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 primary behavioral approaches for uncovering human decision-making processes. However, both approaches face significant limitations: process tracing offers a large and oftentimes confusing number of measures, while process modeling relies on a minimal number 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 the minimal trial amount requirements of DDM fitting. One hundred three participants completed 90 trials in a random dot kinematogram (RDK). A total of 18 cursor measures were taken from the mouse cursor tracking literature and used to predict drift rate, threshold separation, and non-decision time of the DDM via partial least squares regression. Four cursor measures contributed significantly 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.