Colour contrast influences perceived shape in combined shading and texture patterns
Bibliographic record
Abstract
The 'colour-shading effect' describes the phenomenon whereby a chromatic pattern influences perceived shape-from-shading in a luminance pattern. Specifically, the depth corrugations perceived in sinusoidal luminance gratings can be enhanced by spatially non-aligned, and suppressed by spatially aligned sinusoidal chromatic gratings. Here we examine whether colour contrast can influence perceived shape in patterns that combine shape-from-shading with shape-from-texture. Stimuli consisted of sinusoidal modulations of texture (defined by orientation), luminance and colour. When the texture and luminance modulations were suitably combined, one obtained a vivid impression of a corrugated depth surface. The addition of a colour grating to the texture-luminance combination was found to enhance the impression of depth when out-of-phase with the luminance modulation, and suppress the impression of depth when in-phase with the luminance modulation. The degree of depth enhancement and depth suppression was approximately constant across texture amplitude when measured linearly. In the absence of the luminance grating however, the colour grating had no phase-dependent affect on perceived depth. These results show that colour contrast modulates the contribution of shading to perceived shape in combined shading and texture patterns.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".