The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.
The Medial Prefrontal Cortex Shapes Dopamine Reward Prediction Errors under State Uncertainty
Open accessAbstract
Animals make predictions based on currently available information. In natural settings, sensory cues may not reveal complete information, requiring the animal to infer the "hidden state" of the environment. The brain structures important in hidden state inference remain unknown. A previous study showed that midbrain dopamine neurons exhibit distinct response patterns depending on whether reward is delivered in 100% (task 1) or 90% of trials (task 2) in a classical conditioning task. Here we found that inactivation of the medial prefrontal cortex (mPFC) affected dopaminergic signaling in task 2, in which the hidden state must be inferred ("will reward come or not?"), but not in task 1, where the state was known with certainty. Computational modeling suggests that the effects of inactivation are best explained by a circuit in which the mPFC conveys inference over hidden states to the dopamine system. VIDEO ABSTRACT.
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
“The brain may have dedicated machinery, perhaps in prefrontal cortex [12,13], for computing belief states, which could then be provided to downstream areas, such as the basal ganglia, to perform standard RL algorithms such as TD learning [14].”
From Emergence of belief-like representations through reinforcement learning (2023) · cites this paper as [13]
In the citing paper
“As a working example, we will consider the probabilistic associative learning paradigm where dopamine RPEs were shown to be consistent with a belief representation [7,13].”
From Emergence of belief-like representations through reinforcement learning (2023) · cites this paper as [13]
In the citing paper
“In both tasks, mice were trained to associate an odor cue with probabilistic delivery of a liquid reward 1.2-2.8slater.”
From Emergence of belief-like representations through reinforcement learning (2023) · cites this paper as [13]
In the citing paper
“Previous work has shown that, in animals, prefrontal cortex activity is a necessary component of animals' state representations [13].”
From Emergence of belief-like representations through reinforcement learning (2023) · cites this paper as [13]
In the citing paper
“This work found that inactivating prefrontal cortex in the Starkweather task led to animals' RPEs in Task 2 resembling the RPEs of Task 1.”
From Emergence of belief-like representations through reinforcement learning (2023) · cites this paper as [13]
In the citing paper
“For instance, previous work has suggested that prefrontal cortex may perform state estimation in tasks with hidden states [6,7,12,13].”
From Emergence of belief-like representations through reinforcement learning (2023) · cites this paper as [13]
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
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
Learning to predict by the methods of temporal differences
Sutton, R. S. (1988) · Machine Learning · doi:10.1007/bf00115009 · Cited by 5,148
Discrete coding of reward probability and uncertainty by dopamine neurons
Fiorillo, C. D., Tobler, P. N., & Schultz, W. (2003) · Science · doi:10.1126/science.1077349 · Cited by 2,085
Sesack, S. R., Deutch, A. Y., Roth, R. H., & Bunney, B. S. (1989) · Journal of Comparative Neurology · doi:10.1002/cne.902900205 · Cited by 1,568
Differential projections of the infralimbic and prelimbic cortex in the rat
Vertes, R. P. (2004) · Synapse · doi:10.1002/syn.10279 · Cited by 1,550
