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Record W2040517851 · doi:10.1037/0278-7393.31.4.736

Auditory Attentional Capture During Serial Recall: Violations at Encoding of an Algorithm-Based Neural Model?

2005· article· en· W2040517851 on OpenAlex

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

VenueJournal of Experimental Psychology Learning Memory and Cognition · 2005
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsRecallInterstimulus intervalEncoding (memory)Sequence (biology)Task (project management)Speech recognitionComputer scienceCognitive psychologyPsychologyInterval (graph theory)AlgorithmMathematicsNeuroscience

Abstract

fetched live from OpenAlex

A novel attentional capture effect is reported in which visual-verbal serial recall was disrupted if a single deviation in the interstimulus interval occurred within otherwise regularly presented task-irrelevant spoken items. The degree of disruption was the same whether the temporal deviant was embedded in a sequence made up of a repeating item or a sequence of changing items. Moreover, the effect was evident during the presentation of the to-be-remembered sequence but not during rehearsal just prior to recall, suggesting that the encoding of sequences is particularly susceptible. The results suggest that attentional capture is due to a violation of an algorithm rather than an aggregate-based neural model and further undermine an attentional capture-based account of the classical changing-state irrelevant sound 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.

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 score0.081
Threshold uncertainty score0.462

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 communication0.0000.001
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 head0.043
GPT teacher head0.336
Teacher spread0.293 · 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