The Distinct Effects of Empathic Accuracy for a Romantic Partner’s Appeasement and Dominance Emotions
Bibliographic record
Abstract
When is accurately reading other people’s emotions costly and when is it beneficial? We aimed to identify whether the association between empathic accuracy and both relationship quality and motivation to change varies depending on the type of emotion being detected: appeasement (e.g., embarrassment) or dominance (e.g., anger). Romantic partners (couples: N = 111; individuals: N = 222) discussed a characteristic they wanted their partner to change and rated their own emotions and perceptions of their partner’s emotions. Relationship quality was self-reported and objectively coded. Using multilevel response-surface analysis, we tested preregistered hypotheses about whether empathic accuracy for appeasement and dominance emotions was differentially associated with relationship quality and motivation to change. For appeasement emotions, empathic accuracy predicted higher relationship quality. For dominance emotions, higher intensity of felt emotions—not empathic accuracy—predicted lower relationship quality. Empathic accuracy did not predict the motivation to change. These results suggest that the benefits of empathic accuracy can depend on the emotion type.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".