Establishing norms for error-related brain activity during the arrow Flanker task among young adults
Bibliographic record
Abstract
Psychological assessments typically rely on self-report and behavioral measures. Augmenting these with neurophysiological measures of the construct in question may increase the accuracy and predictive power of these assessments. Moreover, thinking about neurophysiological measures from an assessment perspective may facilitate under-utilized research approaches (e.g., brain-based recruitment of participants). However, the lack of normative data for most neurophysiological measures has prevented the comparison of individual responses to the general population, precluding these approaches. The current work examines the distributions of two event-related potentials (ERPs) commonly used in individual differences research: the error-related negativity (ERN) and error positivity (Pe). Across three lab sites, 800 unselected participants between the ages of 18 and 30 performed the arrow version of a Flanker task while EEG was recorded. Percentile scores and distributions for ERPs on error trials, correct trials, and the difference (ΔERN, ΔPe; error minus correct) at Fz, Cz and Pz are reported. The 25th, 50th, and 75th percentile values for the ΔERN at Cz were -2.37 μV, -5.41 μV, and -8.65 μV, respectively. The same values for ΔPe at Cz were 7.51 μV, 11.18 μV, and 15.55 μV. Females displayed significantly larger ΔPe magnitudes and smaller ΔERN magnitudes than males. Additionally, normative data for behavioral performance (accuracy, post-error slowing, and reaction time) on the Flanker task is reported. Results provide a means by which ERN and Pe amplitudes of young adults elicited by the arrow Flanker task can be benchmarked, facilitating the classification of neural responses as 'large,' 'medium,' or 'small'. The ability to classify responses in this manner is a necessary step towards expanded use of these measures in assessment and research settings. These norms may not apply to ERPs elicited by other tasks, and future work should establish similar norms using other tasks.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".