Salience or value: what drives priority in pain-cognition interactions?
Bibliographic record
Abstract
ABSTRACT: The evolutionary function of pain is to protect against injury by capturing attention and motivating nocifensive behavior. This makes pain inherently salient and capable of disrupting the pursuit of competing goals. However, this interference can sometimes be overridden by concurrent demands. We have yet to discover how priority is assigned when pursuing competing goals while experiencing pain: through attentional resource competition (salience-based) or motivational conflict (value-based), or both. We investigated these different mechanisms in a study in which 40 healthy adults completed the cognitive branching paradigm, which comprises 2 nested backward letter-matching tasks, 1 with a low-value reward ($0.05) and the other with a high-value reward ($1). Participants performed the tasks first without stimulation and then under 2 conditions: tonic painful heat and tonic nonpainful electric stimuli that were matched for salience. We analyzed performance using linear mixed models and conducted post-hoc Bayesian tests for significant interactions. We found that pain selectively disrupted the low-value task, but not the high-value task, indicating that priority assignment was value-based, whereas the electric stimulus had no impact on task performance. In addition, sex-disaggregated analyses showed that this effect was exclusively seen in males, whereas females exhibited no significant impact of pain on task performance. Our findings suggest that motivational conflict, rather than resource competition, determines priority assignment when pain is concurrent to competing demands. In addition, we show for the first time, sexually dimorphic mechanisms in pain-cognition interactions, which contributes to an emerging literature of sex differences in pain mechanisms and behaviours.
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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 | 0.004 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".