Genetic Triple Dissociation Reveals Multiple Roles for Dopamine in Reinforcement Learning
What are the genetic and neural components that support adaptive learning from positive and negative outcomes? Here, we show with genetic analyses that three independent dopaminergic mechanisms contribute to reward and avoidance learning in humans. A polymorphism in the DARPP-32 gene, associated wit...
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Veröffentlicht in: | Proceedings of the National Academy of Sciences - PNAS 2007-10, Vol.104 (41), p.16311-16316 |
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description | What are the genetic and neural components that support adaptive learning from positive and negative outcomes? Here, we show with genetic analyses that three independent dopaminergic mechanisms contribute to reward and avoidance learning in humans. A polymorphism in the DARPP-32 gene, associated with striatal dopamine function, predicted relatively better probabilistic reward learning. Conversely, the C957T polymorphism of the DRD2 gene, associated with striatal D2 receptor function, predicted the degree to which participants learned to avoid choices that had been probabilistically associated with negative outcomes. The Val/Met polymorphism of the COMT gene, associated with prefrontal cortical dopamine function, predicted participants' ability to rapidly adapt behavior on a trial-to-trial basis. These findings support a neurocomputational dissociation between striatal and prefrontal dopaminergic mechanisms in reinforcement learning. Computational maximum likelihood analyses reveal independent gene effects on three reinforcement learning parameters that can explain the observed dissociations. |
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Here, we show with genetic analyses that three independent dopaminergic mechanisms contribute to reward and avoidance learning in humans. A polymorphism in the DARPP-32 gene, associated with striatal dopamine function, predicted relatively better probabilistic reward learning. Conversely, the C957T polymorphism of the DRD2 gene, associated with striatal D2 receptor function, predicted the degree to which participants learned to avoid choices that had been probabilistically associated with negative outcomes. The Val/Met polymorphism of the COMT gene, associated with prefrontal cortical dopamine function, predicted participants' ability to rapidly adapt behavior on a trial-to-trial basis. These findings support a neurocomputational dissociation between striatal and prefrontal dopaminergic mechanisms in reinforcement learning. Computational maximum likelihood analyses reveal independent gene effects on three reinforcement learning parameters that can explain the observed dissociations.</description><identifier>ISSN: 0027-8424</identifier><identifier>EISSN: 1091-6490</identifier><identifier>DOI: 10.1073/pnas.0706111104</identifier><identifier>PMID: 17913879</identifier><language>eng</language><publisher>United States: National Academy of Sciences</publisher><subject>Adolescent ; Adult ; Algorithms ; Biological Sciences ; Brain ; Brain - physiology ; Catechol O-Methyltransferase - genetics ; Dopamine - genetics ; Dopamine - physiology ; Dopamine and cAMP-Regulated Phosphoprotein 32 - genetics ; Female ; Genetics ; Genetics, Behavioral ; Genotypes ; Human genetics ; Humans ; Learning ; Learning modules ; Learning rate ; Male ; Maximum likelihood method ; Medical genetics ; Models, Genetic ; Models, Psychological ; Negative feedback ; Neurons ; Neurotransmitters ; Polymorphism ; Polymorphism, Genetic ; Positive feedback ; Receptors ; Receptors, Dopamine D2 - genetics ; Reinforcement (Psychology) ; Social Sciences ; Working memory</subject><ispartof>Proceedings of the National Academy of Sciences - PNAS, 2007-10, Vol.104 (41), p.16311-16316</ispartof><rights>Copyright 2007 The National Academy of Sciences of the United States of America</rights><rights>Copyright National Academy of Sciences Oct 9, 2007</rights><rights>2007 by The National Academy of Sciences of the USA 2007</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c623t-dd5929678cdff1e9a9ab22879d835cb91b4c5f757d12ee780c3ef668bbe036073</citedby><cites>FETCH-LOGICAL-c623t-dd5929678cdff1e9a9ab22879d835cb91b4c5f757d12ee780c3ef668bbe036073</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Uhttp://www.pnas.org/content/104/41.cover.gif</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/25449311$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/25449311$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>230,314,723,776,780,799,881,27903,27904,53769,53771,57995,58228</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/17913879$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Frank, Michael J.