Effect of a visual-based sensory motor task on muscle tuning during a dynamic balance task
Bibliographic record
Abstract
In this research, we explored visually-based sensory motor learning when a transformation was applied to the trajectory used to move and track a visual target in a virtual environment. The virtual task was controlled by the subject's center of foot pressure (COP), where the COP position was mapped to an on-screen cursor. Target balloons appeared randomly on the screen; the subject was instructed to move the COP-controlled cursor to intersect and burst the balloons. A transformation was applied to the movement trajectory, which rotated the on-screen cursor counter-clockwise by 60 degrees . The transformation required the subjects to update their spatial reference coordinates between the physical COP position and the on-screen cursor. To investigate learning during the transformation, electromyogram (EMG) data was recorded from the tibialis anterior and peroneus longus muscles. The muscle activity, calculated as the root mean square (RMS) of the EMG data, was calculated for each muscle as a function of the movement direction during movement initiation. The preferred direction (PD) for each muscle was then determined as the directional sum of the RMS values. The results showed a shift in the preferred direction (PD) of the muscles with learning.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".