The Zeus Problem: Why Representational Content Biases Cannot Explain Faith in Gods
Why this work is in the frame
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Bibliographic record
Abstract
Abstract In a recent article, Barrett (2008) argued that a collection of five representational content features can explain both why people believe in God and why people do not believe in Santa Claus or Mickey Mouse. In this model ‐ and within the cognitive science of religion as a whole ‐ it is argued that representational content biases are central to belief. In the present paper, we challenge the notion that representational content biases can explain the epidemiology of belief. Instead, we propose that representational content biases might explain why some concepts become widespread, but that context biases in cultural transmission are necessary to explain why people come to believe in some counterintuitive agents rather than others. Many supernatural agents, including those worshipped by other cultural groups, meet Barrett’s criteria. Nevertheless, people do not come to believe in the gods of their neighbors. This raises a new challenge for the cognitive science of religion: the Zeus Problem. Zeus contains all of the features of successful gods, and was once a target for widespread belief, worship, and commitment. But Zeus is no longer a target for widespread belief and commitment, despite having the requisite content to fulfill Barrett’s criteria. We analyze Santa Claus, God, and Zeus with both content and context biases, finding that context ‐ not content ‐ explains belief. We argue that a successful cognitive science of religious belief needs to move beyond simplistic notions of cultural evolution that only include representational content biases.
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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 | 0.000 | 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 it