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Record W2065326643 · doi:10.1080/09297040802464148

Cognitive Control in Children with ADHD-C: How efficient are they?

2008· article· en· W2065326643 on OpenAlexaff
Kate D. Randall, Karin C. Brocki, Kimberly A. Kerns

Bibliographic record

VenueChild Neuropsychology · 2008
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsCognitionPsychologyAttention deficit hyperactivity disorderSelection (genetic algorithm)Control (management)Developmental psychologyResponse inhibitionPopulationInterference (communication)Cognitive psychologyEffects of sleep deprivation on cognitive performanceClinical psychologyPsychiatryMachine learningMedicineArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

The literature on children with attention deficit/hyperactivity disorder, combined type (ADHD-C), is currently inconclusive as to the nature of deficits in two forms of cognitive control - interference control and response selection (Nigg, 2006). This paper examined the performance of children with ADHD-C on interference control and response selection conflict tasks that required both speed and accuracy. The data was analyzed utilizing a new efficiency method to more effectively analyze overall responses. Both interference control and response selection conditions were combined within tasks allowing for a closer comparison of how children with ADHD-C perform on these specific types of cognitive control. Computerized tasks were administered to 62 boys, ages 7 to 12 (31 controls, 31 ADHD-C). Results revealed deficits in efficient performance for children with ADHD-C on interference control tasks and response selection tasks hypothesized to involve high cognitive control demand. These results highlight the utility of analyzing efficiency data to identify deficits in performance for children with ADHD-C and to foster an increased understanding of cognitive control functioning in this clinical population.

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 score0.017
Threshold uncertainty score0.759

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)0.0000.000

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 head0.028
GPT teacher head0.291
Teacher spread0.263 · 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

Citations22
Published2008
Admission routes1
Has abstractyes

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