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Record W2115112102 · doi:10.1152/jn.00830.2004

fMRI Activation in the Human Frontal Eye Field Is Correlated With Saccadic Reaction Time

2005· article· en· W2115112102 on OpenAlexaff
Jason D. Connolly, Melvyn A. Goodale, Herbert C. Goltz, Douglas P. Munoz

Bibliographic record

VenueJournal of Neurophysiology · 2005
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsCanadian Institutes of Health Research
Fundersnot available
KeywordsIntraparietal sulcusSaccadeSaccadic maskingFrontal eye fieldsFunctional magnetic resonance imagingPsychologyStimulus (psychology)NeuroscienceSupplementary eye fieldEye movementSaccadic suppression of image displacementNeural activityCognitive psychology

Abstract

fetched live from OpenAlex

Variation in response latency to identical sensory stimuli has been attributed to variation in neural activity mediating preparatory set. Here we report evidence for a relationship between saccadic reaction time (SRT) and set-related brain activity measured with event-related functional magnetic resonance imaging. We measured hemodynamic activation time-courses during a preparatory "gap" period, during which no visual stimulus was present and no saccades were made. The subjects merely anticipated appearance of the target. Saccade direction and latency were recorded during scanning, and trials were sorted according to SRT. Both the frontal (FEF) and supplementary eye fields showed pre-target preparatory activity, but only in the FEF was this activity correlated with SRT. Activation in the intraparietal sulcus did not show any preparatory activity. These data provide evidence that the human FEF plays a central role in saccade initiation; pre-target activity in this region predicts both the type of eye movement (whether the subject will look toward or away from the target) and when a future saccade will occur.

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)0.001
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 score0.727
Threshold uncertainty score0.274

Codex and Gemma teacher scores by category

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 integrity0.0000.001
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 head0.264
Teacher spread0.242 · 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

Citations137
Published2005
Admission routes1
Has abstractyes

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