Misunderstanding the affective consequences of everyday social interactions: The hidden benefits of putting one's best face forward.
Bibliographic record
Abstract
Positive self-presentation may have beneficial consequences for mood that are typically overlooked. Across a series of studies, participants underestimated how good they would feel in situations that required them to put their best face forward. In Studies 1 and 2A, participants underestimated the emotional benefits of interacting with an opposite sex stranger versus the benefits of interacting with a romantic partner. In Study 2B, participants who were instructed to engage in self-presentation felt happier after interacting with their romantic partner than participants who were not given this instruction, although other participants serving as forecasters did not anticipate such benefits. Increasing the generalizability of this self-presentation effect across contexts, the authors demonstrated that participants also underestimated how good they would feel before and after being evaluated by another person (Studies 3 and 4). This failure to recognize the affective benefits of putting one's best face forward may underlie forecasting errors regarding the emotional consequences of the most common forms of social interactions.
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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 | 0.008 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".