Caring is costly: People avoid the cognitive work of compassion.
Bibliographic record
Abstract
Compassion-the warm, caregiving emotion that emerges from witnessing the suffering of others-has long been considered an important moral emotion for motivating and sustaining prosocial behavior. Some suggest that compassion draws from empathic feelings to motivate prosocial behavior, whereas others try to disentangle these processes to examine their different functions for human prosociality. Many suggest that empathy, which involves sharing in others' experiences, can be biased and exhausting, whereas warm compassionate concern is more rewarding and sustainable. If compassion is indeed a warm and positive experience, then people should be motivated to seek it out when given the opportunity. Here, we ask whether people spontaneously choose to feel compassion, and whether such choices are associated with perceiving compassion as cognitively costly. Across all studies, we found that people opted to avoid compassion when given the opportunity, reported compassion to be more cognitively taxing than empathy and objective detachment, and opted to feel compassion less often to the degree they viewed compassion as cognitively costly. We also revealed two important boundary conditions: first, people were less likely to avoid compassion for close (vs. distant) others, and this choice difference was associated with viewing compassion for close others as less cognitively costly. Second, in the final study we found that with more contextually enriched and immersive pleas for help, participants preferred to escape feeling compassion, although their preference did not differ from also escaping remaining objectively detached. These results temper strong arguments that compassion is an easier route to prosocial motivation. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".