Taming anger and trusting others: Roles of skin conductance, anger regulation, and trust in children's aggression
Bibliographic record
Abstract
Proactive and reactive aggression subtypes are distinguishable as early as the preschool years. However, their early physiological and social-emotional correlates have not been examined simultaneously. We tested whether children's skin conductance level, anger regulation, and trust in others were differentially related to their proactive and reactive aggression. Four-year-olds and their primary caregivers were recruited from a large Canadian city (N = 150). Controlling for reactive aggression, higher trust was associated with lower proactive aggression, but only for children with low anger regulation or skin conductance level. Controlling for proactive aggression, lower anger regulation was related to higher reactive aggression, and higher trust was related to higher reactive aggression for children with high skin conductance level. Findings highlight the unique and collective relations of physiology, emotion regulation, and trust to different forms of aggression in early childhood. Statement of contribution What is already known on this subject Proactive and reactive aggression subtypes are distinguishable as early as the preschool years. Unique physiological and social-emotional correlates of each subtype have been studied in middle and late childhood. Trust is a critical milestone for positive social interactions in early childhood and has been linked to aggression. What the present study adds Physiological and social-emotional correlates are uniquely linked to subtypes of aggression already at age 4. Trust is differentially linked to aggression subtypes as a function of anger regulation and skin conductance level.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".