Thin-slicing study of the oxytocin receptor (OXTR) gene and the evaluation and expression of the prosocial disposition
Bibliographic record
Abstract
Individuals who are homozygous for the G allele of the rs53576 SNP of the oxytocin receptor (OXTR) gene tend to be more prosocial than carriers of the A allele. However, little is known about how these differences manifest behaviorally and whether they are readily detectable by outside observers, both critical questions in theoretical accounts of prosociality. In the present study, we used thin-slicing methodology to test the hypotheses that (i) individual differences in rs53576 genotype predict how prosocial observers judge target individuals to be on the basis of brief observations of behavior, and (ii) that variation in targets' nonverbal displays of affiliative cues would account for these judgment differences. In line with predictions, we found that individuals homozygous for the G allele were judged to be more prosocial than carriers of the A allele. These differences were completely accounted for by variations in the expression of affiliative cues. Thus, individual differences in rs53576 are associated with behavioral manifestations of prosociality, which ultimately guide the judgments others make about the individual.
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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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 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".