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Record W2027459979 · doi:10.1037/0278-7393.34.1.243

When does between-sequence phonological similarity promote irrelevant sound disruption?

2008· article· en· W2027459979 on OpenAlexfundno aff
John E. Marsh, François Vachon, Dylan M. Jones

Bibliographic record

VenueJournal of Experimental Psychology Learning Memory and Cognition · 2008
Typearticle
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsnot available
FundersEconomic and Social Research CouncilFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsRecallPsychologySerial position effectFree recallSimilarity (geometry)DistractionCognitive psychologySequence (biology)PhonologyIntrusionLinguisticsArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

Typically, the phonological similarity between to-be-recalled items and TBI auditory stimuli has no impact if recall in serial order is required. However, in the present study, the authors have shown that the free recall, but not serial recall, of lists of phonologically related to-be-remembered items was disrupted by an irrelevant sound stream (end rhymes) sharing similar phonological content. These findings can be explained by the notion that between-sequence phonological similarity effects emerge when category-cueing processes become an important determinant for recall, such as when shared category information can be used as a retrieval aid to cue list items or plausible list candidates. In this case, the presence of categorically similar irrelevant items impairs the retrieval of list items and leads to intrusion error. Implications of these results for theories of auditory distraction are discussed.

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 score0.047
Threshold uncertainty score0.489

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 studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.111
GPT teacher head0.375
Teacher spread0.263 · 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

Citations24
Published2008
Admission routes1
Has abstractyes

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