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Isolating the Neural Substrates of Visually Guided Attention Orienting in Humans.

2022· article· en· W4223441913 on OpenAlexafffund
John J. McDonald, Daniel Tay, David Prime, Steven A. Hillyard

Bibliographic record

VenuePubMed · 2022
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsSimon Fraser University
FundersCanada Research ChairsGovernment of Canada
KeywordsStimulus (psychology)PsychologySensory systemNeuroscienceN2pcOrienting responseNeural correlates of consciousnessCognitionSaccadic maskingVisual N1Cognitive psychologyVisual perceptionEye movementPerceptionVisual attentionHabituation

Abstract

fetched live from OpenAlex

The study of visual attention orienting has been an important impetus for the field of cognitive neuroscience. Seminal reaction-time studies demonstrated that a suddenly appearing visual stimulus attracts attention involuntarily, but the neural processes associated with visually guided attention orienting have been difficult to isolate because they are intertwined with sensory processes that trigger the orienting. Here, we disentangled orienting activity from sensory activity using scalp recordings of event-related electrical activity in the human brain. A specific neural index of visually guided attention orienting was identified. Surprisingly, whereas peripheral sensory stimulation is processed initially and predominantly by the contralateral visual cortex, this electrophysiological index of visual orienting was recorded over the cerebral hemisphere that was ipsilateral to the attention-capturing stimulus.

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: Bench or experimental · Consensus signal: Bench or experimental
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 head0.206
GPT teacher head0.358
Teacher spread0.152 · 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 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

Citations5
Published2022
Admission routes2
Has abstractyes

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