Hierarchical Bayesian Modeling of Human Decision-Making Using Wiener Diffusion

Wiener diffusion accounts of human decision-making are among the most successful and best developed formal models in the psychological sciences. We reconsider these models from a Bayesian perspective, using graphical modeling, and Markov Chain Monte-Carlo methods for posterior sampling. By analyzing seminal data from a brightness discrimination task, we show how the Bayesian approach offers several avenues for extending and improving diffusion models. These possibilities include the hierarchical modeling of stimulus properties, and modeling the role of contaminant processes in generating experimental data. We also argue that the Bayesian approach challenges some basic assumptions of previous diffusion models, involving how variability in decision-making should be interpreted. We conclude that adopting a Bayesian approach to relating diffusion models and human decisionmaking data will sharpen the theoretical and empirical questions, and improve our understanding of a basic human cognitive ability. BAYESIAN DIFFUSION DECISION-MAKING 2