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Record W3044183707 · doi:10.1017/s030500092000029x

Targeted adaptation in infants following live exposure to an accented talker

2020· article· en· W3044183707 on OpenAlexafffundabout
Melissa Paquette‐Smith, Angela Cooper, Elizabeth K. Johnson

Bibliographic record

VenueJournal of Child Language · 2020
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsUniversity of Toronto
FundersCanada Research Chairs
KeywordsPsychologyStress (linguistics)Mandarin ChineseActive listeningVocabularyVocabulary developmentReading (process)Adaptation (eye)LinguisticsPronunciationFace (sociological concept)Developmental psychologyCognitive psychologyCommunication

Abstract

fetched live from OpenAlex

Infants struggle to understand familiar words spoken in unfamiliar accents. Here, we examine whether accent exposure facilitates accent-specific adaptation. Two types of pre-exposure were examined: video-based (i.e., listening to pre-recorded stories; Experiment 1) and live interaction (reading books with an experimenter; Experiments 2 and 3). After video-based exposure, Canadian English-learning 15- to 18-month-olds failed to recognize familiar words spoken in an unfamiliar accent. However, after face-to-face interaction with a Mandarin-accented talker, infants showed enhanced recognition for words produced in Mandarin English compared to Australian English. Infants with live exposure to an Australian talker were not similarly facilitated, perhaps due to the lower vocabulary scores of the infants assigned to the Australian exposure condition. Thus, live exposure can facilitate accent adaptation, but this ability is fragile in young infants and is likely influenced by vocabulary size and the specific mapping between the speaker and the listener's phonological system.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.913
Threshold uncertainty score0.962

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.026
GPT teacher head0.341
Teacher spread0.315 · 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 designObservational
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

Citations10
Published2020
Admission routes3
Has abstractyes

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