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Record W2105244423 · doi:10.1073/pnas.0907573106

Cross-modal cueing of attention alters appearance and early cortical processing of visual stimuli

2009· article· en· W2105244423 on OpenAlexaff
Viola S. Störmer, John J. McDonald, Steven A. Hillyard

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2009
Typearticle
Languageen
FieldPsychology
TopicMultisensory perception and integration
Canadian institutionsSimon Fraser University
FundersNational Eye InstituteNational Institute of Mental Health
KeywordsContrast (vision)Visual cortexPsychologyCued speechN2pcVisual perceptionBlindsightNeuroscienceSensory systemVisual processingPerceptionP200CrossmodalVisual N1Cognitive psychologyAudiologyArtificial intelligenceComputer scienceMedicine

Abstract

fetched live from OpenAlex

The question of whether attention makes sensory impressions appear more intense has been a matter of debate for over a century. Recent psychophysical studies have reported that attention increases apparent contrast of visual stimuli, but the issue continues to be debated. We obtained converging neurophysiological evidence from human observers as they judged the relative contrast of visual stimuli presented to the left and right visual fields following a lateralized auditory cue. Cross-modal cueing of attention boosted the apparent contrast of the visual target in association with an enlarged neural response in the contralateral visual cortex that began within 100 ms after target onset. The magnitude of the enhanced neural response was positively correlated with perceptual reports of the cued target being higher in contrast. The results suggest that attention increases the perceived contrast of visual stimuli by boosting early sensory processing in the visual cortex.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

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 science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.075
GPT teacher head0.424
Teacher spread0.349 · 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 designObservational
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

Citations192
Published2009
Admission routes1
Has abstractyes

Explore more

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