Extending energy optimization in goal-directed aiming from movement kinematics to joint angles
Bibliographic record
Abstract
Goal-directed aiming movements are organized in a manner that optimizes speed, accuracy and energy expenditure. Energy optimization has been demonstrated as an undershoot bias in primary submovement endpoint locations, especially in conditions where corrections to target overshoots must be made against gravity. Two-component models of upper limb movement have not yet considered how joint angle displacements are organized to deal with the energy constraints associated with moving the upper limb in aiming tasks. This study was performed to address this limitation. Participants performed aiming movements to near, middle and far targets in the up and down directions with the index finger and two types of rod extensions (short and long) attached to the index finger. Movements with the rod extensions were expected to invoke different energy optimizing strategies in the up and down directions by allowing distal joints the opportunity to contribute to end effector displacement. Primary submovements undershot the far target to a greater extent in the downward direction compared to the upward direction, showing that movement kinematics show energy optimization in a manner that considers the effects of gravity. Importantly, as rod length increased, shoulder elevation was minimized in movements to the far up target and elbow extension was minimized in movements to the far down target. Contrary to our expectations, distal joints were not employed in either movement direction to optimize energy expenditure. While the overall results suggest energy optimization in the control of joint angles, they appear to be independent of the force of gravity. Acknowledgments: NSERC
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".