Inter-individual predictors of pain inhibition during performance of a competing cognitive task
Bibliographic record
Abstract
The main function of pain is to automatically draw attention towards sources of potential injury. However, pain sometimes needs to be inhibited in order to address or pursue more relevant tasks. Elucidating the factors that influence how people manage this relationship between pain and task performance is essential to understanding the disruptive nature of pain and its variability between individuals. Here, 41 healthy adults completed a challenging working memory task (2-back task) while receiving painful thermal stimulations. Examining the trial-by-trial relationship between pain perception and task performance revealed that pain's disruptive effects on performance were mediated by self-reported pain intensity, and that the analgesic effects of a competing task were influenced by task performance. We found that higher pain catastrophizing, higher trait anxiety, and lower trait mindfulness were associated with larger trade-offs between pain perception and task performance, suggesting that these psychological factors can predict increased fluctuations between disruption by pain and analgesia from a competing task. Altogether these findings provide an important and novel perspective on our understanding of individual differences in the interplay between pain and ongoing task performance.
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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 | 0.008 |
| 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 | 0.001 | 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".