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Record W2908005395 · doi:10.1111/infa.12279

The Use of Pitch Accent in Word–Object Association by Monolingual Japanese Infants

2018· article· en· W2908005395 on OpenAlexaff
Ryoko Mugitani, Tessei Kobayashi, Akiko Hayashi, Laurel Fais

Bibliographic record

VenueInfancy · 2018
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsUniversity of British Columbia
FundersCurtin University of Technology
KeywordsPsychologyPitch accentNonsenseAssociation (psychology)Contrast (vision)Stress (linguistics)Context (archaeology)Object (grammar)Task (project management)Word (group theory)Word learningWord AssociationCommunicationLinguisticsAudiologySpeech recognitionArtificial intelligenceProsodyComputer scienceVocabularyHistoryMedicine

Abstract

fetched live from OpenAlex

This study investigated the lexical use of Japanese pitch accent in Japanese-learning infants. A word-object association task revealed that 18-month-old infants succeeded in learning the associations between two nonsense objects paired with two nonsense words minimally distinguished by pitch pattern (Experiment 1). In contrast, 14-month-old infants failed (Experiment 2). Eighteen-month-old infants succeeded even for sounds that contained only the prosodic information (Experiment 3). However, a subsequent experiment revealed that 14-month-old infants succeeded in an easier single word-object task using pitch contrast (Experiment 4). These findings indicate that pitch pattern information is robustly available to 18-month-old Japanese monolingual infants in a minimal pair word-learning situation, but only partially accessible in the same context for 14-month-old infants.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.024
GPT teacher head0.315
Teacher spread0.290 · 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 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

Citations4
Published2018
Admission routes1
Has abstractyes

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