MétaCan
Menu
Back to cohort
Record W2325643089 · doi:10.1080/21622965.2015.1124276

Children's planning performance in the Zoo Map task (BADS-C): Is it driven by general cognitive ability, executive functioning, or prospection?

2016· article· en· W2325643089 on OpenAlexafffund
Nicola Ballhausen, Caitlin E. V. Mahy, Alexandra Hering, Babett Voigt, Katharina M. Schnitzspahn, Prune Lagner, Andreas Ihle, Matthias Kliegel

Bibliographic record

VenueApplied Neuropsychology Child · 2016
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsBrock University
FundersNatural Sciences and Engineering Research Council of CanadaSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsProspectionPsychologyTask (project management)Executive functionsCognitionCognitive mapDysexecutive syndromeNaturalismTask analysisCognitive psychologyDevelopmental psychologyApplied psychologySocial psychologyEngineering

Abstract

fetched live from OpenAlex

A minimal amount of research has examined the cognitive predictors of children's performance in naturalistic, errand-type planning tasks such as the Zoo Map task of the Behavioral Assessment of the Dysexecutive Syndrome for Children (BADS-C). Thus, the current study examined prospection (i.e., the ability to remember to carry out a future intention), executive functioning, and intelligence markers as predictors of performance in this widely used naturalistic planning task in 56 children aged 7- to 12-years-old. Measures of planning, prospection, inhibition, crystallized intelligence, and fluid intelligence were collected in an individual differences study. Regression analyses showed that prospection (rather than traditional measures of intelligence or inhibition) predicted planning, suggesting that naturalistic planning tasks such as the Zoo Map task may rely on future-oriented cognitive processes rather than executive problem solving or general knowledge.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.846

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 integrity0.0000.001
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 head0.024
GPT teacher head0.311
Teacher spread0.288 · 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

Citations12
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueApplied Neuropsychology ChildSame topicAttention Deficit Hyperactivity DisorderFrench-language works237,207