Methodological choices in event-related potential (ERP) research and their impact on internal consistency reliability and individual differences: An examination of the error-related negativity (ERN) and anxiety.
Bibliographic record
Abstract
Researchers in clinical psychophysiology make several methodological decisions during the analysis of event-related potentials (ERPs). In the current study, we review these choices from the perspective of individual differences. We focus on baseline period and reference scheme (i.e., average, mastoid, current source density), as well as choices regarding where (i.e., single electrode site vs. pooling of sites), when (i.e., area, area around peak), and how (i.e., subtraction- or regression-based difference scores) to quantify ERPs. To illustrate the impact of these analytic pathways on internal consistency reliability and individual differences, we focus on the error-related negativity (ERN) and anxiety-and present data from 2 samples: 1st, in adults with diagnosed generalized anxiety disorder (GAD); 2nd, in relation to continuous self-reported symptoms of GAD in a large community sample of female adolescents. Results generally indicated similar internal consistency and between-subjects effect sizes across all evaluated methods. Nonetheless, some patterns of variation emerged, such as that, across both data sets, difference-based ERN measures, especially with mastoid reference, yielded more robust associations with GAD diagnosis and symptoms, despite somewhat lower internal consistency. The current analyses suggest that the association between ERN and anxiety is robust across a range of commonly used methodological choices. The present study is an example of how systematic analyses of analytic strategies on measures of internal consistency and between-subjects variability could help inform individual-differences ERP research. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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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 | 0.260 | 0.373 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".