Adolescents with psychopathic traits report reductions in physiological responses to fear
Bibliographic record
Abstract
BACKGROUND: Psychopathy is characterized by profound affective deficits, including shallow affect and reduced empathy. Recent research suggests that these deficits may apply particularly to negative emotions, or to certain negative emotions such as fear. Despite increased focus on the cognitive and neural underpinnings of psychopathy, little is known about how psychopathy is associated with emotional deficits across a range of emotions. In addition, the relationship between psychopathy and the subjective experience of emotion has not yet been assessed. METHODS: Eighteen 10-17-year-olds with psychopathic traits and 24 comparison children and adolescents reported on their subjective experiences of emotion during five recent emotionally evocative life events, following a paradigm developed by Scherer and colleagues (Scherer & Wallbott, 1994). Group comparisons were then performed to assess variations in subjective experiences across emotions. RESULTS: As predicted, psychopathy was associated with reductions in the subjective experience of fear relative to other emotions. Children and adolescents with psychopathic traits reported fewer symptoms associated with sympathetic nervous system arousal during fear-evoking experiences. CONCLUSIONS: Rather than being related to uniformly impoverished emotional experience, psychopathic traits appear to be associated with greater deficits in subjective experiences of fear. This pattern of responding supports and extends previous observations that psychopathy engenders deficits in fear learning, physiological responses to threats, and the recognition of fear in others.
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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 | 0.001 |
| 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) | 0.003 | 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".