Data-driven group comparisons of eye fixations to dynamic stimuli

Abstract

Recent advances in software and hardware have allowed eye tracking to move away from static images to more ecologically relevant video streams. The analysis of eye tracking data for such dynamic stimuli, however, is not without challenges. The frame by frame coding of regions of interest (ROIs) is labour intensive, and computer vision techniques to automatically code such ROIs are not yet mainstream, restricting the use of such stimuli. Combined with the more general problem of defining relevant ROIs for video frames, methods are needed that facilitate data analysis. Here we present a first evaluation of an easy-to-implement data-driven method with the potential to address these issues. To test the new method, we examined the differences in eye movements of self-reported politically left- or right-wing leaning participants to video clips of left- and right-wing politicians. The results show that our method can accurately predict group membership on the basis of eye movement patterns, isolate video clips which best distinguish people on the political left-right spectrum and reveal the section of each video clip with the largest group differences. Our methodology thereby aids the understanding of group differences in gaze behaviour, and the identification of critical stimuli for follow-up studies or for use in saccade diagnosis.

An Eye Tracking Investigation of Young People’s Gaze Behaviour to Gambling and Non-Gambling Moving Adverts

“…Our findings compliment previous findings that revealed differences in gaze variability across observers [51, 58] by demonstrating differences in gaze variability between groups of participants. For instance, Dror and colleagues found that that eye movements are at least partially determined by the visual input, with gaze patterns of several observers on the same natural movie less variable than those made on different movies.…”

“…We present a simple but effective data-driven method [51] to examine group differences in viewing patterns of young people who self-identify as having a low or high craving to gambling when watching gambling advertisements. Eye tracking research on gambling advertising is still relatively new, and so the methodology is not yet fully optimised.…”

“…Figure 2a, b illustrates the method employed for data analysis. We applied a recently developed data-driven method for analysing eye tracking data for dynamic stimuli [51]. The authors identified two methods to quantify group differences in eye movement patterns: (1) separate comparisons of horizontal and vertical gaze position (comparing central tendency differences between groups – either horizontally or vertically, or both) and (2) comparisons of the distance to the group centres (comparing divergence difference between groups).…”

“…In the present study, we undertake one of the first applications of a newly developed data-driven method for analysing eye tracking data for dynamic stimuli [51]. We use this in a sample of young people to examine whether it can reveal group differences in video clips depicting gambling advertisements.…”