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
Whole-Brain Mapping of Direct Inputs to Midbrain Dopamine Neurons
Watabe‐Uchida, M., Zhu, L., Ogawa, S. K., Vamanrao, A., & Uchida, N. (2012) · Neuron · doi:10.1016/j.neuron.2012.03.017 · Cited by 1,277
Self-citationPrefrontal cortex in the rat: Projections to subcortical autonomic, motor, and limbic centers
Gabbott, P. L. A., Warner, T. A., Jays, P. R. L., Salway, P., & Busby, S. J. (2005) · Journal of Comparative Neurology · doi:10.1002/cne.20738 · Cited by 1,227
The hippocampus as a predictive map
Stachenfeld, K. L., Botvinick, M. M., & Gershman, S. J. (2017) · Nature Neuroscience · doi:10.1038/nn.4650 · Cited by 1,000
Self-citation
Cited by
Sorted by citations
A Neural Circuit Mechanism for Encoding Aversive Stimuli in the Mesolimbic Dopamine System
de Jong, J. W., Afjei, S. A., Dorocic, I. P., Peck, J. R., Liu, C. J., Kim, C. K., Tian, L., Deisseroth, K. A., & Lammel, S. (2018) · Neuron · doi:10.1016/j.neuron.2018.11.005 · Cited by 525
The Relationship Between Uncertainty and Affect
Anderson, E. C., Carleton, R. N., Diefenbach, M., & Han, P. K. J. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.02504 · Cited by 332
The prefrontal cortex, pathological anxiety, and anxiety disorders
Kenwood, M. M., Kalin, N. H., & Barbas, H. (2021) · Neuropsychopharmacology · doi:10.1038/s41386-021-01109-z · Cited by 379
Reinforcement learning in artificial and biological systems
Neftci, E. O., & Averbeck, B. B. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0025-4 · Cited by 268
Dopamine release in the nucleus accumbens core signals perceived saliency
Kutlu, M. G., Zachry, J. E., Melugin, P. R., Cajigas, S. A., Chevee, M. F., Kelly, S. J., Kutlu, B., Tian, L., Siciliano, C. A., & Calipari, E. S. (2021) · Current Biology · doi:10.1016/j.cub.2021.08.052 · Cited by 238
Gershman, S. J., & Uchida, N. (2019) · Nature Reviews Neuroscience · doi:10.1038/s41583-019-0220-7 · Cited by 236
Self-citationThe 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
Cools, R., & Arnsten, A. F. T. (2021) · Neuropsychopharmacology · doi:10.1038/s41386-021-01100-8 · Cited by 216
The successor representation: Its computational logic and neural substrates
Gershman, S. J. (2018) · The Journal of Neuroscience · doi:10.1523/jneurosci.0151-18.2018 · Cited by 191
Self-citationSolié, C., Girard, B., Righetti, B., Tapparel, M., & Bellone, C. (2022) · Nature Neuroscience · doi:10.1038/s41593-021-00972-9 · Cited by 186
Behavioral, Physiological, and Neural Signatures of Surprise during Naturalistic Sports Viewing
Antony, J. W., Hartshorne, T. H., Pomeroy, K., Gureckis, T. M., Hasson, U., McDougle, S. D., & Norman, K. A. (2020) · Neuron · doi:10.1016/j.neuron.2020.10.029 · Cited by 150
Dopaminergic and Prefrontal Basis of Learning from Sensory Confidence and Reward Value
Lak, A., Okun, M., Moss, M. M., Gurnani, H., Farrell, K., Wells, M. J., Reddy, C. B., Kepecs, Á., Harris, K. D., & Carandini, M. (2019) · Neuron · doi:10.1016/j.neuron.2019.11.018 · Cited by 137
A neural network for information seeking
White, J. K., Bromberg-Martin, E. S., Heilbronner, S. R., Zhang, K., Pai, J., Haber, S. N., & Monosov, I. E. (2019) · Nature Communications · doi:10.1038/s41467-019-13135-z · Cited by 129
Reinforcement-learning in fronto-striatal circuits
Averbeck, B. B., & O’Doherty, J. P. (2021) · Neuropsychopharmacology · doi:10.1038/s41386-021-01108-0 · Cited by 132
Prefrontal Cortex Predicts State Switches during Reversal Learning
Bartolo, R., & Averbeck, B. B. (2020) · Neuron · doi:10.1016/j.neuron.2020.03.024 · Cited by 132
Anterior Cingulate Cortex and the Control of Dynamic Behavior in Primates
