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Record W3172245559 · doi:10.1002/dev.22150

The reliability of visual ERP components in children across the first year of school

2021· article· en· W3172245559 on OpenAlexaff
Michelle K. Jetha, Sidney J. Segalowitz, Lisa M. Gatzke‐Kopp

Bibliographic record

VenueDevelopmental Psychobiology · 2021
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsBrock UniversityCape Breton University
Fundersnot available
KeywordsPsychologyEvent-related potentialHabituationCognitionPerceptionVisual perceptionModerationDevelopmental psychologyP200AudiologyN100ElectroencephalographyCognitive psychologyNeuroscience

Abstract

fetched live from OpenAlex

Event-related potentials (ERPs) are increasingly used as neurophysiological markers of perceptual and cognitive processes conveying risk for psychopathology. However, little is known about the reliability of ERP components during childhood, a time of substantial brain maturation. In the present study, we examine the early visual ERP components (P1, N170, VPP), frequently examined as indicators of attentional bias, for 110 children at kindergarten (T1) and first grade (T2). Children performed a Go/Nogo task at both time points, with exact stimuli changed to reduce habituation. All components showed increases in absolute amplitude and the P1 and VPP also showed decreases in latency. Retest reliability across time was good to very good for amplitude measures (Pearson rs ranging from .54 for N170 to .69 for P1) and low to very good for latencies (rs from .34 for P1 to .60 for N170), despite the change in visual stimuli. Although there was some evidence of moderation by sex, early visual ERP components appear to be a reliable measure of individual differences in attention processing in middle childhood. This has implications for the use of early visual ERP components as trait-like markers for individual differences in perceptual processes in developmental research.

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 score0.048
Threshold uncertainty score0.342

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.084
GPT teacher head0.375
Teacher spread0.292 · 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

Citations6
Published2021
Admission routes1
Has abstractyes

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