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Introducing the kollaR package: A user-friendly open-access solution for eye-tracking analysis and visualization.

Johan Lundin Kleberg1,2, Astrid E Z Hallman3, Rebecka Astenvald4

  • 1Department of Psychology, Stockholm University, Frescativägen 8, 106 91, Stockholm, Sweden. johan.lundin.kleberg@su.se.

Behavior Research Methods
|December 8, 2025
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Summary
This summary is machine-generated.

This study introduces kollaR, an R package for eye-tracking analysis, aiding researchers in selecting fixation classification algorithms. It ensures reliable data analysis for cognitive and developmental research, especially with challenging datasets.

Keywords:
Area of InterestEye trackingFixationsR PackageSaccadesSoftware

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Area of Science:

  • Cognitive Science
  • Developmental Psychology
  • Human-Computer Interaction

Background:

  • Eye tracking is vital for cognitive and developmental research, relying on accurate fixation and saccade identification.
  • Event classification algorithms significantly impact eye-tracking study outcomes, particularly with compromised data quality in special populations.
  • Selecting appropriate algorithms is crucial for valid eye-tracking data analysis.

Purpose of the Study:

  • Introduce kollaR, an open-source R package for comprehensive eye-tracking analysis.
  • Facilitate the selection and comparison of different event classification algorithms.
  • Enable robust AOI-based analyses and data visualization for publication.

Main Methods:

  • Developed kollaR, an R library for event classification, AOI analysis, and data visualization.
  • Included functionalities for comparing various fixation classification algorithms.
  • Validated kollaR against existing software implementations.

Main Results:

  • Demonstrated consistency of kollaR's fixation classification with other software.
  • Showcased kollaR's utility with real data from typically developing and neurodevelopmental individuals.
  • Illustrated identification of validity threats in both high- and low-quality eye-tracking data.

Conclusions:

  • kollaR provides a valuable tool for reproducible and rigorous eye-tracking data analysis.
  • The package aids researchers in selecting appropriate algorithms and assessing data quality.
  • Facilitates reliable insights from eye-tracking studies across diverse populations and data qualities.