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The Dopaminergic Midbrain Encodes the Expected Certainty about Desired Outcomes
Open accessAbstract
Dopamine plays a key role in learning; however, its exact function in decision making and choice remains unclear. Recently, we proposed a generic model based on active (Bayesian) inference wherein dopamine encodes the precision of beliefs about optimal policies. Put simply, dopamine discharges reflect the confidence that a chosen policy will lead to desired outcomes. We designed a novel task to test this hypothesis, where subjects played a "limited offer" game in a functional magnetic resonance imaging experiment. Subjects had to decide how long to wait for a high offer before accepting a low offer, with the risk of losing everything if they waited too long. Bayesian model comparison showed that behavior strongly supported active inference, based on surprise minimization, over classical utility maximization schemes. Furthermore, midbrain activity, encompassing dopamine projection neurons, was accurately predicted by trial-by-trial variations in model-based estimates of precision. Our findings demonstrate that human subjects infer both optimal policies and the precision of those inferences, and thus support the notion that humans perform hierarchical probabilistic Bayesian inference. In other words, subjects have to infer both what they should do as well as how confident they are in their choices, where confidence may be encoded by dopaminergic firing.
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“This approach has been used, for example, to identify links between activity in neuromodulatory nuclei and computational trajectories, such as (precision-weighted) prediction errors or uncertainty (111, 115–117).”
From Can Bayesian Theories of Autism Spectrum Disorder Help Improve Clinical Practice? (2016) · cites this paper as [117]
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Citations
Cites
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
Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., & Joliot, M. (2002) · NeuroImage · doi:10.1006/nimg.2001.0978 · Cited by 16,319
A Stochastic Approximation Method
Robbins, H., & Monro, S. (1951) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177729586 · Cited by 9,643
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 fast diffeomorphic image registration algorithm
Ashburner, J. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.07.007 · Cited by 8,122
Conflict monitoring and cognitive control
Botvinick, M. M., Braver, T. S., Barch, D. M., Carter, C. S., & Cohen, J. D. (2001) · Psychological Review · doi:10.1037/0033-295x.108.3.624 · Cited by 7,351
Rao, R. P. N., & Ballard, D. H. (1999) · Nature Neuroscience · doi:10.1038/4580 · Cited by 5,483 · cited 2×
“It can be thought of as the discrete time version of predictive coding—that now predominates in models of perceptual inference and learning (Srinivasan 1982; Rao and Ballard 1999; Seth 2011; Bastos et al. 2012).”
BackgroundMaldjian, J. A., Laurienti, P. J., Kraft, R., & Burdette, J. H. (2003) · NeuroImage · doi:10.1016/s1053-8119(03)00169-1 · Cited by 5,066
A theory of cortical responses.
Friston, K. (2005) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2005.1622 · Cited by 4,578
Self-citationPredictive reward signal of dopamine neurons
Schultz, W. (1998) · Journal of Neurophysiology · doi:10.1152/jn.1998.80.1.1 · Cited by 4,564
Conflict monitoring and anterior cingulate cortex: an update
Botvinick, M. M., Cohen, J. D., & Carter, C. S. (2004) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2004.10.003 · Cited by 3,623
Canonical Microcircuits for Predictive Coding
Bastos, A. M., Usrey, W. M., Adams, R. A., Mangun, G. R., Fries, P., & Friston, K. J. (2012) · Neuron · doi:10.1016/j.neuron.2012.10.038 · Cited by 2,421 · cited 2×
“It can be thought of as the discrete time version of predictive coding—that now predominates in models of perceptual inference and learning (Srinivasan 1982; Rao and Ballard 1999; Seth 2011; Bastos et al. 2012).”
