Individual differences in cognitive performance under pain linked to region-specific alpha power modulations
Bibliographic record
Abstract
Chronic pain is associated with reduced cognitive function, potentially due to a diversion of cognitive resources to processing pain. However, reduced cognitive function during pain is not always consistently evidenced, perhaps due to individual differences in the attentional cost of pain processing. In the current electroencephalography investigation, we therefore examined differences in the top-down modulation of oscillatory activity in a cognitive task, between participants who performed better, and worse, during experimentally induced neuropathic-like pain. We employed a cross-modal attention task in which visual cues indicated whether participants needed to judge the visual orientation or discriminate the auditory pitch of an upcoming target. The visual and auditory targets were presented either simultaneously or individually, enabling us to assess the "cost" of having a distractor present in each modality. Participants engaged in the task under two conditions: prolonged pain via the capsaicin-heat pain model, and pain-free. Participants less "costed" by visual distraction during pain demonstrated a greater increase in alpha power (8-12 Hz) over frontal/central electrodes during pain. This may reflect inhibition of regions related to the processing of painful stimuli (somatosensory cortex), which could increase availability of resources to meet task-demands - a possible task-favouring pattern. In conclusion, our results support the notion that better cognitive function during pain is associated with a behavioral strategy.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".