Cargando…
Reinforcement Learning and Dopamine in Schizophrenia: Dimensions of Symptoms or Specific Features of a Disease Group?
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 sc...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2013
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3870301/ https://www.ncbi.nlm.nih.gov/pubmed/24391603 http://dx.doi.org/10.3389/fpsyt.2013.00172 |
_version_ | 1782296690110234624 |
---|---|
author | Deserno, Lorenz Boehme, Rebecca Heinz, Andreas Schlagenhauf, Florian |
author_facet | Deserno, Lorenz Boehme, Rebecca Heinz, Andreas Schlagenhauf, Florian |
author_sort | Deserno, Lorenz |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-3870301 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-38703012014-01-03 Reinforcement Learning and Dopamine in Schizophrenia: Dimensions of Symptoms or Specific Features of a Disease Group? Deserno, Lorenz Boehme, Rebecca Heinz, Andreas Schlagenhauf, Florian Front Psychiatry Psychiatry 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. Frontiers Media S.A. 2013-12-23 /pmc/articles/PMC3870301/ /pubmed/24391603 http://dx.doi.org/10.3389/fpsyt.2013.00172 Text en Copyright © 2013 Deserno, Boehme, Heinz and Schlagenhauf. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Psychiatry Deserno, Lorenz Boehme, Rebecca Heinz, Andreas Schlagenhauf, Florian Reinforcement Learning and Dopamine in Schizophrenia: Dimensions of Symptoms or Specific Features of a Disease Group? |
title | Reinforcement Learning and Dopamine in Schizophrenia: Dimensions of Symptoms or Specific Features of a Disease Group? |
title_full | Reinforcement Learning and Dopamine in Schizophrenia: Dimensions of Symptoms or Specific Features of a Disease Group? |
title_fullStr | Reinforcement Learning and Dopamine in Schizophrenia: Dimensions of Symptoms or Specific Features of a Disease Group? |
title_full_unstemmed | Reinforcement Learning and Dopamine in Schizophrenia: Dimensions of Symptoms or Specific Features of a Disease Group? |
title_short | Reinforcement Learning and Dopamine in Schizophrenia: Dimensions of Symptoms or Specific Features of a Disease Group? |
title_sort | reinforcement learning and dopamine in schizophrenia: dimensions of symptoms or specific features of a disease group? |
topic | Psychiatry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3870301/ https://www.ncbi.nlm.nih.gov/pubmed/24391603 http://dx.doi.org/10.3389/fpsyt.2013.00172 |
work_keys_str_mv | AT desernolorenz reinforcementlearninganddopamineinschizophreniadimensionsofsymptomsorspecificfeaturesofadiseasegroup AT boehmerebecca reinforcementlearninganddopamineinschizophreniadimensionsofsymptomsorspecificfeaturesofadiseasegroup AT heinzandreas reinforcementlearninganddopamineinschizophreniadimensionsofsymptomsorspecificfeaturesofadiseasegroup AT schlagenhaufflorian reinforcementlearninganddopamineinschizophreniadimensionsofsymptomsorspecificfeaturesofadiseasegroup |