Distraction from pain depends on task demands and motivation
Bibliographic record
Abstract
Introduction: Pain captures attention automatically, yet we can inhibit pain when we are motivated to perform other tasks. Previous studies show that engaging in a cognitively demanding task reduces pain compared with a task that is minimally demanding, yet the effects of motivation on this pain-reducing effect remain largely unexplored. Objectives: In this study, we hypothesized that motivating people to engage in a task with high demands would lead to more cognitive resources directed toward the task, thereby amplifying its pain-reducing effects. Methods: On different trials, participants performed an easy (left-right arrow discrimination) or demanding (2-back) cognitive task while receiving nonpainful or painful heat stimuli. In half of the trials, monetary rewards were offered to motivate participants to engage and perform well in the task. Results: Results showed an interaction between task demands and rewards, whereby offering rewards strengthened the pain-reducing effect of a distracting task when demands were high. This effect was reinforced by increased 2-back performance when rewards were offered, indicating that both task demands and motivation are necessary to inhibit pain. Conclusions: When task demands are low, motivation to engage in the task will have little impact on pain because performance cannot further increase. When motivation is low, participants will spend minimal effort to perform well in the task, thus hindering the pain-reducing effects of higher task demands. These findings suggest that the pain-reducing properties of distraction can be optimized by carefully calibrating the demands and motivational value of the task.
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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) | 0.001 | 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 | 0.001 |
| 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".