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Stable Representations of Decision Variables for Flexible Behavior
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Decisions occur in dynamic environments. In the framework of reinforcement learning, the probability of performing an action is influenced by decision variables. Discrepancies between predicted and obtained rewards (reward prediction errors) update these variables, but they are otherwise stable between decisions. Although reward prediction errors have been mapped to midbrain dopamine neurons, it is unclear how the brain represents decision variables themselves. We trained mice on a dynamic foraging task in which they chose between alternatives that delivered reward with changing probabilities. Neurons in the medial prefrontal cortex, including projections to the dorsomedial striatum, maintained persistent firing rate changes over long timescales. These changes stably represented relative action values (to bias choices) and total action values (to bias response times) with slow decay. In contrast, decision variables were weakly represented in the anterolateral motor cortex, a region necessary for generating choices. Thus, we define a stable neural mechanism to drive flexible behavior.
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“Also, the dynamic foraging task engages regions of the brain that are not necessary for the dynamic Pavlovian task.43”
From Serotonin neurons modulate learning rate through uncertainty (2022) · cites this paper as [43]
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Fiji: an open-source platform for biological-image analysis
Schindelin, J., Arganda-Carreras, I., Frise, E., Kaynig, V., Longair, M., Pietzsch, T., Preibisch, S., Rueden, C., Saalfeld, S., Schmid, B., et al. (2012) · Nature Methods · doi:10.1038/nmeth.2019 · Cited by 66,890
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,268
A neural substrate of prediction and reward
Schultz, W., Dayan, P., & Montague, P. R. (1997) · Science · doi:10.1126/science.275.5306.1593 · Cited by 9,388
A robust and high-throughput Cre reporting and characterization system for the whole mouse brain
Madisen, L., Zwingman, T. A., Sunkin, S. M., Oh, S. W., Zariwala, H. A., Gu, H., Ng, L. L., Palmiter, R. D., Hawrylycz, M. J., Jones, A. R., et al. (2010) · Nature Neuroscience · doi:10.1038/nn.2467 · Cited by 6,907
The Neural Basis of Decision Making
Gold, J. I., & Shadlen, M. N. (2007) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.29.051605.113038 · Cited by 3,764
(2012) · Springer eBooks · doi:10.1007/978-1-4419-1428-6_5086 · Cited by 3,601
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Dayan, P., & Abbott, L. F. (2001) · Cited by 3,153
Independent optical excitation of distinct neural populations
Klapoetke, N. C., Murata, Y., Kim, S. S., Pulver, S. R., Birdsey-Benson, A., Cho, Y. K., Morimoto, T. K., Chuong, A. S., Carpenter, E. J., Tian, Z., et al. (2014) · Nature Methods · doi:10.1038/nmeth.2836 · Cited by 2,344
Cortical substrates for exploratory decisions in humans
Daw, N. D., O'Doherty, J. P., Dayan, P., Seymour, B., & Dolan, R. J. (2006) · Nature · doi:10.1038/nature04766 · Cited by 2,224
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Yoshida, E., Kondo, M., Nakae, K., Ako, R., Terada, S.-I., Hatano, N., Liu, L., Kobayashi, K., Ishii, S., & Matsuzaki, M. (2025) · Nature Communications · doi:10.1038/s41467-025-58272-w · Cited by 3
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Stoll, F. M., & Rudebeck, P. H. (2019) · Neuron · doi:10.1016/j.neuron.2019.08.018
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Hassam, A. (1997) · TLS, the Times literary supplement/Times literary supplement on CD-ROM/TLS. Times literary supplement
Hamaguchi, K., Takahashi-Aoki, H., & Watanabe, D. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.12.27.474215
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Kvitsiani, D., López-Yépez, J. S., Barta, A., Moltesen, M., Martin, J., Woo, T.-F., & Hulme, O. J. (2023) · Research Square · doi:10.21203/rs.3.rs-2389438/v1
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Rule-based modulation of a sensorimotor transformation across cortical areasPreprint
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Locantore, J., Liu, Y., White, J., Wallace, J. B., Beron, C. C., Sabatini, B. L., & Wallace, M. L. (2024) · eLife · doi:10.7554/elife.100488.1
Augusto, E., Kouskoff, V., Chenouard, N., Giraudet, M., Peltier, L., de Miranda, A., Louis, A., Alonso, L., & Gambino, F. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.29.620931
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Murphy, C. E., Wang, H., Ortega, H. K., Kwan, A. C., & Atilgan, H. (2024) · eLife · doi:10.7554/elife.103001
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Murphy, C. E., Wang, H., Ortega, H. K., Kwan, A. C., & Atilgan, H. (2024) · eLife · doi:10.7554/elife.103001.1
Imani-Almas, E., Mahmoudi, J., Farhoudi, M., Sadigh‐Eteghad, S., & Kalan, A. E. (2025) · Metabolic Brain Disease · doi:10.1007/s11011-025-01695-7
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Kouskoff, V. (2021) · theses.fr (ABES) · doi:10.70675/a8f4e479z2341z4f88zaf5bz91e7451d469d
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Metadata
- Authors
- Bari, B. A., Grossman, C. D., Lubin, E. E., Rajagopalan, A. E., Cressy, J. I., & Cohen, J. Y.
- Affiliations
- Johns Hopkins University (US)
- Corresponding author
- Cohen, Jeremiah Y.
- ORCID
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2019-09
- Volume
- 103
- Issue
- 5
- Pages
- 922-933.e7
- ISSN
- 0896-6273 (ISSN-L)
- PMC
- PMC7169950
- OpenAlex
- W2953886068
- Cited by
- 173
- References
- 91
- Citations by year
- 2026: 19 · 2025: 25 · 2024: 25 · 2023: 30 · 2022: 20 · 2021: 30 · 2020: 19 · 2019: 3
- FWCI
- 10.46
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Neuron (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Neurological Disorders and Stroke
- Brain and Behavior Research Foundation
- Esther A. and Joseph Klingenstein Fund
- National Institute on Drug Abuse
- Whitehall Foundation
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- decision variables, behavioral flexibility, reward prediction error, medial prefrontal cortex, dorsomedial striatum, choice bias, reaction time (psychology), dopamine, mice
- MeSH terms
- Animals, Appetitive Behavior, Decision Making, Motor Cortex, Neural Pathways, Neurons, Probability, Reinforcement, Psychology, Reward, Neostriatum, Prefrontal Cortex, Mice, Electrophysiological Phenomena
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