Differential distributions of red–green and blue–yellow cone opponency across the visual field
Bibliographic record
Abstract
The color vision of Old World primates and humans uses two cone-opponent systems; one differences the outputs of L and M cones forming a red-green (RG) system, and the other differences S cones with a combination of L and M cones forming a blue-yellow (BY) system. In this paper, we show that in human vision these two systems have a differential distribution across the visual field. Cone contrast sensitivities for sine-wave grating stimuli (smoothly enveloped in space and time) were measured for the two color systems (RG & BY) and the achromatic (Ach) system at a range of eccentricities in the nasal field (0-25 deg). We spatially scaled our stimuli independently for each system (RG, BY, & Ach) in order to activate that system optimally at each eccentricity. This controlled for any differential variations in spatial scale with eccentricity and provided a comparison between the three systems under equivalent conditions. We find that while red-green cone opponency has a steep decline away from the fovea, the loss in blue-yellow cone opponency is more gradual, showing a similar loss to that found for achromatic vision. Thus only red-green opponency, and not blue-yellow opponency, can be considered a foveal specialization of primate vision with an overrepresentation at the fovea. In addition, statistical calculations of the level of chance cone opponency in the two systems indicate that selective S cone connections to postreceptoral neurons are essential to maintain peripheral blue-yellow sensitivity in human vision. In the red-green system, an assumption of cone selectivity is not required to account for losses in peripheral sensitivity. Overall, these results provide behavioral evidence for functionally distinct neuro-architectural origins of the two color systems in human vision, supporting recent physiological results in primates.
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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 | 0.001 | 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) | 0.001 | 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".