Smiling won't necessarily make you feel better: Response-focused emotion regulation strategies have little impact on cognitive, behavioural, physiological, and subjective outcomes
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Response-focused emotion regulation (RF-ER) strategies may alter people's evoked emotions, influencing intrapersonal outcomes. Researchers have found that participants engaging in expressive suppression (ES; a RF-ER strategy) experience increased sympathetic nervous system arousal, affect, and lowered memory accuracy. It is unclear, however, whether all RF-ER strategies exert maladaptive effects. Expressive dissonance (ED; displaying an expression opposite from how one feels) is a RF-ER strategy, and likely considered "maladaptive". As outlined by the facial feedback hypothesis, however, smiling may increase positive emotion, suggesting it may be an adaptive strategy. We compared the effects of ED and ES to a control condition on psychophysiology, memory, and affect, to assess whether ED is an adaptive RF-ER strategy, relative to ES, in response to negative stimuli. We recruited women only to account for known gender-based differences in emotion regulation. METHODS: We randomly assigned 144 women-identifying participants to engage in ED, ES, or to naturally observe, while viewing negative and arousing images. We recorded electrodermal activity and self-reported affect throughout and participants completed memory tasks after the picture task. RESULTS: We ran a series of repeated measures and one-way ANOVAs and found no differences between groups across outcomes. LIMITATIONS: The generalizability of our findings may be limited to young, undergraduate women. CONCLUSION: Engaging in ES or ED may not differentially impact outcomes among young, undergraduate women, shedding doubt on a conclusion in past literature that specific strategies are categorically adaptive or maladaptive. Future research exploring RF-ER strategies among diverse populations is warranted.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".