Pupil Dilation as an Indicator of Cognitive Workload in Human-Computer Interaction

Pupil dilation is known to quickly respond to changes in the brightness in the visual field and a person's cognitive workload while performing a visual task. Pupil dilation is rarely analyzed in usability studies although it can be measured by most video-based eye-tracking systems and yields highly relevant workload information. This is mainly due to two problems: First, the variety of factors that can influence pupil dilation, and second, the distortion of pupil-size data by eye movements: The size of the pupil as seen by the eye-tracker camera depends on the person's gaze angle. In the present study, we developed and implemented a neural-network based calibration interface for eye-tracking systems, which is capable of almost completely eliminating the geometry-based distortion of pupil-size data for any human subject. Moreover, we compared the effects of cognitive workload and display brightness on pupil dilation and investigated the interaction of these two factors. The results of our study considerably facilitate the use of pupil dilation as a quick and reliable indicator of a person's cognitive workload.