BackgroundSelf-citationThe debate over dopamine’s role in reward: the case for incentive salience
Berridge, K. C. (2007) · Psychopharmacology · doi:10.1007/s00213-006-0578-x · Cited by 2,347
RELATIVE AND ABSOLUTE STRENGTH OF RESPONSE AS A FUNCTION OF FREQUENCY OF REINFORCEMENT 1, 2
Herrnstein, R. J. (1961) · Journal of the Experimental Analysis of Behavior · doi:10.1901/jeab.1961.4-267 · Cited by 2,281
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
Self-citationBarto, A. (1998) · IFAC Proceedings Volumes · doi:10.1016/s1474-6670(17)38315-5 · Cited by 2,172
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
(2003) · Cited by 1,680
Modulation of memory fields by dopamine Dl receptors in prefrontal cortex
Williams, G. V., & Goldman-Rakic, P. S. (1995) · Nature · doi:10.1038/376572a0 · Cited by 1,593
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
Self-citationNeural Mechanisms for Interacting with a World Full of Action Choices
Cisek, P., & Kalaska, J. F. (2010) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.051508.135409 · Cited by 1,543
Context, cortex, and dopamine: A connectionist approach to behavior and biology in schizophrenia.
Cohen, J. D., & Servan‐Schreiber, D. (1992) · Psychological Review · doi:10.1037/0033-295x.99.1.45 · Cited by 1,513
Bayesian model selection for group studies
Stephan, K. E., Penny, W. D., Daunizeau, J., Moran, R. J., & Friston, K. J. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.03.025 · Cited by 1,423
Self-citationThe mysterious motivational functions of mesolimbic dopamine
Salamone, J. D., & Correa, M. (2012) · Neuron · doi:10.1016/j.neuron.2012.10.021 · Cited by 1,418
A free energy principle for the brain
Friston, K., Kilner, J., & Harrison, L. (2006) · Journal of Physiology-Paris · doi:10.1016/j.jphysparis.2006.10.001 · Cited by 1,369
Self-citationWhen the world becomes ‘too real’: a Bayesian explanation of autistic perception
Pellicano, E., & Burr, D. (2012) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2012.08.009 · Cited by 1,156
Cohen, J. D., McClure, S. M., & Yu, A. J. (2007) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2007.2098 · Cited by 1,121
Predictive coding: a fresh view of inhibition in the retina
Srinivasan, M. V., Laughlin, S. B., & Dubs, A. (1982) · Proceedings of the Royal Society of London. Series B. Biological Sciences · doi:10.1098/rspb.1982.0085 · Cited by 1,042 · cited 1×
“Keywords: active inference, confidence, dopamine, neuroeconomics, precision”
An Interoceptive Predictive Coding Model of Conscious Presence
Seth, A. K., Suzuki, K., & Critchley, H. D. (2012) · Frontiers in Psychology · doi:10.3389/fpsyg.2011.00395 · Cited by 950 · cited 1×
“volved in perceptual inference (Rao and Ballard 1999), action selection (Cisek and Kalaska 2010), and (more speculatively) interoceptive inference (Seth et al. 2011).”
BackgroundA causal link between prediction errors, dopamine neurons and learning
Steinberg, E. E., Keiflin, R., Boivin, J. R., Witten, I. B., Deisseroth, K. A., & Janak, P. H. (2013) · Nature Neuroscience · doi:10.1038/nn.3413 · Cited by 932
Frank, M. J. (2005) · Journal of Cognitive Neuroscience · doi:10.1162/0898929052880093 · Cited by 925
Action and behavior: a free-energy formulation
Friston, K. J., Daunizeau, J., Kilner, J., & Kiebel, S. J. (2010) · Biological Cybernetics · doi:10.1007/s00422-010-0364-z · Cited by 866 · cited 1×
“Expected precision needs to be widely broadcast, since it plays a crucial role in hierarchical inference (Friston et al. 2010; Friston 2011).”
BackgroundSelf-citationThe Computational Anatomy of Psychosis
Adams, R. A., Stephan, K. E., Brown, H. R., Frith, C. D., & Friston, K. J. (2013) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2013.00047 · Cited by 850 · cited 1×
“2013) or failures of sensory attenuation in schizophrenia (Adams et al. 2013).”
