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Record W1992946687 · doi:10.1207/s15326942dn2903_3

Error-Related Electrocortical Responses Are Enhanced in Children With Obsessive–Compulsive Behaviors

2006· article· en· W1992946687 on OpenAlexafffund
Diane L. Santesso, Sidney J. Segalowitz, Louis A. Schmidt

Bibliographic record

VenueDevelopmental Neuropsychology · 2006
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsBrock University
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsError-related negativityPsychologyObsessive compulsiveAssociation (psychology)ElectrophysiologyNegativity effectElectroencephalographyEvent-related potentialScalpAnterior cingulate cortexAudiologyNeuroscienceDevelopmental psychologyClinical psychologyCognitionMedicinePsychotherapist

Abstract

fetched live from OpenAlex

The error-related negativity (ERN or Ne) and positivity (Pe) are event-related potential components elicited during simple discrimination tasks after an error response. The ERN and Pe have a fronto-central scalp distribution and may be an indirect measure of anterior cingulate (AC) activity as it relates to performance monitoring. Brain imaging studies suggest that obsessive-compulsive disorder (OCD) is associated with exaggerated activity of the AC while electrophysiological studies have found an association between OCD and pronounced ERNs in adults. The present study explored the relation between obsessive-compulsive behaviors, the ERN, and the Pe in a sample of nonclinical 10-year-old children. It was found that more parent-reported obsessive-compulsive behaviors were associated with larger ERN and Pe components in the children. Results suggest unique contributions of the ERN and Pe in predicting obsessive-compulsive behaviors.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.039
GPT teacher head0.325
Teacher spread0.286 · 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

Citations137
Published2006
Admission routes2
Has abstractyes

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