Is Psychopathy a Mental Disorder or an Adaptation? Evidence From a Meta-Analysis of the Association Between Psychopathy and Handedness
Bibliographic record
Abstract
Psychopathy has historically been conceptualized as a mental disorder, but there is growing evidence that it may instead be an alternative, adaptive life history strategy designed by natural selection. Although the etiology of mental disorders is not fully understood, one likely contributor is perturbations affecting neurodevelopment. Nonright-handedness is a sign of such perturbations, and therefore can be used to test these competing models. If psychopathy is a mental disorder, psychopaths should show elevated rates of nonright-handedness. However, an adaptive strategy perspective expects psychopaths to be neurologically healthy and therefore predicts typical rates of nonright-handedness. We meta-analyzed 16 studies that investigated the association between psychopathy and handedness in various populations. There was no difference in the rates of nonright-handedness between community participants high and low in psychopathy. Furthermore, there was no difference between psychopathic and nonpsychopathic offenders in rates of nonright-handedness, though there was a tendency for offenders scoring higher on the Interpersonal/Affective dimension of psychopathy to have lower rates of nonright-handedness, and for offenders scoring higher on the Behavioral dimension of psychopathy to have higher rates of nonright-handedness. Lastly, there was no difference in rates of nonright-handedness between psychopathic and nonpsychopathic mental health patients. Thus, our results fail to support the mental disorder model and partly support the adaptive strategy model. We discuss limitations of the meta-analysis and implications for theories of the origins of psychopathy.
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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 | 0.026 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.024 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".