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Record W2288204110 · doi:10.1523/eneuro.0129-15.2016

Long-Latency Feedback Coordinates Upper-Limb and Hand Muscles during Object Manipulation Tasks

2016· article· en· W2288204110 on OpenAlex
Frédéric Crevecoeur, Jean‐Louis Thonnard, Philippe Lefèvre, Stephen H. Scott

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueeNeuro · 2016
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsQueen's University
Fundersnot available
KeywordsGRASPVisual feedbackLatency (audio)Upper limbPhysical medicine and rehabilitationComputer scienceObject (grammar)PsychologyNeuroscienceCommunicationComputer visionArtificial intelligenceMedicine

Abstract

fetched live from OpenAlex

Suppose that someone bumps into your arm at a party while you are holding a glass of wine. Motion of the disturbed arm will engage rapid and goal-directed feedback responses in the upper-limb. Although such responses can rapidly counter the perturbation, it is also clearly desirable not to destabilize your grasp and/or spill the wine. Here we investigated how healthy humans maintain a stable grasp following perturbations by using a paradigm that requires spatial tuning of the motor response dependent on the location of a virtual target. Our results highlight a synchronized expression of target-directed feedback in shoulder and hand muscles occurring at ∼60 ms. Considering that conduction delays are longer for the more distal hand muscles, these results suggest that target-directed responses in hand muscles were initiated before those for the shoulder muscles. These results show that long-latency feedback can coordinate upper limb and hand muscles during object manipulation tasks.

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.

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 score0.720
Threshold uncertainty score0.361

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 communication0.0000.000
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 head0.022
GPT teacher head0.231
Teacher spread0.209 · 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