Children's emotion recognition and aggression: A multi‐cohort longitudinal study
Bibliographic record
Abstract
Difficulty recognizing negative emotions (NEs) in children is linked to increased antisocial traits and externalizing problems. However, crucial aspects of this relation remain unclear, such as: whether NE recognition is associated with externalizing problems in general or only a particular subcomponent (i.e., aggression); whether subcomponents of NE recognition (i.e., insensitivity and misspecifications) are relatively more important; and how these relations change over the course of development. We assessed emotion recognition, overt aggression, attention deficit hyperactivity disorder (ADHD) symptoms, and oppositional defiant disorder (ODD) symptoms in an ethnically diverse sample of Canadian children (N = 150; 4-year-olds, N = 148; 8-year-olds) and followed up with them 1 year later (86.9% retention). Emotion recognition was assessed using a behavioral task and caregivers reported on children's externalizing symptoms. Children with lower NE recognition had higher initial, but not subsequent, overt aggression, even when controlling for nonaggressive externalizing symptoms (i.e., ADHD and ODD symptoms). NE recognition was not concurrently or longitudinally associated with nonaggressive externalizing symptoms. Age and gender did not moderate these findings. Both higher NE insensitivity (e.g., reporting a sad face appears neutral) and misspecifications (e.g., reporting a sad face appears angry) were significantly associated with higher concurrent overt aggression. In conclusion, both NE insensitivity and misspecifications were found to be uniquely important for children's overt aggression. These findings highlight the importance of different forms of NE recognition and differentiating between aggressive and nonaggressive externalizing problems in children.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".