When two eyes are worse than one: Binocular summation for chromatic, interocular-anti-phase stimuli
Bibliographic record
Abstract
Numerous studies have shown that sensitivity to binocular targets is higher than to its monocular components, a phenomenon known as binocular summation. Binocular summation has been demonstrated with luminance contrast targets that are not only interocularly in-phase, that is, identical in both eyes, but also interocularly anti-phase, that is, of opposite polarity in the two eyes. Here we show that for the detection of anti-phase targets defined along the red-cyan and violet-lime axes of cardinal color space two eyes are more often than not worse than one. We suggest this is because channels that detect interocular differences, or S- channels are relatively insensitive to chromatic stimuli. We tested this idea by measuring binocular summation for chromatic anti-phase targets in the context of a chromatic surround that itself was either interocularly in-phase or anti-phase. The anti-phase surrounds reduced even further binocular summation for the anti-phase targets whereas the in-phase surrounds increased the level of summation. We show that a model that combines via probability summation the independent activities of adding S+ and differencing S- channels gave a good account of the data, especially for the anti-phase targets. We conclude that binocular adding and differencing channels play an important role in binocular color vision.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".