The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Dopamine reward prediction error coding
Open accessAbstract
Reward prediction errors consist of the differences between received and predicted rewards. They are crucial for basic forms of learning about rewards and make us strive for more rewards-an evolutionary beneficial trait. Most dopamine neurons in the midbrain of humans, monkeys, and rodents signal a reward prediction error; they are activated by more reward than predicted (positive prediction error), remain at baseline activity for fully predicted rewards, and show depressed activity with less reward than predicted (negative prediction error). The dopamine signal increases nonlinearly with reward value and codes formal economic utility. Drugs of addiction generate, hijack, and amplify the dopamine reward signal and induce exaggerated, uncontrolled dopamine effects on neuronal plasticity. The striatum, amygdala, and frontal cortex also show reward prediction error coding, but only in subpopulations of neurons. Thus, the important concept of reward prediction errors is implemented in neuronal hardware.
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“This facilitates the formation of LTM cause/effect relations between particular needs and their adequate aims and objects, which in turn yields the iterative “reward prediction error” cycle that codes ongoing learning from experience (see Schultz, 2016).”
From The Hard Problem of Consciousness and the Free Energy Principle (2019) · cites this paper as Schultz, 2016
Experimental — accuracy not yet measuredIn the citing paper
“The reward prediction error, in which changes in dopamine firing encode differences between an expected vs. actual reward outcome, allows for dynamic learning from experience and updates in reward expectations (Gradin et al., 2011; Schultz, 2016).”
From The effects of early life stress on reward processing (2018) · cites this paper as Schultz, 2016
In the citing paper
“Dopamine signaling to the basal ganglia, including the nucleus accumbens, also plays a role in what is known as the reward prediction error, a subconstruct of approach motivation.”
From The effects of early life stress on reward processing (2018) · cites this paper as Schultz, 2016
In the citing paper
“It was thus shown that dopaminergic neurons increase the firing rate for unpredicted reward outcomes only, whereas expected rewards provoke no change in dopamine firing rate.”
From Circuit Mechanisms of Reward, Anhedonia, and Depression (2019) · cites this paper as Schultz, 2016
In the citing paper
“This is also true for aversive stimuli, with decreased firing rates of dopaminergic neurons below baseline.”
From Circuit Mechanisms of Reward, Anhedonia, and Depression (2019) · cites this paper as Schultz, 2016
In the citing paper
“The increase of firing rate is affected in a size-dependent manner (Schultz, 2016).”
From Circuit Mechanisms of Reward, Anhedonia, and Depression (2019) · cites this paper as Schultz, 2016
In the citing paper
“given that, according to reinforcement learning theory, the US response is a driver of learning, while the CS response is considered a consequence of learning6,18–20.”
From Dopamine dynamics as a regulatory mechanism for shifting between defensive and reward-seeking behaviors (2026) · cites this paper as [20]
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
Rao, R. P. N., & Ballard, D. H. (1999) · Nature Neuroscience · doi:10.1038/4580 · Cited by 5,483
Predictive reward signal of dopamine neurons
Schultz, W. (1998) · Journal of Neurophysiology · doi:10.1152/jn.1998.80.1.1 · Cited by 4,564
Increasing risk: I. A definition
Rothschild, M., & Stiglitz, J. (1970) · Journal of Economic Theory · doi:10.1016/0022-0531(70)90038-4 · Cited by 3,760
Olds, J., & Milner, P. (1954) · Journal of Comparative and Physiological Psychology · doi:10.1037/h0058775 · Cited by 3,176
Kapur, S. (2003) · American Journal of Psychiatry · doi:10.1176/appi.ajp.160.1.13 · Cited by 2,813
Dopamine-dependent prediction errors underpin reward-seeking behaviour in humans
Pessiglione, M., Seymour, B., Flandin, G., Dolan, R. J., & Frith, C. D. (2006) · Nature · doi:10.1038/nature05051 · Cited by 1,584
Temporal difference models and reward-related learning in the human brain
O'Doherty, J. P., Dayan, P., Friston, K., Critchley, H., & Dolan, R. J. (2003) · Neuron · doi:10.1016/s0896-6273(03)00169-7 · Cited by 1,522
Toward a modern theory of adaptive networks: Expectation and prediction.
