Empathy, defending, and functional connectivity while witnessing social exclusion
Bibliographic record
Abstract
Peers are present for most bullying episodes. Peers who witness bullying can play an important role in either stopping or perpetuating the behavior. Defending can greatly benefit victimized peers. Empathy is strongly associated with defending. Yet, less is known about defenders’ neural response to witnessing social distress, and how this response may relate to the link between empathy and defending. Forty-six first-year undergraduate students (Mage = 17.7; 37 women), with varied history of peer defending, underwent fMRI scanning while witnessing a depiction of social exclusion. Functional connectivity analysis was performed across brain regions that are involved in cognitive empathy, empathetic distress, and compassion. History of defending was positively associated with functional connectivity (Exclusion > Inclusion) between the left orbitofrontal cortex (OFC) – medial prefrontal cortex (MPFC), and right OFC – left and right amygdalae. Defending was negatively associated with functional connectivity between the left OFC – anterior cingulate cortex. The relationship between history of defending and empathy (specifically, empathetic perspective taking) was moderated by functional connectivity of the right OFC – left amygdala. These findings suggest that coactivation of brain regions involved in compassionate emotion regulation and empathetic distress play a role in the relationship between empathy and peer defending.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".