The influence of group membership and individual differences in psychopathy and perspective taking on neural responses when punishing and rewarding others
Bibliographic record
Abstract
Understanding how neural processes involved in punishing and rewarding others are altered by group membership and personality traits is critical in order to gain a better understanding of how socially important phenomena such as racial and group biases develop. Participants in an fMRI study (n = 48) gave rewards (money) or punishments (electroshocks) to in-group or out-group members. The results show that when participants rewarded others, greater activation was found in regions typically associated with receiving rewards such as the striatum and medial orbitofrontal cortex, bilaterally. Activation in those regions increased when participants rewarded in-group compared to out-group members. Punishment led to increased activation in regions typically associated with Theory of Mind including the medial prefrontal cortex and posterior superior temporal sulcus, as well as regions typically associated with perceiving others in pain such as the dorsal anterior cingulate cortex, anterior insula and lateral orbitofrontal cortex. Interestingly, in contrast to the findings regarding reward, activity in these regions was not moderated by whether the target of the punishment was an in- or out-group member. Additional regression analysis revealed that participants who have low perspective taking skills and higher levels of psychopathy showed less activation in the brain regions identified when punishing others, especially when they were out-group members. In sum, when an individual is personally responsible for delivering rewards and punishments to others, in-group bias is stronger for reward allocation than punishments, marking the first neuroscientific evidence of this dissociation.
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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 | 0.002 |
| 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) | 0.002 | 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".