Assessing MMN and P3b reliably within-participant -- a comparison between the local-global paradigm and two specialized oddball sequences.
Bibliographic record
Abstract
Mismatch negativity (MMN) and P3b are well known for their clinical utility. There exists no gold standard, however, to acquire these markers. This may explain why the within-individual sensitivity of MMN/P3b is often quite poor and why seemingly identical markers can behave differently across studies. Here we compare two traditional paradigms for MMN or P3b assessment with the recently more popular local-global paradigm which promises to assess MMN and P3b orthogonally within one oddball sequence. All three paradigms were administered to healthy participants (N=15) with concurrent EEG. A clear MMN and local effect were found for 15/15 participants. The P3b and global effect were found for 14/15 and 13/15 participants, respectively. There were no systematic differences between the global effect and P3b. Indeed, P3b amplitude was highly correlated across paradigms. The local effect differed clearly from the MMN, however. It occurred earlier compared to MMN and was followed by a much more prominent P3a effect. The two sets of peak latencies and amplitudes were also not correlated across paradigms. We conclude that the local-global paradigm is effective in evoking the traditional P3b component, but it does not capture the MMN. Caution should therefore be exercised when comparing the local effect and MMN across studies. Nevertheless, the within-individual sensitivity of both MMN and the local effect was satisfactory. The within-individual sensitivity of P3b was lower than expected in a healthy control group, which may explain the often-low sensitivity of P3b in patients with disorders of consciousness.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".