Respiratory response to passive limb movement is suppressed by a cognitive task
Bibliographic record
Abstract
Feedback from muscles stimulates ventilation at the onset of passive movement. We hypothesized that central neural activity via a cognitive task source would interact with afferent feedback, and we tested this hypothesis by examining the fast changes in ventilation at the transition from rest to passive leg movement, under two conditions: 1) no task and 2) solving a computer-based puzzle. Resting breathing was greater in condition 2 than in condition 1, evidenced by an increase in mean +/- SE breathing frequency (18.2 +/- 1.1 vs. 15.0 +/- 1.2 breaths/min, P = 0.004) and ventilation (10.93 +/- 1.16 vs. 9.11 +/- 1.17 l/min, P < 0.001). In condition 1, the onset of passive movement produced a fast increase in mean +/- SE breathing frequency (change of 2.9 +/- 0.4 breaths/min, P < 0.001), tidal volume (change of 233 +/- 95 ml, P < 0.001), and ventilation (change of 6.00 +/- 1.76 l/min, P < 0.001). However, in condition 2, the onset of passive movement only produced a fast increase in mean +/- SE breathing frequency (change of 1.3 +/- 0.4 breaths/min, P = 0.045), significantly smaller than in condition 1 (P = 0.007). These findings provide evidence for an interaction between central neural cognitive activity and the afferent feedback mechanism, and we conclude that the performance of a cognitive task suppresses the respiratory response to passive movement.
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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 | 0.003 |
| 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 | 0.000 |
| 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".