Gazepath: An eye-tracking analysis tool that accounts for individual differences and data quality
Daan R van Renswoude1,2, Maartje E J Raijmakers3,4,5,6, Arnout Koornneef4
1Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands. D.R.vanRenswoude@uva.nl.
Behavior Research Methods
|June 9, 2017
Summary
New R-package gazepath improves eye-tracking data analysis for diverse populations. It accounts for individual differences and data quality, offering a more accurate parsing method for researchers studying cognitive processes.
Area of Science:
- Cognitive Science
- Developmental Psychology
- Human-Computer Interaction
Background:
- Eye-tracking is vital for studying cognitive, emotional, and attentional processes across all ages and populations.
- Standard eye-tracking parsing algorithms often fail to account for significant inter- and intra-individual differences.
- Existing methods are typically optimized for adult data, limiting their applicability to diverse groups like infants.
Purpose of the Study:
- Introduce gazepath, an R-package designed for robust eye-tracking data parsing.
- Develop a method that accommodates individual variations and data quality issues in eye-tracking analysis.
- Provide a user-friendly tool with a graphical interface for broader accessibility.
Main Methods:
- Developed gazepath, an R-package integrating solutions from adult and infant eye-tracking literature.
- Implemented a graphical user interface (GUI) using Shiny for ease of use.
- Validated gazepath using diverse datasets, including infant free-viewing, adult reading, and noisy infant data.
Main Results:
- gazepath demonstrated effective parsing for both infant and adult free-viewing data, outperforming standard EyeLink parsing.
- The package successfully controlled for spurious correlations between fixation durations and data quality in infant data.
- gazepath proved effective even with noisy infant eye-tracking data from a Tobii tracker at a low 60 Hz sampling rate.
Conclusions:
- gazepath offers a flexible and accurate eye-tracking data parsing solution adaptable to various populations and data qualities.
- The R-package enhances the reliability of eye-tracking research by addressing limitations of standard algorithms.
- Researchers can utilize gazepath to improve the analysis of cognitive, emotional, and attentional processes in diverse study groups.
