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Record W1984041204 · doi:10.1093/applin/amu028

Event-related Potentials (ERPs) in Second Language Research: A Brief Introduction to the Technique, a Selected Review, and an Invitation to Reconsider Critical Periods in L2

2014· article· en· W1984041204 on OpenAlexafffund
Karsten Steinhauer

Bibliographic record

VenueApplied Linguistics · 2014
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsCentre for Research on Brain Language and MusicMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsPsychologyEvent (particle physics)LinguisticsCognitive psychologyEvent-related potentialApplied linguisticsCognitive sciencePhilosophyCognitionNeuroscience

Abstract

fetched live from OpenAlex

This article provides a selective overview of recent event-related brain potential (ERP) studies in L2 morpho-syntax, demonstrating that the ERP evidence supporting the critical period hypothesis (CPH) may be less compelling than previously thought. The article starts with a general introduction to ERP methodology and language-related ERP profiles in native speakers. The second section presents early ERP studies supporting the CPH, discusses some of their methodological problems, and follows up with data from more recent studies avoiding these problems. It is concluded that well-controlled ERP studies support the convergence hypothesis, according to which L2 learners initially differ from native speakers and then converge on native-like neurocognitive processing mechanisms. The fact that ERPs in late L2 learners at high levels of proficiency are often indistinguishable from those of native speakers suggests that age-of-acquisition effects in SLA are not primarily driven by maturational constraints.

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)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.005
Science and technology studies0.0000.002
Scholarly communication0.0020.004
Open science0.0010.001
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0030.004

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 head0.032
GPT teacher head0.378
Teacher spread0.346 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations119
Published2014
Admission routes2
Has abstractyes

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