Brain structural correlates of psychopathic traits in elite female combat-sports athletes
Bibliographic record
Abstract
Psychopathy is characterized by glibness and superficial charm, as well as a lack of empathy, guilt, and remorse, and is often accompanied by antisocial behavior. The cerebral bases of this syndrome have been mostly studied in violent subjects or those with a criminal history. However, the antisocial component of psychopathy is not central to its conceptualization and, in fact, psychopathic traits are present in well-adjusted, non-criminal individuals within the general population. Interestingly, certain psychopathy characteristics appear to be particularly pronounced in some groups or professions. Importantly, as these so-called adaptive or successful psychopaths do not show antisocial tendencies or have significant psychiatric comorbidities, they may represent an ideal population to study this trait. Here we investigated such a group, specifically elite female judo athletes, and compared them to matched non-athletes. Participants completed psychopathy, anger, perspective-taking and empathic concern questionnaires and underwent structural magnetic resonance imaging (MRI). Grey matter density (GMD) was computed using voxel-based morphometry from the T1-weighted images. Athletes scored significantly higher in primary psychopathy and anger, and lower in empathy and perspective taking. They also exhibited smaller GMD in the right Temporal Pole, left Occipital Cortex, and left Amygdala/Hippocampus. GMD values for the latter cluster significantly correlated with primary psychopathy scores across both groups. These results confirm and extend previous findings to a little-studied population and provide support for the conceptualization of psychopathy as a dimensional personality trait which, not only is not necessarily associated with antisocial behavior, but may potentially have adaptive value.
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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 | 0.001 | 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) | 0.002 | 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".