Decoding da Vinci: quantitative depth from monocular occlusions
Bibliographic record
Abstract
Nakayama and Shimojo (1990) demonstrated that quantitative depth percepts could be generated by monocular occlusions, a phenomenon they called da Vinci stereopsis. They used a configuration where a monocular bar was placed to one side of a binocular rectangle. When an occlusion interpretation was possible, the bar appeared behind the rectangle at a distance that increased as the lateral separation between the bar and the rectangle increased. Gillam, Cook and Blackburn (2003) argued that quantitative depth perception in da Vinci stereopsis was due to double-matching of the bar with the edge of the rectangle. They showed that when the monocular bar was replaced with a monocular dot only qualitative depth percepts remained. However, their stimulus differed from the original in ways that promoted double-matching and the range of separations of the monocular feature from the rectangle was different for the bar and the dot. To evaluate the contributions of monocular occlusions and double-matching to quantitative depth percepts in da Vinci arrangements, we have replicated and extended the Nakayama and Shimojo and Gillam et al. experiments. We reproduced the original stimuli precisely and used the same range of separations for the bar stimuli as for the dot stimuli. We also compared perceived depth from disparity in the bar and dot stimuli when they were presented binocularly. Three of six observers were able to see quantitative depth with the dot stimulus though less depth was perceived than when a monocular bar was used. Interestingly, we found a similar difference in perceived depth when the bar and the dot were presented binocularly. Taken together our results provide evidence that quantitative depth in da Vinci arrangements is based, at least in part, on monocular occlusions, and that this phenomenon depends on the properties of the monocular object and is subject to inter-observer differences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".