</creatorcontrib><creatorcontrib>Moustafa, Ahmed A.</creatorcontrib><creatorcontrib>Haughey, Heather M.</creatorcontrib><creatorcontrib>Curran, Tim</creatorcontrib><creatorcontrib>Hutchison, Kent E.</creatorcontrib><title>Genetic Triple Dissociation Reveals Multiple Roles for Dopamine in Reinforcement Learning</title><title>Proceedings of the National Academy of Sciences - PNAS</title><addtitle>Proc Natl Acad Sci U S A</addtitle><description>What are the genetic and neural components that support adaptive learning from positive and negative outcomes? Here, we show with genetic analyses that three independent dopaminergic mechanisms contribute to reward and avoidance learning in humans. A polymorphism in the DARPP-32 gene, associated with striatal dopamine function, predicted relatively better probabilistic reward learning. Conversely, the C957T polymorphism of the DRD2 gene, associated with striatal D2 receptor function, predicted the degree to which participants learned to avoid choices that had been probabilistically associated with negative outcomes. The Val/Met polymorphism of the COMT gene, associated with prefrontal cortical dopamine function, predicted participants' ability to rapidly adapt behavior on a trial-to-trial basis. These findings support a neurocomputational dissociation between striatal and prefrontal dopaminergic mechanisms in reinforcement learning. Computational maximum likelihood analyses reveal independent gene effects on three reinforcement learning parameters that can explain the observed dissociations.</description><subject>Adolescent</subject><subject>Adult</subject><subject>Algorithms</subject><subject>Biological Sciences</subject><subject>Brain</subject><subject>Brain - physiology</subject><subject>Catechol O-Methyltransferase - genetics</subject><subject>Dopamine - genetics</subject><subject>Dopamine - physiology</subject><subject>Dopamine and cAMP-Regulated Phosphoprotein 32 - genetics</subject><subject>Female</subject><subject>Genetics</subject><subject>Genetics, Behavioral</subject><subject>Genotypes</subject><subject>Human genetics</subject><subject>Humans</subject><subject>Learning</subject><subject>Learning modules</subject><subject>Learning rate</subject><subject>Male</subject><subject>Maximum likelihood method</subject><subject>Medical genetics</subject><subject>Models, Genetic</subject><subject>Models, Psychological</subject><subject>Negative feedback</subject><subject>Neurons</subject><subject>Neurotransmitters</subject><subject>Polymorphism</subject><subject>Polymorphism, Genetic</subject><subject>Positive feedback</subject><subject>Receptors</subject><subject>Receptors, Dopamine D2 - genetics</subject><subject>Reinforcement (Psychology)</subject><subject>Social Sciences</subject><subject>Working memory</subject><issn>0027-8424</issn><issn>1091-6490</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2007</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqF0sFu1DAQBmALgehSOHMCRRyQOKQdO05sX5BQCwVpEVJVDpwsx5kUrxJ7sZMK3h6nu-oCB5pLpMw3v-zJEPKcwgkFUZ1uvUknIKCh-QH-gKwoKFo2XMFDsgJgopSc8SPyJKUNAKhawmNyRIWilRRqRb5doMfJ2eIquu2AxblLKVhnJhd8cYk3aIZUfJ6H6bZ6GQZMRR9icR62ZnQeC7cw5_M3iyP6qVijid7566fkUZ-b8dn-fUy-fnh_dfaxXH-5-HT2bl3ahlVT2XW1YqoR0nZ9T1EZZVrG8tk6WdW2VbTltu5FLTrKEIUEW2HfNLJtEaomz-CYvN3lbud2xM7mM0Qz6G10o4m_dDBO_13x7ru-DjeaAWcMqhzweh8Qw48Z06RHlywOg_EY5qQbyaHK87oX5kCQlNYZvvoHbsIcfZ5CNrSqBb1N-y-iAFxmdLpDNoaUIvZ396Kglw3Qywbowwbkjpd_juPg9788gzd7sHQe4rjmVNOmolT38zBM-HPKtrjHZvJiRzZpCvHOsJpztdR_A9vEzpU</recordid><startdate>20071009</startdate><enddate>20071009</enddate><creator>Frank, Michael J.</creator><creator>Moustafa, Ahmed A.</creator><creator>Haughey, Heather M.</creator><creator>Curran, Tim</creator><creator>Hutchison, Kent E.</creator><general>National Academy of Sciences</general><general>National Acad Sciences</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QG</scope><scope>7QL</scope><scope>7QP</scope><scope>7QR</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TK</scope><scope>7TM</scope><scope>7TO</scope><scope>7U9</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>H94</scope><scope>M7N</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20071009</creationdate><title>Genetic Triple Dissociation Reveals Multiple Roles for Dopamine in Reinforcement Learning</title><author>Frank, Michael J. ; Moustafa, Ahmed A. ; Haughey, Heather M. ; Curran, Tim ; Hutchison, Kent E.