Sequence Models in Eye Tracking: Predicting Pupil Diameter During Learning

Abstract

A deep learning framework for predicting pupil diameter using eye tracking data is described. Using a variety of input, such as fixation positions, durations, saccades and blink-related information, we assessed the performance of a sequence model in predicting future pupil diameter in a student population as they watched educational videos in a controlled setting. Through assessing student performance on a post-viewing test, we report that deep learning sequence models may be useful for separating components of pupil responses that are linked to luminance and accommodation from those that are linked to cognition and arousal.