Binocularly matched luminance contrast reduces sensitivity to between-eye but not within-eye differences in hue and saturation
Bibliographic record
Abstract
Detection of between-eye differences in both hue and chromatic contrast (saturation) were measured using dichoptically superimposed colour patches. Sensitivity was found to be highest at isoluminance and decreased with the addition of task-irrelevant spatially contiguous binocular (i.e. same in both eyes) luminance contrast. However, when the members of each dichoptic pair were presented side-by-side on the screen and thus both viewed with the same eye, the luminance contrast had no effect on the detection of their differences. If the effect of the luminance contrast was simply to dilute, or ‘desaturate’ the chromatic signals we would expect thresholds to increase for the within-eye as well as the between-eye (dichoptic) conditions. We suggest that binocular luminance contrast reduces the interocular suppression between dichoptic colours, causing the dichoptic colours to blend and as a result render their differences harder to detect. Our hypothesis is that binocularly matched luminance contrast promotes the interpretation that disparate colours are nevertheless part of the same object, and we term this the “object commonality hypothesis”. Meeting abstract presented at VSS 2015
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".