English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Reinforcement learning and dopamine in schizophrenia: Dimensions of symptoms or specific features of a disease group?

Deserno, L., Boehme, R., Heinz, A., & Schlagenhauf, F. (2013). Reinforcement learning and dopamine in schizophrenia: Dimensions of symptoms or specific features of a disease group? Frontiers in Psychiatry, 4: 172. doi:10.3389/fpsyt.2013.00172.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0014-72D1-D Version Permalink: http://hdl.handle.net/21.11116/0000-0003-86DA-0
Genre: Journal Article

Files

show Files
hide Files
:
Deserno_Reinforcement.pdf (Publisher version), 395KB
Name:
Deserno_Reinforcement.pdf
Description:
-
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
-
License:
-

Locators

show

Creators

show
hide
 Creators:
Deserno, Lorenz1, 2, Author              
Boehme, Rebecca2, Author
Heinz, Andreas2, Author
Schlagenhauf, Florian1, 2, Author              
Affiliations:
1Max Planck Fellow Group Cognitive and Affective Control of Behavioural Adaptation, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_1753350              
2Department of Psychiatry and Psychotherapy, Charité University Medicine Berlin, Germany, ou_persistent22              

Content

show
hide
Free keywords: Schizophrenia; Dopamine; Computational modeling; Reinforcement learning; Aberrant salience; Prediction error; fMRI; PET imaging
 Abstract: Abnormalities in reinforcement learning are a key finding in schizophrenia and have been proposed to be linked to elevated levels of dopamine neurotransmission. Behavioral deficits in reinforcement learning and their neural correlates may contribute to the formation of clinical characteristics of schizophrenia. The ability to form predictions about future outcomes is fundamental for environmental interactions and depends on neuronal teaching signals, like reward prediction errors. While aberrant prediction errors, that encode non-salient events as surprising, have been proposed to contribute to the formation of positive symptoms, a failure to build neural representations of decision values may result in negative symptoms. Here, we review behavioral and neuroimaging research in schizophrenia and focus on studies that implemented reinforcement learning models. In addition, we discuss studies that combined reinforcement learning with measures of dopamine. Thereby, we suggest how reinforcement learning abnormalities in schizophrenia may contribute to the formation of psychotic symptoms and may interact with cognitive deficits. These ideas point toward an interplay of more rigid versus flexible control over reinforcement learning. Pronounced deficits in the flexible or model-based domain may allow for a detailed characterization of well-established cognitive deficits in schizophrenia patients based on computational models of learning. Finally, we propose a framework based on the potentially crucial contribution of dopamine to dysfunctional reinforcement learning on the level of neural networks. Future research may strongly benefit from computational modeling but also requires further methodological improvement for clinical group studies. These research tools may help to improve our understanding of disease-specific mechanisms and may help to identify clinically relevant subgroups of the heterogeneous entity schizophrenia.

Details

show
hide
Language(s): eng - English
 Dates: 2013-09-102013-12-072013-12-23
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.3389/fpsyt.2013.00172
PMID: 24391603
PMC: PMC3870301
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Frontiers in Psychiatry
  Abbreviation : Front Psychiatry
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Lausanne, Switzerland : Frontiers Research Foundation
Pages: - Volume / Issue: 4 Sequence Number: 172 Start / End Page: - Identifier: Other: 1664-0640
CoNE: https://pure.mpg.de/cone/journals/resource/16640640