The modulation of somatosensory resonance by psychopathic traits and empathy
Bibliographic record
Abstract
A large number of neuroimaging studies have shown neural overlaps between first-hand experiences of pain and the perception of pain in others. This shared neural representation of vicarious pain is thought to involve both affective and sensorimotor systems. A number of individual factors are thought to modulate the cerebral response to other's pain. The goal of this study was to investigate the impact of psychopathic traits on the relation between sensorimotor resonance to other's pain and self-reported empathy. Our group has previously shown that a steady-state response to non-painful stimulation is modulated by the observation of other people's bodily pain. This change in somatosensory response was interpreted as a form of somatosensory gating (SG). Here, using the same technique, SG was compared between two groups of 15 young adult males: one scoring very high on a self-reported measure of psychopathic traits [60.8 ± 4.98; Levenson's Self-Report Psychopathy Scale (LSRP)] and one scoring very low (42.7 ± 2.94). The results showed a significantly greater reduction of SG to pain observation for the high psychopathic traits group compared to the low psychopathic traits group. SG to pain observation was positively correlated with affective and interpersonal facet of psychopathy in the whole sample. The high psychopathic traits group also reported lower empathic concern (EC) scores than the low psychopathic traits group. Importantly, primary psychopathy, as assessed by the LSRP, mediated the relation between EC and SG to pain observation. Together, these results suggest that increase somatosensory resonance to other's pain is not exclusively explained by trait empathy and may be linked to other personality dimensions, such as psychopathic traits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".