Detection of between-eye differences in color: Interactions with luminance
Bibliographic record
Abstract
Between-eye differences in color or luminance result in the appearance of luster, which provides a cue for detecting between-eye differences. We measured thresholds for detecting between-eye differences in both hue and chromatic contrast (saturation) in dichoptically superimposed color patches. Sensitivity was found to be highest at isoluminance and decreased with the addition of task-irrelevant, spatially coextensive, 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 viewed with the same eye, the added 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 and not just the between-eye (dichoptic) conditions. We suggest that the presence of binocular luminance contrast reduces the interocular suppression between the dichoptic colors, causing the dichoptic color pairs to blend, thus rendering their differences harder to detect.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".