Contrast normalization in colour vision: the effect of luminance contrast on colour contrast detection
Bibliographic record
Abstract
While contrast normalization is well known to occur in luminance vision between overlaid achromatic contrasts, and in colour vision between overlaid colour contrasts, it is unknown whether it transfers between colour and luminance contrast. Here we investigate whether contrast detection in colour vision can be normalized by achromatic contrast, or whether this is a selective process driven only by colour contrast. We use a method of cross-orientation masking, in which colour detection is masked by cross-oriented achromatic contrast, over a range of spatio-temporal frequencies (0.375-1.5 cpd, 2-8 Hz). We find that there is virtually no cross-masking of colour by achromatic contrast under monocular or binocular conditions for any of the spatio-temporal frequencies tested, although we find significant facilitation at low spatio-temporal conditions (0.375 cpd, 2 Hz). These results indicate that the process of contrast normalization is colour selective and independent of achromatic contrast, and imply segregated chromatic signals in early visual processing. Under dichoptic conditions, however, we find a strikingly different result with significant masking of colour by achromatic contrast. This indicates that the dichoptic site of suppression is unselective, responding similarly to colour and luminance contrast, and suggests that dichoptic suppression has a different origin from monocular or binocular suppression.
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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 | 0.003 |
| 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 | 0.000 | 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".