The presence of an avatar can reduce cybersickness in Virtual Reality
Bibliographic record
Abstract
Virtual Reality (VR) applications are increasingly being utilized for research, healthcare, and education. Despite their benefits, many VR users report motion sickness-like sensations (cybersickness), such as headache, disorientation, or nausea. Previous studies suggest that the sense of presence (“being there”) in the virtual world may contribute to the severity of cybersickness; however, results have been contradictory, with some studies reporting a negative and some reporting a positive relationship between the two. The goal of the current study was to further investigate how presence and cybersickness are related. Participants ( N = 54) were exposed to a VR scene presented on a head-mounted display showing a 15-minute-long passive movement through space. The level of presence was manipulated by including an avatar (astronaut suit with hand-tracking) or no avatar in the virtual environment. Results showed that the avatar group reported significantly less severe cybersickness compared to the no-avatar group. We also found significant, negative correlations between some of the presence metrics (immersion, sensory fidelity) and cybersickness, indicating that cybersickness severity decreased as the level of presence increased. These findings suggest that more immersive VR experiences using an avatar may potentially reduce the risk of experiencing cybersickness.
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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 | 0.000 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".