Religion priming and an oxytocin receptor gene (<i>OXTR</i>) polymorphism interact to affect self-control in a social context
Bibliographic record
Abstract
Using a genetic moderation approach, this study examines how an experimental prime of religion impacts self-control in a social context, and whether this effect differs depending on the genotype of an oxytocin receptor gene (OXTR) polymorphism (rs53576). People with different genotypes of OXTR seem to have different genetic orientations toward sociality, which may have consequences for the way they respond to religious cues in the environment. In order to determine whether the influence of religion priming on self-control is socially motivated, we examine whether this effect is stronger for people who have OXTR genotypes that should be linked to greater rather than less social sensitivity (i.e., GG vs. AA/AG genotypes). The results showed that experimentally priming religion increased self-control behaviors for people with GG genotypes more so than people with AA/AG genotypes. Furthermore, this Gene × Religion interaction emerged in a social context, when people were interacting face to face with another person. This research integrates genetic moderation and social psychological approaches to address a novel question about religion's influence on self-control behavior, which has implications for coping with distress and psychopathology. These findings also highlight the importance of the social context for understanding genetic moderation of psychological effects.
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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 | 0.001 |
| 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".