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An EMG Study of the Lip Muscles During Covert Auditory Verbal Hallucinations in Schizophrenia

2013· article· en· W2060288352 on OpenAlexaff
Lucile Rapin, Marion Dohen, Mircea Polosan, Pascal Perrier, Hélène Lœvenbruck

Bibliographic record

VenueJournal of Speech Language and Hearing Research · 2013
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsCovertSchizophrenia (object-oriented programming)Articulation (sociology)PsychologyElectromyographyAudiologyPsychosisSpeech productionPhysical medicine and rehabilitationCognitive psychologyMedicinePsychiatrySpeech recognitionComputer science

Abstract

fetched live from OpenAlex

PURPOSE: Auditory verbal hallucinations (AVHs) are speech perceptions in the absence of external stimulation. According to an influential theoretical account of AVHs in schizophrenia, a deficit in inner-speech monitoring may cause the patients' verbal thoughts to be perceived as external voices. The account is based on a predictive control model, in which individuals implement verbal self-monitoring. The authors examined lip muscle activity during AVHs in patients with schizophrenia to check whether inner speech occurred. METHOD: Lip muscle activity was recorded during covert AVHs (without articulation) and rest. Surface electromyography (EMG) was used on 11 patients with schizophrenia. RESULTS: Results showed an increase in EMG activity in the orbicularis oris inferior muscle during covert AVHs relative to rest. This increase was not due to general muscular tension because there was no increase of muscular activity in the forearm muscle. CONCLUSION: This evidence that AVHs might be self-generated inner speech is discussed in the framework of a predictive control model. Further work is needed to better describe how inner speech is controlled and monitored and the nature of inner-speech-monitoring-dysfunction. This will lead to a better understanding of how AVHs occur.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.267
Threshold uncertainty score0.365

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.038
GPT teacher head0.368
Teacher spread0.330 · 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 teacher head, 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

Citations28
Published2013
Admission routes1
Has abstractyes

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