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Nonlinearities in bilingual visual word recognition: An introduction to generalized additive modeling

Published online by Cambridge University Press:  17 March 2021

Koji Miwa Open the ORCID record for Koji Miwa [Opens in a new window]  and
Harald Baayen Open the ORCID record for Harald Baayen [Opens in a new window]
Koji Miwa*
Affiliation:
Graduate School of Humanities, Nagoya University, Nagoya
Harald Baayen
Affiliation:
Department of Linguistics, Eberhard Karls UniversityTuebingen, Tuebingen
*
Address for correspondence: Koji Miwa, Bunkeisogokan Room 711, Graduate School of Humanities, Nagoya University, Foro-cho, Chikusa-ku Nagoya, 464-8601, Japan Email: kojimiwa@nagoya-u.jp
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Abstract

This paper introduces the generalized additive mixed model (GAMM) and the quantile generalized additive mixed model (QGAMM) through reanalyses of bilinguals’ lexical decision data from Dijkstra et al. (2010) and Miwa et al. (2014). We illustrate how regression splines can be used to test for nonlinear effects of cross-language similarity in form as well as for controlling experimental trial effects. We further illustrate the tensor product smooth for a nonlinear interaction between cross-language semantic similarity and word frequency. Finally, we show how the QGAMM helps clarify whether the effect of a particular predictor is constant across distributions of RTs.

Keywords

nonlinearitygeneralized additive modelingquantile regressioncognate processingvisual word recognition
Type
Review Article
Information
Bilingualism: Language and Cognition , Volume 24 , Issue 5 , November 2021 , pp. 825 - 832
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Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press

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