Exploring unintentional drifts in finger force production and muscle activity: A study of finger independence
Bibliographic record
Abstract
Human beings cannot move or produce force with their fingers independently from each other. Finger independence is constrained by the central nervous system which coordinates force production via multi-finger synergies, among additional mechanical and peripheral neural factors. Finger interdependencies represented in the central nervous system rely on integrating tactile, proprioceptive, and visual feedback on task performance. The primary purpose of this thesis was to explore drifts in finger interdependencies in the absence of visual feedback. Twenty right-handed participants (10 females and 10 males, aged 18-29 years) performed a series of isometric, single finger flexion and extension exertions with digits II-V. The right arms of the participants were braced in a mid-prone position, with their right wrist at 0° flexion and digits II-V secured to uniaxial force transducers. The activity of flexor digitorum superficialis (FDS) 2-5 and extensor digitorum communis (EDC) 2-5 were recorded via surface electromyography. Participants performed 30 second static, single finger flexion and extension exertions at 15% and 30% of their maximum voluntary contraction (MVC) with digits 2-5. A single repetition of each exertion was performed in two conditions: (1) with continuous visual force feedback, and (2) with visual feedback removed following 10 s. When feedback was given for the whole trial, the uninstructed fingers drifted towards greater involuntary force production (~4% MVC between the four fingers) while FDS and EDC activity generally increased over time. Removing visual feedback on the instructed finger induced consistent downward force drifts in its force production at 15% and 30% MVC flexion and 30% MVC extension, along with decreased extrinsic finger muscle activity. In the flexion conditions, removing feedback also eliminated the upward uninstructed finger force drifts.
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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 | 0.002 |
| 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) | 0.001 | 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".