Timing Finger Opening in Overarm Throwing Based on a Spatial Representation of Hand Path
Bibliographic record
Abstract
Previous studies on overarm throwing have suggested that throwing accuracy depends on a precise central timing mechanism. In the present study, we investigated an alternative hypothesis: that central control of finger opening is based on an internal positional representation of handpath. Angular positions of each segment of the middle finger, thumb, and arm were recorded with the search-coil technique as subjects made slow, medium, and fast throws at a target 3.1 m away. Onset of ball release from the hand was strongly correlated with extension at the proximal interphalangeal joint (PIJ). The velocity of this finger joint opening varied with the speed of the throw. In agreement with the hypothesis, at a fixed hand angular position in space, there was no difference across subjects in the amplitude of extension at the PIJ for throws of different speeds. That is, for these two parameters, a fast throw was the same as a slow throw that was sped-up. This occurred irrespective of whether the trunk was constrained (sitting throws) or unconstrained (standing throws). No equivalent relation was found between extension at the PIJ and elbow extension. These findings support the idea that precisely timed finger opening in overarm throwing depends, not on a central timing controller that triggers a step-like (ballistic) finger opening at the right moment in throws of different speeds, but on a central spatial controller that matches angular positions of finger opening to the intended handpath.
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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 | 0.004 |
| 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 | 0.001 |
| 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".