Robust, scalable Cognitive Psychometrics with the Hierarchical Bayesian EZ-DDM

In previous work, we introduced a probabilistic formulation of the EZ drift diffusion model (EZ-DDM) that enables hyper-efficient Bayesian inference of the drift rate, boundary separation, and non-decision time parameters from binary choice and response time (RT) data. By deriving sampling distributions for the summary statistics underlying the EZ estimators, we constructed a “proxy” likelihood that can be implemented in JAGS, Stan, and PyMC. This formulation supports hierarchical Bayesian models with excellent sampling properties, allowing researchers to decompose variability in model parameters across individuals, experimental conditions, and stimuli. We now present a robust extension of the hierarchical Bayesian EZ-DDM that addresses the sensitivity of standard EZ-based estimators to contaminant RTs. The robust variant replaces the mean and variance of RTs with quartile-based statistics, while preserving the generative model structure. Simulation studies varying sample size, trial count, effect size, and contamination level show that the robust implementation matches the accuracy of the standard hierarchical EZ-DDM on clean data and remains stable under contamination, without sacrificing computational efficiency. Beyond simulations, we illustrate the potential of our robust hierarchical Bayesian EZ-DDM with two experimental datasets: a large lexical decision study and a recognition memory study with symbolic stimuli. In both cases, we model the effect of stimulus-level properties on model parameters indexing information processing, response caution, and non-decision time components, and explore how these effects vary across participants and conditions. Together, these examples demonstrate how the robust hierarchical EZ-DDM supports scalable, experiment-based inferences about cognitive processes under realistic data conditions.