Drift Diffusion Models as an Interpretable Framework for Modeling Decision-Making Behavior

Abstract

The Drift Diffusion Model (DDM) provides a principled, interpretable framework for modeling binary choices and response-time distributions. In this chapter, we synthesize DDM theory and applied practice, and we contribute a ready-to-use workflow that combines (i) concise exposition of core assumptions and parameters, (ii) recommended estimation strategies and diagnostics (including hierarchical Bayesian inference), and (iii) a synthetic-data parameter-recovery example that illustrates best practices for ensuring identifiability and robustness. We review empirical applications in perception, cognitive control, and clinical research, and critically evaluate recent extensions such as collapsing boundaries and hybrid machine–cognitive integrations. We close with concrete recommendations for study design, model comparison, and ethical deployment in clinical and AI settings. This chapter aims to bridge theory and practice, equipping researchers with both conceptual clarity and actionable tools for diffusion-model analyses.