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dc.contributor.authorHeinz, Andreas
dc.contributor.authorMurray, Graham
dc.contributor.authorSchlagenhauf, Florian
dc.contributor.authorSterzer, Philipp
dc.contributor.authorGrace, Anthony A
dc.contributor.authorWaltz, James A
dc.date.accessioned2018-12-13T00:30:41Z
dc.date.available2018-12-13T00:30:41Z
dc.date.issued2019-09-11
dc.identifier.issn0586-7614
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/286775
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.languageeng
dc.publisherOxford University Press (OUP)
dc.subjectBrain
dc.subjectHippocampus
dc.subjectCorpus Striatum
dc.subjectPrefrontal Cortex
dc.subjectTemporal Lobe
dc.subjectNeural Pathways
dc.subjectHumans
dc.subjectDopamine
dc.subjectBayes Theorem
dc.subjectDelusions
dc.subjectCognition
dc.subjectLearning
dc.subjectReward
dc.subjectPsychotic Disorders
dc.subjectSchizophrenia
dc.subjectSchizophrenic Psychology
dc.subjectNeurophysiology
dc.subjectSynaptic Transmission
dc.subjectModels, Neurological
dc.subjectModels, Psychological
dc.subjectLimbic Lobe
dc.subjectCognitive Neuroscience
dc.subjectReinforcement, Psychology
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
dc.identifier.doi10.17863/CAM.34082
rioxxterms.versionofrecord10.1093/schbul/sby154
rioxxterms.versionAM
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2019-09
dc.contributor.orcidMurray, Graham [0000-0001-8296-1742]
dc.identifier.eissn1745-1701
rioxxterms.typeJournal Article/Review
cam.issuedOnline2018-11-02
rioxxterms.freetoread.startdate2019-11-02


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