Abstract
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.
Highlights
Eye-tracking has become a popular tool in many psychological disciplines
Gazepath is capable of dealing with low-quality eye-tracking data in terms of robustness and precision, but is well suited for high-quality data. We show this by examining correlations between data quality and outcome measures and assessing the distribution of fixation durations when the gazepath method is used, compared to the standard classification methods
Less than 5% of the EyeLink fixations were split and these splits cannot fully account for the difference. This means that there may be another difference between the two methods that accounts for the difference in median fixation durations
Summary
Eye-tracking is used to study reading abilities (Rayner, Castelhano, & Yang, 2009) and real-world scene perception (Henderson, 2003) in different types of populations and age groups. Eye-tracking can provide insights into reading behavior differences between children with and without dyslexia (e.g., Behav Res (2018) 50:834–852. The fact that eye-tracking can be used in such a broad range of populations is one of its main advantages (Karatekin, 2007). This implies that there are most likely individual differences that should be taken into account, especially when comparing different populations. This paper presents gazepath: an R-package developed to detect fixations in eye-tracking data while accounting for individual differences
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