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Record W2998000777 · doi:10.1075/lab.18028.tit

Spoken word processing in bilingual older adults

2020· article· en· W2998000777 on OpenAlexaff
Debra Titone, Julie Mercier, Aruna Sudarshan, Irina Pivneva, Jason W. Gullifer, Shari R. Baum

Bibliographic record

VenueLinguistic Approaches to Bilingualism · 2020
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsMcGill University
Fundersnot available
KeywordsWord (group theory)LinguisticsComputer scienceNatural language processingSpoken languageSpoken wordPsychologyArtificial intelligenceCommunication

Abstract

fetched live from OpenAlex

Abstract We investigated whether bilingual older adults experience within- and cross-language competition during spoken word recognition similarly to younger adults matched on age of second language (L2) acquisition, objective and subjective L2 proficiency, and current L2 exposure. In a visual world eye-tracking paradigm, older and younger adults, who were French-dominant or English-dominant English-French bilinguals, listened to English words, and looked at pictures including the target (field), a within-language competitor (feet) or cross-language (French) competitor (fille, “girl”), and unrelated filler pictures while their eye movements were monitored. Older adults showed evidence of greater within-language competition as a function of increased target and competitor phonological overlap. There was some evidence of age-related differences in cross-language competition, however, it was quite small overall and varied as a function of target language proficiency. These results suggest that greater within- and possibly cross-language lexical competition during spoken word recognition may underlie some of the communication difficulties encountered by healthy bilingual older adults.

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)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
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 score0.697
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.139
GPT teacher head0.284
Teacher spread0.145 · 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.

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

Citations0
Published2020
Admission routes1
Has abstractyes

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