Think outside the box: Making up casual hypotheses from unreliable evidence
Bibliographic record
Abstract
Human adults think of the natural world as orchestrated by rules, yet many of them are neither equally rigid nor clearly evident. Some are beset by exceptions, and others are not intuitive. The problem of rule learning is especially salient in development, as children are continuously learning how the world works. What cognitive mechanisms underpin this rule learning? We propose a computational model for formulating and testing hypotheses in naturalistic contexts, that combines Bayesian inference under uncertainty over self-generated and social evidence with formal rules and optimistic information-seeking heuristics. We validate our model experimentally, showing that it explains 7- to 10-year-olds' behavior in a rule-based, physical task, including the distribution and the types of evidence children sampled. The proposed model outperforms both a purely rule-based Bayesian hypothesis search and a resource-rational random sampling approach. Our results suggest that children implement an internal mechanism for generating and testing a limited number of hypotheses, including formal programmatic rules and heuristics generated from salient problem features to seek more evidence when formal rule generation fails.
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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 | 0.009 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".