  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.
