Authors’ reply to the commentary on “Establishing norms for error-related brain activity during the arrow Flanker task among young adults”
Bibliographic record
Abstract
In their commentary on our article, "Establishing norms for error-related brain activity during the arrow Flanker task among young adults" (Imburgio et al., 2020), Clayson and colleagues (2021) voiced their concerns about our development of norms for an event-related potential measure of error monitoring, the error-related negativity (ERN). The central flaw in their commentary is the idea that because we don't know all the factors that can affect the ERN, it should not be normed. We respond to this idea, while also reiterating points made in our original manuscript: a) at present, the reported norms are not intended to be used for individual clinical assessment and b) our norms should be considered specific to the procedures (i.e., recording and processing parameters) and task used (i.e., arrow Flanker). Contrary to Clayson and colleagues' claims, we believe that information about the distribution of the ERN (i.e., our norms) in a large sample representative of those used in much of the ERN literature (i.e., unselected young adults) will be useful to the field and that this information stands to increase, not decrease, understanding of the ERN.
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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 | 0.007 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.051 | 0.046 |
| Insufficient payload (model declined to judge) | 0.005 | 0.008 |
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".