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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: [email protected]
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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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References

Baayen, RH (2010) A real experiment is a factorial experiment? The Mental Lexicon 5, 149–157.10.1075/ml.5.1.06baaCrossRefGoogle Scholar
Baayen, RH, Davidson, DJ and Bates, DM (2008) Mixed-effects modeling with crossed random effects for subjects and items. Journal of Memory and Language 59, 390–412.CrossRefGoogle Scholar
Baayen, RH, van Rij, J, De Cat, C and Wood, SN (2018) Autocorrelated errors in experimental data in the language sciences: Some solutions offered by Generalized Additive Mixed Models. In Speelman, D, Heylen, K and Geeraerts, D (eds), Mixed Effects Regression Models in Linguistics, (pages 49–69). Berlin: Springer.CrossRefGoogle Scholar
Baayen, RH, Vasishth, S, Kliegl, R and Bates, D (2017) The cave of shadows: Addressing the huma factor with generalized additive mixed models. Journal of Memory and Language 94, 206–234.CrossRefGoogle Scholar
Brysbaert, M and New, B (2009) Moving beyond Kucera and Francis: A critical evaluation of current word frequency norms and the introduction of a new and improved word frequency measure for American English. Behavior Research Methods 41, 977–990.10.3758/BRM.41.4.977CrossRefGoogle ScholarPubMed
Chuang, YY, Fon, J, Papakyritsis, I and Baayen, RH (2020) Analyzing phonetic data with generalized additive mixed models. In Ball, MJ (Ed.), Handbook of clinical phonetics. London: Routledge.Google Scholar
Dijkstra, T, Grainger, J and van Heuven, WJB (1999) Recognition of cognates and interlingual homographs: The neglected role of phonology. Journal of Memory and language 41, 496–518.10.1006/jmla.1999.2654CrossRefGoogle Scholar
Dijkstra, T, Miwa, K, Brummelhuis, B, Sappeli, M and Baayen, RH (2010) How cross-language similarity and task demands affect cognate recognition. Journal of Memory and Language 62, 284–301.10.1016/j.jml.2009.12.003CrossRefGoogle Scholar
Dijkstra, T and van Heuven, WJB (2002) The architecture of the bilingual word recognition system: From identification to decision. Bilingualism: Language and Cognition 5, 175–197.CrossRefGoogle Scholar
Fasiolo, M, Goude, Y, Nedellec, R and Wood, SN (2017) Fast calibrated additive quantile regression. URL: https://arxiv.org/abs/1707.03307Google Scholar
Hendrix, P, Bolger, P and Baayen, H (2017) Distinct ERP signatures of word frequency, phrase frequency, and prototypicality in speech production. Journal of Experimental Psychology: Learning, Memory, and Cognition 43, 128–149.Google ScholarPubMed
Miwa, K, Dijkstra, T, Bolger, P and Baayen, RH (2014) Reading English with Japanese in mind: Effects of frequency, phonology, and meaning in different-script bilinguals. Bilingualism: Language and Cognition 17, 445–463.CrossRefGoogle Scholar
Murakami, A (2016) Modeling systematicity and individuality in nonlinear second language development: The case of English grammatical morphemes. Language Learning 66, 834–871.CrossRefGoogle Scholar
Pinheiro, JC and Bates, DM (2000) Mixed-effects models in S and S-PLUS. New York: Springer.10.1007/978-1-4419-0318-1CrossRefGoogle Scholar
Ratcliff, R (1979) Group reaction time distributions and an analysis of distribution statistics. Psychological Bulletin 86, 446–461.CrossRefGoogle Scholar
Schmidtke, D, Matsuki, K and Kuperman, V (2017) Surviving blind decomposition: A distributional analysis of the time-course of complex word recognition. Journal of Experimental Psychology: Learning, Memory, and Cognition 43, 1793–1820.Google ScholarPubMed
van Rij, J, Hendriks, P, van Rijn, H, Baayen, RH and Wood, SN (2019) Analyzing the time course of pupilometric data. Trends in Hearing 23, 1–22.10.1177/2331216519832483CrossRefGoogle Scholar
van Rij, J, Wieling, M, Baayen, R and van Rijn, H (2017) “itsadug: Interpreting time series and autocorrelated data using GAMMs.” R package version 2.3.Google Scholar
Wieling, M (2018) Analyzing dynamic phonetic data using generalized additive mixed modeling: a tutorial focusing on articulatory differences between L1 and L2 speakers of English. Journal of Phonetics 70, 86–116.CrossRefGoogle Scholar
Wood, SN (2003) Thin-plate regression splines. Journal of the Royal Statistical Society (B) 65, 95–114.CrossRefGoogle Scholar
Wood, SN (2004) Stable and efficient multiple smoothing parameter estimation for generalized additive models. Journal of the American Statistical Association 99, 673–686.CrossRefGoogle Scholar
Wood, SN (2013) On p-values for smooth components of an extended generalized additive model. Biometrika 100, 221–228.CrossRefGoogle Scholar
Wood, SN (2017) Generalized additive models: An introduction with R (2nd edition). Chapman and Hall/CRC.CrossRefGoogle Scholar
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