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Dopamine prediction error responses integrate subjective value from different reward dimensions
Open accessAbstract
Significance: Most real-world rewards have multiple dimensions, such as amount, risk, and type. Here we show that within a bounded set of such multidimensional rewards monkeys’ choice behavior fulfilled several core tenets of rational choice theory; namely, their choices were stochastically complete and transitive. As such, in selecting between rewards, the monkeys behaved as if they maximized value on a common scale. Dopamine neurons encoded prediction errors that reflected that scale. A particular reward dimension influenced dopamine activity only to the extent that it influenced choice. Thus, vastly different reward types such as juice and food activated dopamine neurons in accordance with subjective value derived from the different rewards. This neuronal signal could serve to update value signals for economic choice.
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“Dopamine neurons show higher activations in response to the preferred juice, and their activity also correlates with the indifference amounts in choices between risky and safe rewards, indicating that the neurons consistently code subjective rather than objective reward value9.”
From Dopamine reward prediction-error signalling: A two-component response (2016) · cites this paper as [9]
In the citing paper
“Stimuli of all sensory modalities are effective in eliciting this initial response component; those tested include loud sounds, intense light flashes, rapidly moving visual objects and liquids touching the mouth2,9,16,19,20,35.”
From Dopamine reward prediction-error signalling: A two-component response (2016) · cites this paper as [9]
In the citing paper
“these changes fail to occur with more-controlled arm, mouth and eye movements in a considerable range of studies2,5,9,13,151,152,154”
From Dopamine reward prediction-error signalling: A two-component response (2016) · cites this paper as [9]
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
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
Self-citationIndividual Choice Behavior: A Theoretical Analysis.
Cane, V. R., & Luce, R. D. (1960) · Journal of the Royal Statistical Society Series A (General) · doi:10.2307/2343282 · Cited by 2,538
A framework for studying the neurobiology of value-based decision making
Rangel, A., Camerer, C., & Montague, P. R. (2008) · Nature Reviews Neuroscience · doi:10.1038/nrn2357 · Cited by 2,184
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
Self-citationRegulation of parkinsonian motor behaviours by optogenetic control of basal ganglia circuitry
Kravitz, A. V., Freeze, B. S., Parker, P. R. L., Kay, K., Thwin, M. T., Deisseroth, K., & Kreitzer, A. C. (2010) · Nature · doi:10.1038/nature09159 · Cited by 1,885
A Discounting Framework for Choice With Delayed and Probabilistic Rewards.
Green, L., & Myerson, J. (2004) · Psychological Bulletin · doi:10.1037/0033-2909.130.5.769 · Cited by 1,676
Neurons in the orbitofrontal cortex encode economic value
Padoa-Schioppa, C., & Assad, J. A. (2006) · Nature · doi:10.1038/nature04676 · Cited by 1,598
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 · cited 1×
“D1R-mediated dopamine activity enhances the suppression of working memory fields in the PFC, sculpting and sharpening the response of the PFC network for greater task fidelity (46, 47).”
BackgroundTwo 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
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
Midbrain 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
Schultz, W., Apicella, P., & Ljungberg, T. (1993) · The Journal of Neuroscience · doi:10.1523/jneurosci.13-03-00900.1993 · Cited by 1,291
Self-citationWhole-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
Adaptive Coding of Reward Value by Dopamine Neurons
Tobler, P. N., Fiorillo, C. D., & Schultz, W. (2005) · Science · doi:10.1126/science.1105370 · Cited by 1,276
Self-citationPhasic Firing in Dopaminergic Neurons Is Sufficient for Behavioral Conditioning
Tsai, H.-C., Zhang, F., Adamantidis, A., Stuber, G. D., Bonci, A., de Lecea, L., & Deisseroth, K. (2009) · Science · doi:10.1126/science.1168878 · Cited by 1,266
Dichotomous dopaminergic control of striatal synaptic plasticity
Shen, W., Flajolet, M., Greengard, P., & Surmeier, D. J. (2008) · Science · doi:10.1126/science.1160575 · Cited by 1,190
PEST: Efficient Estimates on Probability Functions
Taylor, M. M., & Creelman, C. D. (1967) · The Journal of the Acoustical Society of America · doi:10.1121/1.1910407 · Cited by 1,173
Individual choice behavior: A theoretical analysis.
