How Individual Differences in Empathy Predict Moments of Empathy in Everyday Life
Bibliographic record
Abstract
Do trait empathy measures predict how people experience empathy in daily life? Despite considerable research on empathy, we know surprisingly little about how trait measures relate to real-world empathic experiences. In this preregistered analysis of 7,343 experience sampling surveys from a near-representative sample of 246 U.S. adults, we map the connections between validated trait empathy measures and state experiences of empathy. Each component of state empathy-including emotion sharing, perspective taking, and compassion-was significantly predicted by theoretically relevant trait measures. However, trait empathy explained limited variance in daily experiences overall, ranging from just 3% for emotion sharing to 15% for perceived empathic efficacy. Adding emotional valence as a predictor improved model fit and variance explained for most state experiences, highlighting the crucial role of context. Our findings validate trait empathy measures while revealing their limitations in predicting real-world experiences.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".