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Separating Visual and Motor Components of Motor Cortex Activation for Multiple Reach Targets: A Visuomotor Adaptation Study

2015· article· en· W2338330638 on OpenAlexfundno aff
Tineke Grent-‘t-Jong, Robert Oostenveld, W. Pieter Medendorp, Peter Praamstra

Bibliographic record

VenueJournal of Neuroscience · 2015
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsnot available
FundersMedical Research CouncilQueen's UniversityNederlandse Organisatie voor Wetenschappelijk Onderzoek
KeywordsAdaptation (eye)PsychologyNeuroscienceWorkspaceMovement (music)Sensory systemMotor cortexTask (project management)Motor systemVisual cortexCommunicationCognitive psychologyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Ethologically inspired models of movement preparation view the sensorimotor system as sampling information from the environment in a parallel fashion in preparation for multiple potential actions. In support, the configuration of the physical workspace, manipulated by the number or spatial separation of potential targets, has been shown to modulate sensorimotor neural activity. It is unclear, however, whether this modulation is driven by the sensory layout of the workspace or through the associated motor plans. Here, we combine a delayed-movement pre-cuing task with visuomotor adaptation to address this question in human subjects while recording MEG. By dissociating visual and motor coordinates of two targets using visuomotor adaptation, the task was designed to evaluate, in a selective fashion, the effects of visual and movement target separation on movement preparatory activity. The results did not allow the intended comparison due to an unanticipated effect of the direction of visuomotor adaptation on baseline oscillatory power in beta and low-gamma bands. Fortuitously, this effect was dependent on whether the adaptation direction decreased or increased the angular separation between alternative movements. That is, there was a sustained reduction of oscillatory power, which was stronger at small compared with large target separation. These results support a direct influence of movement target separation on motor cortex neural activity, mediated by lateral interactions between simultaneously active motor plans. The results further demonstrate a novel effect of visuomotor adaptation on motor cortex oscillatory activity, with properties that support the local nature of learned changes in visuomotor mapping. SIGNIFICANCE STATEMENT: There is growing evidence that the motor cortex routinely prepares for different movements simultaneously, each suited to a possible course of events in the immediate environment. The preparatory motor cortex activity for different movements can be seen as a competition between groups of neurons. This competition is influenced by how similar the alternative movements are; for example, in terms of direction, determined by the proximity of alternative movement goals. This study investigates whether the proximity of alternative reach goals has a direct influence on motor cortex activity (in the form of brain oscillations) or if it has an effect only through conscious evaluation of the separation between targets. We establish that there is a direct effect, supporting the biased competition model of action selection.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.109
GPT teacher head0.335
Teacher spread0.226 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations18
Published2015
Admission routes1
Has abstractyes

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