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Record W1973719932 · doi:10.1152/jn.00669.2011

Rapid feedback corrections during a bimanual postural task

2012· article· en· W1973719932 on OpenAlexaff
Mohsen Omrani, Jörn Diedrichsen, Stephen H. Scott

Bibliographic record

VenueJournal of Neurophysiology · 2012
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsKingston Health Sciences CentreQueen's University
FundersBiotechnology and Biological Sciences Research Council
KeywordsCursor (databases)Computer sciencePhysical medicine and rehabilitationTask (project management)Visual feedbackPsychologyArtificial intelligenceMedicine

Abstract

fetched live from OpenAlex

An important observation in motor physiology is that even the fastest feedback responses can be modified in a task-dependent manner. However, whether or not such responses in one limb can be modulated based on online sensory feedback from other limbs is still unknown. We tested this using a bimanual postural control task, in which the two hands either controlled two separate cursors (double-cursor task) or a single cursor displayed at the spatial average between the hands (single-cursor task). In the first experiment, the two hands were symmetrically perturbed outwards. In the double-cursor task, the participants therefore had to return their hands to the targets, whereas in the single-cursor task no correction was necessary. Within 50 ms, the electromyographic activity showed significantly smaller responses in the single- compared with the double-cursor task. In the second experiment, the perturbation direction of the left hand (inward/outward) was randomized, such that participants could not preplan their response before perturbation onset. Results show that the behavior of the right arm in the one-cursor task depended on online feedback coming from the left arm: the muscular response was modulated within 75 ms based on directionally specific information of the left arm. These results suggest that sensory feedback from one limb can quickly modify the perturbation response of another limb in a task-dependent manner.

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 score0.971
Threshold uncertainty score0.311

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 head0.029
GPT teacher head0.254
Teacher spread0.225 · 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

Citations67
Published2012
Admission routes1
Has abstractyes

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