High Psychopathic Trait Individuals’ Decisions to Empathize are Influenced by Power Dynamics
Bibliographic record
Abstract
Abstract The lack of empathy associated with heightened psychopathic traits is commonly attributed to fundamental emotional and/or cognitive deficits. However, recent studies showing that psychopathic individuals are capable of normative empathy in certain contexts suggest their reduced empathy may instead reflect reduced motivation to empathize. To further evaluate these possibilities,158 university students completed self-report measures of psychopathic traits and motivations to empathize and performed an Empathic Choice Task which presented various social situations and asked them to freely choose to either empathize virtuously (for the target’s benefit), empathize non-virtuously (for their own benefit), or merely observe. Results indicated that psychopathic traits were unrelated to the overall frequency of empathic choices. However, post-hoc analyses indicated that the motivations underlying these choices varied as a function of situational power dynamics, and this was increasingly so for individuals higher in psychopathic traits. Specifically, psychopathic traits were positively correlated with virtuous empathy when targets were depicted in positions of power over the participant, but positively related to non-virtuous empathy when they were depicted in positions of power over the target. These results support motivational theories of psychopathy and highlight the strategic sensitivity of high psychopathic trait individuals to complex socio-contextual dynamics.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".