Sutton, R. S., & Barto, A. G. (1981) · Psychological Review · doi:10.1037/0033-295x.88.2.135 · Cited by 1,450
Two types of dopamine neuron distinctly convey positive and negative motivational signals
Matsumoto, M., & Hikosaka, O. (2009) · Nature · doi:10.1038/nature08028 · Cited by 1,378
Flagged first, then by citations
Research works printed but not linked: 3
- Pavlov PI., 1927, Conditioned Reflexes. London, UK: Oxford University Press:
- Rescorla RA., 1972, Classical Conditioning II: Current Research and Theory. New York, NY: Appleton Century Crofts:, 64
- von Neumann J., 1944, The Theory of Games and Economic Behavior. Princeton, NJ: Princeton University Press:
Cited by
Sorted by citations
Items 401–500 of 595
Proia, P., Sclafani, A., Pagliaro, A., Alioto, A., Boatta, A., Baldassano, S., Messina, G., Loi, E., Cortis, C., Sangiorgio, A., et al. (2026) · Brain Sciences · doi:10.3390/brainsci16070694 · Cited by 1
Nadolsky, S., Kessel, S., Krumm, Z. A., & Tinsley, G. M. (2026) · Obesity Pillars · doi:10.1016/j.obpill.2026.100302 · Cited by 1
Region-specific weighting of sensory intensity and reward prediction error by dopamine signals
Bouchard, S.-J., Boutin, J., Lévesque, M., & Breton‐Provencher, V. (2026) · iScience · doi:10.1016/j.isci.2026.117130 · Cited by 1
A Complementary Learning Systems Approach to Temporal Difference Learning
Blakeman, S., & Denis Mareschal, B. (2019) · BIROn (Birkbeck, University of London) · doi:10.48550/arxiv.1905.02636 · Cited by 1
Hyperpolarization-Activated Cyclic Nucleotide-Gated Channels
Masi, A., Romanelli, M. N., Mannaioni, G., & Cerbai, E. (2019) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780190669164.013.23 · Cited by 1
Dynamic modulation of neural feedback processing and attention during spatial probabilistic learning
Pütz, C., van den Berg, B., & Lorist, M. M. (2022) · iScience · doi:10.1016/j.isci.2022.104302 · Cited by 2
Neurobiology and Mechanisms of Nicotine Addiction
Gogineni, H. P., Sachs, D. P. L., & Brunzell, D. H. (2023) · Respiratory medicine · doi:10.1007/978-3-031-24914-3_3 · Cited by 2
Irreversibility in belief dynamics: Unraveling the link to cognitive effort
Vodret, M. (2024) · Physical review. E · doi:10.1103/physreve.110.014304 · Cited by 1
Cue Reactivity in Internet Game Addicts
黄, 鑫. (2024) · Advances in Psychology · doi:10.12677/ap.2024.147508 · Cited by 1
An overview of reward prediction error and its links with dopamine
Yao, T. (2024) · Theoretical and Natural Science · doi:10.54254/2753-8818/44/20240450 · Cited by 1
Learning the Cognitive Skill of Topographic Map Reading Through Adaptive, High-Repetition Training
Reber, P. J., Grandoit, E., Schmidt, K., Dixon, T. C., & McRobert, C. P. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-77857-6_6 · Cited by 1
La dopamine messagère du désir
Szalavitz, M. (2017) · Cerveau & Psycho · doi:10.3917/cerpsy.088.0014 · Cited by 1
Agentology: Crosspollination of Science and Philosophy
Sharov, A. A., & Tønnessen, M. (2021) · Biosemiotics/Biosemiotics. Bookseries · doi:10.1007/978-3-030-89484-9_12 · Cited by 1
Selection of active defensive behaviors relies on extended amygdala dopamine D2 receptorsPreprint
Castell, L., Gall, V. L., Cutando, L., Puighermanal, E., Jercog, D., Tarot, P., Tassou, A., Harrus, A.-G., Rubinstein, M., Nouvian, R., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.02.24.432692 · Cited by 1
