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Record W2021437246 · doi:10.1037/h0087379

Modulation of the attentional blink by differential resource allocation.

2001· article· en· W2021437246 on OpenAlex
David I. Shore, Elizabeth McLaughlin, Raymond M. Klein

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCanadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale · 2001
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsBaycrest Hospital
Fundersnot available
KeywordsAttentional blinkRapid serial visual presentationPsychologyCognitive psychologyTask (project management)CognitionNeuroscience

Abstract

fetched live from OpenAlex

When one masked target (T2) follows another (T1) in close temporal proximity, identification accuracy of the second target is reduced for a period referred to as the attentional blink. Analysis of the attentional blink literature suggests that increasing the difficulty of T1 processing increases the magnitude of the blink. In a previous study that eliminated several untoward features of the typical attentional blink design (e.g., task switching, location switching, and stream contribution), we found no effect on blink magnitude when three levels of T1 difficulty (manipulated in a data-limited manner) were randomly intermixed. Here, when we repeated the previous study using a blocked manipulation of T1 difficulty, which is characteristic of the literature, a significant positive relation between T1 difficulty and blink magnitude was found. Resource allocation put in place to encode T1 in advance of a dual-target trial thus seems to be the critical factor in mediating this relation.

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.182
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.106
GPT teacher head0.346
Teacher spread0.240 · 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