Measuring Mental Effort via Entropy in VR

Abstract

Recognizing changes in users' experienced mental effort is a perennial interest in human-computer interaction research particularly in the design of intelligent user interfaces built to adapt to different levels of mental effort. With virtual reality (VR) applications, for example, many measures of mental workload (e.g., secondary tasks) are highly intrusive and can distort what is being measured. In this paper we investigate the entropy of controller movements as an indicator of mental effort that can be measured unobtrusively. We report a proof-of-concept study that manipulates the experienced mental effort using the popular e-crossing task. As expected, the results show that entropy is increased for people with higher mental effort than for people with lower mental effort and that there is a positive relationship with NASA-TLX scores, the benchmark questionnaire for mental effort. Thus, intelligent user interfaces become capable of detecting mental effort in VR on the basis of controller entropy and could recognize when users need assistance in their decision making.