A tutorial on cognitive modeling for cognitive aging research

Abstract

Cognitive aging researchers are interested in understanding how cognitive processes change in old age, but the relationship between hypothetical latent cognitive processes and observed behavior is often complex and not fully accounted for in standard analyses (e.g., Analysis of variance [ANOVA]). Cognitive models formalize the relationship between underlying processes and observed behavior and are more suitable for identifying what processes are associated with aging. This article provides a tutorial on how to fit and interpret cognitive models to measure age differences in cognitive processes. We work with an example of a two choice discrimination task and describe how to fit models in the highly flexible modeling software Stan. We describe how to use hierarchical modeling to estimate both group and individual effects simultaneously, and we detail model fitting in a Bayesian statistical framework, which, among other benefits, enables aging researchers to quantify evidence for null effects. We contend that more widespread use of cognitive modeling among cognitive aging researchers may be useful for addressing potential issues of nonreplicability in the field, as cognitive modeling is more suitable to addressing questions about what cognitive processes are (or are not) affected by aging.