Visual Factors in Cybersickness: A Literature Survey and Meta-Analysis
Bibliographic record
Abstract
Cybersickness, a common adverse side-effect of virtual-reality exposure, is characterised by a constellation of symptoms including nausea, disorientation, and oculomotor disturbances. This review synthesises findings in the literature to evaluate the influence of low-level visual factors on cybersickness, with an emphasis on motion-sickness-related symptoms. Higher-level visual or multisensory factors such as head-tracking latency, coupled physical motion or semantic content were not considered. We searched the Scopus, Pubmed, ACM Digital Library, IEEE Xplore, Web of Science, Google Scholar, and OVID databases in November 2024 as well as searched backward citations from the selected papers and recent review papers. Experimental studies using human participants published in peer-reviewed journals were selected after abstract screening and full-text review of the screened records. Effects were extracted from the papers and effect sizes were synthesised as standardised mean differences in cybersickness intensity or symptoms. Separate random-effects meta-analyses were performed to quantify the effect sizes for each of the visual factors considered including field of view, motion type, velocity and direction, spatial and temporal resolution, contrast, luminance, and the presence of visual reference frames. Of the 4622 initial records, 97 studies were selected and included in the meta-analyses. The analyses revealed that peripheral field of view restriction and independent visual backgrounds were consistently associated with reductions in cybersickness severity. Conversely, visual oscillation, multidimensional visual motion stimuli, and visually simulated off-vertical axis rotation were found to exacerbate cybersickness. The review also identifies methodological trends and limitations within the literature, and suggests ways to improve the effectiveness of subsequent meta-analyses through study design, data reporting standards and methodological descriptions. These findings highlight avenues for future research, particularly in the context of individual susceptibility and multifactor integration. The results offer actionable insights for the design of virtual-reality systems aimed at mitigating cybersickness and enhancing user comfort.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".