A functional classification of medial frontal negativity ERPs: Theta oscillations and single subject effects
Bibliographic record
Abstract
Theta oscillations in the EEG have been linked to several ERPs that are elicited during performance-monitoring tasks, including the error-related negativity (ERN), no-go N2, and the feedback-related negativity (FRN). We used a novel paradigm to isolate independent components (ICs) in single subjects' (n = 27) EEG accounting for a medial frontal negativity (MFN) to response cue stimuli that signal a potential change in future response demands. Medial frontal projecting ICs that were sensitive to these response cues also described the ERNs, no-go N2s, and, to a lesser extent, the FRNs, that were elicited in letter flanker, go/no-go, and time-estimation tasks, respectively. In addition, percentile bootstrap tests using trimmed means indicated that the medial frontal ICs show an increase in theta activity during the ERN, no-go N2, and FRN across tasks and within individuals. Our results provide an important validation of previous studies by showing that increases in medial frontal theta to cognitively challenging events in multiple paradigms is a reliable effect within individuals and can be elicited by basic stimulus cues that signal the potential need to adjust response control. Thus, medial frontal theta reflects a neural response common to all MFN paradigms and characterizes the general process of controlling attention without the need to induce error commission, inhibited responses, or to present negative feedback.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".