Can children and adults balance majority size with information quality in learning from preferences?
Bibliographic record
Abstract
= 241) balance the number of endorsements for a given option with the quality of the informants' source of information when deciding which of two boxes contains the better option. When choosing between two different boxes endorsed by groups of equal sizes, both children (Experiments 1-3) and adults (Experiment 6) tend to choose boxes endorsed by informants with visual access to the boxes over informants with hearsay. However, children's choices were biased toward the larger group when the size of the group conflicted with the quality of the source of the groups' information (Experiments 4 and 5), while adults more often chose the option endorsed by the group with the higher quality information (Experiment 6). Children were more likely to conform to a majority opinion when compared with both adults and to a normative computational model that endorses a group proportional to the number of independent, direct observations made by that group's informants. These findings suggest that, while adults balance the size of a majority with the quality of the informants' information source, preschoolers can evaluate when groups differ in the source of their information but may assume that the presence of a majority endorsing an option is inherently informative over and above the information source group members' testimony relied on. (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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".