The cave of Shadows. Addressing the human factor with generalized additive mixed models

By Harald Baayen, Shravan Vasishth, Douglas M. Bates, Reinhold Kliegl. 2015

202 citations recorded by the source corpus.

Generalized additive mixed models are introduced as an extension of the generalized linear mixed model which makes it possible to deal with temporal autocorrelational structure in experimental data. This autocorrelational structure is likely to be a consequence of learning, fatigue, or the ebb and flow of attention within an experiment (the `human factor'). Unlike molecules or plots of barley, subjects in psycholinguistic experiments are intelligent beings that depend for their survival on constant adaptation to their environment, including the environment of an experiment. Three data sets illustrate that the human factor may interact with predictors of interest, both factorial and metric. We also show that, especially within the framework of the generalized additive model, in the nonlinear world, fitting maximally complex models that take every possible contingency into account is ill-advised as a modeling strategy. Alternative modeling strategies are discussed for both confirmatory and exploratory data analysis.

Subfields: Food Science

View Paper: https://www.semanticscholar.org/paper/2d20604e2c730265327d1d899b178bde99c11707

Citations in the corpus

• Analyzing dynamic phonetic data using generalized additive mixed modeling: A tutorial focusing on articulatory differences between L1 and L2 speakers of English (2018, unknown): “Trial effects are frequently included in the analysis, in order to take into account effects of repetition (Winter, 2015), fatigue, attention, or learning (Baayen et al., 2017).”
• An Introduction to Mixed Models for Experimental Psychology (2019, unknown): “An even more powerful class of models than discussed here, generalized additive mixed models (GAMMs), is described in Baayen et al. (2017).”