MétaCan
Menu
Back to cohort

Cognitive Control in Children

2006· article· en· W2145492946 on OpenAlexaff
Daniel N. Bub, Michael E. J. Masson, Christopher E. Lalonde

Bibliographic record

VenuePsychological Science · 2006
Typearticle
Languageen
FieldPsychology
TopicCognitive Abilities and Testing
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsPsychologyCognitionControl (management)Cognitive psychologyDevelopmental psychologyPsychiatryArtificial intelligence

Abstract

fetched live from OpenAlex

The development of cognitive control and its relation to overcoming Stroop interference was assessed in a sample (N= 65) of elementary-school children. Subjects alternately performed Stroop color-naming trials and word-reading trials. In separate blocks, the colored Stroop items were non-color words (incongruent condition) or rows of asterisks (neutral condition). Younger children showed both larger Stroop interference in error rates and a greater slowing of word reading in the incongruent condition compared with older children. We conducted analyses of response time distributions that assessed the degree of word-reading suppression applied by younger and older children. Surprisingly, these analyses indicated that younger children engaged in stronger suppression than older children. We propose that greater Stroop interference among younger children is not due to lack of ability to suppress word reading, but instead is the result of a failure to consistently maintain the task set of color naming.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.031
GPT teacher head0.366
Teacher spread0.335 · 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

Citations107
Published2006
Admission routes1
Has abstractyes

Explore more

Same venuePsychological ScienceSame topicCognitive Abilities and TestingFrench-language works237,207