Bayesian Regression Analysis with the Drift-Diffusion Model
Traditional drift-diffusion model (DDM)–phenotype association studies commonly employ a two-step approach—first estimating trial-level DDM parameters, then regressing subject-level phenotypes onto these estimates—introducing substantial estimation bias. To address this, we propose and implement a unified Bayesian hierarchical regression framework that jointly models trial-level diffusion processes and subject-level phenotype associations within a single coherent model, thereby eliminating two-step bias. The framework leverages Markov chain Monte Carlo (MCMC) sampling, enabling flexible covariate specification and cross-subject parameter sharing. We develop RegDDM, an open-source R package providing standardized fitting interfaces and comprehensive diagnostic tools. Empirical analyses and simulation studies demonstrate that our method substantially improves parameter estimation accuracy and statistical inference reliability compared to the two-step approach—particularly under small-sample or weak-effect conditions. This work establishes a robust, scalable, and integrated analytical paradigm for cognitive modeling and individual-differences research.