Online webcam-based eye tracking in cognitive science: A first look

Abstract

Online experimentation is emerging in many areas of cognitive psychology as a viable alternative or supplement to classical in-lab experimentation. While performance- and reaction-time-based paradigms are covered in recent studies, one instrument of cognitive psychology has not received much attention up to now: eye tracking. In this study, we used JavaScript-based eye tracking algorithms recently made available by Papoutsaki et al. (International Joint Conference on Artificial Intelligence, 2016) together with consumer-grade webcams to investigate the potential of online eye tracking to benefit from the common advantages of online data conduction. We compared three in-lab conducted tasks (fixation, pursuit, and free viewing) with online-acquired data to analyze the spatial precision in the first two, and replicability of well-known gazing patterns in the third task. Our results indicate that in-lab data exhibit an offset of about 172 px (15% of screen size, 3.94° visual angle) in the fixation task, while online data is slightly less accurate (18% of screen size, 207 px), and shows higher variance. The same results were found for the pursuit task with a constant offset during the stimulus movement (211 px in-lab, 216 px online). In the free-viewing task, we were able to replicate the high attention attribution to eyes (28.25%) compared to other key regions like the nose (9.71%) and mouth (4.00%). Overall, we found web technology-based eye tracking to be suitable for all three tasks and are confident that the required hard- and software will be improved continuously for even more sophisticated experimental paradigms in all of cognitive psychology.