Monosov, I. E., Haber, S. N., Leuthardt, E. C., & Jezzini, A. (2020) · Current Biology · doi:10.1016/j.cub.2020.10.009 · Cited by 118
Prefrontal deep projection neurons enable cognitive flexibility via persistent feedback monitoring
Spellman, T., Svei, M., Kaminsky, J. C., Manzano-Nieves, G., & Liston, C. M. (2021) · Cell · doi:10.1016/j.cell.2021.03.047 · Cited by 123
The interpretation of computational model parameters depends on the context
Eckstein, M. K., Master, S. L., Xia, L., Dahl, R. E., Wilbrecht, L., & Collins, A. G. E. (2022) · eLife · doi:10.7554/elife.75474 · Cited by 124
Behavior- and Modality-General Representation of Confidence in Orbitofrontal Cortex
Masset, P., Ott, T., Lak, A., Hirokawa, J., & Kepecs, A. (2020) · Cell · doi:10.1016/j.cell.2020.05.022 · Cited by 109
Eckstein, M. K., Wilbrecht, L., & Collins, A. G. E. (2021) · PsyArXiv (OSF Preprints) · doi:10.1016/j.cobeha.2021.06.004 · Cited by 131
Dopaminergic basis for signaling belief updates, but not surprise, and the link to paranoia
Nour, M. M., Dahoun, T., Schwartenbeck, P., Adams, R. A., FitzGerald, T. H. B., Coello, C., Wall, M. B., Dolan, R. J., & Howes, O. D. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1809298115 · Cited by 101
Heterogeneity in striatal dopamine circuits: Form and function in dynamic reward seeking
Collins, A. L., & Saunders, B. T. (2020) · Journal of Neuroscience Research · doi:10.1002/jnr.24587 · Cited by 93
Discovery of hierarchical representations for efficient planning
Tomov, M. S., Yagati, S., Kumar, A., Yang, W., & Gershman, S. J. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007594 · Cited by 100
Self-citationNeural circuitry of information seeking
Bromberg-Martin, E. S., & Monosov, I. E. (2020) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2020.07.006 · Cited by 89
Functional neuroimaging in psychiatry and the case for failing better
Nour, M. M., Liu, Y., & Dolan, R. J. (2022) · Neuron · doi:10.1016/j.neuron.2022.07.005 · Cited by 92
Dopaminergic action prediction errors serve as a value-free teaching signal
Greenstreet, F., Vergara, H. M., Johansson, Y., Pati, S., Schwarz, L., Lenzi, S. C., Geerts, J. P., Wisdom, M., Gubanova, A., Rollik, L. B., et al. (2025) · Nature · doi:10.1038/s41586-025-09008-9 · Cited by 94
The role of state uncertainty in the dynamics of dopamine
Mikhael, J. G., Kim, H. R., Uchida, N., & Gershman, S. J. (2022) · Current Biology · doi:10.1016/j.cub.2022.01.025 · Cited by 88
Self-citationVertechi, P., Lottem, E., Sarra, D., Godinho, B., Treves, I. N., Quendera, T., Lohuis, M. N. O., & Mainen, Z. F. (2020) · Neuron · doi:10.1016/j.neuron.2020.01.017 · Cited by 85
Meta-learning, social cognition and consciousness in brains and machines
Langdon, A., Botvinick, M., Nakahara, H., Tanaka, K., Matsumoto, M., & Kanai, R. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.10.004 · Cited by 87
Anxiety and depression: A top‐down, bottom‐up model of circuit function
LeDuke, D. O., Borio, M., Miranda, R. L., & Tye, K. M. (2023) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.14997 · Cited by 76
Distributional Reinforcement Learning in the Brain
Lowet, A. S., Qiao, Z., Matias, S., Drugowitsch, J., & Uchida, N. (2020) · Trends in Neurosciences · doi:10.1016/j.tins.2020.09.004 · Cited by 75
Self-citationMultiple Dopamine Systems: Weal and Woe of DopaminePreprint
Watabe‐Uchida, M., & Uchida, N. (2018) · Cold Spring Harbor Symposia on Quantitative Biology · doi:10.1101/sqb.2018.83.037648 · Cited by 71
Self-citationDopamine signals as temporal difference errors: recent advances
