The moralization bias of gods’ minds: a cross-cultural test
Bibliographic record
Abstract
There are compelling reasons to expect that cognitively representing any active, powerful deity motivates cooperative behavior. One mechanism underlying this association could be a cognitive bias toward generally attributing moral concern to anthropomorphic agents. If humans cognitively represent the minds of deities and humans in the same way, and if human agents are generally conceptualized as having moral concern, a broad tendency to attribute moral concern—a “moralization bias”—to supernatural deities follows. Using data from 2,228 individuals in 15 different field sites, we test for the existence of such a bias. We find that people are indeed more likely than chance to indicate that local deities care about punishing theft, murder, and deceit. This effect is stable even after holding beliefs about explicitly moralistic deities constant. Additionally, we take a close look at data collected among Hadza foragers and find two of their deities to be morally interested. There is no evidence to suggest that this effect is due to direct missionary contact. We posit that the “moralization bias of gods’ minds” is part of a widespread but variable religious phenotype, and a candidate mechanism that contributes to the well-recognized association between religion and cooperation.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".