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Record W133995631 · doi:10.1139/jpn.0411

Semantics and N400: insights for schizophrenia

2004· article· en· W133995631 on OpenAlexaffvenue
Namita Kumar, J. Bruno Debruille

Bibliographic record

VenueJournal of Psychiatry and Neuroscience · 2004
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsMcGill University Health CentreDouglas Mental Health University Institute
Fundersnot available
KeywordsN400ComprehensionSchizophrenia (object-oriented programming)Semantics (computer science)Semantic memoryPsychologyCognitionContext (archaeology)Cognitive psychologyEvent-related potentialEvent (particle physics)LinguisticsComputer scienceNeurosciencePsychiatry

Abstract

fetched live from OpenAlex

Thought disorder is a hallmark symptom of schizophrenia, which often leads to deficits in social functioning. Some aspects of this cognitive dysfunction are the result of abnormal characteristics in the semantic processes of patients. These abnormalities exist not only at the discourse production level, but at the discourse comprehension level as well. The recording and analysis of event-related potentials has greatly advanced the investigation of the processing of linguistic information. One particular component of event-related potentials, N400, indexes semantic processing. Whereas all meaningful words elicit an N400, the amplitude of this component is much greater in response to words that are unexpected in a given context. As such, it is thought to reflect processes involved in contextual integration, which is the key to correct comprehension. N400 has been found to be abnormal in patients with schizophrenia when compared with healthy controls and, thus, may point toward the underlying cause of semantic deficits of patients with thought disorder.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.024
GPT teacher head0.287
Teacher spread0.263 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations51
Published2004
Admission routes2
Has abstractyes

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Same venueJournal of Psychiatry and NeuroscienceSame topicNeurobiology of Language and BilingualismFrench-language works237,207