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Inhibition Development: Comparison of Neuropsychological and Eye Tracking Measures

2015· article· en· W294867619 on OpenAlexaff
Marc Mainville, Julie Brisson, François Nougarou, Annie Stipanicic, Sylvain Sirois

Bibliographic record

VenueRevista Argentina de Ciencias del Comportamiento · 2015
Typearticle
Languageen
FieldPsychology
TopicCognitive Abilities and Testing
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsNeuropsychologyEye trackingPsychologyCognitive psychologyNeuroscienceOphthalmologyMedicineComputer scienceArtificial intelligenceCognition

Abstract

fetched live from OpenAlex

Inhibition is the ability to stop an automatic response when a stimulus is presented. It is one main component of executive function models. Few studies have evaluated the development of this ability’s in children between five and eight years of age using eye tracking measures. The first objective of this exploratory study is to evaluate the performance difference of younger compared to older children. The second objective is to evaluate if inhibition assessed via three different neuropsychological tests develops at a similar rate as inhibition assessed via two eye tracking tasks. Forty-six children aged 5.7 to 8.4 years completed both types of tests. Results show that one neuropsychological test was sensitive to the children’ increasing inhibition ability, while both eye tracking tests were. Additionally, scores from one eye tracking task correlated with scores from one neuropsychological test. Possible explanations of moderate relations between tasks are discussed.

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)0.005
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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.134
GPT teacher head0.372
Teacher spread0.238 · 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

Citations6
Published2015
Admission routes1
Has abstractyes

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