Dichoptic and binocular detection of hue and saturation differences: the effect of luminance contrast
Bibliographic record
Abstract
Between-eye difference thresholds (BEDTs) were measured for hue and saturation using dichoptically superimposed coloured patches. BEDTs were measured at isoluminance and as a function of added binocular (i.e. same in both eyes) luminance contrast, both increments and decrements. Increasing the binocular luminance contrast increased BEDTs for both hue and saturation. A control experiment showed that the BEDTs were only elevated when the binocular luminance contrast was spatially coextensive within the colour-defined patches. When measured under full binocular viewing conditions however, i.e. when each member of the dichoptic pair was presented at a separate screen location and to both eyes, both hue and chromatic contrast difference thresholds were unaffected by the addition of binocular luminance contrast. These results are hard to explain by the simple dilution of the colour signals by luminance contrast, as thresholds were only elevated in the dichoptic viewing conditions. A model of BEDTs that includes an interocular suppression component whose gain is inversely proportional to the amount of binocular luminance contrast was found to give a good account of the data.
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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 | 0.000 | 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".