Infants’ Social Evaluation of Helpers and Hinderers: A Large-Scale, Multi-Lab, Coordinated Replication Study
Bibliographic record
Abstract
Evaluating others’ actions as praiseworthy or blameworthy is a fundamental aspect of human nature. A seminal study published in 2007 suggested that the ability to form social evaluations based on third-party interactions emerges within the first year of life, considerably earlier than previously thought (Hamlin, Wynn, & Bloom, 2007). In this study, infants demonstrated a preference for a character (i.e., a shape with eyes) who helped, over one who hindered, another character who tried but failed to climb a hill. This study sparked a new line of inquiry into infants’ social evaluations; however, numerous attempts to replicate the original findings yielded mixed results, with some reporting effects not reliably different from chance. These failed replications point to at least two possibilities: (1) the original study may have overestimated the true effect size of infants’ preference for helpers, or (2) key methodological or contextual differences from the original study may have compromised the replication attempts. Here we present a pre-registered, closely coordinated, multi-laboratory, standardized study aimed at replicating the helping/hindering finding using a well-controlled video version of the hill show. We intended to (1) provide a precise estimate of the true effect size of infants’ preference for helpers over hinderers, and (2) determine the degree to which infants’ preferences are based on social features of the Helper/Hinderer scenarios. XYZ labs participated in the study yielding a total sample size of XYZ infants between the ages of 5.5 and 10.5 months. Brief summary of results will be added after data collection.
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".