right figure: ROLL DOWN same-manner variant (left panel) and BOUNCE UP same-path variant (right panel).

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    Four hypothetical scenarios under a Whorfian effect of language type (S vs. V). The y-axis shows the probability of categorizing an event in terms of Manner (rather than Path). Ticks along the x-axis represent a random sample of languages (10 S, 10 V); shapes represent participants (12 per language, as in the present study); error bars show confidence intervals per language; horizontal lines indicate empirical by-type means. Each panel is a random simulation from four distributions with the same underlying by-type mean (S > V). The panels differ with respect to the amount of participant variability (low/high) and language variability (low/high).

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    Proportion of same-manner choices by language (x-axis) and language type (S-framed: red dots and solid lines; V-framed: blue triangles and dashed lines). Shapes show by-participant averages, error bars show 95% confidence intervals of by-subject means (non-parametric bootstrap). The two horizontal lines show average percentage of same-manner choices for each language type. Languages are ordered by increasing mean proportion of same-manner responses.

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    Power analysis based on a sample size as in the present study (7 S-, 12 V-languages) and three estimates of the effect of language type (10,000 simulations per cell). Bar heights show the proportion of significant differences between language types at the .05 level. Panel titles and bar colors show the size of the population-level effect. Horizontal dashed lines mark a power of .08.

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    Power analysis based on balanced language samples of 20, 40 and 80 S/V-languages and three estimates of the effect of language type (10,000 simulations per cell). Bar heights show the proportion of significant differences between language types at the .05 level. Panel titles and bar colors show the size of the population-level effect. Horizontal dashed lines mark a power of .08.

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