Facial resemblance increases the attractiveness of same–sex faces more than other–sex faces
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it