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Record W2091459067 · doi:10.1207/s15327647jcd0403_03

Executive Function and False-Belief Understanding in Preschool Children: Two Tasks Are Harder Than One

2003· article· en· W2091459067 on OpenAlexafffund
Suzanne Hala, Stacey Hug, Annette M. E. Henderson

Bibliographic record

VenueJournal of Cognition and Development · 2003
Typearticle
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsQueen's UniversityUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Calgary
KeywordsPsychologyWorking memoryExecutive functionsInhibitory controlCognitive psychologyRelation (database)CognitionControl (management)Developmental psychologyTest (biology)Function (biology)Attentional controlComputer scienceArtificial intelligenceNeuroscience

Abstract

fetched live from OpenAlex

In this research we examine the relation between executive function (EF) and false-belief (FB) understanding in young children. Specifically, we proposed that performance on tasks combining 2 executive demands: (a) working memory and (b) inhibitory control would be most predictive of performance on FB tasks. Forty-eight children between the ages of 3 and 5 years were given a battery of EF and FB measures. As predicted we found that performance on executive tasks that combined demands for memory and inhibitory control were highly predictive of performance on FB tasks. To further test the relation of EF and FB understanding we also introduced an experimental manipulation designed to reduce the working memory demands of FB tasks. This manipulation did not significantly improve performance. The results from this study provide support for the relation between EF and FB understanding, although the exact nature of the relation requires further clarification.

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)0.002
metaresearch head score (Gemma)0.016
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.002
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 head0.044
GPT teacher head0.277
Teacher spread0.233 · 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

Citations171
Published2003
Admission routes2
Has abstractyes

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