MétaCan
Menu
Back to cohort
Record W4403464675 · doi:10.1016/j.isci.2024.111174

Increased muscle coactivation is linked with fast feedback control when reaching in unpredictable visual environments

2024· article· en· W4403464675 on OpenAlexafffund
Philipp Maurus, Ghadeer Mahdi, Tyler Cluff

Bibliographic record

VenueiScience · 2024
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsHotchkiss Brain InstituteUniversity of Calgary
FundersFaculty of Kinesiology, University of CalgaryNatural Sciences and Engineering Research Council of CanadaUniversity of Calgary
KeywordsCoactivationVisual feedbackFeedback controlControl (management)Computer scienceNeuroscienceHuman–computer interactionPsychologyElectromyographyComputer visionArtificial intelligenceEngineeringControl engineering

Abstract

fetched live from OpenAlex

Humans encounter unpredictable disturbances in daily activities and sports. When encountering unpredictable physical disturbances, healthy participants increase the peak velocity of their reaching movements, muscle coactivation, and responses to sensory feedback. Emerging evidence suggests that muscle coactivation may facilitate responses to sensory feedback and may not solely increase stiffness to resist displacements. We tested this idea by examining how healthy participants alter the control of reaching movements and responses to sensory feedback when encountering variable visuomotor rotations. The rotations changed amplitude and direction between movements, creating unpredictable errors that required fast online corrections. Participants increased the peak velocity of their movements, muscle coactivation, and responses to visual and proprioceptive feedback with the variability of the visuomotor rotations. The findings highlight an increase in neural responsiveness to sensory feedback and suggest that muscle coactivation may prime the nervous system for fast responses to sensory feedback that accommodate properties of unpredictable visual environments.

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)0.000
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 score0.939
Threshold uncertainty score0.430

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.017
GPT teacher head0.239
Teacher spread0.221 · 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

Citations5
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueiScienceSame topicMotor Control and AdaptationFrench-language works237,207