Painted faces: misperceiving shading as pigmentation
Bibliographic record
Abstract
Many lightness illusions reflect the discounting of shadows and shading by the visual system so that equivalent luminances appear as different lightnesses; or conversely, cases where shadows are not discounted because they are instead misperceived as surfaces. We explored a novel form of these illusions created simply by mirroring side-lit objects. When the two halves of a side-lit face are each mirrored to form a pair of symmetric faces, the face formed by the shaded side is perceived to be darkly pigmented. These effects can be measured by a matching task in which observers adjust the lightness or texture of a uniform comparison patch to match the perceived skin tone on either side of the face. The matches are similar for the two sides of the original side-lit face, yet can strongly differ between the two mirrored faces. The magnitude of the illusion is dependent on cues to the actual angle and directionality of the illuminant, as well as the surface structure of the object, and we explore the inferences and stimulus cues underlying this dependence. For example, the effect is largely absent in simple shapes such as uniform spheres, and for faces depends critically on the point at which the image is mirrored and on which directly lit surfaces this includes. Notably, when only one side of the face is shown, the shaded side again defaults to a pigmented percept, though this again diminishes rapidly as cues to the actual lighting are included. Meeting abstract presented at VSS 2014
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".