Color signal information content and the eye of the beholder: a case study in the rhesus macaque
Bibliographic record
Abstract
Animal coloration has provided many classical examples of both natural and sexual selection. Methods to study color signals range from human assessment to models of receiver vision, with objective measurements commonly involving spectrometry or digital photography. However, signal assessment by a receiver is not objective but linked to receiver perception. Here, we use standardized digital photographs of female rhesus macaque (Macaca mulatta) face and hindquarter regions, combined with estimates of the timing of the female fertile phase, to assess how color varies with respect to this timing. We compare objective color measures (camera sensor responses) with models of rhesus vision (retinal receptor stimulation and visual discriminability). Due to differences in spectral separation between camera sensors and rhesus receptors, camera measures overestimated color variation and underestimated luminance variation compared with rhesus macaques. Consequently, objective digital camera measurements can produce statistically significant relationships that are probably undetectable to rhesus macaques, and hence biologically irrelevant, while missing variation in the measure that may be relevant. Discrimination modeling provided results that were most meaningful (as they were directly related to receiver perception) and were easiest to relate to underlying physiology. Further, this gave new insight into the function of such signals, revealing perceptually salient signal luminance changes outside of the fertile phase that could potentially enhance paternity confusion. Our study demonstrates how, even for species with similar visual systems to humans, models of vision may provide more accurate and meaningful information on the form and function of visual signals than objective color measures do.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".