A Bayesian hierarchical diffusion model decomposition of performance in approach-avoidance tasks
Krypotos, Angelos-Miltiadis Beckers, Tom × Kindt, Merel Wagenmakers, Eric-Jan #
Taylor & Francis
Cognition & Emotion vol:29 pages:1424-1444
Common methods for analyzing response time tasks, frequently used across different disciplines of psychology, suffer from a number of limitations such as the failure to directly measure the underlying latent processes of interest and the inability to take into account the uncertainty associated with each individual’s point estimate of performance. Here we discuss a Bayesian hierarchical diffusion model and apply it to response time data. This model allows researchers to decompose performance into meaningful psychological processes and to account optimally for individual differences and commonalities, even with relatively sparse data. We highlight the advantages of the Bayesian hierarchical diffusion model decomposition by applying it to performance on Approach-Avoidance Tasks, widely used in the emotion and psychopathology literature. Model fits for two experimental data sets demonstrate that the model performs well. The Bayesian hierarchical diffusion model overcomes important limitations of current analysis procedures and provides deeper insight in latent psychological processes of interest.