Analyzing spatial data from mouse tracker methodology: An entropic approach.

Area of Science:

• Cognitive Science
• Human-Computer Interaction
• Psychology

Background:

• Mouse tracker methodology analyzes computer-mouse trajectories to study cognitive dynamics in tasks like categorization and decision-making.
• Current geometric approaches to trajectory analysis face limitations due to the noisy nature of movement paths and disregard motor pauses.

Purpose of the Study:

• To present a novel approach, EMOT (Entropy-based MOvement والتتبع), for analyzing computer-mouse trajectories.
• To overcome the limitations of existing methods by quantifying movement features using entropy and modeling trajectories with fast movements and motor pauses.

Main Methods:

• Developed the EMOT approach to quantify movement features in terms of entropy.
• Modeled mouse trajectories as composed of fast movements and motor pauses.
• Utilized a dedicated entropy decomposition analysis for model parameter estimation.

Main Results:

• The EMOT approach provides a robust method for extracting spatial descriptors from noisy mouse trajectories.
• The entropy-based quantification effectively captures movement features, including motor pauses, which are often overlooked.
• Evaluation on categorization tasks demonstrated the characteristics and effectiveness of the EMOT approach.

Conclusions:

• EMOT offers an improved methodology for analyzing mouse trajectories in cognitive science research.
• The approach enhances the understanding of motor components in cognitive dynamics.
• This method provides a more comprehensive analysis of movement patterns compared to traditional geometric methods.