Stereoscopic perception of real depths at large distances
Bibliographic record
Abstract
There has been no direct examination of stereoscopic depth perception at very large observation distances and depths. We measured perceptions of depth magnitude at distances where it is frequently reported without evidence that stereopsis is non-functional. We adapted methods pioneered at distances up to 9 m by R. S. Allison, B. J. Gillam, and E. Vecellio (2009) for use in a 381-m-long railway tunnel. Pairs of Light Emitting Diode (LED) targets were presented either in complete darkness or with the environment lit as far as the nearest LED (the observation distance). We found that binocular, but not monocular, estimates of the depth between pairs of LEDs increased with their physical depths up to the maximum depth separation tested (248 m). Binocular estimates of depth were much larger with a lit foreground than in darkness and increased as the observation distance increased from 20 to 40 m, indicating that binocular disparity can be scaled for much larger distances than previously realized. Since these observation distances were well beyond the range of vertical disparity and oculomotor cues, this scaling must rely on perspective cues. We also ran control experiments at smaller distances, which showed that estimates of depth and distance correlate poorly and that our metric estimation method gives similar results to a comparison method under the same conditions.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".