Estimating the sensorimotor components of cybersickness

The user base of the virtual reality (VR) medium is growing, and many of these users will experience cybersickness. Accounting for the vast interindividual variability in cybersickness forms a pivotal step in solving the issue. Most studies of cybersickness focus on a single factor (e.g., balance, sex, or vection), while other contributors are overlooked. Here, we characterize the complex relationship between cybersickness and several measures of sensorimotor processing. In a single session, we conducted a battery of tests of balance control. vection responses, and vestibular sensitivity to self-motion. Following this, we measured cybersickness after VR exposure. We constructed a principal components regression model using the measures of sensorimotor processing. The model significantly predicted 37% of the variability in cybersickness measures, with 16% of this variance being accounted for by a principal component that represented balance control measures. The strongest predictor was participants' sway path length during vection, which was inversely related to cybersickness [r(28) = -0.53, P = 0.002] and uniquely accounted for 75% of the variance in cybersickness scores across participants. Vection strength reports and measures of vestibular sensitivity were not significant predictors of cybersickness. We discuss the possible role of sensory reweighting in cybersickness that is suggested by these results, and we identify other factors that may account for the remaining variance in cybersickness. The results reiterate that the relationship between balance control and cybersickness is anything but straightforward. NEW & NOTEWORTHY The advent of consumer virtual reality provides a pressing need for interventions that combat sickness in simulated environments (cybersickness). This research builds on multiple theories of cybersickness etiology to develop a predictive model that distinguishes between individuals who are/are not likely to experience cybersickness. In the future this approach can be adapted to provide virtual reality users with curated content recommendations based on more efficient measurements of sensorimotor processing.