Da Vinci decoded: Does da Vinci stereopsis rely on disparity?
Bibliographic record
Abstract
In conventional stereopsis, the depth between two objects is computed based on the retinal disparity in the position of matching points in the two eyes. When an object is occluded by another object in the scene, so that it is visible only in one eye, its retinal disparity cannot be computed. Nakayama and Shimojo (1990) found that a precept of quantitative depth between the two objects could still be established for such stimuli and proposed that this precept is based on the constraints imposed by occlusion geometry. They named this and other occlusion-based depth phenomena "da Vinci stereopsis." Subsequent research found quantitative depth based on occlusion geometry in several other classes of stimuli grouped under the term da Vinci stereopsis. However, Nakayama and Shimojo's findings were later brought into question by Gillam, Cook, and Blackburn (2003), who suggested that quantitative depth in their stimuli was perceived based on conventional disparity. In order to understand whether da Vinci stereopsis relies on one type of mechanism or whether its function is stimulus dependent we examine the nature and source of depth in the class of stimuli used by Nakayama and Shimojo (1990). We use three different psychophysical and computational methods to show that the most likely source for depth in these stimuli is occlusion geometry. Based on these experiments and previous data we discuss the potential mechanisms responsible for processing depth from monocular features in da Vinci stereopsis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| 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 teacher head, 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".