What can topology changes in the oddball N2 reveal about underlying processes?
Bibliographic record
Abstract
A prominent theory of the N2 event-related potential component holds that the 'oddball' N2 is generated in the anterior cingulate cortex. However, observations of oddball N2s with posterior scalp distributions are inconsistent with this hypothesis. We suggest that variability in the topology of the oddball N2 is a key characteristic of the component that can inform theories of its neural basis. We propose that the oddball N2 reflects cortex-wide noradrenergic modulation of the ongoing cortical activity and thus should have a topology that varies systematically according to task specifics. Participants engaged in an oddball task with male and female faces tinted either yellow or blue, counting targets according to color or sex. Between blocks, targets were frequent or infrequent, counterbalanced across task (attend color, attend sex), and category (blue male, yellow male, blue female, yellow female). We created difference waves by subtracting frequent from infrequent category trials to isolate the oddball N2. When participants attended to color the oddball N2 was maximal over frontal-central areas and when they attended to sex it was maximal over lateral-occipital areas. Thus, the oddball N2 has a variable scalp distribution that depends on the relative engagement of cortical areas, consistent with noradrenergic modulation having the greatest impact in those areas mostly engaged by the task at hand.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".