Beyond Residuals: Toward a Comprehensive Understanding of Religion
Bibliographic record
Abstract
We argue that while the statistical brain hypothesis offers a valuable framework for understanding religious belief, its explanatory power is limited if it focuses solely on epistemic motives. Rigoli and LEnnon propose that religious beliefs emerge as an attempt to explain residual errors – discrepancies between predictions and observations – by attributing them to supernatural agents. While we acknowledge the strengths of this approach, we contend that a comprehensive account of religion must go beyond belief formation and incorporate the emotional, intersubjective, and cultural dimensions that shape religious life. Although a broader interpretation of epistemic processes could encompass these elements, the paper by Rigoli and Lennon does not fully explore their significance. By integrating a perspective that includes the role of rituals, embodied practices, and existential meaning, we suggest that the statistical brain hypothesis can be expanded into a more robust framework, capable of capturing the full complexity of religious cognition and experience.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".