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Eye Movement Analysis: A Kernel Density Estimation Approach for Saccade Direction and Amplitude.

Paula Fehlinger1, Bernhard Ertl2, Bianca Watzka1

  • 1Didactics of Physics and Technology, I. Institute of Physics IA, RWTH Aachen University, Sommerfeldstraße 16, 52074 Aachen, Germany.

Journal of Eye Movement Research
|February 20, 2026
PubMed
Summary

New eye-tracking analysis reveals distinct problem-solving strategies by modeling eye movement distributions. This advanced method enhances understanding of cognitive processes during learning and task performance.

Keywords:
amplitude and direction of saccadeseye trackingsolution strategies

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Area of Science:

  • Cognitive Science
  • Educational Technology
  • Data Analysis

Background:

  • Eye movements during tasks offer insights into cognitive processes and problem-solving strategies.
  • Eye-tracking technology is vital for educational research, capturing how individuals interact with information.
  • Conventional saccade analysis methods (histograms, polar diagrams) have limitations in representing complex directional and amplitude patterns.

Purpose of the Study:

  • To introduce a novel kernel density estimation (KDE) approach for analyzing eye movement data, specifically saccades.
  • To improve the accuracy and depth of understanding in eye movement analysis for cognitive and educational research.
  • To identify subtle differences in solution strategies that traditional methods might miss.

Main Methods:

  • Utilized a KDE approach tailored for eye movement data structure.
  • Employed the von Mises kernel for the circular distribution of saccade direction.
  • Applied a Gaussian kernel for the distribution of saccade amplitude.

Main Results:

  • Developed continuous probability distributions for saccade direction and amplitude.
  • Demonstrated improved accuracy in representing eye movement patterns compared to traditional methods.
  • Uncovered previously undetected differences in solution strategies within an empirical dataset.

Conclusions:

  • The proposed KDE method provides a more nuanced understanding of cognitive processes during task engagement and learning.
  • This approach enhances the analysis of eye movements, revealing connections to underlying cognitive strategies.
  • Findings support the development of more effective, individualized teaching methods to improve learner outcomes.