Incremental hypothesis revision in causal reasoning across development
Bibliographic record
Abstract
We explore whether children’s strategies on a causal learning task show a bias observed in adults towards “exploitative” hypothesis revision. Adults and children (ages 4–6) were presented with evidence which initially seemed to conform to a simple, salient rule (e.g. blue blocks activate a machine), but then encountered evidence that violated this rule. The true rule in the "near" condition was more complex, but could be reached through iterative revision of the salient rule, while in the "distant" condition, the true rule was comparatively simple, but incremental revision could not yield the true rule. Participants then predicted the behaviour of a set of new blocks. Adults performed better in the near condition, while in the distant condition adults did not appear to revise their initial hypothesis significantly. Unlike adults, children's overall performance in both conditions was similar, while condition differences may reflect a broader search for alternative solutions.
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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 | 0.000 | 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) | 0.003 | 0.002 |
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; both teacher heads agree on what is shown here.
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".