The many faces of empathy and their relation to prosocial action and aggression inhibition
Bibliographic record
Abstract
This article discusses the emotional reactions most commonly associated with empathy and their relation to prosocial or altruistic action, aggression inhibition, and understanding others. In What is Empathy?, I characterize the distinct emotional reactions most commonly associated with empathy: empathy, sympathy, personal distress, and emotional contagion. In Measures of Empathy, I discuss the most common measures of dispositional and situational empathy. In Empathy, Prosocial Action, and Altruism, I consider the evidence that empathy, sympathy, and personal distress induce prosocial motivation. I conclude that sympathy is most strongly associated with prosocial, even altruistic, motivation. In Empathy and Aggression Inhibition, I examine the evidence that empathy inhibits aggression. The evidence is inconclusive. In Empathy and Mindreading, I briefly discuss empathy and mindreading, with an eye toward recent evidence concerning mirror neurons. I conclude by linking our current understanding of empathy to the philosophical tradition, and by offering some speculative remarks. WIREs Cogn Sci 2012, 3:253-263. doi: 10.1002/wcs.1165 For further resources related to this article, please visit the WIREs website.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".