Starkweather, C. K., & Uchida, N. (2020) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2020.08.014 · Cited by 71
Self-citationAvian pallial circuits and cognition: A comparison to mammals
Güntürkün, O., von Eugen, K., Packheiser, J., & Pusch, R. (2021) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2021.08.007 · Cited by 63
Primate Orbitofrontal Cortex Codes Information Relevant for Managing Explore–Exploit Tradeoffs
Costa, V. D., & Averbeck, B. B. (2020) · Journal of Neuroscience · doi:10.1523/jneurosci.2355-19.2020 · Cited by 65
Integrating Models of Interval Timing and Reinforcement Learning
Petter, E. A., Gershman, S. J., & Meck, W. H. (2018) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2018.08.004 · Cited by 59
Self-citationDopaminergic signaling of uncertainty and the aetiology of gambling addiction
Zack, M. H., George, R. S., & Clark, L. (2019) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2019.109853 · Cited by 62
Dopamine-independent effect of rewards on choices through hidden-state inference
Blanco-Pozo, M., Akam, T., & Walton, M. E. (2024) · Nature Neuroscience · doi:10.1038/s41593-023-01542-x · Cited by 69
Hidden state inference requires abstract contextual representations in the ventral hippocampus
Mishchanchuk, K., Gregoriou, G. C., Qü, A. J., Kastler, A., Huys, Q. J. M., Wilbrecht, L., & MacAskill, A. F. (2024) · Science · doi:10.1126/science.adq5874 · Cited by 56
The anticipation of events in time
Grabenhorst, M., Michalareas, G., Maloney, L. T., & Poeppel, D. (2019) · Nature Communications · doi:10.1038/s41467-019-13849-0 · Cited by 59
Visual categories and concepts in the avian brain
Pusch, R., Clark, W., Rose, J., & Güntürkün, O. (2022) · Animal Cognition · doi:10.1007/s10071-022-01711-8 · Cited by 48
Emergence of belief-like representations through reinforcement learning
Hennig, J. A., Romero Pinto, S. A., Yamaguchi, T., Linderman, S. W., Uchida, N., & Gershman, S. J. (2023) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1011067 · Cited by 41
Self-citationLin, W. C., Liu, C. J., Kosillo, P., Tai, L.-H., Galarce, E. M., Bateup, H. S., Lammel, S., & Wilbrecht, L. (2022) · Current Biology · doi:10.1016/j.cub.2022.06.089 · Cited by 40
Goal-directed learning in adolescence: neurocognitive development and contextual influences
Wilbrecht, L., & Davidow, J. Y. (2024) · Nature reviews. Neuroscience · doi:10.1038/s41583-023-00783-w · Cited by 47
A stress-sensitive frontostriatal circuit supporting effortful reward-seeking behavior
Fetcho, R. N., Parekh, P. K., Chou, J., Kenwood, M. M., Chalençon, L., Estrin, D. J., Johnson, M. M., & Liston, C. M. (2023) · Neuron · doi:10.1016/j.neuron.2023.10.020 · Cited by 44
Takahashi, Y. K., Stalnaker, T. A., Mueller, L. E., Harootonian, S. K., Langdon, A., & Schoenbaum, G. M. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01310-x · Cited by 37
An Active-Inference Approach to Second-Person Neuroscience
Lehmann, K., Bolis, D., Friston, K. J., Schilbach, L., Ramstead, M. J. D., & Kanske, P. (2023) · Perspectives on Psychological Science · doi:10.1177/17456916231188000 · Cited by 53
Behavior-relevant top-down cross-modal predictions in mouse neocortex
Han, S., & Helmchen, F. (2024) · Nature Neuroscience · doi:10.1038/s41593-023-01534-x · Cited by 43
Rational inattention and tonic dopamine
Mikhael, J. G., Lai, L., & Gershman, S. J. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008659 · Cited by 34
Self-citationChen, W., Liang, J., Wu, Q., & Han, Y. (2024) · Nature Communications · doi:10.1038/s41467-024-50388-9 · Cited by 32
A systems-neuroscience model of phasic dopamine.
