Does Body Postural Configuration Affect Upper Limb Performance During Point-to-Point Hand Movements?
Bibliographic record
Abstract
Adopting a postural configuration may be regarded as preparation for the performance of an upcoming movement. However, it is unclear how different postural configurations affect motor performance. The aim of the current study was to examine how body posture - sitting versus standing - influences fast and accurate planar point-to-point hand movements. Twenty-three healthy adults performed a "Go/No-go" paradigm while doing repetitive point-to-point movements. Arousal levels, which may change due to the change in posture, were independently manipulated by using a sham threat of electrical stimulation. Upper limb kinematics, center of pressure displacement, and galvanic skin responses were recorded in four test conditions: sitting and standing with and without arousal manipulation. Descriptive performance measures were computed and analyzed using multiple analyses of variance. A difference in arousal level was observed in the two conditions with the arousal manipulation, but no difference in arousal level was found between sitting and standing. Center of pressure displacement onset was found to be earlier in the two standing conditions compared to those in sitting. No difference was found in upper limb performance between the two postures, nor due to the arousal manipulation. We concluded that under the tested conditions, body posture does not appear to affect upper limb performance.
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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 | 0.001 |
| 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".