Pain-related emotions modulate experimental pain perception and autonomic responses
Bibliographic record
Abstract
The effect of emotions on pain perception is generally recognized but the underlying mechanisms remain unclear. Here, emotions related to pain were induced in healthy volunteers using hypnosis, during 1-min immersions of the hand in painfully hot water. In Experiment 1, hypnotic suggestions were designed to induce various positive or negative emotions. Compared to a control condition with hypnotic-relaxation, negative emotions produced robust increases in pain. In Experiment 2, induction of pain-related anger and sadness were found to increase pain. Pain increases were associated with increases in self-rated desire for relief and decreases in expectation of relief, and with increases in arousal, negative affective valence and decreases in perceived control. In Experiment 3, hypnotic suggestions specifically designed to increase and decrease the desire for relief produced increases and decreases in pain, respectively. In all three experiments, emotion-induced changes in pain were most consistently found on ratings of pain unpleasantness compared to pain intensity. Changes in pain-evoked cardiac responses (R-R interval decrease), measured in experiments 2 and 3, were consistent with changes in pain unpleasantness. Correlation and multiple regression analyses suggest that negative emotions and desire for relief influence primarily pain affect and that pain-evoked autonomic responses are strongly associated with pain affect. These results confirm the hypothesized influence of the desire for relief on pain perception, and particularly on pain affect, and support the functional relation between pain affect and autonomic nociceptive responses. This study provides further experimental confirmation that pain-related emotions influence pain perception and pain-related physiological responses.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".