Neural computations associated with goal-directed choice
Abstract
In goal-directed decision-making, animals choose between actions that are associated with different reward outcomes (e.g., foods) and with different costs (e.g., effort). Rapid advances have been made over the past few years in our understanding of the computations associated with goal-directed choices, and of how those computations are implemented in the brain. We review some important findings, with an emphasis on computational models, human fMRI, and monkey neurophysiology studies.
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The periaqueductal gray sends monosynaptic reciprocal projections to ventromedial prefrontal cortex and to anterior mid-cingulate cortex, orbitofrontal cortex, and other areas involved in action value and defensive behavior.
“It also sends monosynaptic, reciprocal projections to the vmPFC—which is essential for value updating in the RL framework and likely for behavioral choice as well31—and to aMCC32, OFC and other areas involved in determining action value and coordinating defensive behavior18,33.”
From Representation of aversive prediction errors in the human periaqueductal gray (2014) · cites this paper as [31]
Ventromedial prefrontal cortex (VMPFC) plays a critical role in the computation of value both at decision and outcome.
“the ventromedial prefrontal cortex (VMPFC) plays a critical role in the computation of value both at decision and outcome (Rangel and Hare, 2010;Padoa-Schioppa, 2011;Levy and Glimcher, 2012).”
From Informatic parcellation of the network involved in the computation of subjective value (2014) · cites this paper as Rangel and Hare, 2010
Prior work has established that the ventromedial prefrontal cortex (vmPFC) plays a central role in computing the value of potential choice options, with activity in this region reflecting the dynamic evolution of a value comparison.
“Prior work has established that the ventromedial prefrontal cortex (vmPFC) plays a central role in computing the value of potential choice options1-5, with activity in this region reflecting the dynamic evolution of a value comparison6.”
From Confidence in value-based choice (2013) · cites this paper as [1]
The striatum signals not only the presence or absence of a reward, but also the value of the reward.
“That is, the striatum (along with other components of the reward system such as ventromedial prefrontal cortex discussed elsewhere 31) signals not only the presence or absence of a reward, but also the value of the reward.”
From The social brain and reward: Social information processing in the human striatum (2014) · cites this paper as [31]
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Neural correlates of mindfulness meditation-related anxiety relief
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Pain, decisions, and actions: a motivational perspective
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Assessment of Eating Behavior Disturbance and Associated Neural Networks in Frontotemporal Dementia
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- Authors
- Rangel, A., & Hare, T.
- Affiliations
- California Institute of Technology (US)
- Corresponding author
- Rangel, Antonio
- Venue
- Current Opinion in Neurobiology
- Publisher
- Elsevier BV
- Published
- 2010-04
- Volume
- 20
- Issue
- 2
- Pages
- 262-270
- ISSN
- 0959-4388 (ISSN-L)
- OpenAlex
- W2008709921
- Cited by
- 532
- References
- 80
- Citations by year
- 2026: 11 · 2025: 15 · 2024: 19 · 2023: 18 · 2022: 29 · 2021: 30 · 2020: 33 · 2019: 30 · 2018: 40 · 2017: 42 · 2016: 39 · 2015: 44 · 2014: 47 · 2013: 53 · 2012: 39
- FWCI
- 3.11
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Current Opinion in Neurobiology (publishedVersion)
- PubMed (publishedVersion)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion)
- Funders
- National Science Foundation
- Gordon and Betty Moore Foundation
- 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
- decision-making, reward, effort cost, computational model, neural computation, action selection
- MeSH terms
- Animals, Brain, Cognition, Computer Simulation, Decision Making, Goals, Humans, Judgment, Motivation, Primates, Models, Animal, Executive Function
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