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Record W2038770166 · doi:10.1152/jn.01268.2004

Timing Finger Opening in Overarm Throwing Based on a Spatial Representation of Hand Path

2005· article· en· W2038770166 on OpenAlexaff
Jon Hore, Sherry Watts

Bibliographic record

VenueJournal of Neurophysiology · 2005
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsWestern University
Fundersnot available
KeywordsThrowingComputer scienceThumbMathematicsAngular displacementPhysicsGeometryAnatomyMedicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.625
Threshold uncertainty score0.258

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.051
GPT teacher head0.296
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations32
Published2005
Admission routes1
Has abstractyes

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