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Record W3193526787 · doi:10.1027/0269-8803/a000286

Test-Retest Reliability of Event-Related Potentials Across Three Tasks

2021· article· en· W3193526787 on OpenAlexaff
Simon Morand‐Beaulieu, Marie-Ange Perrault, Marc E. Lavoie

Bibliographic record

VenueJournal of Psychophysiology · 2021
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsUniversité de MontréalInstitut universitaire en santé mentale de MontréalInstitut Universitaire en Santé Mentale de Québec
Fundersnot available
KeywordsPsychologyEvent-related potentialP3bIntraclass correlationN100CognitionElectroencephalographyOddball paradigmReliability (semiconductor)AudiologyCognitive psychologyDevelopmental psychologyPsychometricsNeuroscience

Abstract

fetched live from OpenAlex

Abstract. Event-related potentials (ERPs) constitute a useful and cost-effective method to assess the neural underpinnings of multiple cognitive processes. ERPs have been used to track changes in cognitive processes in longitudinal and clinical studies. However, few studies have assessed their test-retest reliability (i.e., their consistency across time). Therefore, in the current study, we aimed to assess the test-retest reliability of ERPs (P100, N100, P200, N200, P3b, lateralized readiness potentials) across three tasks. In two assessments separated by approximately 4 months, ERPs were recorded in 26 healthy participants, during two oddball tasks (motor and counting) and a stimulus-response compatibility paradigm. Pearson’s correlations and intraclass correlations were used to assess the test-retest reliability of ERPs. Correlations between ERPs elicited by the three tasks were assessed with Pearson’s correlations. Our analyses revealed moderate to very strong test-retest reliability for most ERP components across the three tasks. Test-retest reliability did not differ between the motor and counting oddball tasks. Most ERPs were also correlated across paradigms. Therefore, these results confirm that ERPs have the potential to be reliable markers to serve as robust assessment tools in longitudinal or clinical studies.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score0.565

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.072
GPT teacher head0.397
Teacher spread0.325 · 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 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

Citations17
Published2021
Admission routes1
Has abstractyes

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