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c623t-dd5929678cdff1e9a9ab22879d835cb91b4c5f757d12ee780c3ef668bbe036073</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2007</creationdate><topic>Adolescent</topic><topic>Adult</topic><topic>Algorithms</topic><topic>Biological Sciences</topic><topic>Brain</topic><topic>Brain - physiology</topic><topic>Catechol O-Methyltransferase - genetics</topic><topic>Dopamine - genetics</topic><topic>Dopamine - physiology</topic><topic>Dopamine and cAMP-Regulated Phosphoprotein 32 - genetics</topic><topic>Female</topic><topic>Genetics</topic><topic>Genetics, Behavioral</topic><topic>Genotypes</topic><topic>Human genetics</topic><topic>Humans</topic><topic>Learning</topic><topic>Learning modules</topic><topic>Learning rate</topic><topic>Male</topic><topic>Maximum likelihood method</topic><topic>Medical genetics</topic><topic>Models, Genetic</topic><topic>Models, Psychological</topic><topic>Negative feedback</topic><topic>Neurons</topic><topic>Neurotransmitters</topic><topic>Polymorphism</topic><topic>Polymorphism, Genetic</topic><topic>Positive feedback</topic><topic>Receptors</topic><topic>Receptors, Dopamine D2 - genetics</topic><topic>Reinforcement (Psychology)</topic><topic>Social Sciences</topic><topic>Working memory</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Frank, Michael J.</creatorcontrib><creatorcontrib>Moustafa, Ahmed A.</creatorcontrib><creatorcontrib>Haughey, Heather M.</creatorcontrib><creatorcontrib>Curran, Tim</creatorcontrib><creatorcontrib>Hutchison, Kent E.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Oncogenes and Growth Factors Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Proceedings of the National Academy of Sciences - PNAS</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Frank, Michael J.</au><au>Moustafa, Ahmed A.</au><au>Haughey, Heather M.</au><au>Curran, Tim</au><au>Hutchison, Kent E.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Genetic Triple Dissociation Reveals Multiple Roles for Dopamine in Reinforcement Learning</atitle><jtitle>Proceedings of the National Academy of Sciences - PNAS</jtitle><addtitle>Proc Natl Acad Sci U S A</addtitle><date>2007-10-09</date><risdate>2007</risdate><volume>104</volume><issue>41</issue><spage>16311</spage><epage>16316</epage><pages>16311-16316</pages><issn>0027-8424</issn><eissn>1091-6490</eissn><abstract>What are the genetic and neural components that support adaptive learning from positive and negative outcomes? Here, we show with genetic analyses that three independent dopaminergic mechanisms contribute to reward and avoidance learning in humans. A polymorphism in the DARPP-32 gene, associated with striatal dopamine function, predicted relatively better probabilistic reward learning. Conversely, the C957T polymorphism of the DRD2 gene, associated with striatal D2 receptor function, predicted the degree to which participants learned to avoid choices that had been probabilistically associated with negative outcomes. The Val/Met polymorphism of the COMT gene, associated with prefrontal cortical dopamine function, predicted participants' ability to rapidly adapt behavior on a trial-to-trial basis. These findings support a neurocomputational dissociation between striatal and prefrontal dopaminergic mechanisms in reinforcement learning. Computational maximum likelihood analyses reveal independent gene effects on three reinforcement learning parameters that can explain the observed dissociations.</abstract><cop>United States</cop><pub>National Academy of Sciences</pub><pmid>17913879</pmid><doi>10.1073/pnas.0706111104</doi><tpages>6</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Adolescent Adult Algorithms Biological Sciences Brain Brain - physiology Catechol O-Methyltransferase - genetics Dopamine - genetics Dopamine - physiology Dopamine and cAMP-Regulated Phosphoprotein 32 - genetics Female Genetics Genetics, Behavioral Genotypes Human genetics Humans Learning Learning modules Learning rate Male Maximum likelihood method Medical genetics Models, Genetic Models, Psychological Negative feedback Neurons Neurotransmitters Polymorphism Polymorphism, Genetic Positive feedback Receptors Receptors, Dopamine D2 - genetics Reinforcement (Psychology) Social Sciences Working memory |
title | Genetic Triple Dissociation Reveals Multiple Roles for Dopamine in Reinforcement Learning |
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