The Quest for Truth: Experimenter Identity Impacts Children’s Response to Surprising Information
Bibliographic record
Abstract
Abstract Much of what children know about the world is learned from information provided by others, and children’s endorsement of this information depends on the social attributes of the person providing the information (e.g., their accent, attractiveness, etc.). Previous work on how the identity of a person providing information (i.e., informant) influences children’s learning has tended to focus on a highly specific, simplified learning context, where children are provided with conflicting claims from two individuals (e.g., one foreign- and one locally accented speaker) and are immediately asked to indicate whose information they endorse more. In the current study, we investigated the effect of informant identity on 5- to 7-year-old children’s (N = 144) learning in a more real-world context, where children encountered surprising information from only one person (a foreign- or locally accented speaker), and were subsequently given the opportunity to engage further with that information (by testing for themselves whether the information was true). In contrast to previous research using a forced choice method, almost all children initially endorsed the surprising claim; however, their subsequent testing of the claim and later endorsement did differ based on whether children were interacting with a foreign- or locally accented speaker. These results highlight the need to investigate the influence of social factors on selective learning in more ecologically valid contexts, which, importantly, consider the influence of an informant at multiple points throughout the learning process.
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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 | 0.006 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".