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

TL;DR

This paper introduces generalized additive mixed models to account for temporal autocorrelation in experimental data, addressing the dynamic and adaptive nature of human subjects in psycholinguistic studies.

Contribution

It extends generalized linear mixed models by incorporating autocorrelational structures, providing a new approach to model human factors in experimental data.

Findings

Human factors interact with predictors in psycholinguistic data.

Maximally complex models may be ill-advised in nonlinear modeling.

Alternative strategies are discussed for data analysis.

Abstract

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.