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Attenuation of Neural Responses in Primary Visual Cortex during the Attentional Blink

2008· article· en· W2073120432 on OpenAlexaff
Mark Williams, Troy A. W. Visser, Ross Cunnington, Jason B. Mattingley

Bibliographic record

VenueJournal of Neuroscience · 2008
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersNational Health and Medical Research CouncilMedical Research Council
KeywordsVisual cortexAttentional blinkNeurosciencePsychologyAttenuationCortex (anatomy)Cognitive psychologyAudiologyMedicineOpticsCognitionPhysics

Abstract

fetched live from OpenAlex

Information-processing bottlenecks are characteristic of many cognitive and neural systems. One such bottleneck is revealed by tasks in which rapidly successive stimulus events must be reported. Here, observers missed the second of two visual targets if it occurred within 700 ms of the first [an "attentional blink" (AB)], even though this second target could be reported accurately when the first item was ignored. Isolating neural responses to such rapid events has proven difficult because current magnetic resonance imaging methods rely on relatively sluggish changes in the brain's physiological response to sensory inputs. Here, we overcame this limitation by presenting successive visual targets at different spatial locations, thereby exploiting the retinotopic organization of early cortical visual areas to distinguish neural activity associated with successive target events. We show that neural activity in primary visual cortex is significantly modulated during the AB, and that this activity mirrors behavioral measures of target identification accuracy. The findings suggest that the neural signature of perceptual suppression during processing of rapidly successive stimuli is evident at the earliest stages of cortical sensory processing.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.917
Threshold uncertainty score0.273

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.086
GPT teacher head0.343
Teacher spread0.257 · 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.

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

Citations44
Published2008
Admission routes1
Has abstractyes

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