Empathic accuracy in couples: A daily diary study of relationship-related emotions.
Bibliographic record
Abstract
Empathic accuracy-the ability to accurately infer one's partner's emotions-has important implications for couples' relational well-being. Although distinct emotions convey various needs and elicit different responses between romantic partners, research on empathic accuracy-its patterns, underlying processes and relational consequences-across a spectrum of discrete emotions directed towards the partner or the relationship remains sparse. This study employed a 35-day dyadic daily diary design to examine empathic accuracy in couples, focusing on seven emotions (joy, feeling loved, anger, contempt, sadness, fear, and guilt) while also investigating the reliance on bias of assumed similarity, the moderating role of the target's social sharing, and the links between empathic accuracy and perceived partner responsiveness (PPR). The sample included 327 couples who reported on their own emotions, their perceptions of their partner's emotions, their perceptions of their own social sharing and their perception of their partner's responsiveness. Results showed that partners tend to hold a slight negativity bias when inferring each other's emotions. However, most are adept at tracking changes in their partner's emotions, especially when partners verbalize how they are feeling, and they strongly rely on their own emotions to make such inferences. In addition, the intensity of felt or perceived emotions-rather than empathic accuracy-were associated with PPR, though some distinct patterns emerged across emotions. These results provide partial support for error-management theory and highlight the importance of examining emotions beyond valence, as both similarities and distinctions emerge in patterns of empathic accuracy and their links to relational outcomes. (PsycInfo Database Record (c) 2025 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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".