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Record W2768225677 · doi:10.1017/s1366728917000554

Lexical entrenchment and cross-language activation: Two sides of the same coin for bilingual reading across the adult lifespan

2017· article· en· W2768225677 on OpenAlexaff
Veronica Whitford, Debra Titone

Bibliographic record

VenueBilingualism Language and Cognition · 2017
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsMcGill UniversityCentre for Research on Brain Language and Music
Fundersnot available
KeywordsPsychologyNeuroscience of multilingualismLinguisticsReading (process)Word lists by frequencyWord (group theory)MultilingualismCognitionCognitive psychologySentence

Abstract

fetched live from OpenAlex

We used eye movement measures of paragraph reading to examine whether two consequences of bilingualism, namely, reduced lexical entrenchment (i.e., reduced lexical quality and accessibility arising from less absolute language experience) and cross-language activation (i.e., simultaneous co-activation of target- and non-target-language lexical representations) interact during word processing in bilingual younger and older adults. Specifically, we focused on the interaction between word frequency (a predictor of lexical entrenchment) and cross-language neighborhood density (a predictor of cross-language activation) during first- and second-language reading. Across both languages and both age groups, greater cross-language (and within-language) neighborhood density facilitated word processing, indexed by smaller word frequency effects. Moreover, word frequency effects and, to a lesser extent, cross-language neighborhood density effects were larger in older versus younger adults, potentially reflecting age-related changes in lexical accessibility and cognitive control. Thus, lexical entrenchment and cross-language activation multiplicatively influence bilingual word processing across the adult lifespan.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.062
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
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 head0.034
GPT teacher head0.362
Teacher spread0.329 · 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

Citations29
Published2017
Admission routes1
Has abstractyes

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