Colour Vision Brings Clarity to Shadows
Bibliographic record
Abstract
We have revealed a new role for colour vision in visual scene analysis: colour vision facilitates shadow identification. Shadows are important features of the visual scene, providing information about the shape, depth, and movement of objects. To be useful for perception, however, shadows must be distinguished from other types of luminance variation, principally the variation in object reflectance. A potential cue for distinguishing shadows from reflectance variations is colour, since chromatic changes typically occur at object but not shadow boundaries. We tested whether colour cues were exploited by the visual system for shadow identification, by comparing the ability of human test subjects to identify simulated shadows on chromatically variegated versus achromatically variegated backgrounds with identical luminance compositions. Performance was superior with the chromatically variegated backgrounds. Furthermore, introducing random colour contrast across the shadow boundaries degraded their identification. These findings demonstrate that the visual system exploits inbuilt assumptions about the relationships between colour and luminance in the natural visual world.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".