The evolutionary background to (mis)understanding an uncertain world
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Misunderstandings of causality are often referred to as superstitions. More formally, superstitious behaviours can be defined as actions (or inactions) that are performed in order to increase the probability that a beneficial outcome arises when there is no causal relationship between the action and the outcome. While superstitious behaviours are common in humans, they also arise in non-human animals. Although behaving superstitiously may on first reflection appear always maladaptive, recent models have shown that superstitions will readily arise as a by-product of adaptive learning, in which individuals seek to balance gaining new information about the world with exploiting their current information. In short, if a behavior appears associated with a beneficial outcome, it may not be worthwhile experimenting and losing out on this benefit to determine whether the association has arisen by chance. The models help explain why superstitions get started, and indicate the types of superstitious behaviours that are likely to persist. In support, empiricists have widely observed that superstitions are more likely to develop when the perceived benefit of adopting a behaviour is high compared to the cost of not adopting it and when the number of opportunities to test one’s understanding is low. Collectively, therefore, while superstitions are commonly presented as entirely irrational behaviours, they can actually represent a smart strategy, promoted by natural selection, in situations where causal relationships are uncertain.
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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 | 0.014 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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