EZ Cognitive Psychometrics: Bayesian hypothesis testing with the EZ diffusion model

In previous work, we developed a probabilistic EZ drift diffusion model (EZ-DDM) that allows for Bayesian hierarchical extensions with latent variables and metaregression structures. The EZ-DDM provides closed-form estimators for the drift rate, boundary separation, and nondecision time parameters using three summary statistics: the accuracy rate and the mean and variance of the response times. The EZ hierarchical Bayesian drift diffusion model (EZ-HBDDM) is a computationally efficient proxy model for the hierarchical three-parameter DDM built from the sampling distributions of the EZ summary statistics. In this work, we evaluate the efficacy of the EZ-HBDDM for hypothesis testing. We report on numerical experiments in which we generated data from a hierarchical DDM, with person-specific boundary and nondecision time parameters and a within-subject design for drift rate across two or more conditions and with various effect sizes. Hypotheses were evaluated using Bayes factors capturing the ratio between the prior to posterior mass near zero. We additionally evaluated the efficiency of the procedure for various data sizes, from very small to very large. We also evaluate the performance of a robust implementation of the EZ-DDM.