Interactions of Pain Intensity and Cognitive Load: The Brain Stays on Task
Bibliographic record
Abstract
Pain naturally draws one's attention. However, humans are capable of engaging in cognitive tasks while in pain, although it is not known how the brain represents these processes concurrently. There is some evidence for a cortical interaction between pain- and cognitive-related brain activity, but the outcome of this interaction may depend on the relative load imposed by the pain versus the task. Therefore, we used 3 levels of cognitive load (multisource interference task) and 2 levels of pain intensity (median nerve stimulation) to examine how functional magnetic resonance imaging activity in regions identified as pain-related or cognitive-related responds to different combinations of pain intensity and cognitive load. Overall, most pain-related or cognitive-related brain areas showed robust responses with little modulation. However, during the more intense pain, activity in primary sensorimotor cortex, secondary somatosensory cortex/posterior insula, anterior insula, paracentral lobule, caudal anterior cingulate cortex, cerebellum, and supplementary motor area was modestly attenuated by the easy task and in some cases the difficult task. Conversely, cognitive-related activity was not modulated by pain, except when cognitive load was minimal during the control task. These findings support the notion that brain networks supporting pain perception and cognition can be simultaneously active.
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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 | 0.004 |
| 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 | 0.001 |
| 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".