Children’s causal inferences from conflicting testimony and observations.
Bibliographic record
Abstract
Preschoolers use both direct observation of statistical data and informant testimony to learn causal relationships. Can children integrate information from these sources, especially when source reliability is uncertain? We investigate how children handle a conflict between what they hear and what they see. In Experiment 1, 4-year-olds were introduced to a machine and 2 blocks by a knowledgeable informant who claimed to know which block was better at activating the machine, or by a naïve informant who guessed. Children then observed probabilistic evidence contradicting the informant and were asked to identify the block that worked better. Next, the informant claimed to know which of 2 novel blocks was a better activator, and children chose 1 block to try themselves. After observing conflicting data, children were more likely to say the informant's block was better when the informant was knowledgeable than when she was naïve. Children also used the statistical data to evaluate the informant's reliability and were less likely to try the novel block she endorsed than children in a baseline group who did not observe data. In Experiment 2, children saw conflicting deterministic data; the majority chose the block that consistently activated the machine as better than the endorsed block. Children's causal inferences varied with the confidence of the informant and strength of the statistical data, and informed their future trust in the informant. Children consider the strength of both social and physical causal cues even when they disagree and integrate information from these sources in a rational way.
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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 | 0.008 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".