Statistical analysis of the mismatch negativity: To a dilemma, an answer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper offers a new innovative outlook on mismatch negativity (MMN) analysis. Indeed, researchers in this field encounter difficulties when attempting to objectively quantify the MMN component waveform. Advantages taken from already existing amplitude and area under the curve measures were used in order to thwart weaknesses from each individual measure. The present paper can also be used as a guideline that describes each step required in the execution of the proposed technique to MMN analysis. Ce travail suggère une nouvelle approche à l’analyse de la MMN. En effet, certains problèmes sont engendrés par les outils couramment utilisés pour analyser la MMN, notamment l’amplitude et l’aire sous la courbe. La technique suggérée afin de développer une mesure objective de la MMN propose d’utiliser les forces des deux techniques précédemment nommées afin de pallier à leurs faiblesses respectives. Le présent travail se veut également un mode d’emploi quant à la façon d’appliquer les étapes nécessaires à la réalisation de cette nouvelle approche à l’analyse de la MMN. We first wanted to do a tutorial about the BrainVision Analyser program, which processes raw EEG data both for spontaneous EEG analyses and for evoked potentials. However, its user-friendly workspace designed to allow users to interactively compute complex analysis tasks combined with the already existing comprehensive Vision
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.000 | 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 it