A State Space Approach to Dynamic Modeling of Mouse-Tracking Data

Area of Science:

• Cognitive Psychology
• Experimental Psychology
• Psychometrics

Background:

• Mouse-tracking is increasingly used to measure real-time cognitive processes like decision-making.
• Current analysis methods summarize trajectories, potentially losing rich movement variability.
• A unified framework is needed to better represent mouse-tracking data dynamics.

Purpose of the Study:

• To present a novel, unified framework for analyzing mouse-tracking data.
• To utilize stochastic state-space modeling for a comprehensive data representation.
• To improve the analysis and interpretation of mouse-tracking experiments.

Main Methods:

• Developed a stochastic state-space model for mouse-tracking trajectories.
• Employed a Metropolis-Hastings algorithm and non-linear recursive filter for model estimation.
• Applied the framework to a lexical decision task case study.

Main Results:

• The proposed dynamic modeling approach offers a unified representation of hand movement variability.
• The method effectively captures individual movement dynamics and experimental variables.
• Analysis of a lexical decision task demonstrated the framework's potential.

Conclusions:

• Dynamic modeling significantly enhances the analysis of mouse-tracking data.
• This unified framework allows for richer insights into cognitive processes.
• The approach improves the conclusions researchers can draw from mouse-tracking studies.