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Record W4408279919 · doi:10.1101/2025.03.03.641240

Dual agonist and antagonist muscle vibration produces a bias in end point with no change in variability

2025· preprint· en· W4408279919 on OpenAlexaff
Gregg Eschelmuller, J. Timothy Inglis, Hyosub E. Kim, Romeo Chua

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2025
Typepreprint
Languageen
FieldMedicine
TopicEffects of Vibration on Health
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAgonistAntagonistDual (grammatical number)PhysicsNeuroscienceChemistryInternal medicineMedicinePsychologyReceptorPhilosophy

Abstract

fetched live from OpenAlex

Abstract Muscle spindles provide critical proprioceptive feedback about muscle length to the central nervous system (CNS). Single muscle tendon vibration can stimulate muscle spindles, causing participants to misjudge limb position, while dual muscle tendon vibration is thought to produce a noisy proprioceptive system. It is currently unclear exactly how the CNS uses kinesthetic feedback from the agonist and antagonist muscles during target-directed reaches. The purpose of the current project was to investigate the effects of agonist, antagonist, and dual agonist/antagonist during target-directed reaching. Using an elbow extension task, we found that antagonist muscle vibration produced an undershooting effect relative to the no-vibration control, while agonist muscle vibration produced an overshooting effect relative to the no-vibration control. Neither of the single muscle vibrations produced any change in the variable error of the movements. While it was originally hypothesized that dual agonist/antagonist vibration would increase participants variable error with no change in bias, the opposite was found. Participants undershot relative to the no-vibration control with no change in variable error. Overall, the results from this study suggest that dual vibration does not necessarily create a noisy proprioceptive system but can produce a bias in end point.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.227
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.028
GPT teacher head0.277
Teacher spread0.249 · 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.

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

Citations1
Published2025
Admission routes1
Has abstractyes

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