Evidence of Motor Equivalence in a Pointing Task Involving Locomotion
Bibliographic record
Abstract
A pointing task was performed both while subjects stood beside and while subjects walked past targets that involved differing movement amplitudes and differing sizes. The hand kinematics were considered relative both to a fixed frame of reference in the movement environment (end effector kinematics) and to the subject's body (kinematics of the hand alone). From the former view, there were few differences between standing and walking versions of the task, indicating similarity of the kinematics of the hand. However, when the hand was considered alone, marked differences in the kinematics and spatial trajectories between standing and walking were achieved. Furthermore, kinematic analyses of the trunk showed that subjects used differing amounts of both flexion-extension and rotation movements at the waist depending on whether they were standing or walking as well as on the constraints imposed by target width and movement amplitude. The present results demonstrate the existence of motor equivalence in a combined upper and lower extremity task and that this motor equivalence is a control strategy to cope with increasing task demands. Given the complexity involved in controlling the arm, the torso, and the legs (during locomotion), the movements involved in the present tasks appear to be planned and controlled by considering the whole body as a single unit.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".