The reliability of visual ERP components in children across the first year of school
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".