Generalized additive models (GAMs) are a powerful generalization of regression modelling that makes it possible to examine response variables that vary non-linearly as a function of a predictor variable, using smooths. These smooths are able to capture data undulation as a function of time. As a result they have great potential for providing precise predictions on pitch contours, gesture motion, and other phenomena of interest in multimodal communication research, which present great variability and noisiness. GAMs, alongside developments such as GAMMs (mixed) or PAMMs (piece-wise), allow us to model this variability as non-linear responses to multiple independent variables, isolating their individual effects. The penalization methods implemented in GAMs give the user a powerful tool for modelling nonlinearity while being protected against undersmoothing.

This talk by Cristóbal Pagán Cánovas discusses an initial attempt to apply GAMs to Red Hen data.

References

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Baayen, R. H., & Linke, M. (in press). An introduction to the generalized additive model. In S. T. Gries & M. Paquot (Eds.), A practical handbook of corpus linguistics. Springer.

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Kösling, K., Kunter, G., Baayen, H., & Plag, I. (2013). Prominence in Triconstituent Compounds: Pitch Contours and Linguistic Theory. Language and Speech, 56(4), 529–554. https://doi.org/10.1177/0023830913478914

Miwa, K., & Baayen, H. (2021). Nonlinearities in bilingual visual word recognition: An introduction to generalized additive modeling. Bilingualism: Language and Cognition, 1–8. https://doi.org/10.1017/S1366728921000079

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Wood, S. N. (2017). Generalized Additive Models: An Introduction with R, Second Edition. Chapman & Hall/CRC Texts in Statistical Science.