Control of Finger Grip Forces in Overarm Throws Made by Skilled Throwers
Bibliographic record
Abstract
In an overarm throw, as the hand opens and the ball rolls along the fingers, the ball exerts a back force on the fingers. Previous studies suggested that skilled throwers compensate for this back force by producing an appropriate finger flexor torque to oppose the back force, but it was unclear how this is controlled by the CNS. We investigated whether the increase in finger flexor torque is timed precisely to occur late in the throw as the fingers open or whether the increase occurs throughout the throw to anticipate the increase in hand acceleration. Recreational ball players threw balls of different weights and diameters at different speeds from both a sitting and standing position while arm joint rotations were recorded with the search-coil technique. Force transducers were taped to the distal and middle phalanges of the middle finger and subjects released the ball from this finger. Passive forces on the finger were also recorded in "fake" throws in which the ball was taped to the finger and subjects did not grip the ball. These skilled throwers correctly anticipated the magnitude of the back force from the ball on the finger because the mean amplitude of finger extension did not increase in throws made with a large range of increasing back forces. This was achieved by subjects gripping the ball during the backswing with a force proportional to ball weight and intended ball speed (acceleration) and progressively increasing the grip force throughout the backswing and forward throw. The magnitude of this grip force during the forward throw was not affected by ball texture. After ball release from the fingertip, the finger flexed in proportion to the peak force on the finger before ball release. It is concluded, in a skilled fast overarm throw where large, fast-changing forces on the fingers result from the sum of motions at all arm joints, that finger flexor torque is progressively increased throughout the throw in an anticipatory (predictive) fashion to counteract the progressively increasing back force from the ball.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".