Perceptual averaging of dichoptic mixtures of colour contrast promoted by task-irrelevant luminance contrast
Bibliographic record
Abstract
Aim: Previous studies have shown that under certain conditions the perceived contrast of a dichoptic mixture of two different luminance contrasts is similar to that of the larger of the two contrasts when presented binocularly, a scenario termed winner-take-all. We ask whether dichoptic mixtures of different colour (chromatic) contrast obey winner-take-all, or instead obey the alternative scenario of averaging, in which the perceived contrast of the dichoptic mix is the average of the two contrasts. We also consider the effect of adding task-irrelevant luminance contrast to the dichoptic colour contrast mixtures. Methods: Subjects adjusted the contrast of a disk in one eye that was dichoptically superimposed on a test disk of fixed contrast in the other eye, until the perceived contrast of the mixture equalled that of a separate, fixed-in-contrast reference disk presented binocularly. Results: For isoluminant red, cyan, violet and lime disks the settings were close to winner-take-all. However, when a fixed amount of luminance contrast was added equally to all disks, i.e. was task-irrelevant, the settings shifted significantly towards averaging. Conclusion: The shift away from winner-take-all towards averaging caused by task-irrelevant luminance contrast is consistent with reduced interocular suppression between the different dichoptic colour contrasts, and constitutes a new form of interaction between colour and luminance contrast in binocular vision. Meeting abstract presented at VSS 2014
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".