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Record W4387227800 · doi:10.1017/9781108178501.007

Cognitive and Neurocognitive Effects of Bilingualism

2023· book-chapter· en· W4387227800 on OpenAlexaff

Bibliographic record

VenueCambridge University Press eBooks · 2023
Typebook-chapter
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsNeurocognitiveNeuroscience of multilingualismPsychologyCognitionCognitive psychologyPerceptionBrain activity and meditationTimelineDevelopmental psychologyNeuroscienceElectroencephalography

Abstract

fetched live from OpenAlex

This chapter presents criteria characterizing the “bilingual experience” examined on three different levels: language processing, cognitive processing, and structural and functional changes in the brain. On all three levels, numerous studies have been conducted and have yielded inconclusive results. A bilingual experience is a change-inducing event leading to speedy adaptations on different levels of processing, with brain changes at its basis to accommodate for additional demands and specific requirements which are dependent on the length and intensity of the bilingual experience. A surge of proposals on how to measure the bilingual experience has recently appeared in the literature. The brain adapts from early on, even in infants, allowing for early indications of the effects of bilingual experience, in particular on perception and attentional aspects. The experience-dependent alterations in the brain at various locations, intensities, and timelines seem to align with our current understanding of the cognitive neuroscientific effects of bilingualism much more than previous views of separate brain areas involved in the processing and representation of each language.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.033
GPT teacher head0.239
Teacher spread0.206 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueCambridge University Press eBooks→Same topicNeurobiology of Language and Bilingualism→French-language works237,207→