(Not) showing you feel good, can be bad: The consequences of breaking expressivity norms for positive emotions
Bibliographic record
Abstract
Are there optimal levels of showing one feels good? Examining four positive emotions (gratitude, interest, feeling moved, triumph), we demonstrate in two pre-registered experiments (n = 901) that even for pleasant feelings, showing too much – or too little – can lead to negative social consequences. Expressers who downplay their gratitude, and to a lesser degree interest, are deprived of social contact and power. Restrained displays of feeling moved are also met with reduced contact. For triumph, amplified expressers are socially avoided, yet at the same time, those who downplay their victory are seen to be less powerful. We demonstrate the role of person-perception mechanisms (warmth and competence) as underlying explanators for these effects. Taken together, our findings contribute to the growing literature on the social consequences of emotional expressions, by pointing to divergent outcomes for norm violations relating to different positive emotions.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".