Practicing a Maximal Performance Task: A Cooperative Strategy for Muscle Activity
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Bibliographic record
Abstract
The effect of practice on predicting elbow flexion movement time was studied. Participants (N = 18) performed 400 elbow flexion trials to a target in the horizontal plane. The trials were distributed equally over four sessions. The goal was to decrease the movement time (MT) for the same degree of accuracy. The electromyographic (EMG) activity of the biceps and triceps brachii was monitored with standard Beckman Ag/AgCl surface electrodes. The EMG measures formed two variable sets within one prediction equation. One variable set was composed of the onset of muscle activity relative to the start of movement (motor time) and the duration of muscle activity. The other variable set consisted of the mean amplitude value of the entire burst and of the first 30 ms (Q30) of activity. As the maximal speed of limb movement increased, the duration of muscle activity (motor time and EMG duration) decreased, and the magnitude of muscle activity (MAV and Q30) increased. Most of the change in the duration of muscle activity occurred in Session 1, while the magnitude of muscle activity continued to increase until Session 3. Multiple regression analysis revealed a cooperative strategy between the magnitude and duration of muscle activity. Early in learning, participants adjusted the magnitude of muscle activity to increase limb movement speed. As practice continued, alterations in the duration of muscle activity became more important, while the magnitude changes were less involved. Late in learning, both dimensions of muscle activity were used to decrease MT. We suggest that the interplay between the magnitude and duration of muscle activity may be due to: (a) cognitive factors related to the division of attention in a motor skill, (b) an increase in the frequency of motor unit firing that affects both dimensions of muscle activity, or (c) some combination of (a) and (b).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 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) | 0.000 | 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 it