Emotion recognition links to reactive and proactive aggression across childhood: A multi-study design
Bibliographic record
Abstract
Abstract Difficulty recognizing negative emotions is linked to aggression in children. However, it remains unclear how certain types of emotion recognition (insensitivities vs. biases) are associated with functions of aggression and whether these relations change across childhood. We addressed these gaps in two diverse community samples (study 1: aged 4 and 8; N = 300; study 2: aged 5 to 13, N = 374). Across studies, children performed a behavioral task to assess emotion recognition (sad, fear, angry, and happy facial expressions) while caregivers reported children’s overt proactive and reactive aggression. Difficulty recognizing fear (especially in early childhood) and sadness was associated with greater proactive aggression. Insensitivity to anger – perceiving angry faces as showing no emotion – was associated with increased proactive aggression, especially in middle-to-late childhood. Additionally, greater happiness bias – mistaking negative emotions as being happy – was consistently related to higher reactive aggression only in early childhood. Together, difficulty recognizing negative emotions was related to proactive aggression, however, the strength of these relations varied based on the type of emotion and developmental period assessed. Alternately, difficulty determining emotion valence was related to reactive aggression in early childhood. These findings demonstrate that distinct forms of emotion recognition are important for understanding functions of aggression across development.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".