The DOI matches its Crossref record; 90 of the 91 references it cites were found in a registry or an index.
Stable Representations of Decision Variables for Flexible Behavior
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering
Quiroga, R. Q., Nadasdy, Z., & Ben-Shaul, Y. (2004) · Neural Computation · doi:10.1162/089976604774201631 · Cited by 2,119
(1986) · Cited by 2,022
Balleine, B. W., & O'Doherty, J. P. (2010) · Neuropsychopharmacology · doi:10.1038/npp.2009.131 · Cited by 1,775
The importance of mixed selectivity in complex cognitive tasks
Rigotti, M., Barak, O., Warden, M. R., Wang, X.-J., Daw, N. D., Miller, E. K., & Fusi, S. (2013) · Nature · doi:10.1038/nature12160 · Cited by 1,639
Rapid, reversible activation of AgRP neurons drives feeding behavior in mice
Krashes, M. J., Koda, S., Ye, C., Rogan, S. C., Adams, A. C., Cusher, D. S., Maratos-Flier, E., Roth, B. L., & Lowell, B. B. (2011) · Journal of Clinical Investigation · doi:10.1172/jci46229 · Cited by 1,578
Spontaneous behaviors drive multidimensional, brainwide activity
Stringer, C., Pachitariu, M., Steinmetz, N., Reddy, C. B., Carandini, M., & Harris, K. D. (2019) · Science · doi:10.1126/science.aav7893 · Cited by 1,510
The Role of the Dorsal Striatum in Reward and Decision-Making: Figure 1.
Balleine, B. W., Delgado, M. R., & Hikosaka, O. (2007) · The Journal of Neuroscience · doi:10.1523/jneurosci.1554-07.2007 · Cited by 1,390
Neuron-type-specific signals for reward and punishment in the ventral tegmental area
Cohen, J. Y., Haesler, S., Vong, L., Lowell, B. B., & Uchida, N. (2012) · Nature · doi:10.1038/nature10754 · Cited by 1,318
Self-citationMidbrain dopamine neurons encode a quantitative reward prediction error signal
Bayer, H. M., & Glimcher, P. W. (2005) · Neuron · doi:10.1016/j.neuron.2005.05.020 · Cited by 1,304
Synaptic theory of working memory
Mongillo, G., Barak, O., & Tsodyks, M. (2008) · Science · doi:10.1126/science.1150769 · Cited by 1,253
A Designer AAV Variant Permits Efficient Retrograde Access to Projection Neurons
Tervo, D. G. R., Hwang, B., Viswanathan, S., Gaj, T., Lavzin, M., Ritola, K., Lindo, S. E., Michael, S. C., Кулешова, Е. П., Ojala, D. S., et al. (2016) · Neuron · doi:10.1016/j.neuron.2016.09.021 · Cited by 1,220
Tonic dopamine: Opportunity costs and the control of response vigor
Niv, Y., Daw, N. D., Joel, D., & Dayan, P. (2007) · Psychopharmacology · doi:10.1007/s00213-006-0502-4 · Cited by 1,133
Madisen, L., Garner, A. R., Shimaoka, D., Chuong, A. S., Klapoetke, N. C., Li, L., van der Bourg, A., Niino, Y., Egolf, L., Monetti, C., et al. (2015) · Neuron · doi:10.1016/j.neuron.2015.02.022 · Cited by 1,114
Matching Behavior and the Representation of Value in the Parietal Cortex
Sugrue, L. P., Corrado, G. S., & Newsome, W. T. (2004) · Science · doi:10.1126/science.1094765 · Cited by 1,030
(2010) · Springer eBooks · doi:10.1007/978-0-387-30164-8_588 · Cited by 978
Choice-specific sequences in parietal cortex during a virtual-navigation decision task
Harvey, C. D., Coen, P., & Tank, D. W. (2012) · Nature · doi:10.1038/nature10918 · Cited by 951
Optimal decision making and the anterior cingulate cortex
Kennerley, S. W., Walton, M. E., Behrens, T. E. J., Buckley, M. J., & Rushworth, M. F. S. (2006) · Nature Neuroscience · doi:10.1038/nn1724 · Cited by 948
Representation of Action-Specific Reward Values in the Striatum
Samejima, K., Ueda, Y., Doya, K., & Kimura, M. (2005) · Science · doi:10.1126/science.1115270 · Cited by 939
Mesolimbic dopamine signals the value of work
Hamid, A., Pettibone, J. R., Mabrouk, O. S., Hetrick, V. L., Schmidt, R., Weele, C. M. V., Kennedy, R. T., Aragona, B. J., & Berke, J. D. (2015) · Nature Neuroscience · doi:10.1038/nn.4173 · Cited by 848
Flow of cortical activity underlying a tactile decision in mice
Guo, Z. V., Li, N., Huber, D., Ophir, E., Gutnisky, D., Ting, J. T., Feng, G., & Svoboda, K. (2014) · Neuron · doi:10.1016/j.neuron.2013.10.020 · Cited by 715 · cited 1×
“ALM (the tongue premotor cortex), which is necessary for generating reward-guided licks (Komiyama et al., 2010; Guo et al., 2014; Li et al., 2015).”
