Bibliographic record
Abstract
Leonardo da Vinci (1452-1519) identified two stimulus situations that cannot be painted faithfully on a canvas: (a) when two objects are located in the same direction with respect to the painter's head, and (b) when parts of a surface are visible to one eye, but occluded from the other eye. He analysed these situations in terms of rays being emitted from the two eyes and, aside from the origin of the rays, the projective geometry he used was correct. His analyses showed that what can be seen from two vantage points cannot be represented on a canvas, because a 'correct' painting must be created from a single 'station point'. He was struck by the consequence of this fact that the depth seen on a canvas cannot match that of viewing the scene with two eyes. Subsequent visual scientists focused on Leonardo's observation about the lack of vivid depth in a picture. We argue that a complete understanding of what we see in the two stimulus situations requires consideration of visual direction in addition to visual depth. More specifically, we argue that the visual directions of the two objects, (a) above, and the visual direction of the monocular areas, (b) above, are dependent upon the constraint that two opaque objects cannot be represented in the same direction. Demonstrations that readers can perform, and that support this argument, are provided on the Perception website at http://www.perceptionweb.com/perc0102/ono.html.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".