Interactions cognition-émotion et personnalité psychopathique : des trajectoires distinctes vers les comportements antisociaux et violents
Bibliographic record
Abstract
Researchers have long acknowledged heterogeneity among persons who exhibit antisocial and violent behaviours. The study of psychopathic personality or psychopathy can help elucidate this heterogeneity through examination of the different facets that constitute this disorder. In particular, the distinct correlates of the interpersonal-affective traits (Factor 1) and the impulsive-antisocial traits (Factor 2) of psychopathy suggest at least two possible pathways to antisocial behaviours. Building on basic studies in cognitive and affective neuroscience, we provide a focused, non-comprehensive review of work identifying the biopsychological mechanisms involved in these two pathways, with special attention to studies using event-related potential (ERP) methods. In specific, a series of studies are discussed which examined affective and cognitive processes that may distinguish offenders high on psychopathic traits from other offenders, with emphasis on alterations in emotion-cognition interactions related to each factor of psychopathy. The set of findings reviewed highlight a central conclusion: Factor 1 represents a pathway involving reduced emotional responding, exacerbated by attentional abnormalities, that make for a more deliberate and emotionally insensitive offender profile. In contrast, Factor 2 characterizes a pathway marked by emotional and behavioural dysregulation and cognitive control dysfunctions, particularly in emotional contexts. Implications for identifying etiological processes and the further understanding of antisocial and violent behaviours are discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".