Familiarity affects the assessment of female facial signals of fertility by free-ranging male rhesus macaques
Bibliographic record
Abstract
Animals signal their reproductive status in a range of sensory modalities. Highly social animals, such as primates, have access not only to such signals, but also to prior experience of other group members. Whether this experience affects how animals interpret reproductive signals is unknown. Here, we explore whether familiarity with a specific female affects a male's ability to assess that female's reproductive signals. We used a preferential looking procedure to assess signal discrimination in free-ranging rhesus macaques, a species in which female facial luminance covaries with reproductive status. We collected images of female faces throughout the reproductive cycle, and using faecal hormone analysis to determine ovulation, categorized images as coming from a female's pre-fertile, ovulating, or post-fertile period. We printed colour-calibrated stimuli of these faces, reproducing stimuli perceptually the same in colour and luminance to the original appearance of females. These images were presented to males who were either unfamiliar or familiar with stimuli females. Overall, males distinguished ovulatory from pre-ovulatory faces. However, a significant proportion of males did so only among males familiar with stimuli females. These experiments demonstrate that familiarity may increase a receiver's ability to use a social partner's signals to discern their reproductive status.
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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 | 0.002 |
| 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) | 0.001 | 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".