Trustworthy but not lust-worthy: context-specific effects of facial resemblance
Bibliographic record
Abstract
If humans are sensitive to the costs and benefits of favouring kin in different circumstances, a strong prediction is that cues of relatedness will have a positive effect on prosocial feelings, but a negative effect on sexual attraction. Indeed, positive effects of facial resemblance (a potential cue of kinship) have been demonstrated in prosocial contexts. Alternatively, such effects may be owing to a general preference for familiar stimuli. Here, I show that subtly manipulated images of other-sex faces were judged as more trustworthy by the participants they were made to resemble than by control participants. In contrast, the effects of resemblance on attractiveness were significantly lower. In the context of a long-term relationship, where both prosocial regard and sexual appeal are important criteria, facial resemblance had no effect. In the context of a short-term relationship, where sexual appeal is the dominant criterion, facial resemblance decreased attractiveness. The results provide evidence against explanations implicating a general preference for familiar-looking stimuli and suggest instead that facial resemblance is a kinship cue to which humans modulate responses in a context-sensitive manner.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".