Self-Resemblance and Social Rejection
Bibliographic record
Abstract
Humans perceive and treat self-resembling others in ways that suggest that self-resemblance is a cue of kinship. However, we know little about how individuals respond to treatment by self-resembling others. Here we approach this problem by connecting facial self-resemblance to social rejection. Given that individuals should expect to cooperate with kin, we hypothesized that (1) social inclusion by perceived kin should elicit lesser feelings of rejection and (2) social exclusion by perceived kin should elicit greater feelings of rejection relative to inclusion or exclusion, respectively, by nonkin. To test these hypotheses, we recruited 90 participants to play two games of Cyberball, a virtual ball-tossing game, with separate pairs of ostensible partners. In one game, the ostensible partners were programed to fully include the participants in group play and, in the other game, they were programed to exclude the participants after a few rounds; the order of inclusion and exclusion was counterbalanced across participants. Partner faces were digitally manipulated to be either self- or nonself-resembling, and these conditions were also counterbalanced. Rejection feelings differed significantly as a function of self-resemblance between the inclusion and exclusion conditions, but only for participants who experienced inclusion first. Moreover, for these individuals, inclusion by self-resembling partners led to significantly lesser feelings of rejection than did inclusion by nonself-resembling partners. To explain this effect, we explore potential mechanisms of kin recognition and social rejection. Although nuanced, our results suggest that perceptions of kinship can moderate psychological responses to the actions of others.
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.
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 | 0.001 | 0.006 |
| 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 | 0.001 | 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".