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Record W2155685600 · doi:10.1017/s136672891300062x

Learning two languages from birth shapes pre-attentive processing of vowel categories: Electrophysiological correlates of vowel discrimination in monolinguals and simultaneous bilinguals

2013· article· en· W2155685600 on OpenAlexaffabout
Monika Molnar, Linda Polka, Shari R. Baum, Karsten Steinhauer

Bibliographic record

VenueBilingualism Language and Cognition · 2013
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsCentre for Research on Brain Language and MusicMcGill University
Fundersnot available
KeywordsMismatch negativityVowelPsychologyPerceptionActive listeningContext (archaeology)Speech perceptionNeuroscience of multilingualismEvent-related potentialLinguisticsFirst languageAudiologyElectroencephalographySpeech recognitionCommunicationComputer science

Abstract

fetched live from OpenAlex

Using event-related brain potentials (ERPs), we measured pre-attentive processing involved in native vowel perception as reflected by the mismatch negativity (MMN) in monolingual and simultaneous bilingual (SB) users of Canadian English and Canadian French in response to various pairings of four vowels: English /u/, French /u/, French /y/, and a control /y/. The monolingual listeners exhibited a discrimination pattern that was shaped by their native language experience. The SB listeners, on the other hand, exhibited a MMN pattern that was distinct from both monolingual listener groups, suggesting that the SB pre-attentive system is tuned to access sub-phonemic detail with respect to both input languages, including detail that is not readily accessed by either of their monolingual peers. Additionally, simultaneous bilinguals exhibited sensitivity to language context generated by the standard vowel in the MMN paradigm. The automatic access to fine phonetic detail may aid SB listeners to rapidly adjust their perception to the variable listening conditions that they frequently encounter.

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 score0.058
Threshold uncertainty score0.671

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 head0.289
Teacher spread0.274 · 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

Citations21
Published2013
Admission routes2
Has abstractyes

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