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

Area of Science:

• Cognitive Psychology
• Computational Neuroscience
• Behavioral Science

Background:

• Process tracing and modeling are key for understanding decision-making.
• Process tracing yields numerous measures, while process modeling requires many trials.
• Existing methods have limitations in scope and data requirements.

Purpose of the Study:

• To integrate mouse cursor tracking with the drift diffusion model (DDM).
• To reduce the number of cursor measures needed for analysis.
• To overcome the minimal trial requirement for DDM fitting.

Main Methods:

• 103 participants completed 90 trials in a random dot kinematogram (RDK) task.
• 18 mouse cursor measures were collected and analyzed.
• Partial least squares regression predicted DDM parameters (drift rate, threshold separation, non-decision time) from cursor measures.

Main Results:

• Four cursor measures significantly predicted DDM parameters.
• Reduced trial counts showed these measures, plus response time and accuracy, outperformed traditional model fitting.
• The combined approach remained stable in predicting DDM parameters.

Conclusions:

• This study highlights key mouse cursor measures for decision-making research.
• The method lowers the barrier for using mouse tracking in psychological research.
• It provides a viable approach for DDM analysis with limited experimental trials.