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Record W4377943229 · doi:10.1017/s1366728923000330

The role of morphological configuration in language control during bilingual production and comprehension

2023· article· en· W4377943229 on OpenAlexaff
Shuang Liu, Junjun Huang, John W. Schwieter, Huanhuan Liu

Bibliographic record

VenueBilingualism Language and Cognition · 2023
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsMcMaster UniversityWilfrid Laurier University
Fundersnot available
KeywordsComprehensionLinguisticsLanguage productionNounCued speechComputer sciencePsychologyControl (management)Production (economics)VerbCommunicationNatural language processingCognitive psychologyArtificial intelligenceCognition

Abstract

fetched live from OpenAlex

Abstract When bilinguals switch between their two languages, they often alternate between words whose formation rules in one language are different from the other (e.g., a noun-verb compound in one language may be a verb-noun compound in another language). In this study, we analyze behavioral performance and electrophysiological activity to examine the effects of morphological configuration on language control during production and comprehension. Chinese–English bilinguals completed a joint naming-listening task involving cued language switching. The findings showed differential effects of morphological configuration on language production and comprehension. In production, morphological configuration was processed sequentially, suggesting that bilingual production may be a combination of sequential processing and inhibition of morphological levels and language interference. In comprehension, however, bottom-up control processes appear to mask the influence of sequential processing on language switching. Together, these findings underscore differential functionalities of language control in speaking and listening.

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)0.002
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 score0.013
Threshold uncertainty score0.482

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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 head0.015
GPT teacher head0.267
Teacher spread0.252 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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