Bibliographic record
Abstract
From an early age, children explore their environment in a way suggesting that they reason about causal variables and seek causal explanations.Indeed, following extensive studies of problem-solving abilities in chimpanzees, Povinelli (Folk Physics for Apes, Oxford University Press, 2000) proposed that this ability to reason about unobservable variables is unique to humans.Following on from this, Povinelli and Dunphy-Lelii (Canadian Journal of Experimental Psychology, 55(2), [187][188][189][190][191][192][193][194][195] 2001) addressed the question whether chimpanzees would explore objects with the aim of elucidating unobservable and surprising object properties.Chimpanzees, unlike preschool children, did not show increased object exploration following a change in the unobservable properties of an object.We critically discuss these findings and argue that more research using a greater variety of methods and with a larger number of species is required to support the hypothesis that only humans engage in explanation seeking.We conclude by highlighting avenues for future research based on developmental and comparative research aimed at object exploration and information seeking conducted since the original investigation by Povinelli and Dunphy-Lelii.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".