Our new article on linguistic relativity and statistical issues in cross-linguistic studies has just appeared in the journal Cognitive Semantics:
Satellite- vs. Verb-Framing Underpredicts Nonverbal Motion Categorization: Insights from a Large Language Sample and Simulations
Montero-Melis, Guillermo and Eisenbeiss, Sonja and Narasimhan, Bhuvana and Ibarretxe-Antuñano, Iraide and Kita, Sotaro and Kopecka, Anetta and Lüpke, Friederike and Nikitina, Tatiana and Tragel, Ilona and Florian Jaeger, T. and Bohnemeyer, Juergen, Cognitive Semantics, 3, 36-61 (2017), DOI:https://doi.org/10.1163/23526416-00301002
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.
downloadable preprint: https://www.academia.edu/29808551/Satellite-_vs._verb-framing_underpredicts_nonverbal_motion_categorization_Insights_from_a_large_language_sample_and_simulations._Cognitive_Semantics._UPDATED_11_29_16_with_minor_cuts_for_proofs_
One of the issues we discussed in this article is the need to achieve enough statistical power for experimental studies in psycholinguistics. This issue is debated by a lot of researchers at the moment, see e.g. recent work by Shravan Vasishth and colleagues – and references cited in these two publications:
Shravan Vasishth and Andrew Gelman. The Illusion of Power: How the statistical significance filter leads to overconfident expectations of replicability. Submitted to conference: Cognitive Science, London, UK, 2017. [ http ]
Matuschek, Hannes, Reinhold Kliegl, Shravan Vasishth, Harald Baayen, and Douglas Bates. “Balancing Type I error and power in linear mixed models.” Journal of Memory and Language 94 (2017): 305-315.
I will soon update my stats pages with more papers discussing statistical modeling, power, etc. Thus, please keep the suggestions coming
Happy experimenting in the field or lab – or field lab!