Can physical motions prevent disorientation in naturalistic VR?
Bibliographic record
Abstract
Most virtual reality simulators have a serious flaw: Users tend to get easily lost and disoriented as they navigate. According to the prevailing opinion, this is because of the lack of actual physical motion to match the visually simulated motion: E.g., using HMD-based VR, Klatzky et al. [1] showed that participants failed to update visually simulated rotations unless they were accompanied by physical rotation of the observer, even if passive. If we use more naturalistic environments (but no salient landmarks) instead of just optic flow, would physical motion cues still be needed to prevent disorientation? To address this question, we used a paradigm inspired by Klatzky et al.: After visually displayed passive movements along curved streets in a city environment, participants were asked to point back to where they started. In half of the trials the visually displayed turns were accompanied by a matching physical rotation. Results showed that adding physical motion cues did not improve pointing performance. This suggests that physical motions might be less important to prevent disorientation if visuals are naturalistic enough. Furthermore, unexpectedly two participants consistently failed to update the visually simulated heading changes, even when they were accompanied by physical rotations. This suggests that physical motion cues do not necessarily improve spatial orientation ability in VR (by inducing obligatory spatial updating). These findings have noteworthy implications for the design of effective motion simulators.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".