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Record W1919054501 · doi:10.1111/ejn.12976

Transient visual responses reset the phase of low‐frequency oscillations in the skeletomotor periphery

2015· article· en· W1919054501 on OpenAlexafffund
Daniel K. Wood, Chao Gu, Brian D. Corneil, Paul L. Gribble, Melvyn A. Goodale

Bibliographic record

VenueEuropean Journal of Neuroscience · 2015
Typearticle
Languageen
FieldNeuroscience
TopicNeural dynamics and brain function
Canadian institutionsRobarts Clinical TrialsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsReset (finance)Transient (computer programming)Low frequencyPhase (matter)Low-frequency oscillationPhysicsAcousticsAudiologyComputer scienceMedicineEconomics

Abstract

fetched live from OpenAlex

We recorded muscle activity from an upper limb muscle while human subjects reached towards peripheral targets. We tested the hypothesis that the transient visual response sweeps not only through the central nervous system, but also through the peripheral nervous system. Like the transient visual response in the central nervous system, stimulus-locked muscle responses (< 100 ms) were sensitive to stimulus contrast, and were temporally and spatially dissociable from voluntary orienting activity. Also, the arrival of visual responses reduced the variability of muscle activity by resetting the phase of ongoing low-frequency oscillations. This latter finding critically extends the emerging evidence that the feedforward visual sweep reduces neural variability via phase resetting. We conclude that, when sensory information is relevant to a particular effector, detailed information about the sensorimotor transformation, even from the earliest stages, is found in the peripheral nervous system.

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)0.003
metaresearch head score (Gemma)0.004
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 score0.864
Threshold uncertainty score0.470

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.099
GPT teacher head0.339
Teacher spread0.240 · 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

Citations65
Published2015
Admission routes2
Has abstractyes

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