Is Primate Cone Ratio Variation Functional and Adaptive?
Bibliographic record
Abstract
Variation in animal perception provides excellent opportunities for studying adaptation. Unusually, primates exhibit a great deal of inter- and intra-specific visual system variation. Here, we discuss what is known about the retinal cone mosaic, and the sources of variation in primate cone types and their relative expression. We focus on catarrhines (African and Asian monkeys and apes and humans), which have evolved uniform trichromacy, exhibiting short- (S), medium- (M), and long-wave (L) cones. Catarrhines generally exhibit high inter-specific consistency in the peak sensitivities of their L and M sensitive cones. One under-explored component of variation is the relative expression of those cones, that is, the L:M ratio. Across catarrhines, the mean L:M ratio is 1:1, with some limited intraspecific variation. Intriguingly, humans show two big differences compared to other catarrhines. Firstly, their mean L:M ratio is shifted to 2:1. Secondly, they show vast (75-fold) intraspecific L:M ratio variation. We discuss evidence as to whether this difference in the mean ratio, and this high intraspecific variation, are likely to have functional consequences, concluding that indeed this variation likely impacts color perception. We finish by suggesting possible explanations for the higher mean ratio of L:M cones in humans, highlighting similarities with other aspects of our color vision that differ from other catarrhines. We hope that the suggestions and questions we raise will inspire future research on primate cone ratios.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".