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Record W4379797334 · doi:10.1017/s136672892300038x

An ERP study on novel word learning in an immersive virtual reality context

2023· article· en· W4379797334 on OpenAlexaff
Lu Jiao, Mengrui Zhu, Zijie Xu, Guanzhu Zhou, John W. Schwieter, Cong Liu

Bibliographic record

VenueBilingualism Language and Cognition · 2023
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsWilfrid Laurier University
FundersInstitute of Psychology, Chinese Academy of SciencesNatural Science Foundation of Shandong ProvinceNational Natural Science Foundation of China
KeywordsContext (archaeology)Virtual realitySet (abstract data type)PsychologyComputer scienceTask (project management)Human–computer interaction

Abstract

fetched live from OpenAlex

Abstract In this behavioral and electrophysiological study, we compare novel word learning, particularly lexical form acquisition, in an immersive virtual reality (VR) context with a picture-word (PW) association context. We also test whether inhibitory control and age of second language acquisition (L2 AoA) have modulating effects. Chinese speakers of L2 English learned two sets of German words, one set in each of the contexts. Behavioral performance from a subsequent recognition task indicated that responses to VR-learned words were faster than PW-leaned words. ERPs revealed that VR-learned words elicited more negative N100 and N200 waveforms than PW-learned words. Moreover, a significant relationship between L2 AoA and N200 amplitude was observed for VR-learned words. Taken together, the results suggest that the multi-sensory, interactive experience simulated by an immersive VR context has a positive effect on early lexical form acquisition of novel words.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.500
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0000.000
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 head0.064
GPT teacher head0.352
Teacher spread0.289 · 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

Citations13
Published2023
Admission routes1
Has abstractyes

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