Comparison of stereoscopic and non-stereoscopic optic flow displays
Bibliographic record
Abstract
Non-stereoscopic optic flow can evoke vection of an inaccurate magnitude (Jenkin, Redlick, Harris, ARVO 2000). Stereoscopic information can affect vection, shortening its latency and increasing its duration (Palmisano, Percept. Psych. 1996 58: 1168). Does stereoscopic information influence the magnitude of vection? Methods: Subjects wore alternating shutter glasses (96Hz) to view either a non-stereoscopic or a binocular, 3-dimensional virtual corridor projected onto a large display surface comprised of two orthogonal walls with their corner straight ahead at a distance of 3.5m. Subjects viewed a virtual target at between 4-32m which was extinguished before forward self-motion at either constant velocity (0.4m/s–6.4m/s) or acceleration (0.025m/s/s–1.0m/s/s) was simulated. Subjects reported when they perceived they had reached the previously presented target position. Results: When subjects viewed the large, non-stereoscopic display, we reproduced our previous results obtained in a non-stereoscopic HMD. Subjects overestimated their motion at slower accelerations (constant velocity and 0.025–0.4m/s/s), and were accurate at higher accelerations (0.4–1.0 m/s/s). When subjects viewed the display with stereoscopic cues, they overestimated their motion at both slower (0.025–0.4m/s/s) and higher accelerations (0.4m/s/s – 1m/s/s). Conclusion: Adding stereopsis, far from improving the accuracy of distance judgments, surprisingly was associated with overestimation even of high acceleration movements (which were judged accurately in non- stereoscopic displays).
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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 | 0.002 |
| 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) | 0.002 | 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".