Domain-specific associations between social cognition and aggression in schizophrenia spectrum disorders
Bibliographic record
Abstract
Aggression in schizophrenia spectrum disorders (SSD) is rare but elevated relative to the general population. Existing studies have not identified reliable personal predictors of aggression in SSD. In line with social information processing models suggesting that difficulties interpreting social cues and others' intentions may lead to aggression, we evaluated whether social cognitive domains or global social cognition could be modifiable risk factors in SSD. We examined aggression and social cognition in 59 participants with SSD and 43 healthy volunteers (HV). Self-reported aggression was measured via the Reactive-Proactive Aggression Questionnaire (RPAQ). Social cognition was assessed using five tasks measuring emotion processing, theory of mind , and social perception. Group differences were analyzed using Mann-Whitney-Wilcoxon tests. Multiple regressions examined effects of social cognition on aggression, controlling for demographic and clinical covariates. Supplemental mediation analyses tested whether impairments in emotion processing, theory of mind , or overall social cognition explained the relationship between SSD diagnosis and increased aggression. Reported aggression was higher in the SSD group, and social cognitive abilities were impaired across domains ( p < .001). Better emotion processing (β = −0.35, p = .03) and theory of mind (β = −0.32, p = .03) predicted lower aggression in SSD, even when accounting for demographic and neurocognitive variables. Exploratory models adjusting for overall psychiatric symptom severity showed that theory of mind remained significant, while emotion processing attenuated. However, social cognition did not mediate the relationship between diagnosis and aggression. Future studies should examine other social processing factors, such as attributional bias.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".