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Record W2159980428 · doi:10.1093/cercor/bhl172

Visually Guided Reaching Depends on Motion Area MT+

2007· article· en· W2159980428 on OpenAlexaff
David Whitney, Amanda Ellison, Nichola J. Rice, Derek H. Arnold, Melvyn A. Goodale, Vincent Walsh, David Milner

Bibliographic record

VenueCerebral Cortex · 2007
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsWestern University
FundersNational Eye Institute
KeywordsMotion (physics)Transcranial magnetic stimulationTrajectoryMovement (music)Task (project management)Object (grammar)Computer scienceComputer visionArtificial intelligenceNeuroscienceFunctional magnetic resonance imagingMechanism (biology)CommunicationPsychologyPhysical medicine and rehabilitationCognitive psychologyStimulationMedicinePhysics

Abstract

fetched live from OpenAlex

Visual information is crucial for goal-directed reaching. A number of studies have recently shown that motion in particular is an important source of information for the visuomotor system. For example, when reaching a stationary object, movement of the background can influence the trajectory of the hand, even when the background motion is irrelevant to the object and task. This manual following response may be a compensatory response to changes in body position, but the underlying mechanism remains unclear. Here we tested whether visual motion area MT+ is necessary to generate the manual following response. We found that stimulation of MT+ with transcranial magnetic stimulation significantly reduced a strong manual following response. MT+ is therefore necessary for generating the manual following response, indicating that it plays a crucial role in guiding goal-directed reaching movements by taking into account background motion in scenes.

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)0.000
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.098
GPT teacher head0.351
Teacher spread0.252 · 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

Citations342
Published2007
Admission routes1
Has abstractyes

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