Gazepath: An eye-tracking analysis tool that accounts for individual differences and data quality

Daan R. van Renswoude*, Maartje E.J. Raijmakers, Arnout Koornneef, Scott P. Johnson, Sabine Hunnius, Ingmar Visser

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review


Eye-trackers are a popular tool for studying cognitive, emotional, and attentional processes in different populations (e.g., clinical and typically developing) and participants of all ages, ranging from infants to the elderly. This broad range of processes and populations implies that there are many inter- and intra-individual differences that need to be taken into account when analyzing eye-tracking data. Standard parsing algorithms supplied by the eye-tracker manufacturers are typically optimized for adults and do not account for these individual differences. This paper presents gazepath, an easy-to-use R-package that comes with a graphical user interface (GUI) implemented in Shiny (RStudio Inc 2015). The gazepath R-package combines solutions from the adult and infant literature to provide an eye-tracking parsing method that accounts for individual differences and differences in data quality. We illustrate the usefulness of gazepath with three examples of different data sets. The first example shows how gazepath performs on free-viewing data of infants and adults, compared to standard EyeLink parsing. We show that gazepath controls for spurious correlations between fixation durations and data quality in infant data. The second example shows that gazepath performs well in high-quality reading data of adults. The third and last example shows that gazepath can also be used on noisy infant data collected with a Tobii eye-tracker and low (60 Hz) sampling rate.

Original languageEnglish
Pages (from-to)834-852
Number of pages19
JournalBehavior Research Methods
Issue number2
Publication statusPublished - 1 Apr 2018
Externally publishedYes


  • Infant eye movements
  • Eye-tracking methodology
  • Fixation duration
  • Attention
  • Event detection


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