A reevaluation of the tolerance to vertical misalignment in stereopsis
Bibliographic record
Abstract
The stereoscopic system tolerates some vertical misalignment of the images in the eyes. However, the reported tolerance for an isolated line stimulus (approximately 4 degrees) is greater than for a random-dot stereogram (RDS, approximately 45 arcmin). We hypothesized that the greater tolerance can be attributed to monoptic depth signals (E. Hering, 1861; M. Kaye, 1978; L. M. Wilcox, J. M. Harris, & S. P. McKee, 2007). We manipulated the vertical misalignment of a pair of isolated stereoscopic dots to assess the contribution of each depth signal separately. For the monoptic stimuli, where only one half-image was present, equivalent horizontal and vertical offsets were imposed instead of disparity. Judgments of apparent depth were well above chance, though there was no conventional disparity signal. For the stereoscopic stimuli, one element was positioned at the midline where monoptic depth perception falls to chance but conventional disparity remains. Subjects lost the depth percept at a vertical misalignment of between 44 and 88 arcmin, which is much smaller than the limit found when both signals were provided. This tolerance for isolated stimuli is comparable to the reported tolerance for RDS. We conclude that previous reports of the greater tolerance to vertical misalignment for isolated stimuli arose from the use of monoptic depth signals.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".