Neuralink: A cheat code for evolution?Preprint
Gadda, N. S. (2022) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/w76pz · Cited by 1
Pupillo, F., Ortiz-Tudela, J., Bruckner, R., & Shing, Y. L. (2022) · doi:10.31234/osf.io/4qmzk · Cited by 1
Jansen, M., Lockwood, P. L., Cutler, J., & de Bruijn, E. (2022) · doi:10.31234/osf.io/h7yrz · Cited by 1
Editorial: New challenges and future perspectives in motivation and reward
Chen, W., & Yang, H. (2023) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2023.1293938 · Cited by 1
Distinct roles of dopamine and acetylcholine in delay- and effort-based decision making in humansPreprint
Abdoust, M. E., Froböse, M. I., Schnitzler, A., Schreivogel, E., & Jocham, G. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.11.20.566558 · Cited by 1
Unravelling the Role of the Dopamine D1 Receptor in Anhedonia, Asociality, and Avolition
Witt, K. (2023) · doi:10.26686/wgtn.24646128 · Cited by 1
Parvizi-Wayne, D., & Severs, L. (2024) · doi:10.31234/osf.io/65en8 · Cited by 1
Silva, G. M., Picone, J. A., Kaplan, A. L., Park, C. R., Lira, D. P., Kim, R. K., Truby, N. L., Neve, R. L., Cui, X., & Hamilton, P. J. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.28.610134 · Cited by 1
Low-Power Biopotential Signal Acquisition System for Biomedical Applications
Tasneem, N. T. (2022) · University of North Texas Digital Library (University of North Texas) · doi:10.12794/metadc1944354 · Cited by 1
Thanarajah, S. E., Uckermark, C., Molter, F., Schiweck, C., Meyer, K., Aichholzer, M., Bouzouina, A., Forbes, P., Allen, M., Oehrn, C. R., et al. (2025) · Research Square · doi:10.21203/rs.3.rs-6760313/v1 · Cited by 1
Valjent, E., Castell, L., Gall, V. L., Cutando, L., Petit, C., Puighermanal, E., Kim, H.-R., Jercog, D., Tarot, P., Tassou, A., et al. (2023) · Research Square · doi:10.21203/rs.3.rs-2595678/v1 · Cited by 1
Rossini, C. A. A. (2026) · doi:10.64628/aai.uhnmumemt · Cited by 1
Parent, K. L. (2017) · UA Campus Repository (The University of Arizona)
Effect of time of day on reward circuitry: Further thoughts on methods, prompted by Steel et al 2018Preprint
Murray, G., Orr, C., Byrne, J. E. M., Hughes, M. E., Rossell, S. L., & Johnson, S. L. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.12245
van Leeuwen, J. M. C. (2019) · Utrecht University Repository (Utrecht University)
Optical Imaging of Dopamine Dynamics and Decoding its Role in Arousal and Salience
Cho, J. R. (2019) · CaltechTHESIS (California Institute of Technology) · doi:10.7907/tnga-5x08
Kruse, I. M. (2019) · European Journal of Neuroscience · doi:10.1111/ejn.14519
The Relevant Signals for Sensorimotor Adaptation in Human Reaching
Parvin, D. E. (2019) · eScholarship (California Digital Library)
Individual differences in successful self-regulation of the dopaminergic midbrainPreprint
Hellrung, L., Kirschner, M., Sulzer, J. S., Sladky, R., Scharnowski, F., Herdener, M., & Tobler, P. N. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/863639
Adaptive Economics: A neuroethological approach to the study of preferences, biases, and choice
Bujold, P. M. (2019) · Apollo (University of Cambridge) · doi:10.17863/cam.48007
Imaging of feeding and eating disorders
Kurniadi, N. E., Wierenga, C. E., Berner, L. A., & Kaye, W. H. (2020) · doi:10.1093/med/9780198713005.003.0105
De‐mobilisation of NMDA receptors in midbrain dopamine neurons: a quantum of reward?
