Motivational effects on empathic choices
Bibliographic record
Abstract
Empathy often feels automatic, but variations in empathic responding suggest that, at least some of the time, empathy is affected by one’s motivation to empathize in any particular circumstance. Here, we show that people can be motivated to engage in (or avoid) empathy-eliciting situations with strangers, and that these decisions are driven by subjective value-based estimations of the costs (e.g., cognitive effort) and benefits (e.g., social reward) inherent to empathizing. Across seven experiments (overall N = 1,348), and replicating previous work (Cameron et al., 2019), we found a robust empathy avoidance effect. We also find support for the hypothesis that individuals can be motivated to opt-in to situations requiring empathy that they would otherwise avoid. Participants were more likely to opt into empathy-eliciting situations if 1) they were incentivized monetarily for doing so (Experiments 1a and 1b), and 2) if a more familiar and liked empathy target was available (Experiments 2a and 2b). Framing empathy as explicitly related to one’s moral character and reputation did not motivate participants to engage in empathy (Experiment 3a and 3c), though these null results may be due to a weak manipulation. These findings suggest that empathy can be motivated in multiple ways, and is a process driven by context-specific value-based decision making.
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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 | 0.002 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".