Mollick, J., Hazy, T. E., Krueger, K. A., Nair, A., Mackie, P., Herd, S. A., & O’Reilly, R. C. (2020) · Psychological Review · doi:10.1037/rev0000199 · Cited by 23
Inference as a fundamental process in behavior
Bartolo, R., & Averbeck, B. B. (2020) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2020.06.005 · Cited by 22
Learning differentially shapes prefrontal and hippocampal activity during classical conditioning
Klee, J. L., Souza, B. C., & Battaglia, F. P. (2021) · eLife · doi:10.7554/elife.65456 · Cited by 24
Reappraising the role of dopamine in adolescent risk-taking behaviorPreprint
França, T. F. A., & Pompéia, S. (2022) · PsyArXiv (OSF Preprints) · doi:10.1016/j.neubiorev.2023.105085 · Cited by 24
Margolis, A. E., Liu, R., Conceição, V. A., Ramphal, B., Pagliaccio, D., DeSerisy, M., Koe, E., Selmanovic, E., Raudales, A., Emanet, N., et al. (2022) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2022.104645 · Cited by 23
Effects of Amygdala Lesions on Object-Based Versus Action-Based Learning in Macaques
Taswell, C. A., Costa, V. D., Basile, B. M., Pujara, M. S., Jones, B., Manem, N., Murray, E. A., & Averbeck, B. B. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa241 · Cited by 21
Executive Function Assigns Value to Novel Goal-Congruent Outcomes
McDougle, S. D., Ballard, I. C., Baribault, B., Bishop, S. J., & Collins, A. G. E. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab205 · Cited by 25
20 Years of Aberrant Salience in Psychosis: What Have We Learned?
Corlett, P. R., & Fraser, K. M. (2025) · American Journal of Psychiatry · doi:10.1176/appi.ajp.20240556 · Cited by 22
Prospective contingency explains behavior and dopamine signals during associative learning
Qian, L., Burrell, M., Hennig, J. A., Matias, S., Murthy, V. N., Gershman, S. J., & Uchida, N. (2025) · Nature Neuroscience · doi:10.1038/s41593-025-01915-4 · Cited by 38
Self-citationHow structure informs and transforms chemogenetics
Roth, B. L. (2019) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2019.01.016 · Cited by 19
Medial prefrontal cortex population activity is plastic irrespective of learning
Singh, A. K., Peyrache, A., & Humphries, M. D. (2019) · Journal of Neuroscience · doi:10.1523/jneurosci.1370-17.2019 · Cited by 20
Mahmoodi, A., Luo, S., Harbison, C., Piray, P., & Rushworth, M. F. S. (2024) · Neuron · doi:10.1016/j.neuron.2024.09.001 · Cited by 17
Attention-related modulation of caudate neurons depends on superior colliculus activity
Herman, J. P., Arcizet, F., & Krauzlis, R. J. (2020) · eLife · doi:10.7554/elife.53998 · Cited by 17
Qian, L., Burrell, M., Hennig, J. A., Matias, S., Murthy, V. N., Gershman, S. J., & Uchida, N. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.02.05.578961 · Cited by 16
Self-citationRíos, A., Nonomura, S., Kato, S., Yoshida, J., Matsushita, N., Nambu, A., Takada, M., Hira, R., Kobayashi, K., Sakai, Y., et al. (2023) · Communications Biology · doi:10.1038/s42003-023-05288-x · Cited by 15
Pereira, U., Hou, H., Svoboda, K., & Wang, X. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2318521121 · Cited by 14
Papalini, S., Lange, I., Bakker, J. M., Michielse, S., Marcelis, M. C., Wichers, M., Vervliet, B., van Os, J., van Amelsvoort, T., Goossens, L., et al. (2019) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2019.02.002 · Cited by 14
Gorrell, S., Shott, M. E., Pryor, T. L., & Frank, G. (2023) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2023.07.007 · Cited by 12
Leading indicators of mental representation in construction hazard recognition