BackgroundRole for Cingulate Motor Area Cells in Voluntary Movement Selection Based on Reward
Shima, K., & Tanji, J. (1998) · Science · doi:10.1126/science.282.5392.1335 · Cited by 645
Quantitative measures of cluster quality for use in extracellular recordings
Schmitzer-Torbert, N. C., Jackson, J. C., Henze, D. A., Harris, K., & Redish, A. D. (2005) · Neuroscience · doi:10.1016/j.neuroscience.2004.09.066 · Cited by 642
Prefrontal cortex as a meta-reinforcement learning system
Wang, J. X., Kurth-Nelson, Z., Kumaran, D., Tirumala, D., Soyer, H., Leibo, J. Z., Hassabis, D., & Botvinick, M. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0147-8 · Cited by 596
A motor cortex circuit for motor planning and movement
Li, N., Chen, T., Guo, Z. V., Gerfen, C. R., & Svoboda, K. (2015) · Nature · doi:10.1038/nature14178 · Cited by 571
Midbrain dopamine neurons encode decisions for future action
Morris, G., Nevet, A., Arkadir, D., Vaadia, E., & Bergman, H. (2006) · Nature Neuroscience · doi:10.1038/nn1743 · Cited by 503
DYNAMIC RESPONSE-BY-RESPONSE MODELS OF MATCHING BEHAVIOR IN RHESUS MONKEYS
Lau, B., & Glimcher, P. W. (2005) · Journal of the Experimental Analysis of Behavior · doi:10.1901/jeab.2005.110-04 · Cited by 485
Varazzani, C., San‐Galli, A., Gilardeau, S., & Bouret, S. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.0454-15.2015 · Cited by 482
Medial prefrontal cell activity signaling prediction errors of action values
Matsumoto, M., Matsumoto, K., Abe, H., & Tanaka, K. (2007) · Nature Neuroscience · doi:10.1038/nn1890 · Cited by 462
A whole-brain atlas of inputs to serotonergic neurons of the dorsal and median raphe nuclei
Pollak Dorocic, I., Fürth, D., Xuan, Y., Johansson, Y., Pozzi, L., Silberberg, G., Carlén, M., & Meletis, K. (2014) · Neuron · doi:10.1016/j.neuron.2014.07.002 · Cited by 451
Learning-related fine-scale specificity imaged in motor cortex circuits of behaving mice
Komiyama, T., Sato, T. R., O’Connor, D. H., Zhang, Y.-X., Huber, D., Hooks, B. M., Gabitto, M., & Svoboda, K. (2010) · Nature · doi:10.1038/nature08897 · Cited by 447
Value Representations in the Primate Striatum during Matching Behavior
Lau, B., & Glimcher, P. W. (2008) · Neuron · doi:10.1016/j.neuron.2008.02.021 · Cited by 419
Transient stimulation of distinct subpopulations of striatal neurons mimics changes in action value
Tai, L.-H., Lee, A. M., Benavidez, N., Bonci, A., & Wilbrecht, L. (2012) · Nature Neuroscience · doi:10.1038/nn.3188 · Cited by 399
Serotonergic neurons signal reward and punishment on multiple timescales
Cohen, J. Y., Amoroso, M. W., & Uchida, N. (2015) · eLife · doi:10.7554/elife.06346 · Cited by 388
Self-citationDistinct Roles of Rodent Orbitofrontal and Medial Prefrontal Cortex in Decision Making
Sul, J. H., Kim, H., Huh, N., Lee, D., & Jung, M. W. (2010) · Neuron · doi:10.1016/j.neuron.2010.03.033 · Cited by 378
Lima, S. Q., Hromádka, T., Znamenskiy, P., & Zador, A. M. (2009) · PLoS ONE · doi:10.1371/journal.pone.0006099 · Cited by 356
Reward and choice encoding in terminals of midbrain dopamine neurons depends on striatal target
