Instantaneous and Robust Eye-Activity Based Task Analysis

Abstract

Task analysis using eye-activity has previously been used for estimating cognitive load on a per-task basis. However, since pupil size is a continuous physiological signal, eye-based classification accuracy of cognitive load can be improved by considering cognitive load at a higher temporal resolution and incorporating models of the interactions between the task-evoked pupillary response (TEPR) and other pupillary responses such as the Pupillary Light Reflex into the classification model. In this work, methods of using eye-activity as a measure of continuous mental load will be investigated. Subsequently pupil light reflex models will be incorporated into task analysis to investigate the possibility of enhancing the reliability of cognitive load estimation in varied lighting conditions. This will culminate in the development and evaluation of a classification system which measures rapidly changing cognitive load. Task analysis of this calibre will enable interfaces in wearable optical devices to be constantly aware of the user's mental state and control information flow to prevent information overload and interruptions.