The need to believe: a neuroscience account of religion as a motivated process
Bibliographic record
Abstract
Religious belief has been shown to offer substantial benefits to its adherents, including improved well-being and health. We suggest that these benefits might be explained, at least in part, from a “motivated meaning-making” perspective. This model holds that people are motivated to create and sustain meaning (i.e., a sense of coherency between beliefs, goals, and perceptions of the environment, which provides individuals with the feeling that the world is an orderly place), and that religious beliefs buffer the distress associated with disruptions to meaning, thus leading to decreases in distress. We further propose that religion's palliative attributes can be measured at the level of the brain, specifically in the anterior cingulate cortex (ACC), which produces a “distress signal” upon the detection of errors, conflict, and expectancy violation. Using a social neuroscience paradigm, we investigate four main predictions that arise from this model: (1) religion should be associated with activation in the ACC; (2) religion should decrease activation in the ACC; (3) this attenuation of ACC activity should be related to religion's ability to buffer bodily states of distress, and not to decreases in motivation, attention, or control; (4) religion should have these effects because it provides meaning and thus buffers people from uncertainty. All predictions were supported, thus providing evidence, at the neural level, for the motivated meaning-making model's account of the salutary properties of religion.
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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 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".