Proceedings of the Annual Meeting of the Cognitive Science Society

Decision-making models hold a vital role in the field of cognitive science, serving as a means of describing and predicting human behavior. While classical models with similar assumptions are frequently favored, there is no guarantee they provide the best accounts of behavior. Here, we evaluate BEAST, a model that has demonstrated extraordinary predictive capabilities in diverse settings, but was excluded from a recent large-scale comparison of models because it cannot be analytically estimated. Our evaluation of the model's performance on a large collection of experiments of decisions under risk shows it provides excellent predictions in some domains. We further show how BEAST can be adapted to increase its predictive power in contextualized settings. Our results highlight the importance of a more inclusive approach toward models that may be difficult to analytically estimate to deepen our understanding of the psychological mechanisms underlying human decision making behavior.