Trustworthy but not lust-worthy: context-specific effects of facial resemblance
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
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.
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 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it