Children’s recognition of causal system categories across superficially distinct events.
Bibliographic record
Abstract
A deep understanding of any phenomenon requires knowing how its causal elements are related to one another. Here, we examine whether children treat causal structure as a metric for assessing similarity across superficially distinct events. In two experiments, we presented 156 4-7-year-olds (approximately 55% of participants identified as White, 29% as multiracial, and 12% as Asian) with three-variable narratives in which story events unfold according to a causal chain or a common effect structure. We then asked children to make judgments about which stories are the most similar. In Experiment 1, we presented all events in the context of simple, illustrated stories. In Experiment 2, we removed all low-level linguistic cues that may have supported children's similarity judgments in Experiment 1 and used animated videos to support understanding of the causal elements in each story. Results indicated a gradual shift between 4 and 7 years in children's use of causal structure as a metric of similarity between narratives: While we found that children as young as five were capable of correctly representing the causal structure of each story individually, only 6- and 7-year-olds relied on shared causal structure across stories when making similarity judgments. We discuss these findings in light of children's developing causal and abstract reasoning and propose directions for future work. (PsycInfo Database Record (c) 2025 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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".