In virtual reality (VR), users can experience symptoms of motion sickness, which is referred to as VR sickness or cybersickness. The symptoms include but are not limited to eye fatigue, disorientation, and nausea, which can impair the VR experience of users. Though many studies have attempted to reduce the discomfort, they produced conflicting results with varying degrees of VR sickness. In particular, a visually improved VR does not necessarily result in decreased VR sickness. To understand these unexpected results, we surveyed the causes of VR sickness and measurement of symptoms. We reorganized the causes of the VR sickness into three major factors (hardware, content, and human factors) and investigated the sub-component of each factor. We then surveyed frequently used measures of VR sickness, both subjective and objective approaches. We also investigated emerging approaches for reducing VR sickness and proposed a multimodal fidelity hypothesis to give an insight into future studies.

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Cybersickness refers to a group of uncomfortable symptoms experienced in virtual reality (VR). Among several theories of cybersickness, the subjective vertical mismatch (SVM) theory focuses on an individual’s internal model, which is created and updated through past experiences. Although previous studies have attempted to provide experimental evidence for the theory, most approaches are limited to subjective measures or body sway. In this study, we aimed to demonstrate the SVM theory on the basis of the participant’s eye movements and investigate whether the subjective level of cybersickness can be predicted using eye-related measures. 26 participants experienced roller coaster VR while wearing a head-mounted display with eye tracking. We designed four experimental conditions by changing the orientation of the VR scene (upright vs. inverted) or the controllability of the participant’s body (unrestrained vs. restrained body). The results indicated that participants reported more severe cybersickness when experiencing the upright VR content without controllability. Moreover, distinctive eye movements (e.g. fixation duration and distance between the eye gaze and the object position sequence) were observed according to the experimental conditions. On the basis of these results, we developed a regression model using eye-movement features and found that our model can explain 34.8% of the total variance of cybersickness, indicating a substantial improvement compared to the previous work (4.2%). This study provides empirical data for the SVM theory using both subjective and eye-related measures. In particular, the results suggest that participants’ eye movements can serve as a significant index for predicting cybersickness when considering natural gaze behaviors during a VR experience.

Schizotypy refers to the personality trait of experiencing “psychotic” symptoms and can be regarded as a predisposition of schizophrenia-spectrum psychopathology (Raine, 1991). Cumulative evidence has revealed that individuals with schizotypy, as well as schizophrenia patients, have emotional processing deficits. In the present study, we investigated multimodal emotion perception in schizotypy and implemented the machine learning technique to find out whether a schizotypy group (ST) is distinguishable from a control group (NC), using electroencephalogram (EEG) signals. Forty-five subjects (30 ST and 15 NC) were divided into two groups based on their scores on a Schizotypal Personality Questionnaire. All participants performed an audiovisual emotion perception test while EEG was recorded. After the preprocessing stage, the discriminatory features were extracted using a mean subsampling technique. For an accurate estimation of covariance matrices, the shrinkage linear discriminant algorithm was used. The classification attained over 98% accuracy and zero rate of false-positive results. This method may have important clinical implications in discriminating those among the general population who have a subtle risk for schizotypy, requiring intervention in advance.