Fixation identification: the optimum threshold for a dispersion algorithm.

Area of Science:

• Cognitive Psychology
• Human-Computer Interaction
• Neuroscience

Background:

• Fixation detection algorithms are crucial for analyzing eye-tracking data.
• The parameters of these algorithms, such as dispersion metrics and thresholds, can significantly influence results.
• Understanding these parameters is key to ensuring the reliability and replicability of eye-tracking studies.

Purpose of the Study:

• To investigate how different dispersion metrics and threshold values affect fixation detection algorithms.
• To determine the optimal threshold for the I-DT (Information-based Drift-Tracking) algorithm.
• To assess the impact of algorithm parameters on the number, position, size, and duration of detected fixations.

Main Methods:

• Analysis of gaze data from chess players during a memory recall experiment.
• Systematic variation of threshold values across five different dispersion metrics.
• Generation of scan paths at distinct intervals to evaluate algorithm sensitivity.
• Use of metrics like percentage of points of regard (PORs) used, number of fixations, spatial dispersion, and scan path differences.

Main Results:

• A fixation radius of 1 degree was identified as an optimal threshold.
• This threshold ensures replicable fixation counts and positions.
• Approximately 90% of the captured gaze data is utilized with this threshold.
• The choice of dispersion metric and threshold value significantly impacts fixation detection outcomes.

Conclusions:

• The I-DT algorithm's sensitivity is influenced by dispersion metrics and threshold settings.
• A 1-degree fixation radius provides a robust threshold for reliable eye-tracking data analysis.
• This finding contributes to standardizing fixation detection methods for improved research reproducibility.