TRACK-a new algorithm and open-source tool for the analysis of pursuit-tracking sensorimotor integration processes.

Area of Science:

• Neuroscience
• Cognitive Science
• Human-Computer Interaction

Background:

• Sensorimotor integration is crucial for daily cognitive functions.
• Pursuit-tracking tasks offer ecological assessment of sensorimotor integration.
• Current analysis methods for pursuit-tracking data can be complex and yield ambiguous metrics.

Purpose of the Study:

• To introduce TRACK, an open-source algorithm for analyzing pursuit-tracking performance.
• To develop a novel spatial error metric for enhanced precision in performance analysis.
• To improve the investigation of sensorimotor integration through refined pursuit-tracking analysis.

Main Methods:

• Developed the TRACK algorithm to calculate spatial error.
• Algorithm identifies key events based on cursor and target direction changes (similarity and proximity).
• Applied the algorithm to pursuit-tracking behavioral data.

Main Results:

• The spatial error metric demonstrated higher precision and better fit to behavioral data compared to temporal error.
• The algorithm successfully replicated known effects like learning and practice.
• New insights into pursuit-tracking behavior were revealed through the spatial error metric.

Conclusions:

• The TRACK algorithm and its spatial error metric offer a more precise tool for analyzing pursuit-tracking data.
• This advancement enhances the utility of pursuit-tracking tasks for studying sensorimotor integration.
• The findings pave the way for deeper understanding of sensorimotor processes in complex environments.