MétaCan
Menu
Back to cohort
Record W2942812424 · doi:10.1075/lab.18036.das

A new look at the question of the bilingual advantage

2019· article· en· W2942812424 on OpenAlexaff
Tanya Dash, Pierre Berroir, Ladan Ghazi Saidi, Daniel Adrover‐Roig, Ana Inés Ansaldo

Bibliographic record

VenueLinguistic Approaches to Bilingualism · 2019
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsInstitut Universitaire de Gériatrie de Montréal
Fundersnot available
KeywordsNeuroscience of multilingualismCognitionPsychologyMode (computer interface)Task (project management)Cognitive psychologyControl (management)Default mode networkDevelopmental psychologyComputer scienceNeuroscienceArtificial intelligenceHuman–computer interactionEngineering

Abstract

fetched live from OpenAlex

Abstract Bilingualism has been associated with age-related cognitive advantage. It is important to study cognitive control mechanisms to better understand this phenomenon. We sought to examine proactive and reactive control, as measured by fast and slow responses, respectively. The neural underpinnings of these modes of control were studied in rigorously matched elderly monolinguals and bilinguals, using fMRI performance on a Simon task. The results indicate that bilinguals performed efficiently in proactive mode, as more activation and connectivity were observed in the monolinguals. On the other hand, the monolinguals functioned more efficiently in reactive mode, recruiting fewer brain areas than the bilinguals. These results suggest that bilinguals’ function effortlessly and economically in proactive mode, which is preserved through lifelong use of languages, whereas monolinguals are efficient in reactive mode, which they use more often as a consequence of aging. Thus, frequent use in daily life contributes to efficient functioning in the respective mode of control.

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)0.006
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 score0.092
Threshold uncertainty score0.886

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.064
GPT teacher head0.290
Teacher spread0.226 · 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
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueLinguistic Approaches to BilingualismSame topicNeurobiology of Language and BilingualismFrench-language works237,207