Emotion and Morality: Understanding the Role of Empathy and Other Specific Emotions in Moral Judgment and Moral Behaviour
Bibliographic record
Abstract
Empathy is often considered to be an important emotion for morality.In fact, many researchers argue that morality is impossible without empathy (Howe, 2013).In this research project, I argue against the widely held view that empathy is important for morality.I examine autistic individuals and psychopaths, both known for having an impairment in empathy, in order to show that empathy is not important for morality.I argue that although autistic individuals are deficient in empathy, they display moral behaviour, thus empathy cannot be the core of morality.I argue that while psychopaths are said to lack empathy, they are not wholly devoid of empathy.If it is the case that psychopaths do not fully lack empathy, then there must be more to morality than empathy.I also show that empathy can cause partiality, and thus cannot be the core of morality.In support of a sentimentalist framework of morality, I determine the emotions important for morality by conducting a systematic review of the studies on emotion and morality.I examine and analyze studies conducted on both negative and positive emotions.I conclude that disgust, anger, distress sympathy, elevation, and mirth are important emotions for morality.
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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 | 0.001 | 0.001 |
| 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 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| 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".