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Record W2073818014 · doi:10.1167/10.7.1068

EEG microstates during visually guided reaching

2010· article· en· W2073818014 on OpenAlexaff
John de Grosbois, F. Colino, Olav Krigolson, Matthew Heath, Gordon Binsted

Bibliographic record

VenueJournal of Vision · 2010
Typearticle
Languageen
FieldNeuroscience
TopicEEG and Brain-Computer Interfaces
Canadian institutionsWestern UniversityUniversity of British Columbia
Fundersnot available
KeywordsElectroencephalographyNeurosciencePsychologyVisual fieldPosterior parietal cortexMotor controlArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

The notion that vision's importance in controlling goal-directed reaching movements has been experimentally validated (Woodworth, 1899). Functional MRI and rTMS work has subsequently confirmed that the posterior parietal cortex (PPC) are important for the control of visually guided movements (Culham and Kanwisher, 2001, Desmurget et al, 1999). The temporal resolution is inappropriate to study the cortical dynamics of reaching movements. Therefore, this investigation examined the activation dynamics of movement planning and control as measured by electroencephalography (EEG). Participants completed reaching movements during full-vision (FV), no-vision-delayed (NV) or open-loop (OL). Event-related potential analysis segmented with respect to peak velocity (PV) yielded differences in visual and motor areas following PV. To generate an overall evaluation of the activation across time, ERP waveforms were submitted to a space-oriented field clustering approach (Tunik et al, 2008) to determine epochs of semi-stable field configurations (i.e. microstates) throughout the planning/control of reaches. The results of this micro-state analysis showed that regardless of visual condition, the planning and initiation of movement is characterized by two state transitions: an activation pattern dominated by increasing primary-visual and motor activation (FCz, Oz). NV remained in this early movement state and did not enter any control-based state. During FV, activation shifted following PV to an activation consistent with dorsal (contralateral PPC, frontal and 1o visual areas) guidance of movement. OL transitioned into a bilateral temporal (presumably memory-guided) mode of control that did not exhibit primary visual activation. This had been expected of NV. Thus, even though previous fMRI studies have correctly identified important structures for the control of movement across different visual conditions, they have lacked the temporal resolution to elucidate the pattern of functioning across visually guided reaching movements.

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 head0.020
GPT teacher head0.329
Teacher spread0.309 · 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

Citations2
Published2010
Admission routes1
Has abstractyes

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