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linear mixed model (LMM) instead. As clarified by the title of their paper, their conclusion is: “Using GAMMs to model trial-by-trial
fluctuations in experimental data: More risks but hardly any benefit”.
We address the questions raised by Thul et al. (2020), who clearly demonstrated
that problems can indeed arise when using factor smooths in combination with factorial designs. We show that the problem does not arise when
using by-smooths. Furthermore, we have traced a bug in the implementation of factor smooths in the mgcv package, which will
have been removed from version 1.8–36 onwards.
To illustrate that GAMMs now produce correct estimates, we report simulation studies implementing different by-subject
longitudinal effects. The maximal LMM emerges as slightly conservative compared to GAMMs, and GAMMs provide estimated coefficients that can
be less variable across simulation runs. We also discuss two datasets where time-varying effects interact with numerical predictors in a
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Cited by (6)
Cited by six other publications
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Widmann, Andreas, Björn Herrmann & Florian Scharf
2024. Pupillometry is sensitive to speech masking during story listening: a commentary on the critical role of modeling temporal trends. Journal of Neuroscience Methods► pp. 110299 ff.
Winn, Matthew B.
2024. The Effort of Repairing a Misperceived Word Can Impair Perception of Following Words, Especially for Listeners With Cochlear Implants. Ear & Hearing 45:6 ► pp. 1527 ff.
Heitmeier, Maria, Yu-Ying Chuang & R. Harald Baayen
2023. How trial-to-trial learning shapes mappings in the mental lexicon: Modelling lexical decision with linear discriminative learning. Cognitive Psychology 146 ► pp. 101598 ff.
2022. Comparing Single-Word Insertions and Multi-Word Alternations in Bilingual Speech: Insights from Pupillometry. Languages 7:4 ► pp. 267 ff.
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