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Record W2027276797 · doi:10.1159/000013875

Mismatch Negativity: Different Water in the Same River

2000· review· en· W2027276797 on OpenAlexaff
Terence W. Picton, Claude Alain, Leun J. Otten, Walter Ritter, André Achim

Bibliographic record

VenueAudiology and Neurotology · 2000
Typereview
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsBaycrest HospitalUniversity of Toronto
Fundersnot available
KeywordsMismatch negativitySensory memoryStimulus (psychology)PsychologyAudiologySensory systemEvent-related potentialEngramNeuroscienceCognitive psychologyElectroencephalographyMedicine

Abstract

fetched live from OpenAlex

The mismatch negativity (MMN) is a frontal negative deflection in the human event-related potential that typically occurs when a repeating auditory stimulus changes in some manner. The MMN can be elicited by many kinds of stimulus change, varying from simple changes in a single stimulus feature to abstract changes in the relationship between stimuli. The main intracerebral sources for the MMN are located in the auditory cortices of the temporal lobe. Since it occurs whether or not stimuli are being attended, the MMN represents an automatic cerebral process for detecting change. The MMN is clinically helpful in terms of demonstrating disordered sensory processing or disordered memory in groups of patients. Improvements in the techniques for measuring the MMN and in the paradigms for eliciting it will be needed before the MMN can become clinically useful as an objective measurement of such disorders in individual patients.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.003

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.

Opus teacher head0.093
GPT teacher head0.338
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations582
Published2000
Admission routes1
Has abstractyes

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