Studying cybersickness and sensory conflict theory using a motion-coupled virtual reality system
Bibliographic record
Abstract
6th International Conference on Visually Induced Motion Sensations, Toronto, ON, Canada.Conference Presentation Abstract:Sensory conflict theory attempts to explain the immediate cause of visually induced motion sickness (VIMS) in virtual reality (VR) systems. The discomfort is based on the mismatch in visual–vestibular interaction. Traditional VR experiences often involve visual stimuli without significant amount of physical motion to compensate the missing vestibular stimuli. This study examined whether virtual visual scene coupled with motion sensation could reduce VIMS symptoms. A motion-coupled VR system with HTC Vive head-mounted display (HMD) was developed and used in the experimentation. The mechanical 3-DOF motion platform provides programmed physical motion which supplements the visual stimulus shown on the HMD. In addition to providing coherent visual and vestibular sensations, the study also tested the effect of an Earth-referenced visual scene that provides a fixed visual horizon. This is similar to a past study of using virtual horizon on a ship motion simulator. Anecdotal report suggested that visual Earth reference is important in establishing postural stability in a sea voyage and simulation, while instability of postural is usually correlated with cybersickness. Hence, a generated Earth-referenced visual scene could in theory reduce the effect of VIMS. In the experiment, participants experienced regular programmed visual and motion yaw rotation while viewing a virtual apartment (0.6Hz ±30°) for 3.5 minutes in each trial. Three conditions: purely visual, motion synchronized with visual, and motion with Earth-referenced visual scenes were tested on how motion and visual stimuli interact. Participants were asked to complete the simulator sickness questionnaire (SSQ) (every trial), the 11-points misery scale (every 60 seconds), and the 11-points joyfulness scale (every 60 seconds). These measurements help to create a cybersickness symptom profile and severity measurement for different conditions. Participants were also asked to stand on a balance board for 60 seconds to measure their centre of pressure after each trial. The result of the experiment could provide further insights into the relationships between the two senses and potential ways of relieving VIMS in VR systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".