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Neural mechanisms regulating different forms of risk-related decision-making: Insights from animal models
Open accessAbstract
Over the past 20 years there has been a growing interest in the neural underpinnings of cost/benefit decision-making. Recent studies with animal models have made considerable advances in our understanding of how different prefrontal, striatal, limbic and monoaminergic circuits interact to promote efficient risk/reward decision-making, and how dysfunction in these circuits underlies aberrant decision-making observed in numerous psychiatric disorders. This review will highlight recent findings from studies exploring these questions using a variety of behavioral assays, as well as molecular, pharmacological, neurophysiological, and translational approaches. We begin with a discussion of how neural systems related to decision subcomponents may interact to generate more complex decisions involving risk and uncertainty. This is followed by an overview of interactions between prefrontal-amygdala-dopamine and habenular circuits in regulating choice between certain and uncertain rewards and how different modes of dopamine transmission may contribute to these processes. These data will be compared with results from other studies investigating the contribution of some of these systems to guiding decision-making related to rewards vs. punishment. Lastly, we provide a brief summary of impairments in risk-related decision-making associated with psychiatric disorders, highlighting recent translational studies in laboratory animals.
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“DA neurotransmission is essential for diverse behavioral outputs, including motivation, cognition, reward learning, decision making, salience attribution, and voluntary motor control [121–127].”
From Adolescent nicotine exposure and persistent neurocircuitry changes: unveiling lifelong psychiatric risks (2025) · cites this paper as [124]
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“The comparison of apparent risks or costs to possible reward is a computation performed by circuits supporting motivated behavior, critically including the NAc and the mesocorti-colimbic dopamine system (reviewed in depth elsewhere123-125).”
From Resolving the neural circuits of anxiety (2015) · cites this paper as [124]
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Metadata
- Authors
- Orsini, C. A., Moorman, D. E., Young, J. W., Setlow, B., & Floresco, S. B.
- Affiliations
- University of Florida Health (US)
- University of Massachusetts Amherst (US)
- University of California San Diego (US)
- University of British Columbia (CA)
- Corresponding author
- Floresco, Stan B.
- Venue
- Neuroscience & Biobehavioral Reviews
- Publisher
- Elsevier BV
- Published
- 2015-11
- Volume
- 58
- Pages
- 147-167
- ISSN
- 0149-7634 (ISSN-L)
- PMC
- PMC7913606
- OpenAlex
- W2214549810
- Cited by
- 153
- References
- 267
- Citations by year
- 2026: 6 · 2025: 10 · 2024: 25 · 2023: 10 · 2022: 9 · 2021: 9 · 2020: 13 · 2019: 11 · 2018: 15 · 2017: 21 · 2016: 17 · 2015: 7
- FWCI
- 1.99
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Neuroscience & Biobehavioral Reviews (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- McKnight Brain Institute
- National Institutes of Health (R21-DA032005, R01-DA024671, R01-MH104344)
- Canadian Institutes of Health Research (MOP 89861)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Keywords
- cost-benefit decision making, reward uncertainty, habenula, dopamine signaling, striatal circuits, limbic circuits, mental illness, animal models
- MeSH terms
- Animals, Brain, Decision Making, Humans, Risk-Taking, Translational Research, Biomedical
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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