Facial resemblance increases the attractiveness of same–sex faces more than other–sex faces
Bibliographic record
Abstract
Our reactions to facial self-resemblance could reflect either specialized responses to cues of kinship or by-products of the general perceptual mechanisms of face encoding and mere exposure. The adaptive hypothesis predicts differences in reactions to self-resemblance in mating and prosocial contexts, while the by-product hypothesis does not. Using face images that were digitally transformed to resemble participants, I showed that the effects of resemblance on attractiveness judgements depended on both the sex of the judge and the sex of the face being judged: facial resemblance increased attractiveness judgements of same-sex faces more than other-sex faces, despite the use of identical procedures to manipulate resemblance. A control experiment indicated these effects were caused neither by lower resemblance of other-sex faces than same-sex faces, nor by an increased perception of averageness or familiarity of same-sex faces due to prototyping or mere exposure affecting only same-sex faces. The differential impact of self-resemblance on our perception of same-sex and other-sex faces supports the hypothesis that humans use facial resemblance as a cue of kinship.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".