Mouse-Tracking Substantiates the Contributions of Predispositions and Evaluations in Value-Based Choice

Computational models like the drift diffusion model (DDM) have greatly enhanced our understanding of the mechanisms of decision making, such as predispositions (i.e., starting points) and evaluations (i.e., drift rates). However, a common criticism is that the model parameters are not directly observable and may not reflect the true choice process. Here, we address this concern with several value-based experiments designed to assess these mechanisms more directly with mouse-tracking. We find that people’s initial mouse movements are tied to their predispositions (i.e., starting points) and that their full mouse trajectories are tied to their evaluations (i.e., drift rates). We show that a base-rate manipulation changes people’s predispositions and thus their initial mouse angles. We also find that the speed of errors depends on predispositions. These results substantiate the influence of predispositions in value-based choice. Finally, we show that integrating these mouse-tracking measures into the DDM improves model fit. We find that mouse-tracking can capture the physical manifestations of DDM parameters, validate process-level assumptions, and improve the predictive power of computational models.