Don’t grin when you win: The social costs of positive emotion expression in performance situations.
Bibliographic record
Abstract
People who express positive emotion usually have better social outcomes than people who do not, and suppressing the expression of emotions can have interpersonal costs. Nevertheless, social convention suggests that there are situations in which people should suppress the expression of positive emotions, such as when trying to appear humble in victory. The present research tested whether there are interpersonal costs to expressing positive emotions when winning. In Experiment 1, inexpressive winners were evaluated more positively and rated as lower in hubristic-but not authentic-pride compared with expressive winners. Experiment 2 confirmed that inexpressive winners were perceived as using expressive suppression to downregulate their positive emotion expression. Experiment 3 replicated the findings of Experiment 1, and also found that people were more interested in forming a friendship with inexpressive winners than expressive winners. The effects were mediated by the perception that the inexpressive winner tried to protect the loser's feelings. This research is the first to identify social costs of expressing positive emotion, and highlights the importance of understanding the situational context when determining optimal emotion regulation strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".