LAUSR

Eye activity-based within-task cognitive load measurement (CLM) is currently not feasible in everyday situations. One important issue to be addressed to move such CLM beyond controlled laboratory environments is determining practical methods for mitigating the pupillary light reflex (PLR) effect in CLM. In this paper, four approaches to dealing with the PLR effect within a modified verbal digit span task are investigated: ignore the PLR, exclude PLR data, compensate for PLR and use PLR features for measurement. During experimental work, cognitive load and the PLR were induced with a modified verbal digit span task and changes in brightness of a large monitor, respectively. The “exclude PLR,” “compensate for PLR,” and “use PLR features” methods were found to improve classification performance by up to 18.5% relative to the “ignore PLR” method, which yielded the worst classification accuracy of 58% using an average pupil diameter feature. Features derived from the transient properties of the PLR response associated with cognitive load were found to yield the superior classification accuracy of 70%, which is an improvement compared with previously published approaches which treated the PLR responses as interference. The findings from this paper suggest that the PLR cannot be easily ignored or normalized, and clearly demonstrate the importance of PLR-aware feature extraction for the design of future eyewear-based always-on CLM in conditions that are more realistic than a darkened, controlled laboratory.