Error‐related negativity (ERN) and sustained threat: Conceptual framework and empirical evaluation in an adolescent sample
Bibliographic record
Abstract
The error-related negativity (ERN) currently appears as a physiological measure in relation to three Research Domain Criteria (RDoC) constructs: Cognitive Control, Sustained Threat, and Reward Learning. We propose a conceptual model in which variance in the ERN reflects individual differences in the degree to which errors are evaluated as threatening. We also discuss evidence for the placement of the ERN in the "Sustained Threat" construct, as well as evidence that the ERN may more specifically reflect sensitivity to endogenous threat. Following this, we present data from a sample of 515 adolescent females demonstrating a larger ERN in relation to self-reported checking behaviors, but only in older adolescents, suggesting that sensitivity to internal threat and the ERN-checking relationship may follow a developmental course as adolescents develop behavioral control. In contrast, depressive symptoms were linked to a smaller ERN, and this association was invariant with respect to age. Collectively, these data suggest that the magnitude of the ERN is sensitive both to specific anxiety-related processes and depression, in opposing directions that may reflect variation in internal threat sensitivity. We discuss directions for future research, as well as ways in which findings for the ERN complement and challenge aspects of the current RDoC matrix.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".