Examining empathic accuracy in a standardized task and in a naturalistic interaction: Associations, differences, and links with empathy
Bibliographic record
Abstract
Empathic accuracy (EA) – the ability to infer others' emotions accurately – is typically conceptualized as a perceiver-level skill. This perspective implies that performance on different EA tasks should be correlated. Further, EA should be associated with trait empathy, given the theoretical similarity between the constructs, and that EA tasks are often used as behavioural measures of cognitive empathy. We examined the conceptualization of EA as a perceiver-level skill, and as a measure of perceivers' cognitive empathy. We recruited friend dyads ( N = 137 dyads, M age = 19.61 years, SD age = 1.34 years) and tested associations and differences between EA measured with a personal task (rating the affect of a friend following a supportive interaction), and a standardized task (rating the affect of unknown targets discussing emotional events). Additionally, we examined associations between EA and cognitive and affective empathy. Analyses revealed low correspondence in EA between tasks and videos. EA was higher on each of the standard-task videos compared to in the personal task. Finally, greater self-report affective empathy, but not cognitive empathy, was linked to greater EA in both tasks. These findings challenge the notion that EA is a skill of the perceiver. Implications for conceptualizing and measuring empathic accuracy are discussed.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".