Effects of Opacity of Peripheral Real Scene and Field of View of Mixed Reality on Motion Sickness
Bibliographic record
Abstract
Mixed reality (MR) technology enables seamless transitions between virtual and real environments, but motion sickness (MS) remains an important barrier to widespread adoption. This study investigated how opacity of peripheral real scenes (OPRS) and field of view (FoV) affect MS symptoms in MR environments where dynamic virtual content is presented centrally while static real-world scenes remain visible peripherally. A between-subjects experiment was conducted with 120 participants experiencing a 10-minute roller coaster simulation in MR. The experiment employed a 4 × 3 factorial design with four OPRS levels (0, 0.25, 0.75, and 1) and three FoV levels (30°, 60°, and 90°). Heart rate variability (HRV) was measured using electrocardiography (ECG) to assess physiological responses, and subjective MS was evaluated using the Virtual Reality Sickness Questionnaire (VRSQ). OPRS significantly influenced MS symptoms. Higher OPRS conditions showed increased sympathetic nervous system activation and decreased parasympathetic activity compared to lower OPRS levels, indicating heightened MS. FoV also showed significant effects, with the 90° condition indicating reduced MS symptoms compared to 60°. This finding is contrary to traditional VR studies, which generally report that narrower FoV reduce MS. No significant OPRS × FoV interactions were observed. These results indicate that carefully adjusting the ratio between virtual and real content visibility, particularly OPRS, is essential for developing comfortable MR interfaces.
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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 | 0.008 |
| 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) | 0.002 | 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".