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Autonomic Nervous System Factors Underlying Anxiety in Virtual Environments: A Regression Model for Cybersickness

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2 Author(s)
Susan Bruck ; Macquarie Univ., North Ryde, NSW, Australia ; Paul A. Watters

The ability to predict whether people will experience anxiety is important for recruitment and selection in highly-stressful professions. Using a virtual reality environment (VRE) can provide a tool to predict whether a person will experience anxiety. This paper reports several regression models which suggest observed and self-reported measures of anxiety during and after immersion in a VRE can be used to predict an individual's anxiety response to a simulated stressful environment. We found that respiration was a poor predictor of anxiety, but that cardiac activity accounted for around 39% of variance in self-reported anxiety responses using a four point scale. In contrast, responses from the simulator sickness questionnaire (SSQ) accounted for 98% of variance in anxiety responses. However, only four out of eighteen measures in the SSQ made a significant contribution to the model. The implication for predicting an individual's anxiety responses using self-report or physiological measures is discussed.

Published in:

Virtual Systems and Multimedia, 2009. VSMM '09. 15th International Conference on

Date of Conference:

9-12 Sept. 2009