Optimized sensorimotor activation enhances the control of goal-directed aiming mediated by real-time visuomotor transformations
Bibliographic record
Abstract
Improving motor abilities may result from sensory-motor stimulations involving repetitive mechanical vibratory applications focused on muscles or tendons. These stimulations activate the proprioceptive pathway, critical for effective motion coordination. Optimized focal muscle vibration (o-fmv) paradigms can enhance motor control of goal-directed movements, potentially influencing visuomotor transformations underlying movement coordination, although with uncertain mechanisms. Here, we asked whether the o-fmv enhances the motor control of goal-directed movements, affecting sensorimotor transformations that rely on real-time or stored visual information processing. For this purpose, we applied the o-fmv to muscles that assist with shoulder movements in healthy individuals to affect their proprioception. Then, we studied the immediate and one-week-after effects on upper limb aiming movements mediated by shoulder motion, planned in vision, and executed with or without online visual information. We found that o-fmv improves mean speed, movement smoothness, and accuracy mainly on movements prepared and executed moment-to-moment with online visual information. The improvement begins immediately and increases one week after o-fmv. Therefore, o-fmv lastingly enhances motor control of goal-directed aimings that rely on real-time visual information processing with a minimal impact on those dependent on stored visual information. Our results indicate that o-fmv improves how the brain processes proprioceptive information to convert a visuospatial plan into motor commands, enhancing motion coordination when executing movements through real-time visual pathways route activation. The implication is that o-fmv may induce long-term effects that influence elaborations in the brain’s visual streams, which control goal-directed action by online visuomotor transformations.
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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 | 0.000 |
| 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".