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 the proprioceptive contribution needed for visual-proprioceptive integration during planning and execution stages of motion. We examined whether o-fmv affects goal-directed movements when visual information is available in real time or when it needs to be memorized. We applied the o-fmv to the shoulder muscles in healthy participants to affect their proprioception. Then, we assessed immediate and 1-week-later effects on upper limb aiming toward visual targets. Movements were prepared with vision and executed either with real-time or memorized visual information. O-fmv improved mean speed, smoothness, and accuracy primarily when movements were performed with real-time visual information. These improvements began immediately and continued to increase after 1 week. Minimal effects were observed when movements relied on memorized visual information. Therefore, o-fmv produces lasting improvements in motor control of goal-directed movements supported by real-time visual information. Our findings suggest that o-fmv may enhance the brain's processing of proprioceptive information used during motion planning and execution, potentially leading to long-term changes. These effects might involve stream pathways that coordinate goal-directed actions by integrating real-time visual information with proprioceptive inputs.
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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".