Jones, S. (2021) · The Journal of Physiology · doi:10.1113/jp281559
Rizzo, M. J., & Whitman, G. (2019) · Cambridge University Press eBooks · doi:10.1017/9781139061810.004
Pfaff, D. W. (2018) · Cambridge University Press eBooks · doi:10.1017/9781108377485.007
Knowledge Problems in Paternalist Policymaking
Rizzo, M. J., & Whitman, G. (2019) · Cambridge University Press eBooks · doi:10.1017/9781139061810.008
The Political Economy of Paternalist Policymaking
Rizzo, M. J., & Whitman, G. (2019) · Cambridge University Press eBooks · doi:10.1017/9781139061810.009
Moving the Needle: Cultivating Systemic Change in Juvenile Services
Rhodes, K., Rhodes, A., Bear, W., & Brendtro, L. K. (2021) · Journal of Applied Juvenile Justice Services · doi:10.52935/21.1881545.06
Common Threads, Escape Routes, and Paths Forward
Rizzo, M. J., & Whitman, G. (2019) · Cambridge University Press eBooks · doi:10.1017/9781139061810.011
Slippery Slopes in Paternalist Policymaking
Rizzo, M. J., & Whitman, G. (2019) · Cambridge University Press eBooks · doi:10.1017/9781139061810.010
Pfaff, D. W. (2018) · Cambridge University Press eBooks · doi:10.1017/9781108377485.006
Deficient Foundations for Behavioral Policymaking
Rizzo, M. J., & Whitman, G. (2019) · Cambridge University Press eBooks · doi:10.1017/9781139061810.007
Rizzo, M. J., & Whitman, G. (2019) · Cambridge University Press eBooks · doi:10.1017/9781139061810.006
Pütz, C., van den Berg, B., & Lorist, M. M. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.10.29.466415
Rizzo, M. J., & Whitman, G. (2019) · Cambridge University Press eBooks · doi:10.1017/9781139061810.002
Rizzo, M. J., & Whitman, G. (2019) · Cambridge University Press eBooks · doi:10.1017/9781139061810.003
Rizzo, M. J., & Whitman, G. (2019) · Cambridge University Press eBooks · doi:10.1017/9781139061810.001
Rizzo, M. J., & Whitman, G. (2019) · Cambridge University Press eBooks · doi:10.1017/9781139061810.005
(2019) · Cambridge University Press eBooks · doi:10.1017/9781139061810.012
Goh, J. O. S. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780190057695.013.22
(2020) · Dunod eBooks · doi:10.3917/dunod.hoden.2020.01.0287
Rizzo, M. J., & Whitman, G. (2019) · Cambridge University Press eBooks · doi:10.1017/9781139061810.013
Reuter, J. (2018) · Elsevier eBooks · doi:10.1016/b978-3-437-15340-2.00004-5
Highgate, Q. (2022) · Open Access Victoria University of Wellington | Te Herenga Waka (Figshare) · doi:10.26686/wgtn.21607191
Walsh, A. T., Dayan, P., Olsson, A., & Guitart‐Masip, M. (2022) · doi:10.31234/osf.io/jbuqt
Pereg, M., Hertz, U., Ben-Artzi, I., & Shahar, N. (2022) · doi:10.31234/osf.io/28js3
Cezar, L. C. (2022) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.10.2022.tde-20012023-112002
Genética do comportamento no transtorno por uso de substâncias psicoativas
da Silveira, F. M., Rodrigues, F. D. A. A., & Pinto, M. D. S. (2022) · Journal · doi:10.56238/cpahjournalv1n2-004
New perspectives on financial decisions
Jansen, A. P. (2023) · University of Groningen research database (University of Groningen / Centre for Information Technology) · doi:10.33612/diss.625568340
