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Record W3000001826 · doi:10.1123/mc.2019-0049

Performance-Stabilizing Synergies in a Complex Motor Skill: Analysis Based on the Uncontrolled Manifold Hypothesis

2020· article· en· W3000001826 on OpenAlexaff
Fariba Hasanbarani, Mark L. Latash

Bibliographic record

VenueMotor Control · 2020
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsMcGill University
Fundersnot available
KeywordsThrowingHierarchyMathematicsBall (mathematics)Motor controlMotor learningStability (learning theory)PsychologyPhysical medicine and rehabilitationComputer scienceMathematical analysisMedicineMachine learningNeuroscienceEngineering

Abstract

fetched live from OpenAlex

The authors studied indices of stability (ΔV) of two time-varying variables, hand coordinate and velocity, during accurate throw of a small ball into the basket. Ten participants performed the throwing task with eyes-open (vision) and -closed (no vision) conditions. In the latter condition, participants closed their eyes prior to initiating the throw. The intertrial variance in the joint configuration space (and joint velocity space) was analyzed based on the uncontrolled manifold hypothesis. The results confirmed the presence of both coordinate- and velocity-stabilizing synergies (ΔV > 0). Intertrial variance was larger in the no-vision condition compared with the vision condition. Over the movement duration, ΔV did not change for the coordinate-related analysis but dropped consistently for the velocity-related analysis. The authors interpret the findings within the idea of hierarchical control and trade-off between synergy indices at different levels of the hierarchy.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.050
GPT teacher head0.225
Teacher spread0.175 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations14
Published2020
Admission routes1
Has abstractyes

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