Repository logo
 

Towards a Unifying Cognitive, Neurophysiological, and Computational Neuroscience Account of Schizophrenia.

Simple item page
cam.issuedOnline2018-11-02
dc.contributor.authorHeinz, Andreas
dc.contributor.authorMurray, Graham K
dc.contributor.authorSchlagenhauf, Florian
dc.contributor.authorSterzer, Philipp
dc.contributor.authorGrace, Anthony A
dc.contributor.authorWaltz, James A
dc.contributor.orcidMurray, Graham [0000-0001-8296-1742]
dc.date.accessioned2018-12-13T00:30:41Z
dc.date.available2018-12-13T00:30:41Z
dc.date.issued2019-09-11
dc.description.abstractPsychotic experiences may be understood as altered information processing due to aberrant neural computations. A prominent example of such neural computations is the computation of prediction errors (PEs), which signal the difference between expected and experienced events. Among other areas showing PE coding, hippocampal-prefrontal-striatal neurocircuits play a prominent role in information processing. Dysregulation of dopaminergic signaling, often secondary to psychosocial stress, is thought to interfere with the processing of biologically important events (such as reward prediction errors) and result in the aberrant attribution of salience to irrelevant sensory stimuli and internal representations. Bayesian hierarchical predictive coding offers a promising framework for the identification of dysfunctional neurocomputational processes and the development of a mechanistic understanding of psychotic experience. According to this framework, mismatches between prior beliefs encoded at higher levels of the cortical hierarchy and lower-level (sensory) information can also be thought of as PEs, with important consequences for belief updating. Low levels of precision in the representation of prior beliefs relative to sensory data, as well as dysfunctional interactions between prior beliefs and sensory data in an ever-changing environment, have been suggested as a general mechanism underlying psychotic experiences. Translating the promise of the Bayesian hierarchical predictive coding into patient benefit will come from integrating this framework with existing knowledge of the etiology and pathophysiology of psychosis, especially regarding hippocampal-prefrontal-striatal network function and neural mechanisms of information processing and belief updating.
dc.format.mediumPrint
dc.identifier.doi10.17863/CAM.34082
dc.identifier.eissn1745-1701
dc.identifier.issn0586-7614
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/286775
dc.languageeng
dc.language.isoeng
dc.publisherOxford University Press (OUP)
dc.publisher.urlhttp://dx.doi.org/10.1093/schbul/sby154
dc.subjectcomputational modeling
dc.subjectdelusions
dc.subjectdopamine
dc.subjecthallucinations
dc.subjectprediction error
dc.subjectreward
dc.subjectschizophrenia
dc.subjectBayes Theorem
dc.subjectBrain
dc.subjectCognition
dc.subjectCognitive Neuroscience
dc.subjectCorpus Striatum
dc.subjectDelusions
dc.subjectDopamine
dc.subjectHippocampus
dc.subjectHumans
dc.subjectLearning
dc.subjectLimbic Lobe
dc.subjectModels, Neurological
dc.subjectModels, Psychological
dc.subjectNeural Pathways
dc.subjectNeurophysiology
dc.subjectPrefrontal Cortex
dc.subjectPsychotic Disorders
dc.subjectReinforcement, Psychology
dc.subjectReward
dc.subjectSchizophrenia
dc.subjectSchizophrenic Psychology
dc.subjectSynaptic Transmission
dc.subjectTemporal Lobe
dc.titleTowards a Unifying Cognitive, Neurophysiological, and Computational Neuroscience Account of Schizophrenia.
dc.typeArticle
prism.endingPage1100
prism.issueIdentifier5
prism.publicationDate2019
prism.publicationNameSchizophr Bull
prism.startingPage1092
prism.volume45
rioxxterms.licenseref.startdate2019-09
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.typeJournal Article/Review
rioxxterms.versionAM
rioxxterms.versionofrecord10.1093/schbul/sby154

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
Heinz-et-al_SchizBull_MS_R2_20180924.doc
Size:
561 KB
Format:
Microsoft Word
Description:
Accepted version
Licence
http://www.rioxx.net/licenses/all-rights-reserved
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
DepositLicenceAgreementv2.1.pdf
Size:
150.9 KB
Format:
Adobe Portable Document Format
Download

Collections

Cambridge University Research Outputs