Who Peeked? Children Infer the Likely Cause of Improbable Success
Bibliographic record
Abstract
Some outcomes are brought about by intentional agents with access to information and others are not. Children use a variety of cues to infer the causes of outcomes, such as statistical reasoning (e.g., the probability of the outcome) and theory of mind (e.g., a person's perceptual access, preferences, or knowledge). Here we show that children use these cues to infer cheating, a finding which informs our understanding of the flexibility of children's theory of mind. In four experiments (N = 444), 4- to 7-year-olds saw vignettes about blindfolded agents retrieving 10 gumballs from a distribution of yummy and yucky gumballs. Children were then asked if agents were really blindfolded or had peeked. We manipulated the probability of the outcome (i.e., the correspondence between the distribution sampled from and the outcome produced) and the ordering of the outcome was patterned (e.g., five yummy then five yucky) or haphazard. From age 5, children began to use both cues to infer cheating, and also showed signs of flexibly integrating these cues. Together, these findings show that young children can detect cheaters, and that their theory of mind reasoning is flexible and not based on simple and rigid rules (e.g., equating not-seeing with failure). The findings also suggest that children use probabilistic reasoning to infer knowledge.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".