Towards a Unifying Cognitive, Neurophysiological, and Computational Neuroscience Account of Schizophrenia.
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Authors
Heinz, Andreas
Murray, Graham K
Schlagenhauf, Florian
Sterzer, Philipp
Grace, Anthony A
Waltz, James A
Publication Date
2019-09-11Journal Title
Schizophr Bull
ISSN
0586-7614
Publisher
Oxford University Press (OUP)
Volume
45
Issue
5
Pages
1092-1100
Language
eng
Type
Article
This Version
AM
Physical Medium
Print
Metadata
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Heinz, A., Murray, G. K., Schlagenhauf, F., Sterzer, P., Grace, A. A., & Waltz, J. A. (2019). Towards a Unifying Cognitive, Neurophysiological, and Computational Neuroscience Account of Schizophrenia.. Schizophr Bull, 45 (5), 1092-1100. https://doi.org/10.1093/schbul/sby154
Abstract
Psychotic 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.
Keywords
Brain, Hippocampus, Corpus Striatum, Prefrontal Cortex, Temporal Lobe, Neural Pathways, Humans, Dopamine, Bayes Theorem, Delusions, Cognition, Learning, Reward, Psychotic Disorders, Schizophrenia, Schizophrenic Psychology, Neurophysiology, Synaptic Transmission, Models, Neurological, Models, Psychological, Limbic Lobe, Cognitive Neuroscience, Reinforcement, Psychology
Identifiers
External DOI: https://doi.org/10.1093/schbul/sby154
This record's URL: https://www.repository.cam.ac.uk/handle/1810/286775
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