Cavanagh and Leclerc Shape-From-Shadow Pictures: Do Line Versions Fail Because of the Polarity of the Regions or the Contour?
Bibliographic record
Abstract
Shape-from-shadow perception fails when the contour bordering a shadowed area is reduced to a black line, and the shadow area becomes white. It might be that the polarity of the shadowed and illuminated areas has to be from dark on the shadowed side to light on the illuminated side for successful perception. Or it may be that the line, which has two contours, has one too many for shape-from-shadow processing. Alternatively, the problem might be that one of the contours of the line is incorrectly polarised. To test these explanations, three shape-from-shadow figures were prepared, each depicting the same referent--an elderly person. All three figures had two correctly polarised areas. One figure had a correctly polarised contour at the border between the areas. One had two correctly polarised contours. The other had one correctly polarised contour and one incorrectly polarised contour. The referent of the figure with one incorrectly polarised contour was the one difficult to make out. The result has implications for several theories, including an account of a demonstration by Hering involving penumbra.
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 | 0.001 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".