Dopamine transients are sufficient and necessary for acquisition of model-based associations
Learning to predict reward is thought to be driven by dopaminergic prediction errors, which reflect discrepancies between actual and expected value. Here the authors show that learning to predict neutral events is also driven by prediction errors and that such value-neutral associative learning is a...
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| Published in: | Nature neuroscience Vol. 20; no. 5; pp. 735 - 742 |
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| Main Authors: | , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York
Nature Publishing Group US
01.05.2017
Nature Publishing Group |
| Subjects: | |
| ISSN: | 1097-6256, 1546-1726, 1546-1726 |
| Online Access: | Get full text |
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| Abstract | Learning to predict reward is thought to be driven by dopaminergic prediction errors, which reflect discrepancies between actual and expected value. Here the authors show that learning to predict neutral events is also driven by prediction errors and that such value-neutral associative learning is also likely mediated by dopaminergic error signals.
Associative learning is driven by prediction errors. Dopamine transients correlate with these errors, which current interpretations limit to endowing cues with a scalar quantity reflecting the value of future rewards. We tested whether dopamine might act more broadly to support learning of an associative model of the environment. Using sensory preconditioning, we show that prediction errors underlying stimulus–stimulus learning can be blocked behaviorally and reinstated by optogenetically activating dopamine neurons. We further show that suppressing the firing of these neurons across the transition prevents normal stimulus–stimulus learning. These results establish that the acquisition of model-based information about transitions between nonrewarding events is also driven by prediction errors and that, contrary to existing canon, dopamine transients are both sufficient and necessary to support this type of learning. Our findings open new possibilities for how these biological signals might support associative learning in the mammalian brain in these and other contexts. |
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| AbstractList | Associative learning is driven by prediction errors. Dopamine transients correlate with these errors, which current interpretations limit to endowing cues with a scalar quantity reflecting the value of future rewards. We tested whether dopamine might act more broadly to support learning of an associative model of the environment. Using sensory preconditioning, we show that prediction errors underlying stimulus-stimulus learning can be blocked behaviorally and reinstated by optogenetically activating dopamine neurons. We further show that suppressing the firing of these neurons across the transition prevents normal stimulus-stimulus learning. These results establish that the acquisition of model-based information about transitions between nonrewarding events is also driven by prediction errors and that, contrary to existing canon, dopamine transients are both sufficient and necessary to support this type of learning. Our findings open new possibilities for how these biological signals might support associative learning in the mammalian brain in these and other contexts.Associative learning is driven by prediction errors. Dopamine transients correlate with these errors, which current interpretations limit to endowing cues with a scalar quantity reflecting the value of future rewards. We tested whether dopamine might act more broadly to support learning of an associative model of the environment. Using sensory preconditioning, we show that prediction errors underlying stimulus-stimulus learning can be blocked behaviorally and reinstated by optogenetically activating dopamine neurons. We further show that suppressing the firing of these neurons across the transition prevents normal stimulus-stimulus learning. These results establish that the acquisition of model-based information about transitions between nonrewarding events is also driven by prediction errors and that, contrary to existing canon, dopamine transients are both sufficient and necessary to support this type of learning. Our findings open new possibilities for how these biological signals might support associative learning in the mammalian brain in these and other contexts. Associative learning is driven by prediction errors. Dopamine transients correlate with these errors, which current interpretations limit to endowing cues with a scalar quantity reflecting the value of future rewards. We tested whether dopamine might act more broadly to support learning of an associative model of the environment. Using sensory preconditioning, we show that prediction errors underlying stimulus-stimulus learning can be blocked behaviorally and reinstated by optogenetically activating dopamine neurons. We further show that suppressing the firing of these neurons across the transition prevents normal stimulus-stimulus learning. These results establish that the acquisition of model-based information about transitions between nonrewarding events is also driven by prediction errors and that, contrary to existing canon, dopamine transients are both sufficient and necessary to support this type of learning. Our findings open new possibilities for how these biological signals might support associative learning in the mammalian brain in these and other contexts. Learning to predict reward is thought to be driven by dopaminergic prediction errors, which reflect discrepancies between actual and expected value. Here the authors show that learning to predict neutral events is also driven by prediction errors and that such value-neutral associative learning is also likely mediated by dopaminergic error signals. Associative learning is driven by prediction errors. Dopamine transients correlate with these errors, which current interpretations limit to endowing cues with a scalar quantity reflecting the value of future rewards. We tested whether dopamine might act more broadly to support learning of an