Parker, N. F., Cameron, C. M., Taliaferro, J. P., Lee, J., Choi, J. Y., Davidson, T. J., Daw, N. D., & Witten, I. B. (2016) · Nature Neuroscience · doi:10.1038/nn.4287 · Cited by 348
Determination of antidromic excitation by the collision test: Problems of interpretation
Fuller, J. H., & Schlag, J. D. (1976) · Brain Research · doi:10.1016/0006-8993(76)90284-5 · Cited by 341
Presynaptic partners of dorsal raphe serotonergic and GABAergic neurons
Weissbourd, B., Ren, J., DeLoach, K. E., Guenthner, C. J., Miyamichi, K., & Luo, L. (2014) · Neuron · doi:10.1016/j.neuron.2014.06.024 · Cited by 337
Seo, H., & Lee, D. (2007) · The Journal of Neuroscience · doi:10.1523/jneurosci.2369-07.2007 · Cited by 324
Evolutionary Conservation of the Basal Ganglia as a Common Vertebrate Mechanism for Action Selection
Stephenson‐Jones, M., Samuelsson, E., Ericsson, J., Robertson, B., & Grillner, S. E. (2011) · Current Biology · doi:10.1016/j.cub.2011.05.001 · Cited by 295
Validation of Decision-Making Models and Analysis of Decision Variables in the Rat Basal Ganglia
Ito, M., & Doya, K. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.6157-08.2009 · Cited by 284
Organization of monosynaptic inputs to the serotonin and dopamine neuromodulatory systems
Ogawa, S. K., Cohen, J. Y., Hwang, D., Uchida, N., & Watabe-Uchida, M. (2014) · Cell Reports · doi:10.1016/j.celrep.2014.06.042 · Cited by 249
Self-citationOPTIMIZATION AND THE MATCHING LAW AS ACCOUNTS OF INSTRUMENTAL BEHAVIOR
Baum, W. M. (1981) · Journal of the Experimental Analysis of Behavior · doi:10.1901/jeab.1981.36-387 · Cited by 245
Role of Striatum in Updating Values of Chosen Actions
Kim, H., Sul, J. H., Huh, N., Lee, D., & Jung, M. W. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.2728-09.2009 · Cited by 229
Activation of Dorsal Raphe Serotonergic Neurons Promotes Waiting but Is Not Reinforcing
Fonseca, M. S., Murakami, M., & Mainen, Z. F. (2015) · Current Biology · doi:10.1016/j.cub.2014.12.002 · Cited by 219
Single‐unit recording from antidromically activated optic radiation neurones
Bishop, P. O., Burke, W. J., & Davis, R. (1962) · The Journal of Physiology · doi:10.1113/jphysiol.1962.sp006943 · Cited by 197
Cai, X., Kim, S., & Lee, D. (2011) · Neuron · doi:10.1016/j.neuron.2010.11.041 · Cited by 185
Action Selection and Action Value in Frontal-Striatal Circuits
Seo, M., Lee, E., & Averbeck, B. B. (2012) · Neuron · doi:10.1016/j.neuron.2012.03.037 · Cited by 170
Signals in Human Striatum Are Appropriate for Policy Update Rather than Value Prediction
Li, J., & Daw, N. D. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.6316-10.2011 · Cited by 159
Choice, difficulty, and confidence in the brain
Rolls, E. T., Grabenhorst, F., & Deco, G. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.06.073 · Cited by 148
Activation of serotonin neurons promotes active persistence in a probabilistic foraging task
Lottem, E., Banerjee, D., Vertechi, P., Sarra, D., Lohuis, M. N. O., & Mainen, Z. F. (2018) · Nature Communications · doi:10.1038/s41467-018-03438-y · Cited by 140
The dorsomedial striatum encodes net expected return, critical for energizing performance vigor
