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Record W2950757885 · doi:10.1111/cdev.13270

Preschoolers Flexibly Shift Between Speakers' Perspectives During Real-Time Language Comprehension

2019· article· en· W2950757885 on OpenAlexafffund
Melanie Khu, Craig G. Chambers, Susan A. Graham

Bibliographic record

VenueChild Development · 2019
Typearticle
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsUniversity of TorontoUniversity of Calgary
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of CalgaryKillam TrustsAlberta Children's Hospital FoundationCanada Research ChairsCanada Foundation for Innovation
KeywordsPsychologyComprehensionEye trackingGazeCognitive psychologyCommon groundPerspective (graphical)Task (project management)CognitionInterpretation (philosophy)Developmental psychologyPerspective-takingTask analysisLinguisticsCommunicationSocial psychologyArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

In communicative situations, preschoolers use shared knowledge, or "common ground," to guide their interpretation of a speaker's referential intent. Using eye-tracking measures, this study investigated the time course of 4-year-olds' (n = 95) use of two different speakers' perspectives and assessed how individual differences in this ability related to individual differences in executive function and representational skills. Gaze measures indicated partner-specific common ground guided children's interpretation from the earliest moments of language processing. Nonegocentric online processing was positively correlated with performance on a Level 2 visual perspective-taking task. These results demonstrate that preschoolers readily use the perspectives of multiple partners to guide language comprehension and that more advanced representational skills are associated with the rapid integration of common ground information.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.419
Threshold uncertainty score1.000

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)0.0050.013

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 head0.008
GPT teacher head0.258
Teacher spread0.250 · 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; both teacher heads agree on what is shown here.

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

Citations28
Published2019
Admission routes2
Has abstractyes

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