Infants’ Social Evaluation of Helpers and Hinderers: A Large‐Scale, Multi‐Lab, Coordinated Replication Study
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it