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Record W4361017342 · doi:10.1111/cdev.13925

Preschoolers modulate contrastive inferences during online language comprehension

2023· article· en· W4361017342 on OpenAlexafffund
Narae Ju, Natalie Williams, Julie Sedivy, Craig G. Chambers, Susan A. Graham

Bibliographic record

VenueChild Development · 2023
Typearticle
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsAlberta Children's HospitalUniversity of TorontoMcGill UniversityCanadian Celiac AssociationUniversity of Calgary
FundersSocial Sciences and Humanities Research Council of CanadaAlberta Children's Hospital FoundationAlberta Children's Hospital Research InstituteChildren's Hospital FoundationUniversity of Calgary
KeywordsPsychologyAdjectiveComprehensionLinguisticsCognitive psychologyDevelopmental psychologyNoun

Abstract

fetched live from OpenAlex

This study examined 4- and 5-year-olds' incremental interpretation of size adjectives, focusing on whether contrastive inferences are modulated by speaker behavior. Children (N = 120, 59 females, mostly White, tested between July, 2018 and August, 2019) encountered either a conventional or unconventional speaker who labeled objects in a correspondingly typical or atypical way. Critical utterances contained size adjectives (e.g., "Look at the big duck"). With conventional speakers, gaze measures indicated that children rapidly used the adjective to differentiate members of a contrasting pair, indicating that even 4-year-olds derive contrastive inferences. With unconventional speakers, contrastive inferences were delayed in processing. The findings demonstrate that preschoolers adjust their use of pragmatic cues when presented with evidence disconfirming their default assumptions about a speaker.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.640
Threshold uncertainty score0.999

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)0.0010.002

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 head0.020
GPT teacher head0.291
Teacher spread0.271 · 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 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

Citations2
Published2023
Admission routes2
Has abstractyes

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