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Record W2168851639 · doi:10.1037/a0033949

On the costs of lag-1 sparing.

2013· article· en· W2168851639 on OpenAlexaff
Paul E. Dux, Brad Wyble, Pierre Jolicœur, Roberto Dell’Acqua

Bibliographic record

VenueJournal of Experimental Psychology Human Perception & Performance · 2013
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsUniversité de Montréal
FundersAustralian Research Council
KeywordsRapid serial visual presentationAttentional blinkEncoding (memory)LagPerceptionPsychologyCognitive psychologyComputer scienceNeuroscience

Abstract

fetched live from OpenAlex

The attentional blink (AB) is a dual-target, rapid serial visual presentation (RSVP) deficit thought to represent a failure of perceptual awareness that reflects the dynamics of temporal attention. However, second target (T2) report is typically unimpaired when the targets appear within 150 ms of one another (i.e., lag-1 sparing). In addition, this sparing can be extended if more targets appear sequentially. It is thought that sequential targets are processed in the same attentional window. Here, we investigated the fate of targets processed in these windows and, specifically, the consequence for subsequent targets when an item at lag-1 is reported versus missed. The results demonstrated that target encoding in attentional windows has an all-or-none influence on subsequent item report: When comparing two- and three-target (T1 and T2 not separated by distractors) RSVP streams, there was no difference in AB magnitude for the final target when either T2 or T1 was missed in the three-target condition, but both of these conditions had significantly smaller blinks than those observed when T1 and T2 were accurately reported. A comparison of our results to a computational model of temporal attention demonstrates how structural limitations on the rate of encoding affect perception, even during sparing.

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 categoriesInsufficient 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 score0.059
Threshold uncertainty score0.992

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.001

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 head0.087
GPT teacher head0.387
Teacher spread0.300 · 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

Citations18
Published2013
Admission routes1
Has abstractyes

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