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Record W2342168459 · doi:10.1111/psyp.12618

Enhanced frontal activation underlies sparing from the attentional blink: Evidence from human electrophysiology

2016· article· en· W2342168459 on OpenAlexaff
Roberto Dell’Acqua, Mattia Doro, Paul E. Dux, Talia Losier, Pierre Jolicœur

Bibliographic record

VenuePsychophysiology · 2016
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsPsychologyElectrophysiologyNeuroscienceAttentional blinkCognitive psychologyCognition

Abstract

fetched live from OpenAlex

Using the ERP method, we examined the processing operations elicited by stimuli that appear within the same temporal attention window. Forty subjects searched for letter targets among digit distractors displayed in rapid serial visual presentation (RSVP). ERPs were examined under conditions where a single target was embedded among distractors and compared to those recorded when two consecutive targets were embedded among distractors. Standard and independent component analyses revealed two temporally and topographically distinct ERP responses, a midfrontal P3a component peaking at about 300 ms followed by a midparietal P3b component peaking at about 450 ms. With minimal latency variations, the frontal P3a was amplified when elicited by two consecutive targets relative to a single target. The parietal P3b response was also amplified when elicited by two consecutive targets compared to a single target but, in contrast to P3a, it was also associated with a substantially longer time course. These results provide evidence for the involvement of frontal brain regions in the close-to-concurrent selection of two consecutive targets displayed in RSVP, and of posterior brain regions in the serial encoding of targets in visual working memory. The present findings are discussed in relation to current models of temporal gating of attention and the attentional blink effect.

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 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 score0.241
Threshold uncertainty score0.699

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.180
GPT teacher head0.383
Teacher spread0.204 · 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

Citations20
Published2016
Admission routes1
Has abstractyes

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