Measuring Cognitive Load in Virtual Reality Training via Pupillometry

Pupillometry is known as a reliable technique to measure cognitive load in learning and performance. However, its applicability to virtual reality (VR) environments, an emerging technology for simulation-based training, has not been well-verified in educational contexts. Specifically, the VR display causes light reflexes that confound task-evoked pupillary responses (TEPRs), impairing cognitive load measures. Through this pilot study, we validated whether task difficulty can predict cognitive load as measured by TEPRs corrected for the light reflex and if these TEPRs correlate with cognitive load self-ratings and performance. 14 students in health sciences performed observation tasks in two conditions: difficult versus easy tasks, whilst watching a VR scenario in home health care. Then, a cognitive load self-rating ensued. We used a VR system with a built-in eye-tracker and a photosensor installed to assess pupil diameter and light intensity during the scenario. Employing a method from the human-computer interaction field, we determined TEPRs by modeling the pupil light reflexes using a baseline. As predicted, the difficult task caused significantly larger TEPRs than the easy task. Only in the difficult task condition did TEPRs positively correlate with the performance measure. These results suggest that TEPRs are valid measures of cognitive load in VR training when corrected for the light reflex. It opens up possibilities to use real-time cognitive load for assessment and instructional design for VR training. Future studies should test our findings with a larger sample size, in various domains, involving complex VR functions such as haptic interaction.