Diversity in the emotional modulation of pain perception: An account of individual variability
Bibliographic record
Abstract
BACKGROUND: While emotional state has been shown to modulate pain perception, there has been little consideration for the individual variability in this effect, or what factors may contribute to individual-level differences. The objective of this study was to characterize the variability in emotional modulation of pain in a healthy sample. METHODS: Twenty-five healthy, adult females participated in a heat pain-rating task. After calibration of the appropriate temperature for each participant, the pain-rating task was combined with viewing of positive, neutral, or negative valence images. Participants rated pain intensity and unpleasantness of the painful stimulus. RESULTS: The magnitude of the effect for emotional modulation of pain was markedly variable across individuals. Some participants exhibited greater pain relief from the positive emotional stimuli while others were more susceptible to pain amplification from the negative emotional stimuli. There were also significant correlations between emotional modulation of pain and specific psychological measures (depression and anxiety). CONCLUSION: Overall, inducing a positive emotional state mitigates pain perception, while negative emotional state amplifies it. The magnitudes of these separate pain-modulating effects, however, vary across individuals, and are associated with individual levels of depressive and anxious feelings, even within a non-clinical population. SIGNIFICANCE: The opposite effects of valence on pain amplification and modulation revealed in this study are novel. This study shows that emotional modulation of pain varies markedly across individuals and is related to psychological factors including depression and anxiety. Examining this link in healthy individuals may inform our understanding of the comorbidity between pain and depression/anxiety.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".