Maybe She’ll Say Yes: How Young Learners Acquire and Apply Knowledge about Inconsistent Causal Relationships from Different Domains
Bibliographic record
Abstract
Children are adept at learning the principles and properties governing their environment. However, this environment is often highly inconsistent: causes do not always bring about their effects; people do not always act according to their preferences. Past research shows that young causal learners readily reason from probabilistic evidence, but little is known as to how they reason about that evidence. This study presented preschoolers (N=114) with the behavior of three different causes—one consistently effective, one consistently ineffective, and one inconsistent—from one of three domains (social, mechanical, biological) and asked children to predict the future behavior of each. Children’s predictions not only captured the different degrees of inconsistency observed in the evidence but also reflected differences in prior knowledge and expectations about inconsistency between domains. These results offer a novel, more nuanced look into early causal cognition and often-overlooked complexities of causal learning and reasoning in the real world.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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".