Toddlers’ Affective Responses to Sociomoral Scenes Insights From Physiological Measures
Bibliographic record
Abstract
A growing literature suggests preverbal infants are sensitive to sociomoral scenes and prefer prosocial over antisocial agents. It remains unclear, however, whether and how emotional processes are implicated in infants’ responses to prosocial/antisocial actions. Although a recent study found infants and toddlers showed more positive facial expressions after viewing helping (vs. hindering) events, these findings were based on naïve coder ratings of facial activity; further, effect sizes were small. The current studies examined 18- and 24-month-old toddlers’ real-time reactivity to helping and hindering interactions using three physiological measures of emotion-related processes. At 18 months, activity in facial musculature involved in smiling/frowning was explored via facial electromyography (EMG). At 24 months, stress (sweat) was explored via electrodermal activity (EDA). At both ages, arousal was explored via pupillometry. Behaviorally, infants showed no preferences for the helper over the hinderer across age groups. EMG analyses revealed that 18-month-old infants showed higher corrugator activity (more frowning) during hindering (vs. helping) actions, followed by lower corrugator activity (less frowning) after hindering (vs. helping) actions finished. These findings suggest that antisocial actions elicited negativity, perhaps followed by brief disengagement. EDA analyses revealed no significant event-related differences. Pupillometry analyses revealed that both 18- and 24-month-olds’ pupils were smaller after viewing hindering (vs. helping), replicating recent evidence with 5-month-olds and suggesting that toddlers, too, show less arousal following hindering than following helping. Together, these results provide new evidence with respect to whether and how arousal/affective processes are involved when infants process sociomoral scenarios.
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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".