Joint angles and movement kinematics show energy optimization in goal-directed aiming
Bibliographic record
Abstract
Goal-directed aiming movements are organized to be energy optimal. This strategy has typically been demonstrated as an undershoot bias in the primary sub-movement endpoint location, particularly in conditions where corrections to target overshoots must be made against gravity (e.g. Lyons et al., 2006). This pattern of result is also true for the organization of joint movement, where angular displacements in the joints responsible for moving larger masses are minimized (Fisher et al., 1997). In order to determine whether performers maintain these energy optimal behaviours amidst changes in task constraints, participants performed aiming movements to near, middle and far targets in both the up and down directions. Movements to these targets were performed with the index finger, as well as with short and long rod extensions to the index finger. By ÔÇ£lengtheningÔÇØ the end effector, it was hypothesized that performers would minimize angular displacements in the shoulder and elbow, since these joints were responsible for moving the largest masses. Consistent with this hypothesis, when moving to the far targets, primary sub-movements undershot the target to a greater extent when moving downward when compared to moving upward. Furthermore, 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. These results suggest energy optimization as a mediator of movement.Acknowledgments: NSERC, Queen Elizabeth II Science & Technology, Anne Poucher Scholarship
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".