Estimating mental load in passive and active tasks from pupil and gaze changes using bayesian surprise

Abstract

Eye-based monitoring has been suggested as a means to measure mental load in a non-intrusive way. In most cases, the experiments have been conducted in a setting where the user has been mainly passive. This constraint does not reflect applications where we want to identify mental load of an active user, e.g. during surgery. The main objective of our work is to investigate the potential of an eye tracking device for measuring the mental load in realistic active situations. In our first experiments we calibrate our setup by using a well established passive setup. There, we confirm that our setup can recover reliably eye width in real time, and we can observe the previously reported relationship between pupil width and cognitive load, however, we also observe a very high variance between different test subjects. In a follow up active task experiment, neither pupil width nor eye gaze showed a significant predictive power over workflow disruptions. To address this, we present an approach for estimating the likelihood of workflow disruptions during active fine-motor tasks. Our method combines the eye-based data with the Bayesian Surprise theory and is able to successfully predict user's struggle with correlations of 35% and 75% respectively.