Trait and state individual differences’ impact on rapid immersed symptoms of cybersickness (RISC) trajectories

Abstract

Cybersickness, or sickness attributed to virtual reality (VR), is one of the largest barriers to the daily implementation of VR in entertainment, education, and therapy. Although there is a plethora of research in this area, the underlying mechanisms and predictive factors have yet to be discerned. Existing subjective cybersickness measures of symptom-specific intensities are administered post- exposure and cannot detect symptom onset time, whereas subjective measures administered during VR exposure cannot detect symptom-specific intensities. Additionally, current predictive models of cybersickness fail to include human trait and state factors, which are needed to create a fully comprehensive model of human- specific factors of cybersickness. This dissertation aims to address these concerns. First, a psychometric evaluation of the Simulator Sickness Questionnaire (SSQ) leads to the development of a new symptom-specific immersed measure of cybersickness: the Rapid Immersed Symptoms of Cybersickness (RISC). This new measure is then used to assess human state and trait factors that contribute to cybersickness susceptibility and intensity. Results indicate that both state and trait factors predict the changes in cybersickness trajectory and intensity while in VR. Additionally, the interaction between several state and trait factors significantly impact the change in cybersickness trajectory and intensity. These findings indicate that cybersickness susceptibility and intensity are due to a combination of individual differences and rely on both trait and state factors.