Monocular occlusions determine the perceived shape and depth of occluding surfaces
Bibliographic record
Abstract
Recent experiments have established that monocular areas arising due to occlusion of one object by another contribute to stereoscopic depth perception. It has been suggested that the primary role of monocular occlusions is to define depth discontinuities and object boundaries in depth. Here we use a carefully designed stimulus to demonstrate empirically that monocular occlusions play an important role in localizing depth edges and defining the shape of the occluding surfaces in depth. We show that the depth perceived via occlusion in our stimuli is not due to the presence of binocular disparity at the boundary and discuss the quantitative nature of depth perception in our stimuli. Our data suggest that the visual system can use monocular information to estimate not only the sign of the depth of the occluding surface but also its magnitude. We also provide preliminary evidence that perceived depth of illusory occluders derived from monocular information can be biased by binocular features.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".