Mouse tracking performance: A new approach to analyzing continuous mouse tracking data

Area of Science:

• Cognitive Science
• Human-Computer Interaction
• Data Analysis

Background:

• Contemporary mouse tracking studies often rely on binary-choice tasks, limiting the analysis of diverse mouse movement data.
• Naturalistic data from web users present challenges due to the absence of structured, trial-by-trial experimental design.
• Existing methods are insufficient for analyzing complex, unstructured mouse movement time series.

Purpose of the Study:

• To develop robust analytical tools for unstructured mouse movement data in cognitive science.
• To enable the utilization of naturalistic mouse tracking data beyond traditional experimental paradigms.
• To predict performance in online tasks using whole time series analysis of mouse movements.

Main Methods:

• Application of singular value decomposition (SVD) and detrended fluctuation analysis (DFA) to entire mouse movement time series.
• Introduction of a novel method to represent two-dimensional mouse traces as complex-valued time series.
• Enabling straightforward application of SVD and DFA while preserving crucial spatial information.

Main Results:

• Significant predictive information is contained within the whole time series of unstructured mouse movements.
• The developed methods successfully predict performance in an online task using this time series data.
• Analysis of complex-valued time series effectively captures spatial dynamics of mouse traces.

Conclusions:

• Whole time series analysis of unstructured mouse movement data offers valuable insights for cognitive science.
• The novel complex-valued time series approach enhances the applicability of SVD and DFA to mouse tracking.
• These findings advance the potential of mouse tracking research in understanding cognitive processes through naturalistic computer interactions.