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Executive Processes in Appearance–Reality Tasks: The Role of Inhibition of Attention and Symbolic Representation

2004· article· en· W2066065100 on OpenAlexaff
Ellen Bialystok, Lili Senman

Bibliographic record

VenueChild Development · 2004
Typearticle
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsYork University
Fundersnot available
KeywordsPsychologyRepresentation (politics)Object (grammar)Inhibitory controlCognitive psychologyControl (management)CognitionExecutive functionsTask (project management)Neuroscience of multilingualismAttentional controlMental representationDevelopmental psychologyLinguisticsArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

Two studies addressed the role of representation ability and control of attention on solutions to an appearance-reality task based on two types of objects, real and representational. In Study 1, 67 preschool children (3-, 4-, and 5-year-olds) solved appearance-reality problems and executive processing tasks. There was an interaction between object type (real vs. representational) and question type (appearance vs. reality) on problem difficulty. In addition, representational ability predicted performance on appearance questions and inhibitory control predicted performance on reality questions. In Study 2, 95 children (4- and 5-year-olds) who were monolingual or bilingual solved similar problems. On appearance questions, groups performed equivalently but on reality questions, bilinguals performed better (once language proficiency had been controlled). The difference is attributed to the advanced inhibitory control that comes with bilingualism.

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)0.010
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 score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.015
GPT teacher head0.279
Teacher spread0.265 · 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

Citations232
Published2004
Admission routes1
Has abstractyes

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