Neural underpinnings of behavioural strategies that prioritize either cognitive task performance or pain
Bibliographic record
Abstract
We previously discovered that when faced with a challenging cognitive task in the context of pain, some people prioritize task performance, while in others, pain results in poorer performance. These behaviours, designated respectively as A- and P-types (for attention dominates vs pain dominates), may reflect pain coping strategies, resilience or vulnerabilities to develop chronic pain, or predict the efficacy of treatments such as cognitive behavioural therapy. Here, we used a cognitive interference task and pain stimulation in 80 subjects to interrogate psychophysical, psychological, brain structure and function that distinguish these behavioural strategies. During concurrent pain, the A group exhibited faster task reaction times (RTs) compared to nonpain trials, whereas the P group had slower RTs during pain compared to nonpain trials, with the A group being 143 ms faster than the P group. Brain imaging revealed structural and functional brain features that characterized these behavioural strategies. Compared to the performance-oriented A group, the P group had (1) more gray matter in regions implicated in pain and salience (anterior insula, anterior midcingulate cortex, supplementary motor area, orbitofrontal cortex, thalamus, caudate), (2) greater functional connectivity in sensorimotor and salience resting-state networks, (3) less white matter integrity in the internal and external capsule, anterior thalamic radiation and corticospinal tract, but (4) were indistinguishable based on sex, pain sensitivity, neuroticism, and pain catastrophizing. These data may represent neural underpinnings of how task performance vs pain is prioritized and provide a framework for developing personalized pain therapy approaches that are based on behaviour-structure-function organization.
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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 | 0.001 |
| Scholarly communication | 0.000 | 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".