The effects of emotion regulation strategies on the pain experience
Bibliographic record
Abstract
Although emotion regulation modulates the pain experience, inconsistencies have been identified regarding the impact of specific regulation strategies on pain. Our goal was to examine the effects of emotion suppression and cognitive reappraisal on automatic (ie, nonverbal) and cognitively mediated (ie, verbal) pain expressions. Nonclinical participants were randomized into either a suppression (n = 58), reappraisal (n = 51), or monitoring control (n = 42) condition. Upon arrival to the laboratory, participants completed the Emotion Regulation Questionnaire, to quantify self-reported suppression and reappraisal tendencies. Subsequently, they completed a thermal pain threshold and tolerance task. They were then provided with instructions to use, depending on their experimental condition, suppression, reappraisal, or monitoring strategies. Afterward, they were exposed to experimentally induced pain. Self-report measures of pain, anxiety, and tension were administered, and facial expressions, heart rate, and galvanic skin response were recorded. The Facial Action Coding System was used to quantify general and pain-related facial activity (ie, we defined facial actions that occurred during at least 5% of pain stimulation periods as "pain-related actions"). Reappraisal and suppression induction led to reductions in nonverbal and verbal indices of pain. Moreover, self-reported tendencies to use suppression and reappraisal (as measured by the Emotion Regulation Questionnaire) did not interact with experimental condition in the determination of participants' responses. Results suggest that consciously applying emotion regulation strategies during a painful task can moderate both cognitively mediated (e.g., verbal) and automatic (e.g., facial activity) expressions of pain.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".