BackgroundSelf-citationA Bayesian account of ‘hysteria’
Edwards, M. J., Adams, R. A., Brown, H., Parees, I., & Friston, K. J. (2012) · Brain · doi:10.1093/brain/aws129 · Cited by 825
Self-citationOptimized EPI for fMRI studies of the orbitofrontal cortex
Deichmann, R., Gottfried, J. A., Hutton, C., & Turner, R. (2003) · NeuroImage · doi:10.1016/s1053-8119(03)00073-9 · Cited by 821
The short-latency dopamine signal: a role in discovering novel actions?
Redgrave, P., & Gurney, K. (2006) · Nature Reviews Neuroscience · doi:10.1038/nrn2022 · Cited by 762
From prediction error to incentive salience: mesolimbic computation of reward motivation.
Berridge, K. C. (2012) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2012.07990.x · Cited by 700
A Variational Baysian Framework for Graphical Models
Attias, H. T. (1999) · neural information processing systems · Cited by 694
Image Distortion Correction in fMRI: A Quantitative Evaluation
Hutton, C., Bork, A., Josephs, O., Deichmann, R., Ashburner, J., & Turner, R. (2002) · NeuroImage · doi:10.1006/nimg.2001.1054 · Cited by 667
A Bayesian foundation for individual learning under uncertainty
Mathys, C. (2011) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2011.00039 · Cited by 655
Self-citationRisky business: the neuroeconomics of decision making under uncertainty
Platt, M. L., & Huettel, S. A. (2008) · Nature Neuroscience · doi:10.1038/nn2062 · Cited by 556
Cognition and control in schizophrenia: a computational model of dopamine and prefrontal function
Braver, T. S., Barch, D. M., & Cohen, J. D. (1999) · Biological Psychiatry · doi:10.1016/s0006-3223(99)00116-x · Cited by 507
Probabilistic inference for solving discrete and continuous state Markov Decision Processes
Toussaint, M., & Storkey, A. (2006) · International Conference on Machine Learning · doi:10.1145/1143844.1143963 · Cited by 505
Absolute Coding of Stimulus Novelty in the Human Substantia Nigra/VTA
Bunzeck, N., & Düzel, E. (2006) · Neuron · doi:10.1016/j.neuron.2006.06.021 · Cited by 503
Dopamine: generalization and bonuses
Kakade, S. M., & Dayan, P. (2002) · Neural Networks · doi:10.1016/s0893-6080(02)00048-5 · Cited by 495
The translation of translational control by FMRP: therapeutic targets for FXS
Darnell, J. C., & Klann, E. (2013) · Nature Neuroscience · doi:10.1038/nn.3379 · Cited by 491
Perceptions as Hypotheses: Saccades as Experiments
Friston, K., Adams, R. A., Perrinet, L., & Breakspear, M. (2012) · Frontiers in Psychology · doi:10.3389/fpsyg.2012.00151 · Cited by 462
Self-citationHuys, Q. J. M., Eshel, N., O'Nions, E., Sheridan, L., Dayan, P., & Roiser, J. P. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002410 · Cited by 439
Efficient computation of optimal actions
Todorov, E. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0710743106 · Cited by 417
The Cortical Dopamine System: Role in Memory and Cognition
Goldman, P. S. (1997) · Advances in pharmacology · doi:10.1016/s1054-3589(08)60846-7 · Cited by 393
Dopamine, affordance and active inference
Friston, K. J., Shiner, T., FitzGerald, T., Galea, J. M., Adams, R., Brown, H., Dolan, R. J., Moran, R., Stephan, K. E., & Bestmann, S. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002327 · Cited by 381
Self-citationAsymptotic Distribution of Stochastic Approximation Procedures
Sacks, J. (1958) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177706619 · Cited by 375
Hierarchical Prediction Errors in Midbrain and Basal Forebrain during Sensory Learning
Iglesias, S., Mathys, C., Brodersen, K. H., Kasper, L., Piccirelli, M., den Ouden, H. E. M., & Stephan, K. E. (2013) · Neuron · doi:10.1016/j.neuron.2013.09.009 · Cited by 342
Self-citationThe anatomy of choice: active inference and agency
Friston, K., Schwartenbeck, P., FitzGerald, T., Moutoussis, M., Behrens, T., & Dolan, R. J. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00598 · Cited by 340
Self-citationWhat is optimal about motor control?
