ML-Driven Cognitive Workload Estimation in a VR-based Sustained Attention Task^*

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 that it manipulates ’cognitive load’, by adjusting the intensity of the intervention to suit the participant’s ability level and keep the session enjoyable. This study introduces a novel sustained attention task in Virtual Reality (VR) to predict cognitive load dynamically. Unlike previous research, which often used non-VR settings, simpler tasks, or performance metrics as predictors, our approach aims to measure cognitive load objectively in a more ecologically valid and gamified VR environment. We employed machine learning techniques to enable real-time, personalized cognitive training. This work contributes to the development of more effective cognitive training interventions that can adapt to individual differences and maintain optimal engagement levels.