Visualising spatio-temporal aspects of eye-tracking data

Abstract

Eye movements have a spatial (where people look) but also a temporal (when people look) component. Various types of visualizations aim to show these components simultaneously, but it is unclear how well each of them work and whether the adequacy of each visualization depends on what question is asked about the data or what kind of data is plotted.
In this thesis, four spatio-temporal visualization techniques for eye movements (chord diagram, scan path, scarf plot, space-time cube) were compared in a user study. Participants (N = 25) answered three questions (what region first, what region most, which regions most between) about each visualization, which was based on two types of data-sets (eye movements towards adverts, eye movements towards pairs of gambles). Of the four visuals, three use AOIs (Areas of Interest), the chord diagram, scarfplot and space-time-cube, while only one does not use AOIs (scanpath).
The results show that accuracy of the answers depended on a combination of the dataset, the question that needed to answered, and the type of visualization. For most questions, the scanpath, which did not use AOI information, resulted in lower accuracy than the other graphs. This suggests that AOIs improve the information conveyed by graphs. No effects of experience with reading graphs (for work or not for work) or education on accuracy of the answer was found.
The results suggest that there is no single best visualisation of the spatio-temporal aspects of eye movements. When visualising eye movement data, a user study may therefore be beneficial to determine the optimal visualization of the data-set and research question at hand.

Date of Award	30 Jun 2022
Original language	English
Supervisor	Stefano Bromuri (Examiner) & Frouke Hermens (Co-assessor)