Reappraisal Reconsidered: A Closer Look at the Costs of an Acclaimed Emotion-Regulation Strategy
Bibliographic record
Abstract
Cognitive reappraisal is a common form of emotion regulation that often centers on reframing how one thinks about an emotional situation so that one feels better. Given its demonstrated widespread benefits, two conclusions have been drawn about reappraisal: People can use it easily, and people should use it frequently. We critically examine these conclusions and highlight two fundamental drawbacks of reappraisal: First, people are often unable to use reappraisal successfully, and second, even when successful, using reappraisal to feel better is not always functional. To synthesize current research and inspire future research, we present a conceptual framework that systematically considers these drawbacks and how they may be influenced by individual-centered factors (e.g., the individual’s skill) and situation-centered factors (e.g., a stressor’s intensity) to shape outcomes across time. We then summarize the current literature and highlight the importance of considering reappraisal’s costs and benefits in future research.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".