Analyzing reaction times

TLDR

Reaction times are a key source of information in experimental psychology, yet classical analyses rely on aggregated data, prompting a reconsideration of methodological strategies in light of mixed‑effects modeling. The study advocates empirical flexibility in choosing RT transformations, minimal a‑priori trimming, and model criticism. Mixed‑effects modeling enables prediction of individual responses without prior aggregation, and the authors illustrate this with a large dataset featuring complex random‑effects and interaction evaluation between fixed and random factors. They demonstrate that trial‑to‑trial longitudinal dependencies can be incorporated into the statistical model.

Abstract

Reaction times (RTs) are an important source of information in experimental psychology. Classical methodological considerations pertaining to the statistical analysis of RT data are optimized for analyses of aggregated data, based on subject or item means (c.f., Forster & Dickinson, 1976). Mixed-effects modeling (see, e.g., Baayen, Davidson, & Bates, 2008) does not require prior aggregation and allows the researcher the more ambitious goal of predicting individual responses. Mixed-modeling calls for a reconsideration of the classical methodological strategies for analysing rts. In this study, we argue for empirical exibility with respect to the choice of transformation for the RTs. We advocate minimal a-priori data trimming, combined with model criticism. We also show how trial-to-trial, longitudinal dependencies between individual observations can be brought into the statistical model. These strategies are illustrated for a large dataset with a non-trivial random-effects structure. Special attention is paid to the evaluation of interactions involving fixed-effect factors that partition the levels sampled by random-effect factors.