Exploring the synthesis of mouse cursor tracking and drift diffusion modeling in a perceptual decision-making task.

Process tracing and process modeling are the two prime behavioral approaches to uncover human decision-making processes. However, both approaches face significant limitations: Process tracing offers a large and oftentimes confusing amount of measures, while process modeling relies on a minimal amount of comparable trials for reliable model fitting. In our study, we explore how we can combine mouse cursor tracking and the drift diffusion model (DDM) in order to both reduce the number of cursor measures and circumvent minimal trial amount requirements of DDM fitting. 103 participants completed 90 trials in a random dot kinematogram (RDK). A total of 18 cursor measures were taken from mouse cursor tracking literature and used to predict drift rate, threshold separation, and non-decision time of the DDM via partial least square regression. Four cursor measures contributed a major part to the prediction of the DDM parameters. When reducing the available trials these cursor measures, in combination with response time and accuracy, performed better and remained more stable in the prediction of DDM parameters than model fitting. Our results lower the barrier for applying mouse cursor tracking for novice researchers by highlighting important cursor measures and their mapping to psychological constructs of decision-making, while also offering an approach for behavioral scientists to investigate DDM components in experimental setups with a restricted number of trials.