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Record W2470276299 · doi:10.1075/lab.14026.fri

Phonologically-mediated meaning activation in monolinguals and bilinguals

2016· article· en· W2470276299 on OpenAlexafffund
Deanna C. Friesen, Jiyoon Oh, Ellen Bialystok

Bibliographic record

VenueLinguistic Approaches to Bilingualism · 2016
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHomophoneN400Meaning (existential)Context (archaeology)PhonologyPsychologyLinguisticsPriming (agriculture)Interpretation (philosophy)Cognitive psychologyBeechEvent-related potentialCognitionPhilosophyHistory

Abstract

fetched live from OpenAlex

Abstract The current study investigated how language experience impacts phonologically-mediated meaning activation. Monolinguals and bilinguals made living/non-living judgments on English homophones (e.g., beech, beach) while Event-Related Potentials (ERPs) were recorded. Context was manipulated by making the preceding trial either unrelated (e.g., servant → beech) or semantically-related, creating priming. The related context either strengthened (e.g., oak → beech) or diminished (e.g., oak → beach) a homophone’s meaning. In the unrelated context, both groups utilized phonology similarly to access meaning, as evidenced by a later N400 and a larger late positive component (LPC) for homophones than for non-homophonic words. However, when the context primed the incorrect meaning (e.g., oak → beach), only monolinguals exhibited N400 attenuation and delayed LPCs, indicating that they were mistakenly using phonology and context to access meaning and were then required to reanalyze their interpretation. These results provide insight into how oral language experience impacts phonological activation of meaning.

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)0.001
metaresearch head score (Gemma)0.036
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-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 score0.035
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.036
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.167
GPT teacher head0.284
Teacher spread0.117 · 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

Citations2
Published2016
Admission routes2
Has abstractyes

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