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Record W2411521260 · doi:10.16910/jemr.6.1.1

Localizing the Neural Substrate of Reflexive Covert Orienting

2012· article· en· W2411521260 on OpenAlexaff
Valerie Higenell, Brian J. White, Joshua R. Hwang, Douglas P. Munoz

Bibliographic record

VenueJournal of Eye Movement Research · 2012
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsWestern UniversityQueen's UniversityMcGill UniversityMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsInhibition of returnStimulus (psychology)PsychologyMonocularCovertNeural substrateGazePerceptionNeuroscienceCommunicationCognitive psychologyVisual attentionArtificial intelligenceComputer scienceCognition

Abstract

fetched live from OpenAlex

The capture of covert spatial attention by salient visual events influences subsequent gaze behavior. A task irrelevant stimulus (cue) can reduce (Attention capture) or prolong (Inhi-bition of return) saccade reaction time to a subsequent target stimulus depending on the cue-target delay. Here we investigated the mechanisms that underlie the sensory-based account of AC/IOR by manipulating the visual processing stage where the cue and target interact. In Experiment 1, liquid crystal shutter goggles were used to test whether AC/IOR occur at a monocular versus binocular processing stage (before versus after signals from both eyes converge). In Experiment 2, we tested whether visual orientation selective mechanisms are critical for AC/IOR by using oriented “Gabor” stimuli. We found that the magnitude of AC and IOR was not different between monocular and interocular viewing conditions, or between iso- and ortho-oriented cue-target interactions. The results suggest that the visual mechanisms that contribute to AC/IOR arise at an orientation-independent binocular processing stage.

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 head0.299
GPT teacher head0.487
Teacher spread0.188 · 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

Citations3
Published2012
Admission routes1
Has abstractyes

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