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Record W4246760116 · doi:10.1207/875656401753549807

The Flexible Item Selection Task (FIST): A Measure of Executive Function in Preschoolers

2001· article· en· W4246760116 on OpenAlexfundno aff
Sophie Jacques, Philip David Zelazo

Bibliographic record

VenueDevelopmental Neuropsychology · 2001
Typearticle
Languageen
FieldPsychology
TopicEducational and Psychological Assessments
Canadian institutionsnot available
FundersNational Research Council CanadaNatural Sciences and Engineering Research Council of CanadaNational Science CouncilAmerican Psychological Association
KeywordsPsychologyCognitive flexibilityTask (project management)CognitionSelection (genetic algorithm)Cognitive psychologyDevelopmental psychologyFlexibility (engineering)Set (abstract data type)Artificial intelligenceStatisticsComputer scienceNeuroscience

Abstract

fetched live from OpenAlex

Abstract Abstraction and cognitive flexibility were assessed in 197 preschool children at 2, 3, 4, and 5 years of age using the Flexible Item Selection Task, a task adapted from the Visual-Verbal Test (Feldman & Drasgow, 1951). On this new inductive task, children were shown a set of 3 cards and required to select 2 cards that matched each other on 1 dimension (Selection 1) and then to select a different pair of cards that matched each other on another dimension (Selection 2). Thus, 1 of the 3 cards always had to be selected twice according to different dimensions. Two-year-olds failed to understand basic task requirements as assessed by a criterial measure. Three-year-olds did more poorly on Selection 1 than 4- and 5-year-olds (who performed near ceiling), suggesting that 3-year-olds had difficulty with the abstraction component of the task. Four-year-olds did worse than 5-year-olds on Selection 2, suggesting that they had difficulty with the cognitive flexibility component (i.e., difficulty selecting the same card on more than 1 dimension). Results are discussed in terms of the development of executive function.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.148
Threshold uncertainty score0.884

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.034
GPT teacher head0.325
Teacher spread0.291 · 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.

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

Citations19
Published2001
Admission routes1
Has abstractyes

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