Bayesian Regression Analysis with the Drift-Diffusion Model

TL;DR

This paper introduces a unified Bayesian hierarchical regression approach for the Drift-Diffusion Model, addressing biases in traditional two-step methods, and demonstrates its implementation via the R package RegDDM.

Contribution

It presents a novel integrated Bayesian modeling framework for DDM and regression analysis, improving accuracy and reducing bias compared to traditional methods.

Findings

Unified Bayesian model effectively estimates DDM and regression parameters.

The R package RegDDM simplifies implementation of the integrated approach.

Simulation results show improved parameter recovery and reduced bias.

Abstract

The Drift-Diffusion Model (DDM) is widely used in neuropsychological studies to understand the decision process by incorporating both reaction times and subjects' responses. Various models have been developed to estimate DDM parameters, with some employing Bayesian inference. However, when examining associations between phenotypes of interest and DDM parameters, most studies adopt a two-step approach: first estimating DDM parameters, then applying a separate statistical model to the estimated values. Despite the potential for bias, this practice remains common, primarily due to researchers' unfamiliarity with Bayesian modeling. To address this issue, this tutorial presents the implementations and advantages of fitting a unified Bayesian hierarchical regression model that integrates trial-level drift-diffusion modeling and subject-level regression between DDM parameters and other variables. The R package RegDDM, developed and demonstrated in this tutorial, facilitates this integrated modeling approach.