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An association between prediction errors and risk-seeking: Theory and behavioral evidence
- Source :
- PLoS Computational Biology, PLoS Computational Biology, Vol 17, Iss 7, p e1009213 (2021)
- Publication Year :
- 2021
- Publisher :
- Public Library of Science (PLoS), 2021.
-
Abstract
- 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.<br />Author summary Many of our decisions are based on expectations. Sometimes, however, surprises happen: outcomes are not as expected. Such discrepancies between expectations and actual outcomes are called prediction errors. Our brain recognizes and uses such prediction errors to modify our expectations and make them more realistic—a process known as reinforcement learning. In particular, neurons that release the neurotransmitter dopamine show activity patterns that strongly resemble prediction errors. Interestingly, the same neurotransmitter is also known to regulate risk preferences: dopamine levels control our willingness to take risks. We theorized that, since learning signals cause dopamine release, they might change risk preferences as well. In this study, we test this hypothesis. We find that participants are more likely to make a risky choice just after they experienced an outcome that was better than expected, which is precisely what our theory predicts. This suggests that dopamine signaling can be ambiguous—a learning signal can be mistaken for an impulse to take a risk.
- Subjects :
- Male
Neural substrate
Dopamine
Social Sciences
Biochemistry
Basal Ganglia
Task (project management)
Catecholamines
Learning and Memory
Cognition
0302 clinical medicine
Then test
Medicine and Health Sciences
Learning theory
Psychology
Biology (General)
Amines
Likelihood Functions
0303 health sciences
Ecology
Organic Compounds
Simulation and Modeling
Economics, Behavioral
Uncertainty
Brain
Neurochemistry
Neurotransmitters
Chemistry
Risk-seeking
Computational Theory and Mathematics
Dynamic learning
Modeling and Simulation
Physical Sciences
Female
Anatomy
Neurochemicals
Reinforcement, Psychology
Research Article
Cognitive psychology
Adult
Biogenic Amines
Adolescent
QH301-705.5
Decision Making
Models, Psychological
Research and Analysis Methods
Human Learning
Young Adult
03 medical and health sciences
Cellular and Molecular Neuroscience
Risk-Taking
Reward
Memory
Genetics
Learning
Humans
Computer Simulation
Association (psychology)
Molecular Biology
Ecology, Evolution, Behavior and Systematics
030304 developmental biology
Behavior
Organic Chemistry
Chemical Compounds
Cognitive Psychology
Biology and Life Sciences
Association Learning
Computational Biology
Hormones
Corpus Striatum
Cognitive Science
Dopaminergics
Value (mathematics)
030217 neurology & neurosurgery
Neuroscience
Subjects
Details
- ISSN :
- 15537358
- Volume :
- 17
- Database :
- OpenAIRE
- Journal :
- PLOS Computational Biology
- Accession number :
- edsair.doi.dedup.....937451bb31f6d4fc8cbc230848e750bb