The evolutionary background to (mis)understanding an uncertain world
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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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.020 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".