Zhang, Q., Zhang, D., & Liao, P. (2021) · International Journal of Occupational Safety and Ergonomics · doi:10.1080/10803548.2021.1952005 · Cited by 11
Dissociation and integration of outcome and state uncertainty signals in cognitive control
Alexander, W. H., Deraeve, J., & Vassena, E. (2023) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-023-01091-7 · Cited by 11
Kalbe, F., & Schwabe, L. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab402 · Cited by 10
Dopaminergic and prefrontal dynamics co-determine mouse decisions in a spatial gambling task
Bousseyrol, E., Didienne, S., Takillah, S., Solié, C., Côme, M., Yahia, T. A., Mondoloni, S., Vicq, É., Tricoire, L., Mourot, A., et al. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112523 · Cited by 17
Dopamine-independent state inference mediates expert reward guided decision makingPreprint
Blanco-Pozo, M., Akam, T. E., & Walton, M. E. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.06.25.449995 · Cited by 10
Prediction error in dopamine neurons during associative learning
Amo, R. (2023) · Neuroscience Research · doi:10.1016/j.neures.2023.07.003 · Cited by 11
Ding, M., Tomsick, P. L., Young, R. A., & Jadhav, S. P. (2025) · Journal of Neuroscience · doi:10.1523/jneurosci.1670-24.2025 · Cited by 14
Skirzewski, M., Molotchnikoff, S., Hernandez, L. F., & Maya‐Vetencourt, J. F. (2022) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2021.806376 · Cited by 9
Wang, Z., Lou, S., Ma, X., Guo, H., Liu, Y., Chen, W., Lin, D., & Yang, Y. (2023) · BMC Biology · doi:10.1186/s12915-023-01529-x · Cited by 11
Automated and parallelized spike collision tests to identify spike signal projections
Mitani, K., Kawabata, M., Isomura, Y., & Sakai, Y. (2022) · iScience · doi:10.1016/j.isci.2022.105071 · Cited by 7
Neural basis of learning guided by sensory confidence and reward valuePreprint
Lak, A., Okun, M., Moss, M. M., Gurnani, H., Farrell, K., Wells, M. J., Reddy, C. B., Kepecs, Á., Harris, K. D., & Carandini, M. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/411413 · Cited by 9
Biologically plausible local synaptic learning rules robustly implement deep supervised learning
Konishi, M., Igarashi, K. M., & Miura, K. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1160899 · Cited by 9
Kalhan, S., Garrido, M. I., Hester, R., & Redish, A. D. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.09.032 · Cited by 9
Eckstein, M. K., Wilbrecht, L., & Collins, A. G. E. (2021) · doi:10.31234/osf.io/e7kwx · Cited by 7
Miranda-Barrientos, J. A., Adiraju, S. S., Rehg, J. J., Totty, M. S., Hallock, H. L., Li, Y., Carr, G. V., & Martinowich, K. (2025) · Translational Psychiatry · doi:10.1038/s41398-025-03707-7 · Cited by 6
Mahmud, A., & Karaman, M. A. (2026) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2026.1843871 · Cited by 7
Kato, A., & Morita, K. (2025) · Journal of Neuroscience · doi:10.1523/jneurosci.0170-25.2025 · Cited by 6
Expectancy-related changes in firing of dopamine neurons depend on hippocampusPreprint
Takahashi, Y. K., Zhang, Z., Montesinos-Cartagena, M., Kahnt, T., Langdon, A., & Schoenbaum, G. M. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.07.19.549728 · Cited by 6
Uncertainty and reward histories have distinct effects on decisions after wins and losses
Kalhan, S., Magnard, R., Zhang, Z., Cheng, Y., & Janak, P. H. (2026) · Scientific Reports · doi:10.1038/s41598-026-37554-3 · Cited by 5
Wang, H., Ortega, H. K., Kelly, E. B., Indajang, J., Savalia, N. K., Glaeser-Khan, S., Feng, J., Li, Y., Kaye, A. P., & Kwan, A. C. (2025) · Science Advances · doi:10.1126/sciadv.adr9916 · Cited by 7
