Psychophysiological Effects of Downregulating Negative Emotions: Insights From a Meta-Analysis of Healthy Adults
Bibliographic record
Abstract
Assessing psychophysiological responses of emotion regulation is a cost-efficient way to quantify emotion regulation and to complement subjective report that may be biased. Previous studies have revealed inconsistent results complicating a sound interpretation of these findings. In the present study, we summarized the existing literature through a systematic search of articles. Meta-analyses were used to evaluate effect sizes of instructed downregulation strategies on common autonomic (electrodermal, respiratory, cardiovascular and pupillometric) and electromyographic (corrugator activity, emotion-modulated startle) measures. Moderator analyses were conducted, with moderators including study design, emotion induction, control instruction and trial duration. We identified k = 78 studies each contributing multiple sub-samples and performed 23 meta-analyses for combinations of emotion regulation strategy and psychophysiological measure. Overall, results showed that effects of reappraisal and suppression on autonomic measures were highly inconsistent across studies with rather small mean effect sizes. Electromyography (startle and corrugator activity) showed medium effect sizes that were consistent across studies. Our findings highlight the diversity as well as the low level of standardization and comparability of research in this area. Significant moderation of effects by study design, trial duration, and control condition emphasizes the need for better standardization of methods. In addition, the small mean effect sizes resulting from our analyses on autonomic measures should be interpreted with caution. Findings corroborate the importance of multi-channel approaches.
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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 | 0.019 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.024 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 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".