Infants’ neural responses to helping and hindering scenarios
Bibliographic record
Abstract
A growing literature suggests infants prefer prosocial others over antisocial others. Although recent studies have begun to explore the neural mechanisms underlying these responses (Cowell and Decety, 2015; Gredebäck et al., 2015), these studies were based on relatively small samples and focused on distinct aspects of sociomoral responding. The current preregistered study systematically examined infants' neural responses both to prosocial/antisocial interactions and to prosocial/antisocial characters, using larger samples and two distinct age groups. We found that 6- (but not 12-) month-olds showed higher relative right frontal alpha power (indexing approach motivation) when viewing helping versus hindering scenarios. Consistent with past EEG work, infants showed no group-level manual preferences for the helper. However, analyses of infants' neural responses toward images of the helper versus hinderer revealed that both 6- and 12-month-olds showed differential event-related potential (ERP) responses in the P400 and N290 components (indexing social perception) but not in the Nc component (indexing attentional allocation), suggestive that infants' neural responses to prosocial versus antisocial characters reflect social processing. Together, these findings provide a more comprehensive account of infants' responses to prosocial/antisocial interactions and characters, and support the hypothesis that both motivational and socially relevant processes are implicated in infants' sociomoral responding.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".