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Record W2156166205 · doi:10.1093/cercor/bhp148

TMS Over Human Frontal Eye Fields Disrupts Trans-saccadic Memory of Multiple Objects

2009· article· en· W2156166205 on OpenAlexafffund
S. L. Prime, Michael Vesia, J. Douglas Crawford

Bibliographic record

VenueCerebral Cortex · 2009
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsYork University
FundersCanadian Institutes of Health Research
KeywordsSaccadic maskingSupplementary eye fieldSaccadic eye movementNeurosciencePsychologySaccadic suppression of image displacementFrontal eye fieldsEye movementFrontal cortexCognitive psychologyComputer scienceSaccade

Abstract

fetched live from OpenAlex

We recently showed that transcranial magnetic stimulation (TMS) over the right parietal eye fields disrupts memory of object features and locations across saccades. We applied TMS over the frontal eye fields (FEF) as subjects compared the feature details of visual targets presented either within a single eye fixation (Fixation Task) or across a saccade (Saccade Task). TMS pulses were randomly delivered at one of 3 time intervals around the time of the saccade, or at equivalent times in the Fixation Task. A No-TMS control confirmed that subjects could normally retain approximately 3 visual features. TMS in the Fixation Task had no effect compared with No-TMS, but differences among TMS times were found during right FEF stimulation. TMS over either the right or left FEF disrupted memory performance in the Saccade Task when stimulation coincided most closely with the saccade. The capacity to compare pre-and postsaccadic features was reduced to 1-2 objects, as expected if the spatial aspect of memory was disrupted. These findings suggest that the FEF plays a role in the spatial processing involved in trans-saccadic memory of visual features. We propose that this process employs saccade-related feedback signals similar to those observed in spatial updating.

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 score0.239
Threshold uncertainty score0.999

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 head0.037
GPT teacher head0.319
Teacher spread0.282 · 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

Citations47
Published2009
Admission routes2
Has abstractyes

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