Profiles in empathy: Different empathic responses to emotional and physical suffering.
Bibliographic record
Abstract
Empathy often occurs when individuals witness another suffer. Researchers who study empathy have tried to identify reliable behavioral outcomes, affective responses, and physiological changes associated with its experience. However to date, these markers of empathy have remained elusive. We propose that failing to take into account the features of the suffering that elicited the empathy has contributed to this problem. We hypothesized that emotional and physical suffering generate diverging profiles of empathy with different behavioral, affective, and physiological markers. We first examined how observer's rated 75 different types of suffering. Ratings produced 2 independent clusters-primarily emotional and primarily physical, which classified 80% of suffering events (Study 1). Next we measured behavioral, affective, and physiological markers of empathy for emotional and physical suffering. In a 2-step exploratory (Study 2a) and confirmatory (Study 2b; preregistered) process, participants generated open-ended behavioral responses to suffering scenarios, which were coded, classified into thematic categories, and presented to new participants. We found that emotional suffering elicited more comforting and interpersonal emotion regulation behaviors in others, whereas physical suffering elicited more emergency mobilization behaviors. In Study 3, participants viewed pictures of suffering. Self-reports and coded expressions of compassion were stronger for emotional suffering; anxiety and distress were stronger for physical suffering. In Study 4, participants watched videos of suffering. Emotional and physical suffering elicited increased parasympathetic and sympathetic activation, though coactivation was greater for physical suffering. This work generates a more nuanced and comprehensive understanding of empathy, which addresses current debates and reconciles inconsistencies in its conceptualization. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".