Wang, A. Y., Miura, K., & Uchida, N. (2013) · Nature Neuroscience · doi:10.1038/nn.3377 · Cited by 135
Persistent Activity in Neural Networks with Dynamic Synapses
Barak, O., & Tsodyks, M. V. (2005) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0030035 · Cited by 133
Evidence for Hyperbolic Temporal Discounting of Reward in Control of Movements
Haith, A. M., Reppert, T. R., & Shadmehr, R. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.0424-12.2012 · Cited by 129
A Biophysically Based Neural Model of Matching Law Behavior: Melioration by Stochastic Synapses
Soltani, A., & Wang, X. (2006) · Journal of Neuroscience · doi:10.1523/jneurosci.5159-05.2006 · Cited by 129
Control of movement vigor and decision making during foraging
Yoon, T., Geary, R. B., Ahmed, A. A., & Shadmehr, R. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1812979115 · Cited by 122
Ito, M., & Doya, K. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.1962-14.2015 · Cited by 121
Modulation of Saccade Vigor during Value-Based Decision Making
Reppert, T. R., Lempert, K. M., Glimcher, P. W., & Shadmehr, R. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.2621-15.2015 · Cited by 121
The functional logic of corticostriatal connections
Shipp, S. (2017) · Brain Structure and Function · doi:10.1007/s00429-016-1250-9 · Cited by 118
Stalnaker, T. A. (2010) · Frontiers in Integrative Neuroscience · doi:10.3389/fnint.2010.00012 · Cited by 117
Anastasiades, P. G., Boada, C. M., & Carter, A. G. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy299 · Cited by 117
Dynamic Encoding of Action Selection by the Medial Striatum
Kimchi, E. Y., & Laubach, M. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.5206-08.2009 · Cited by 115
Emergence of Context-Dependent Variability across a Basal Ganglia Network
Woolley, S. C., Rajan, R., Joshua, M., & Doupe, A. J. (2014) · Neuron · doi:10.1016/j.neuron.2014.01.039 · Cited by 109
Exploration Disrupts Choice-Predictive Signals and Alters Dynamics in Prefrontal Cortex
Ebitz, R. B., Albarran, E., & Moore, T. (2017) · Neuron · doi:10.1016/j.neuron.2017.12.007 · Cited by 105
Dynamic Flexibility in Striatal-Cortical Circuits Supports Reinforcement Learning
Gerraty, R. T., Davidow, J. Y., Foerde, K., Galván, A., Bassett, D. S., & Shohamy, D. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.2084-17.2018 · Cited by 102
Murakami, M., Shteingart, H., Loewenstein, Y., & Mainen, Z. F. (2017) · Neuron · doi:10.1016/j.neuron.2017.04.040 · Cited by 92
A dynamic code for economic object valuation in prefrontal cortex neurons
Tsutsui, K., Fabian Grabenhorst, B., Kobayashi, S., & Schultz, W. (2016) · Nature Communications · doi:10.1038/ncomms12554 · Cited by 87
The Role of Orbitofrontal–Amygdala Interactions in Updating Action–Outcome Valuations in Macaques
Fiuzat, E. C., Rhodes, S. E. V., & Murray, E. A. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.1839-16.2017 · Cited by 85
Cell-Type-Specific Contributions of Medial Prefrontal Neurons to Flexible Behaviors
Nakayama, H., Ibáñez-Tallon, I., & Heintz, N. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.3537-17.2018 · Cited by 80
