Interactions between monocular occlusions and binocular disparity in the perceived depth of illusory surfaces
Bibliographic record
Abstract
Monocular occlusions play an important role in stereoscopic depth perception. They signal depth discontinuities and, in certain configurations, create percepts of illusory occluding surfaces. Previous research showed that in these configurations the visual system not only infers the depth sign of the illusory occluder but also the depth magnitude. It is believed that quantitative depth percepts from occlusion arrangements are based on the constraints imposed by the viewing geometry. That is, the minimum (or maximum) possible depth of the illusory occluder is constrained by the line of sight from the eye in which the feature is hidden. This information is used by the visual system to estimate depth even in arrangements where the maximum (or minimum) possible depth is unconstrained. Here we have evaluated the effects of binocular disparity on the localization in depth of illusory occluders for several different stimuli. In each of the stimuli, the presence of monocular occlusions induced the percept of an illusory occluder at a different depth than the occluded object. In a series of psychophysical experiments we measured the perceived depth of the occluder as we manipulated 1) the occlusion geometry and 2) the disparity of a binocular feature placed next to the illusory surface. Subjects used a disparity probe to match the perceived depth in the stimuli. Our results show that the disparity of binocular features biases the perceived depth of the illusory occluders in the direction unconstrained by the viewing geometry. We argue that the extent to which binocular disparity influences depth percepts from occlusions can serve as a litmus test of the contribution of monocular information to quantitative depth percepts.
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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 | 0.000 | 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".