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Record W2258166668 · doi:10.1093/schbul/sbv102

Is the Sense of Agency in Schizophrenia Influenced by Resting-State Variation in Self-Referential Regions of the Brain?

2015· review· en· W2258166668 on OpenAlexafffund
Jeffrey D. Robinson, Nils‐Frederic Wagner, Georg Northoff

Bibliographic record

VenueSchizophrenia Bulletin · 2015
Typereview
Languageen
FieldArts and Humanities
TopicMental Health and Psychiatry
Canadian institutionsUniversity of OttawaRoyal Ottawa Mental Health Centre
FundersCanadian Institutes of Health Research
KeywordsMisattribution of memoryConceptualizationAgency (philosophy)PsychologySense of agencySchizophrenia (object-oriented programming)NeuroimagingAttributionDefault mode networkCognitive psychologyPsychosisResting state fMRINeuroscienceRelevance (law)Psychology of selfCognitionSocial psychologyPsychiatryComputer scienceSociologyPolitical science

Abstract

fetched live from OpenAlex

Schizophrenia is a disturbance of the self, of which the attribution of agency is a major component. In this article, we review current theories of the Sense of Agency, their relevance to schizophrenia, and propose a novel framework for future research. We explore some of the models of agency, in which both bottom-up and top-down processes are implicated in the genesis of agency. We further this line of inquiry by suggesting that ongoing neurological activity (the brain's resting state) in self-referential regions of the brain can provide a deeper level of influence beyond what the current models capture. Based on neuroimaging studies, we suggest that aberrant activity in regions such as the default mode network of individuals with schizophrenia can lead to a misattribution of internally/externally generated stimuli. This can result in symptoms such as thought insertion and delusions of control. Consequently, neuroimaging can contribute to a more comprehensive conceptualization and measurement of agency and potential treatment implications.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.596
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.044
GPT teacher head0.298
Teacher spread0.255 · 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.

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

Citations47
Published2015
Admission routes2
Has abstractyes

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