Wisdom to give: Perspectival metacognition and the calibration of prosocial behavior across cultures
Bibliographic record
Abstract
Humans engage in costly prosocial behavior with unrelated others at scales that short-term self-interest cannot explain. We propose that this capacity relies on perspectival metacognition (PMC)—a reflective reasoning style characterized by intellectual humility, open-mindedness, and self-transcendence beyond immediate concerns. In a preregistered cross-national study (N=13,500; nine countries), participants reflected on recent autobiographical conflicts and an incentivized carbon-offset donation experiment. Psychometric modelling of reflective tendencies revealed a latent PMC factor, which was robustly associated with larger incentivized donations and a general prosocial tendency across six real-world domains (e.g., voting, volunteering, pandemic norm adherence). These associations were consistent across experimental conditions and cultures, equivalent in magnitude to the combined effect of socioeconomic markers, and robust to adjustment for trust and personality traits. Exploratory analyses further suggested a “capacity and scope” account: the link between PMC and prosociality was strongest among individuals with higher resources (income, education) and a more expansive self-concept (inclusion of strangers in the self). These findings identify perspectival metacognition as psychological software that calibrates deliberation toward the collective good, particularly when ecological conditions and social orientation afford it.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".