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Record W4306920882 · doi:10.31739/anamh.2022.2.2

The use of the Eriksen Flanker Task as training instrument for cognitive control in inhibition disorder

2022· article· en· W4306920882 on OpenAlexaff
C.C.C. van Geest, Hessel Engelbregt

Bibliographic record

VenueApplied Neuroscience and Mental Health · 2022
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsCentre for Movement Disorders
Fundersnot available
KeywordsTask (project management)PsychologyCognitionExecutive functionsCognitive psychologyElementary cognitive taskInhibitory controlControl (management)Continuous performance taskCognitive trainingNeuroscienceComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

A frequently used cognitive task to measure participants’ cognitive performance is the so-called Eriksen Flanker task (1974). This task requires a response where you have to indicate the direction of a central arrow, which is flanked with (un)corresponding arrows at its side. The Flanker task has many modified versions, adjusted to the different ways you can use the task to measure different aspects of executive cognitive functions. Such cognitive tasks, although often too simple and straightforward to represent daily life, tell us a lot about one’s executive cognitive functioning. Executive functions are very important for human behavior because they help us to engage with our surroundings and to participate in society. Problems of inhibition may have neural causes and may lead to negative behavioral consequences. Inhibitory problems can be determined by the Flanker task which might also be useful to practice cognitive control in individuals suffering from inhibitory disorders.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.078
GPT teacher head0.334
Teacher spread0.256 · 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 designBench or experimental
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

Citations2
Published2022
Admission routes1
Has abstractyes

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