Unravelling neuromechanical constraints to finger independence
Bibliographic record
Abstract
Abstract Intentional use of a single finger results in involuntary forces and movements among other fingers. Constraints to finger independence are attributed to both neural and mechanical factors, but the contribution of these factors is debated. We hypothesized that neural factors primarily constrain finger independence during isometric exertions whereas mechanical factors impose larger constraints during movements. We investigated changes in finger independence following a ring finger fatigue protocol. We assumed that with fatigue, the ability to actively transmit forces across fingers through neural pathways will be reduced but force transmission passively through mechanical pathways will remain unaffected. Participants performed isometric finger contractions and flexion-extension movements at baseline and following a ring finger fatigue protocol. At baseline, involuntary ring finger forces ranged from 7.3-16.5% MVC. Consistent with our predictions, involuntary ring finger forces decreased by 2.5-8.9% MVC following fatigue. In contrast, involuntary ring finger movement did not change or surprisingly in several cases, increased by greater than 10-20° following fatigue relative to baseline across movement tasks. Our findings demonstrate that the neuromechanical control of finger force versus motion are distinct from each other and can alter the constraints to finger independence in a task-dependent way.
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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 | 0.001 |
| 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".