Feeling out a link between feeling and infant sociomoral evaluation
Bibliographic record
Abstract
Recent research has shown that infants selectively approach prosocial versus antisocial characters, suggesting that foundations of sociomoral development may be present early in life. Despite this, to date, the mental processes involved in infants' prosocial preferences are poorly understood. To explore a possible role of emotions in early social evaluations, the current studies examined whether four samples of infants and toddlers express different emotional reactions after observing prosocial (giving) versus antisocial (taking) events. Experimentally blind coders rated infants' and toddlers' emotional reactions to prosocial and antisocial interactions from video using a 1- to 7-point Likert scale of negative to positive emotion; reactions were rated as more positive after viewing prosocial compared to antisocial interactions in three of four samples. While the observed effects were small, a single-paper meta-analysis suggests that the findings are robust and stable across age. These results support the possibility that emotional reactions play some role in infants' sociomoral evaluations. Statement of contribution What is already known Infants prefer prosocial to antisocial individuals from the first year of life. Emotion plays some role in the sociomoral judgments of children and adults. What this study adds Infants and toddlers express more positive reactions after observing prosocial giving versus antisocial taking acts, though observed effect sizes are small. Naïve coders can predict at a better than chance rate what type of act an infant or toddler just viewed based on their facial expressions. Provides the first evidence that emotion plays some to-be-specified role in infants' and toddlers' sociomoral evaluations.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".