Friston, K. (2011) · Neuron · doi:10.1016/j.neuron.2011.10.018 · Cited by 332
Self-citationBotvinick, M., & Toussaint, M. (2012) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2012.08.006 · Cited by 316
Free Energy, Precision and Learning: The Role of Cholinergic Neuromodulation
Moran, R. J., Campo, P., Symmonds, M., Stephan, K. E., Dolan, R. J., & Friston, K. J. (2013) · The Journal of Neuroscience · doi:10.1523/jneurosci.4255-12.2013 · Cited by 312
Self-citationThe temporal precision of reward prediction in dopamine neurons
Fiorillo, C. D., Newsome, W. T., & Schultz, W. (2008) · Nature Neuroscience · doi:10.1038/nn.2159 · Cited by 310
Optimal control as a graphical model inference problem
Kappen, H. J., Gómez, V., & Opper, M. (2012) · Machine Learning · doi:10.1007/s10994-012-5278-7 · Cited by 296
The anatomy of choice: dopamine and decision-making.
Friston, K., Schwartenbeck, P., FitzGerald, T., Moutoussis, M., Behrens, T., & Dolan, R. J. (2014) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2013.0481 · Cited by 289
Self-citationThermodynamics as a theory of decision-making with information-processing costs
Ortega, P. A., & Braun, D. A. (2013) · Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rspa.2012.0683 · Cited by 279
Excessive cocaine use results from decreased phasic dopamine signaling in the striatum
Willuhn, I., Burgeno, L. M., Groblewski, P. A., & Phillips, P. E. M. (2014) · Nature Neuroscience · doi:10.1038/nn.3694 · Cited by 257
The dopamine hypothesis of drug addiction and its potential therapeutic value
Diana, M. (2011) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2011.00064 · Cited by 251
General duality between optimal control and estimation
Todorov, E. (2008) · 2008 47th IEEE Conference on Decision and Control · doi:10.1109/cdc.2008.4739438 · Cited by 248
Functional imaging of the human dopaminergic midbrain
Düzel, E., Bunzeck, N., Guitart-Masip, M., Wittmann, B., Schott, B. H., & Tobler, P. N. (2009) · Trends in Neurosciences · doi:10.1016/j.tins.2009.02.005 · Cited by 216
Guitart-Masip, M., Fuentemilla, L., Bach, D. R., Huys, Q. J. M., Dayan, P., Dolan, R. J., & Duzel, E. (2011) · The Journal of Neuroscience · doi:10.1523/jneurosci.6376-10.2011 · Cited by 216
Self-citationExploration, novelty, surprise, and free energy minimization
Schwartenbeck, P., FitzGerald, T. H. B., Dolan, R. J., & Friston, K. J. (2013) · Frontiers in Psychology · doi:10.3389/fpsyg.2013.00710 · Cited by 213
Self-citationA Neural Signature of Hierarchical Reinforcement Learning
Fernandes, J. J. F. R., Solway, A., Diuk, C., McGuire, J. T., Barto, A. G., Niv, Y., & Botvinick, M. (2011) · Neuron · doi:10.1016/j.neuron.2011.05.042 · Cited by 207
Politis, M., Loane, C., Wu, K., O’Sullivan, S. S., Woodhead, Z., Kiferle, L., Lawrence, A. D., Lees, A. J., & Piccini, P. (2013) · Brain · doi:10.1093/brain/aws326 · Cited by 191
Helms, G., Draganski, B., Frackowiak, R. S. J., Ashburner, J., & Weiskopf, N. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.03.053 · Cited by 181
Risk prediction and aversion by anterior cingulate cortex
Brown, J. W., & Braver, T. S. (2007) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/cabn.7.4.266 · Cited by 166
Dopamine-modulated dynamic cell assemblies generated by the GABAergic striatal microcircuit
Humphries, M. D., Wood, R., & Gurney, K. (2009) · Neural Networks · doi:10.1016/j.neunet.2009.07.018 · Cited by 129
Neuroanatomy of Dopamine: Reward and Addiction
