Learners’ causal intuitions explain behavior in control of variables tasks.
Bibliographic record
Abstract
Self-directed learners are described as "intuitive scientists," yet they often struggle in assessments of their scientific reasoning skills. We investigate a novel explanation for this apparent gap between formal and informal scientific inquiry behavior. Specifically, we consider whether learners' documented failure to correctly apply the control of variables strategy might stem from a mismatch between task presentation and their intuitions as causal learners. In Experiment 1, children (7- and 9-year-olds) and adults were tested on a version of a traditional multivariate reasoning task (Tschirgi, 1980) that was modified to clarify ambiguous elements of the causal logic in the original design. In all age groups, a significant majority of participants selected informative experiments on this modified task, avoiding confounded actions with positive tangible outcomes. In Experiment 2, we replicate these results with real-world stimuli, and in Experiments 3 and 4, we provide direct evidence that self-directed learners apply specific causal intuitions to experimentation tasks. Together, these findings support a novel alternative interpretation of the apparently paradoxical gap between learners' success in informal exploration and their error-prone experimentation-both behaviors are consistent with an intuitively causal approach to scientific inquiry. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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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 | 0.004 | 0.036 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".