Cunliffe, G., Tan, L. Y., Jung, S., Turner, J., & Gigg, J. (2025) · Communications Biology · doi:10.1038/s42003-025-08546-2 · Cited by 4
Skowron, A., Kosciessa, J. Q., Lorenz, R. C., Hertwig, R., van den Bos, W., & Garrett, D. D. (2025) · Cerebral Cortex · doi:10.1093/cercor/bhaf219 · Cited by 4
Shikano, Y., Yagishita, S., Tanaka, K. F., & Takata, N. (2023) · European Journal of Neuroscience · doi:10.1111/ejn.15945 · Cited by 5
Policy Complexity Suppresses Dopamine Responses
Gershman, S. J., & Lak, A. (2025) · Journal of Neuroscience · doi:10.1523/jneurosci.1756-24.2024 · Cited by 5
Self-citationModeling fashion as an emergent collective behavior of bored individuals
Seiler, J. P.-H., & Rumpel, S. (2023) · Scientific Reports · doi:10.1038/s41598-023-47749-7 · Cited by 4
Expectancy-related changes in firing of dopamine neurons depend on hippocampus
Zhang, Z., Takahashi, Y. K., Montesinos-Cartagena, M., Kahnt, T., Langdon, A., & Schoenbaum, G. M. (2024) · Nature Communications · doi:10.1038/s41467-024-53308-z · Cited by 10
Ding, M., Tomsick, P. L., Young, R. A., & Jadhav, S. P. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.09.611811 · Cited by 4
Influence of Critical Variables on Prefrontal Cortex Activity in Hazard Search
Zhang, Q., & Liao, P. (2020) · ASCE Construction Research Congress (CRC) · doi:10.1061/9780784482872.028 · Cited by 3
Brain mechanism of foraging: reward-dependent synaptic plasticity or neural integration of values?Preprint
Pereira, U., Hou, H., Svoboda, K., & Wang, X. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.09.25.509030 · Cited by 3
Neural mechanisms of confidence propagation in hierarchical partially observable decision-making
Katayama, R., Yoshida, W., Amemori, K., & Ishii, S. (2025) · iScience · doi:10.1016/j.isci.2025.112782 · Cited by 2
The function of mPFC-NAc circuit in decision impulsivity: A study based on an animal model
Zhuo, L., Xiangyu, Z. E. N. G., Bing, W. U., Niu, R., Ping, Y. U., & Weiwen, W. A. N. G. (2022) · Acta Psychologica Sinica · doi:10.3724/sp.j.1041.2023.00556 · Cited by 2
Belief inference for hierarchical hidden states in spatial navigation
Katayama, R., Shiraki, R., Ishii, S., & Yoshida, W. (2024) · Communications Biology · doi:10.1038/s42003-024-06316-0 · Cited by 2
Metadata
- Authors
- Starkweather, C. K., Gershman, S. J., & Uchida, N.
- Affiliations
- Harvard University (US)
- Corresponding author
- Gershman, Samuel J.
- Uchida, Naoshige
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2018-05
- Volume
- 98
- Issue
- 3
- Pages
- 616-629.e6
- ISSN
- 0896-6273 (ISSN-L)
- PMC
- PMC5934341
- OpenAlex
- W2797393690
- Cited by
- 136
- References
- 57
- Citations by year
- 2026: 13 · 2025: 23 · 2024: 14 · 2023: 19 · 2022: 12 · 2021: 17 · 2020: 19 · 2019: 13 · 2018: 6
- FWCI
- 6.26
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Neuron (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- NIH (R01MH095953, R01MH101207)
- CRCNS (R01-1207833, F30MH112242-01A1, T32MH020017, T32GM007753)
- Harvard Brain Science Initiative
- Simons Collaboration on Global Brain
- Harvard Mind Brain and Behavior
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Keywords
- medial prefrontal cortex, dopamine, reward prediction error, state uncertainty, hidden state inference, midbrain dopaminergic neurons, neural inactivation, classical conditioning, computational modeling
- MeSH terms
- Action Potentials, Animals, Dopamine, Forecasting, Male, Mice, Inbred C57BL, Mice, Transgenic, Nerve Net, Odorants, Reward, Prefrontal Cortex, Uncertainty, Mice
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
