Postural adjustments for online corrections of arm movements in standing humans
Bibliographic record
Abstract
The aim of this study was to investigate how humans correct ongoing arm movements while standing. Specifically, we sought to understand whether the postural adjustments in the legs required for online corrections of arm movements are predictive or rely on feedback from the moving limb. To answer this question we measured online corrections in arm and leg muscles during pointing movements while standing. Nine healthy right-handed subjects reached with their dominant arm to a visual target in front of them and aligned with their midline. In some trials, the position of the target would switch from the central target to one of the other targets located 15°, 30°, or 45° to the right of the central (midline) target. For each target correction, we measured the time at which arm kinematics, ground reaction forces, and arm and leg muscle electromyogram significantly changed in response to the target displacement. Results show that postural adjustments in the left leg preceded kinematic corrections in the limb. The corrective postural muscle activity in the left leg consistently preceded the corrective reaching muscle activity in the right arm. Our results demonstrate that corrections of arm movements in response to target displacement during stance are preceded by postural adjustments in the leg contralateral to the direction of target shift. Furthermore, postural adjustments preceded both the hand trajectory correction and the arm-muscle activity responsible for it, which suggests that the central nervous system does not depend on feedback from the moving arm to modify body posture during voluntary movement. Instead, postural adjustments lead the online correction in the arm the same way they lead the initiation of voluntary arm movements. This suggests that forward models for voluntary movements executed during stance incorporate commands for posture that are produced on the basis of the required task demands.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".