An association between prediction errors and risk-seeking: Theory and behavioral evidence.
Reward prediction errors (RPEs) and risk preferences have two things in common: both can shape decision making behavior, and both are commonly associated with dopamine. RPEs drive value learning and are thought to be represented in the phasic release of striatal dopamine. Risk preferences bias choic...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2021-07-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1009213&type=printable |
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| author | Moritz Moeller Jan Grohn Sanjay Manohar Rafal Bogacz |
| author_facet | Moritz Moeller Jan Grohn Sanjay Manohar Rafal Bogacz |
| author_sort | Moritz Moeller |
| collection | DOAJ |
| description | Reward prediction errors (RPEs) and risk preferences have two things in common: both can shape decision making behavior, and both are commonly associated with dopamine. RPEs drive value learning and are thought to be represented in the phasic release of striatal dopamine. Risk preferences bias choices towards or away from uncertainty; they can be manipulated with drugs that target the dopaminergic system. Based on the common neural substrate, we hypothesize that RPEs and risk preferences are linked on the level of behavior as well. Here, we develop this hypothesis theoretically and test it empirically. First, we apply a recent theory of learning in the basal ganglia to predict how RPEs influence risk preferences. We find that positive RPEs should cause increased risk-seeking, while negative RPEs should cause risk-aversion. We then test our behavioral predictions using a novel bandit task in which value and risk vary independently across options. Critically, conditions are included where options vary in risk but are matched for value. We find that our prediction was correct: participants become more risk-seeking if choices are preceded by positive RPEs, and more risk-averse if choices are preceded by negative RPEs. These findings cannot be explained by other known effects, such as nonlinear utility curves or dynamic learning rates. |
| format | Article |
| id | doaj-art-afd61b9f51544179b7133edf6fc32e13 |
| institution | Kabale University |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2021-07-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-afd61b9f51544179b7133edf6fc32e132025-08-20T03:25:16ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582021-07-01177e100921310.1371/journal.pcbi.1009213An association between prediction errors and risk-seeking: Theory and behavioral evidence.Moritz MoellerJan GrohnSanjay ManoharRafal BogaczReward prediction errors (RPEs) and risk preferences have two things in common: both can shape decision making behavior, and both are commonly associated with dopamine. RPEs drive value learning and are thought to be represented in the phasic release of striatal dopamine. Risk preferences bias choices towards or away from uncertainty; they can be manipulated with drugs that target the dopaminergic system. Based on the common neural substrate, we hypothesize that RPEs and risk preferences are linked on the level of behavior as well. Here, we develop this hypothesis theoretically and test it empirically. First, we apply a recent theory of learning in the basal ganglia to predict how RPEs influence risk preferences. We find that positive RPEs should cause increased risk-seeking, while negative RPEs should cause risk-aversion. We then test our behavioral predictions using a novel bandit task in which value and risk vary independently across options. Critically, conditions are included where options vary in risk but are matched for value. We find that our prediction was correct: participants become more risk-seeking if choices are preceded by positive RPEs, and more risk-averse if choices are preceded by negative RPEs. These findings cannot be explained by other known effects, such as nonlinear utility curves or dynamic learning rates.https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1009213&type=printable |
| spellingShingle | Moritz Moeller Jan Grohn Sanjay Manohar Rafal Bogacz An association between prediction errors and risk-seeking: Theory and behavioral evidence. PLoS Computational Biology |
| title | An association between prediction errors and risk-seeking: Theory and behavioral evidence. |
| title_full | An association between prediction errors and risk-seeking: Theory and behavioral evidence. |
| title_fullStr | An association between prediction errors and risk-seeking: Theory and behavioral evidence. |
| title_full_unstemmed | An association between prediction errors and risk-seeking: Theory and behavioral evidence. |
| title_short | An association between prediction errors and risk-seeking: Theory and behavioral evidence. |
| title_sort | association between prediction errors and risk seeking theory and behavioral evidence |
| url | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1009213&type=printable |
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