Patel, K., Rotaru, K., Schmidtke, K. A., Judah, G., Melkonyan, T., & Vlaev, I. (2023) · Research Square · doi:10.21203/rs.3.rs-2820787/v1
Zika, O., Appel, J. E., Klinge, C., Shkreli, L., Browning, M. D., Wiech, K., & Reinecke, A. (2023) · doi:10.31234/osf.io/e5wdv
Characterization of Identified Dopaminergic Neurons in the Mouse Olfactory Bulb and MidbrainPreprint
Lau, M. Y. H., Gadiwalla, S., Jones, S., & Galliano, E. (2023) · doi:10.22541/au.169333976.64394830/v1
Characterization of Identified Dopaminergic Neurons in the Mouse Forebrain and MidbrainPreprint
Lau, M. Y. H., Gadiwalla, S., Jones, S., & Galliano, E. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.08.29.554772
Phase Transitions from Low GA States
Pfaff, D. W. (2018) · Cambridge University Press eBooks · doi:10.1017/9781108377485.009
A Vertically Integrated System
Pfaff, D. W. (2018) · Cambridge University Press eBooks · doi:10.1017/9781108377485.011
Pfaff, D. W. (2018) · Cambridge University Press eBooks · doi:10.1017/9781108377485.008
Brain activities for Open Intelligence : Application of Brain Science for Behavior Adaptation
Shimoda, S. (2023) · Journal of the Robotics Society of Japan · doi:10.7210/jrsj.41.591
Pfaff, D. W. (2018) · Cambridge University Press eBooks · doi:10.1017/9781108377485.002
Pfaff, D. W. (2018) · Cambridge University Press eBooks · doi:10.1017/9781108377485.001
Giant Cells in the Medullary Reticular Formation
Pfaff, D. W. (2018) · Cambridge University Press eBooks · doi:10.1017/9781108377485.003
Pfaff, D. W. (2018) · Cambridge University Press eBooks · doi:10.1017/9781108377485.004
Pfaff, D. W. (2018) · Cambridge University Press eBooks · doi:10.1017/9781108377485.005
Motivation ist entscheidend: eine Perspektive für die Neuro-rehabilitation
Valenzuela, Y. M. (2023) · neuroreha · doi:10.1055/a-2124-4940
Smith, B. J., Lipsett, M., Cosme, D., Braun, V. A., O’Hagan, A. M. B., & Berkman, E. T. (2023) · Imaging Neuroscience · doi:10.1162/imag_a_00024
Tobacco Prevalence and Treatment
Fathi, J. T., & Kathuria, H. (2023) · Respiratory medicine · doi:10.1007/978-3-031-38412-7_3
Non-Linear Stochastic Model for Dopamine CyclePreprint
Šuvak, N., Ðorđević, J., & Milošević, M. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4607463
Pfaff, D. W. (2018) · Cambridge University Press eBooks · doi:10.1017/9781108377485.013
Roots of Consciousness and Its Disorders
Pfaff, D. W. (2018) · Cambridge University Press eBooks · doi:10.1017/9781108377485.010
Liu, Y., Wang, D., Wang, L., Yu, C., Wang, S., Huang, F. L., Wang, T., Jin, S., & Zhou, Q. (2023) · Research Square · doi:10.21203/rs.3.rs-3539931/v1
The Intrigue of Intermittent Reward and Cost: Unveiling Their Influence in Real Life
Ting, C., Liu, X., Lin, R., Shi, G., & Yang, Z. (2024) · Interdisciplinary Humanities and Communication Studies · doi:10.61173/xgzr8f61
L-DOPA and oxytocin influence the neural correlates of performance monitoring for self and others
Jansen, M., Overgaauw, S., & de Bruijn, E. R. A. (2024) · Psychopharmacology · doi:10.1007/s00213-024-06541-9 · Cited by 1
