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Record W3007076955 · doi:10.1017/s1366728920000115

L2 exposure modulates the scope of planning during first and second language production

2020· article· en· W3007076955 on OpenAlexaff
Annie C. Gilbert, Maxime Cousineau‐Pérusse, Debra Titone

Bibliographic record

VenueBilingualism Language and Cognition · 2020
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsMcGill UniversityCentre for Research on Brain Language and Music
Fundersnot available
KeywordsPhraseScope (computer science)Phrase structure rulesFunction (biology)LinguisticsProduction (economics)Speech productionComputer scienceNatural (archaeology)PsychologyVariable (mathematics)Natural language processingArtificial intelligenceSpeech recognitionMathematicsGeographyBiologyGenerative grammarEconomics

Abstract

fetched live from OpenAlex

Abstract The psycholinguistic literature suggests that the length of a to-be-spoken phrase impacts the scope of speech planning, as reflected by different patterns of speech onset latencies. However, it is unclear whether such findings extend to first and second language (L1, L2) speech planning. Here, the same bilingual adults produced multi-phrase numerical equations (i.e., with natural break points) and single-phrase numbers (without natural break points) in their L1 and L2. For single-phrase utterances, both L1 and L2 were affected by L2 exposure. For multi-phrase utterances, L1 scope of planning was similar to what has been previously reported for monolinguals; however, L2 scope of planning exhibited variable patterns as a function of individual differences in L2 exposure. Thus, the scope of planning among bilinguals varies as a function of the complexity of their utterances: specifically, by whether people are speaking in their L1 or L2, and bilingual language experience.

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)0.001
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 score0.044
Threshold uncertainty score0.555

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0000.000

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 head0.025
GPT teacher head0.259
Teacher spread0.234 · 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

Citations8
Published2020
Admission routes1
Has abstractyes

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