associative model of the environment. Using sensory preconditioning, we show that prediction errors underlying stimulus–stimulus learning can be blocked behaviorally and reinstated by optogenetically activating dopamine neurons. We further show that suppressing the firing of these neurons across the transition prevents normal stimulus–stimulus learning. These results establish that the acquisition of model-based information about transitions between nonrewarding events is also driven by prediction errors and that, contrary to existing canon, dopamine transients are both sufficient and necessary to support this type of learning. Our findings open new possibilities for how these biological signals might support associative learning in the mammalian brain in these and other contexts. Learning to predict reward is thought to be driven by dopaminergic prediction errors, which reflect discrepancies between actual and expected value. Here the authors show that learning to predict neutral events is also driven by prediction errors and that such value-neutral associative learning is also likely mediated by dopaminergic error signals. Associative learning is driven by prediction errors. Dopamine transients correlate with these errors, which current interpretations limit to endowing cues with a scalar quantity reflecting the value of future rewards. Here, we tested whether dopamine might act more broadly to support learning of an associative model of the environment. Using sensory preconditioning, we show that prediction errors underlying stimulus-stimulus learning can be blocked behaviorally and reinstated by optogenetically activating dopamine neurons. We further show that suppressing the firing of these neurons across t transition prevents normal stimulus-stimulus learning. These results establish that the acquisition of model-based information about transitions between non-rewarding events is also driven by prediction errors, and that contrary to existing canon, dopamine transients are both sufficient and necessary to support this type of learning. Our findings open new possibilities for how these biological signals might support associative learning in the mammalian brain in these and other contexts. |
| Audience | Academic |
| Author | Sharpe, Melissa J Liu, Melissa A Mueller, Lauren E Jones, Joshua L Schoenbaum, Geoffrey Chang, Chun Yun Batchelor, Hannah M Niv, Yael |
| AuthorAffiliation | 1 NIDA Intramural Research Program, Baltimore, MD 21224 4 Solomon H. Snyder Department of Neuroscience, The Johns Hopkins University, Baltimore, MD 21287 3 Departments of Anatomy & Neurobiology and Psychiatry, University of Maryland School of Medicine, Baltimore, MD 21201 2 Department of Psychology and Neuroscience Institute, Princeton University, Princeton, NJ 08544 |
| AuthorAffiliation_xml | – name: 2 Department of Psychology and Neuroscience Institute, Princeton University, Princeton, NJ 08544 – name: 3 Departments of Anatomy & Neurobiology and Psychiatry, University of Maryland School of Medicine, Baltimore, MD 21201 – name: 4 Solomon H. Snyder Department of Neuroscience, The Johns Hopkins University, Baltimore, MD 21287 – name: 1 NIDA Intramural Research Program, Baltimore, MD 21224 |
| Author_xml | – sequence: 1 givenname: Melissa J surname: Sharpe fullname: Sharpe, Melissa J email: melissa.sharpe@nih.gov organization: NIDA Intramural Research Program, Department of Psychology and Neuroscience Institute, Princeton University – sequence: 2 givenname: Chun Yun surname: Chang fullname: Chang, Chun Yun organization: NIDA Intramural Research Program – sequence: 3 givenname: Melissa A surname: Liu fullname: Liu, Melissa A organization: NIDA Intramural Research Program – sequence: 4 givenname: Hannah M surname: Batchelor fullname: Batchelor, Hannah M organization: NIDA Intramural Research Program – sequence: 5 givenname: Lauren E surname: Mueller fullname: Mueller, Lauren E organization: NIDA Intramural Research Program – sequence: 6 givenname: Joshua L surname: Jones fullname: Jones, Joshua L organization: NIDA Intramural Research Program – sequence: 7 givenname: Yael orcidid: 0000-0002-0259-8371 surname: Niv fullname: Niv, Yael organization: Department of Psychology and Neuroscience Institute, Princeton University – sequence: 8 givenname: Geoffrey orcidid: 0000-0001-8180-0701 surname: Schoenbaum fullname: Schoenbaum, Geoffrey email: geoffrey.schoenbaum@nih.gov organization: NIDA Intramural Research Program, Departments of Anatomy and of Neurobiology and Psychiatry, University of Maryland School of Medicine, Solomon H. Snyder Department of Neuroscience, The Johns Hopkins University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/28368385$$D View this record in MEDLINE/PubMed |
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| Copyright | Springer Nature America, Inc. 2017 COPYRIGHT 2017 Nature Publishing Group Copyright Nature Publishing Group May 2017 |
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| Snippet | Learning to predict reward is thought to be driven by dopaminergic prediction errors, which reflect discrepancies between actual and expected value. Here the... Associative learning is driven by prediction errors. Dopamine transients correlate with these errors, which current interpretations limit to endowing cues with... |
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| SubjectTerms | 631/378/116 631/378/1595 Animal Genetics and Genomics Animals Animals, Genetically Modified Association Learning - physiology Behavior Behavioral Sciences Biological Techniques Biomedicine Conditioning, Psychological Cues Dopamine Dopaminergic Neurons - physiology Female Health aspects Male Neurobiology Neurons Neurosciences Physiological aspects Psychological aspects Rats Reward Rewards (Psychology) Ventral Tegmental Area - physiology |
| Title | Dopamine transients are sufficient and necessary for acquisition of model-based associations |
| URI | https://link.springer.com/article/10.1038/nn.4538 https://www.ncbi.nlm.nih.gov/pubmed/28368385 https://www.proquest.com/docview/1892271081 https://www.proquest.com/docview/1883843813 https://www.proquest.com/docview/1897378078 https://pubmed.ncbi.nlm.nih.gov/PMC5413864 |
| Volume | 20 |
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