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Record W2581557583 · doi:10.1037/xhp0000339

Signal enhancement, not active suppression, follows the contingent capture of visual attention.

2017· article· en· W2581557583 on OpenAlexafffund
Ashley Livingstone, Gregory J. Christie, Richard D. Wright, John J. McDonald

Bibliographic record

VenueJournal of Experimental Psychology Human Perception & Performance · 2017
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsN2pcCued speechVisual searchPsychologyVisual attentionPerceptionCognitive psychologyEvent-related potentialSelective attentionTask (project management)NeuroscienceElectroencephalographyCognition

Abstract

fetched live from OpenAlex

Irrelevant visual cues capture attention when they possess a task-relevant feature. Electrophysiologically, this contingent capture of attention is evidenced by the N2pc component of the visual event-related potential (ERP) and an enlarged ERP positivity over the occipital hemisphere contralateral to the cued location. The N2pc reflects an early stage of attentional selection, but presently it is unclear what the contralateral ERP positivity reflects. One hypothesis is that it reflects the perceptual enhancement of the cued search-array item; another hypothesis is that it is time-locked to the preceding cue display and reflects active suppression of the cue itself. Here, we varied the time interval between a cue display and a subsequent target display to evaluate these competing hypotheses. The results demonstrated that the contralateral ERP positivity is tightly time-locked to the appearance of the search display rather than the cue display, thereby supporting the perceptual enhancement hypothesis and disconfirming the cue-suppression hypothesis. (PsycINFO Database Record

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 categoriesScience and technology studies, Insufficient 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 score0.236
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 studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.136
GPT teacher head0.451
Teacher spread0.315 · 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

Citations35
Published2017
Admission routes2
Has abstractyes

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