Infants’ Social Evaluation of Helpers and Hinderers: A Large‐Scale, Multi‐Lab, Coordinated Replication Study
Bibliographic record
Abstract
Evaluating whether someone's behavior is praiseworthy or blameworthy is a fundamental human trait. A seminal study by Hamlin and colleagues in 2007 suggested that the ability to form social evaluations based on third-party interactions emerges within the first year of life: infants preferred a character who helped, over hindered, another who tried but failed to climb a hill. This sparked a new line of inquiry into the origins of social evaluations; however, replication attempts have yielded mixed results. We present a preregistered, multi-laboratory, standardized study aimed at replicating infants' preference for Helpers over Hinderers. We intended to (1) provide a precise estimate of the effect size of infants' preference for Helpers over Hinderers, and (2) determine the degree to which preferences are based on social information. Using the ManyBabies framework for big team-based science, we tested 1018 infants (567 included, 5.5-10.5 months) from 37 labs across five continents. Overall, 49.34% of infants preferred Helpers over Hinderers in the social condition, and 55.85% preferred characters who pushed up, versus down, an inanimate object in the nonsocial condition; neither proportion differed from chance or from each other. This study provides evidence against infants' prosocial preferences in the hill paradigm, suggesting the effect size is weaker, absent, and/or develops later than previously estimated. As the first of its kind, this study serves as a proof-of-concept for using active behavioral measures (e.g., manual choice) in large-scale, multi-lab projects studying infants.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".