Behavioral and neural responses to social exclusion in women: the role of facial attractiveness and friendliness
Bibliographic record
Abstract
The behavioral and neural responses to social exclusion were examined in women randomized to four conditions, varying in levels of attractiveness and friendliness. Informed by evolutionary theory, we predicted that being socially excluded by attractive unfriendly women would be more distressing than being excluded by unattractive women, irrespective of their friendliness level. Our results contradicted most of our predictions but provide important insights into women's responses to interpersonal conflict. Accounting for rejection sensitivity, P300 event-related potential amplitudes were largest when women were excluded by unattractive unfriendly women. This may be due to an expectancy violation or an annoyance with being excluded by women low on social desirability. An examination of anger rumination rates by condition suggests the latter. Only attractive women's attractiveness ratings were lowered in the unfriendly condition, indicating they were specifically punished for their exclusionary behavior. Women were more likely to select attractive women to compete against with one exception-they selected the Black attractive opponent less often than the White attractive opponent when presented as unfriendly. Finally, consistent with studies on retaliation in relation to social exclusion, women tended to rate competitors who rejected them as being more rude, more competitive, less attractive, less nice, and less happy than non-competitors. The ubiquity of social exclusion and its pointed emotional and physiological impact on women demands more research on this topic.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".