(2018) · Cambridge University Press eBooks · doi:10.1017/9781108377485.012
Rethinking Memory Impairments: Retrieval FailurePreprint
Alfei, J. M., Miller, R. R., Ryan, T. J., & Urcelay, G. P. (2024) · doi:10.31234/osf.io/zjxvu
More widespread and rigid neuronal representation of reward expectation underlies impulsive choicesPreprint
Cowan, R. L., Davis, T. S., Kundu, B., Rahimpour, S., Rolston, J. D., & Smith, E. H. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.11.588637
Campbell, M. G., Green, I., Pinto, S. R., & Uchida, N. (2024) · Elsevier eBooks · doi:10.1016/b978-0-12-820480-1.00131-5
Coletânea Ludovicense de Psicologia - Volume 5
Fernandes, M. A., Dias, E. S., Pereira, L. C. P., Reinaldo, M. D. S., & Muniz, K. V. F. (2024) · Editora Pascal LTDA eBooks · doi:10.29327/5414145
Smart segmentation supports transfer learningPreprint
Jafarpour, A., Knight, R. T., & Buffalo, E. A. (2024) · doi:10.31234/osf.io/tfvhb
Balaban, P. M., & Grigoryan, G. A. (2024) · Успехи физиологических наук · doi:10.31857/s0301179824020063
In-Depth Analysis of the Multiple Mechanisms of Habit Formation and Its Dynamic Influencing Factors
张, 雨. (2024) · Advances in Psychology · doi:10.12677/ap.2024.1410737
Reward mechanisms underlying substance use disorder: A role for learning and memory
Fetterly, T. L., Vera, V. A., & Wood, M. A. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-15754-7.00063-8
Eğitim Yönetiminde Motivasyon Teorileri ve Uygulamadaki Yansımaları
Demiroglari, B. (2025) · Akademisyen Kitabevi eBooks · doi:10.37609/akya.3551
Micanti, F., Caiazza, C., Franzese, L., D'Ambrosio, M., Solini, N., Iasevoli, F., Fornaro, M., de Bartolomeis, A., & Rago, V. (2025) · Eating Behaviors · doi:10.1016/j.eatbeh.2025.101961
Healthy happiness: The effect of happiness in promoting brain health
Broderick, M.-Z. L., Khan, Q., & Moradikor, N. (2025) · Progress in brain research · doi:10.1016/bs.pbr.2025.03.004
Metadata
- Authors
- Schultz, W.
- Affiliations
- University of Cambridge (GB)
- Corresponding author
- Schultz, Wolfram
- ORCID
- Schultz, Wolfram
- Venue
- Dialogues in Clinical Neuroscience
- Publisher
- Informa UK Limited
- Published
- 2016-03-31
- Volume
- 18
- Issue
- 1
- Pages
- 23-32
- ISSN
- 1294-8322 (ISSN-L)
- 1958-5969
- PMC
- PMC4826767
- OpenAlex
- W2419438565
- Cited by
- 597
- References
- 37
- Citations by year
- 2026: 84 · 2025: 83 · 2024: 77 · 2023: 68 · 2022: 64 · 2021: 53 · 2020: 36 · 2019: 62 · 2018: 45 · 2017: 20 · 2016: 4
- FWCI
- 24.37
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Dialogues in Clinical Neuroscience (publishedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- Wellcome Trust (093875)
- 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 › Neural dynamics and brain function
- Keywords
- dopamine, reward prediction error, midbrain dopaminergic neurons, reward learning, drug addiction, neuroplasticity, striatum, amygdala, frontal cortex
- MeSH terms
- Animals, Brain, Dopamine, Forecasting, Humans, Learning, Nerve Net, Neuronal Plasticity, Reward
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
