Directional bias in interpersonal emotion perception
Bibliographic record
Abstract
Accurately understanding others’ emotional states is fundamental to effective social functioning. While extensive research exists on how humans recognize different emotions, little is known about how people assess emotional intensity. Through a preliminary survey and seven multi-site studies (n = 2866), we demonstrate that despite believing they gauge emotions accurately, systematic discrepancies emerge: individuals tend to rate others’ emotions as more intense than those individuals rate themselves, particularly for negative emotions. This bias persists across text-based interactions, recorded videos, and live conversations, with both strangers and romantic partners. Interestingly, while people report preferring accurate judgments of their own emotional intensity, the discrepancy may serve adaptive functions, predicting higher empathic responses with strangers and greater relationship satisfaction in romantic relationships. These findings advance understanding of discrepancies in interpersonal emotional perception, highlighting their potential adaptive roles and providing insight into how they shape our social world and relationship outcomes. Understanding others’ emotions is central to human connection. Here, the authors show that people systematically overestimate others’ emotional intensity, especially for negative emotions, a tendency that may promote empathy and relationship satisfaction.
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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 | 0.005 | 0.018 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".