Executive cognitive functioning and the recognition of facial expressions of emotion in incarcerated violent offenders, non‐violent offenders, and controls
Bibliographic record
Abstract
Violence is a social problem that carries enormous costs; however, our understanding of its etiology is quite limited. A large body of research exists, which suggests a relationship between abnormalities of the frontal lobe and aggression; as a result, many researchers have implicated deficits in so-called "executive function" as an antecedent to aggressive behaviour. Another possibility is that violence may be related to problems interpreting facial expressions of emotion, a deficit associated with many forms of psychopathology, and an ability linked to the prefrontal cortex. The current study investigated performance on measures of executive function and on a facial-affect recognition task in 20 violent offenders, 20 non-violent offenders, and 20 controls. In support of our hypotheses, both offender groups performed significantly more poorly on measures of executive function relative to controls. In addition, violent offenders were significantly poorer on the facial-affect recognition task than either of the other two groups. Interestingly, scores on these measures were significantly correlated, with executive deficits associated with difficulties accurately interpreting facial affect. The implications of these results are discussed in terms of a broader understanding of violent behaviour.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".