The cave of shadows: Addressing the human factor with generalized additive mixed models

- GAMs can model autocorrelations in experimental data.
- These autocorrelations arise due to learning, fatigue, or fluctuations in attention.
- Three data sets illustrate that this human factor interacts with predictors of interest.
- Within the framework of GAMs, maximal models are ill-advised.
- Alternative regression modeling strategies are discussed.

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.