Neural responses to emotional and neutral facial expressions in chronically violent men
Bibliographic record
Abstract
BACKGROUND: Abnormal neural responses to others' emotions, particularly cues of threat and distress, have been implicated in the development of chronic violence. We examined neural responses to several emotional cues within a prospectively identified group of chronically violent men. We also explored the association between neural responses to social emotions and psychopathic features. METHODS: We compared neural responses to happy, sad, angry, fearful and neutral faces between chronically violent (n = 22) and nonviolent (n = 20) men using functional magnetic resonance imaging (fMRI). Participants were prospectively identified from a longitudinal study based on information collected from age 7 to 27 years. We assessed psychopathic features using a self-report measure administered in adulthood. RESULTS: The chronically violent men exhibited significantly reduced neural responses in the dorsomedial prefrontal cortex to all faces, regardless of the emotional content, compared with nonviolent men. We also observed a hyperactive amygdala response to neutral faces in chronically violent men, but only within the context of viewing happy faces. Moreover, they exhibited a greater dorsomedial prefrontal cortex response to mildly fearful faces than nonviolent men. These abnormalities were not associated with psychopathic features in chronically violent men. LIMITATIONS: It remains unclear whether the observed neural abnormalities preceded or are a consequence of persistent violence, and these results may not generalize to chronically violent women. CONCLUSION: Chronically violent men exhibit a reduced neural response to facial cues regardless of emotional content. It appears that chronically violent men may view emotionally ambiguous facial cues as potentially threatening and implicitly re-interpret subtle cues of fear in others so they no longer elicit a negative response.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".