Signals for Previous Goal Choice Persist in the Dorsomedial, but Not Dorsolateral Striatum of Rats
Kim, H., Lee, D., & Jung, M. W. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.2422-12.2013 · Cited by 75
Action and Outcome Activity State Patterns in the Anterior Cingulate Cortex
Hyman, J. M., Whitman, J., Emberly, E. G., Woodward, T. S., & Seamans, J. K. (2012) · Cerebral Cortex · doi:10.1093/cercor/bhs104 · Cited by 69
Arco, A. D., Park, J., Wood, J. T., Kim, Y., & Moghaddam, B. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.0450-17.2017 · Cited by 60
Striatal action-value neurons reconsidered
Elber-Dorozko, L., & Loewenstein, Y. (2018) · eLife · doi:10.7554/elife.34248 · Cited by 53
Simon, N. W., Wood, J. T., & Moghaddam, B. (2015) · Journal of Neurophysiology · doi:10.1152/jn.00884.2015 · Cited by 51
When Does Reward Maximization Lead to Matching Law?
Sakai, Y., & Fukai, T. (2008) · PLoS ONE · doi:10.1371/journal.pone.0003795 · Cited by 36
Model-based reinforcement learning under concurrent schedules of reinforcement in rodentsPreprint
Huh, N., Jo, S., Kim, H., Sul, J. H., & Jung, M. W. (2009) · Learning & Memory · doi:10.1101/lm.1295509 · Cited by 31
The Eye of the Laboratory Mouse Remains Anatomically Adapted for Natural Conditions
Shupe, J. M., Kristan, D. M., Austad, S. N., & Stenkamp, D. L. (2005) · Brain Behavior and Evolution · doi:10.1159/000088857 · Cited by 21
Morita, K., Jitsev, J., & Morrison, A. (2016) · Behavioural Brain Research · doi:10.1016/j.bbr.2016.05.017 · Cited by 16
Ueda, Y., Yamanaka, K., Noritake, A., Enomoto, K., Matsumoto, N., Yamada, H., Samejima, K., Inokawa, H., Hori, Y., Nakamura, K., et al. (2017) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2017.00066 · Cited by 15
Iwata, J.-I., Shima, K., Tanji, J., & Mushiake, H. (2013) · Experimental Brain Research · doi:10.1007/s00221-013-3442-3 · Cited by 9
Cited by
Sorted by citations
The role of the medial prefrontal cortex in cognition, ageing and dementia
Jobson, D. D., Hase, Y., Clarkson, A. N., & Kalaria, R. N. (2021) · Brain Communications · doi:10.1093/braincomms/fcab125 · Cited by 338
The mouse prefrontal cortex: Unity in diversity
Le Merre, P., Ährlund-Richter, S., & Carlén, M. (2021) · Neuron · doi:10.1016/j.neuron.2021.03.035 · Cited by 207
A brain-wide map of neural activity during complex behaviour
Meshulam, L., Angelaki, D. E., Benson, J., McRoberts, I., Noel, J., Arlandis, J., Bonacchi, N., Bougrova, K., Catarino, J. A., Cazettes, F., et al. (2025) · Nature · doi:10.1038/s41586-025-09235-0 · Cited by 238
Dopamine, Updated: Reward Prediction Error and Beyond
Lerner, T. N., Holloway, A. L., & Seiler, J. L. (2020) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2020.10.012 · Cited by 195
Serotonin neurons modulate learning rate through uncertainty
Grossman, C. D., Bari, B. A., & Cohen, J. Y. (2022) · Current Biology · doi:10.1016/j.cub.2021.12.006 · Cited by 164
Self-citation
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
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 ›
Are you an author, or do you see a problem with this page? Request a review
