Test-Retest Reliability of Event-Related Potentials Across Three Tasks
Bibliographic record
Abstract
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.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".