Comparing the effectiveness of different displays in enhancing illusions of self-movement (vection)
Why this work is in the frame
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Bibliographic record
Abstract
Illusions of self-movement (vection) can be used in virtual reality (VR) and other applications to give users the embodied sensation that they are moving when physical movement is unfeasible or too costly. Whereas a large body of vection literature studied how various parameters of the presented visual stimulus affect vection, little is known how different display types might affect vection. As a step toward addressing this gap, we conducted three experiments to compare vection and usability parameters between commonly used VR displays, ranging from stereoscopic projection and 3D TV to high-end head-mounted display (HMD, NVIS SX111) and recent low-cost HMD (Oculus Rift). The last experiment also compared these two HMDs in their native full field of view (FOV) and a reduced, matched FOV of 72° × 45°. Participants moved along linear and curvilinear paths in the virtual environment, reported vection onset time, and rated vection intensity at the end of each trial. In addition, user ratings on immersion, motion sickness, vection, and overall preference were recorded retrospectively and compared between displays. Unexpectedly, there were no significant effects of display on vection measures. Reducing the FOV for the HMDs (from full to 72° × 45°) decreased vection onset latencies, but did not affect vection intensity. As predicted, curvilinear paths yielded earlier and more intense vection. Although vection has often been proposed to predict or even cause motion sickness, we observed no correlation for any of the displays studied. In conclusion, perceived self-motion and other user experience measures proved surprisingly tolerant toward changes in display type as long as the FOV was roughly matched. This suggests that display choice for vection research and VR applications can be largely based on other considerations as long as the provided FOV is sufficiently large.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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 it