Taber, K. H., Black, D. N., Porrino, L. J., & Hurley, R. A. (2012) · Journal of Neuropsychiatry · doi:10.1176/appi.neuropsych.24.1.1 · Cited by 111
Brown, J. W., & Braver, T. S. (2007) · Brain Research · doi:10.1016/j.brainres.2007.06.080 · Cited by 92
Active Inference, Evidence Accumulation, and the Urn Task
FitzGerald, T. H. B., Schwartenbeck, P., Moutoussis, M., Dolan, R. J., & Friston, K. J. (2014) · Neural Computation · doi:10.1162/neco_a_00699 · Cited by 79
Self-citationAge-related changes in brain structural covariance networks
Li, X., Pu, F., Fan, Y., Niu, H., Li, S., & Li, D. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00098 · Cited by 76
Luciw, M., Kompella, V. R., Kazerounian, S., & Schmidhuber, J. (2013) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2013.00009 · Cited by 31
Mathys, C. D. (2012) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-a-007595146 · Cited by 11
Self-citationOptimal Limit-Cycle Control recast as Bayesian Inference
Tassa, Y., Erez, T., & Todorov, E. (2011) · IFAC Proceedings Volumes · doi:10.3182/20110828-6-it-1002.00903 · Cited by 6
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Self-citationActive inference and epistemic value
Friston, K., Rigoli, F., Ognibene, D., Mathys, C., Fitzgerald, T., & Pezzulo, G. (2015) · Cognitive Neuroscience · doi:10.1080/17588928.2015.1020053 · Cited by 731
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Carhart-Harris, R., & Nutt, D. (2017) · Journal of Psychopharmacology · doi:10.1177/0269881117725915 · Cited by 739
Friston, K., FitzGerald, T., Rigoli, F., Schwartenbeck, P., O⿿Doherty, J., & Pezzulo, G. (2016) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2016.06.022 · Cited by 674
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Metadata
- Authors
- Schwartenbeck, P., FitzGerald, T. H. B., Mathys, C., Dolan, R., & Friston, K.
- Affiliations
- Wellcome Centre for Human Neuroimaging (GB)
- Corresponding author
- Schwartenbeck, Philipp
- Venue
- Cerebral Cortex
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-07-23
- Volume
- 25
- Issue
- 10
- Pages
- 3434-3445
- ISSN
- 1047-3211 (ISSN-L)
- 1460-2199
- PMC
- PMC4585497
- OpenAlex
- W2128918015
- Cited by
- 219
- References
- 80
- Citations by year
- 2026: 20 · 2025: 19 · 2024: 13 · 2023: 19 · 2022: 26 · 2021: 19 · 2020: 26 · 2019: 15 · 2018: 20 · 2017: 12 · 2016: 17 · 2015: 10 · 2014: 3
- FWCI
- 7.81
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Cerebral Cortex (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- SDL (submittedVersion, cc-by)
- UEA Digital Repository (University of East Anglia) (submittedVersion, other-oa)
- UCL Discovery (University College London) (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Funders
- Wellcome Trust (091593, 091593/Z/10/, 098362, 098362/Z/12/Z, 091593/Z/10/Z, 088130, 098362/Z/12/)
- University College London (098362/Z/12/Z)
- Max-Planck-Gesellschaft (098362/Z/12/Z)
- 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
- dopamine, midbrain, Bayesian inference, active inference, decision-making, surprise minimization, utility maximization, hierarchical Bayesian inference
- MeSH terms
- Adult, Bayes Theorem, Brain Mapping, Cerebral Cortex, Choice Behavior, Conflict, Psychological, Dopamine, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Risk, Substantia Nigra, Ventral Tegmental Area, Young Adult
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