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Record W2085155034 · doi:10.1037/0096-1523.33.1.124

Visual search is postponed during the attentional blink until the system is suitably reconfigured.

2007· article· en· W2085155034 on OpenAlexafffund
Shahab Ghorashi, Daniel Smilek, Vincent Di Lollo

Bibliographic record

VenueJournal of Experimental Psychology Human Perception & Performance · 2007
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsSimon Fraser UniversityUniversity of WaterlooUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaMichael Smith Health Research BC
KeywordsVisual searchAttentional blinkTask (project management)PsychologyComputer scienceFunction (biology)Eye movementArtificial intelligenceCognitive psychologyNeuroscienceCognitionEngineering

Abstract

fetched live from OpenAlex

J. S. Joseph, M. M. Chun, and K. Nakayama (1997) found that pop-out visual search was impaired as a function of intertarget lag in an attentional blink (AB) paradigm in which the 1st target was a letter and the 2nd target was a search display. In 4 experiments, the present authors tested the implication that search efficiency should be similarly impaired (steeper search slopes at shorter lags). A conventional AB deficit was found, but, contrary to expectations, search slopes were invariant with lag. These results suggest that no search can be carried out during the period of the AB. Instead, the search is postponed until after the 1st target has been processed. The authors conclude that efficient visual search cannot be carried out unless the visual system is configured appropriately for the search task. If the initial configuration is inappropriate, processing of the 2nd target is held in abeyance until the system has been suitably reconfigured.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.069
GPT teacher head0.405
Teacher spread0.335 · 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

Citations17
Published2007
Admission routes2
Has abstractyes

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