Learning From Others: Selective Requests by 3-Year-Olds of Three Cultures
Bibliographic record
Abstract
Humans are unique in their propensity to intentionally instruct and subsequently learn a wide range of information from others. We investigated when and how young children become socially resourceful in using others’ expertise, and whether the early propensity to request for help varies across diverse societies. We tested and compared 44 two- to four-year-old children growing up in urban United States and Japan, and rural Canada. Children were faced with two experimenters who demonstrated different abilities (successful vs. unsuccessful) in a toy retrieving task. We measured children’s propensity to request for help and the relative selectivity of requests to one experimenter over another. Results show significant cross-cultural differences. U.S. children’s request behavior differed significantly from the other two societies on three of the four measures. Specifically, U.S. children requested more overall, whereas Japanese children ceased manipulation (“give up”), and Canadian children continued to try on their own. Only the U.S. children show clear selective requests to the successful experimenter. On the last measure (gaze behavior), the U.S. and Canadian children look more to the successful model during the test phase than the unsuccessful model. These findings have implications for social learning research as well as the generalizability of developmental science.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".