Predictive Modelling of Cognitive Workload in VR: An Eye-Tracking Approach

Cognitive training can boost and sharpen the brain's abilities to remember, focus, and switch between different tasks. One of the key elements of cognitive training is cognitive load. It allows a manipulation of the intensity of the intervention to suit the participant's ability level and keep the session enjoyable, i.e. neither too frustrating/hard nor too boring/easy). However, measuring cognitive workload in an objective way is still under-researched and difficult. Here, we have developed a novel sustained attention Virtual Reality (VR) task, using Unity, that aims to predict load in a controlled manner. We demonstrate promising results in that machine learning algorithms can identify perceived as well as objective difficulty of the game accurately, using a combination of eye-tracking and physiological data obtained directly within the VR environment.