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Record W2138866057 · doi:10.1093/cercor/bhi085

Modulation of Antisaccades by Transcranial Magnetic Stimulation of the Human Frontal Eye Field

2005· article· en· W2138866057 on OpenAlexaff
Bettina Olk, Erik C. Chang, Alan Kingstone, Tony Ro

Bibliographic record

VenueCerebral Cortex · 2005
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSaccadeSaccadic maskingTranscranial magnetic stimulationAntisaccade taskEye movementPsychologyNeuroscienceFrontal eye fieldsSaccadic eye movementSupplementary eye fieldVisual fieldStimulation

Abstract

fetched live from OpenAlex

It has been suggested that the frontal eye field (FEF), which is involved with the inhibition and generation of saccades, is engaged to a different degree in pro- and antisaccades. Pro- and antisaccades are often assessed in separate experimental blocks. In such cases, saccade inhibition is required for antisaccades but not for prosaccades. To more directly assess the role of the FEF in saccade inhibition and generation, a new paradigm was used in which inhibition was necessary on pro- and antisaccade trials. Participants looked in the direction indicated by a target ('<' or '>') that appeared in the left or right visual field. When the pointing direction and the location were congruent, prosaccades were executed; otherwise antisaccades were required. Saccadic latencies were measured in blocks without and with single pulse transcranial magnetic stimulation (TMS) to the right FEF or a right posterior control site. Results showed that antisaccades generated into the hemifield ipsilateral to the TMS were significantly delayed after TMS over the FEF, but not the posterior control site. This result is interpreted in terms of a modulation of saccade inhibition to the contralateral visual field due to disruption of processing in the FEF.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.152
Threshold uncertainty score1.000

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)0.0010.000

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 head0.027
GPT teacher head0.302
Teacher spread0.275 · 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.

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

Citations53
Published2005
Admission routes1
Has abstractyes

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