Luce, R. D. (2005) · doi:10.1037/14396-000 · Cited by 1,157
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
Inverted-U dopamine D1 receptor actions on prefrontal neurons engaged in working memory
Vijayraghavan, S., Wang, M., Birnbaum, S. G., Williams, G. V., & Arnsten, A. F. T. (2007) · Nature Neuroscience · doi:10.1038/nn1846 · Cited by 1,013
Neural Economics and the Biological Substrates of Valuation
Montague, P. R., & Berns, G. S. (2002) · Neuron · doi:10.1016/s0896-6273(02)00974-1 · Cited by 994
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
A 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
A cellular mechanism of reward-related learning
Reynolds, J. N. J., Hyland, B. I., & Wickens, J. R. (2001) · Nature · doi:10.1038/35092560 · Cited by 879
Gonon, F. G. (1988) · Neuroscience · doi:10.1016/0306-4522(88)90307-7 · Cited by 676
Activation of VTA GABA Neurons Disrupts Reward Consumption
van Zessen, R., Phillips, J. L., Budygin, E. A., & Stuber, G. D. (2012) · Neuron · doi:10.1016/j.neuron.2012.02.016 · Cited by 646
GABA Neurons of the VTA Drive Conditioned Place Aversion
Tan, K. R., Yvon, C., Turiault, M., Mirzabekov, J. J., Doehner, J., Labouèbe, G., Deisseroth, K. A., Tye, K. M., & Lüscher, C. (2012) · Neuron · doi:10.1016/j.neuron.2012.02.015 · Cited by 635
Neurobiology of economic choice: a good-based model
Padoa-Schioppa, C. (2011) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-061010-113648 · Cited by 600
Handbook of Mathematical Psychology
McGinnis, R., Luce, R. D., Bush, R. R., & Galanter, E. (1967) · The American Journal of Psychology · doi:10.2307/1421001 · Cited by 551
Double dissociation of value computations in orbitofrontal and anterior cingulate neurons
Kennerley, S. W., Behrens, T. E. J., & Wallis, J. D. (2011) · Nature Neuroscience · doi:10.1038/nn.2961 · Cited by 485
Free choice activates a decision circuit between frontal and parietal cortex
Pesaran, B., Nelson, M. J., & Andersen, R. A. (2008) · Nature · doi:10.1038/nature06849 · Cited by 465
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
Influence of Reward Delays on Responses of Dopamine Neurons
Kobayashi, S., & Schultz, W. (2008) · Journal of Neuroscience · doi:10.1523/jneurosci.1600-08.2008 · Cited by 440
Self-citationTransient 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
Utility of Gains and Losses: Measurement-Theoretical and Experimental Approaches
Luce, R. D. (2000) · Medical Entomology and Zoology · Cited by 396
Hong, S., Jhou, T. C., Smith, M., Saleem, K. S., & Hikosaka, O. (2011) · The Journal of Neuroscience · doi:10.1523/jneurosci.1384-11.2011 · Cited by 390
Lewis, D. A., Melchitzky, D. S., Sesack, S. R., Whitehead, R. E., Auh, S., & Sampson, A. (2001) · Journal of Comparative Neurology · doi:10.1002/cne.1092 · Cited by 352
Reward value-based gain control: divisive normalization in parietal cortex
Louie, K., Grattan, L. E., & Glimcher, P. W. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.1237-11.2011 · Cited by 308
Williams, S. M., & Goldman-Rakic, P. S. (1993) · Cerebral Cortex · doi:10.1093/cercor/3.3.199 · Cited by 284
Dopamine Neurons Can Represent Context-Dependent Prediction Error
Nakahara, H., Itoh, H., Kawagoe, R., Takikawa, Y., & Hikosaka, O. (2004) · Neuron · doi:10.1016/s0896-6273(03)00869-9 · Cited by 276
Burton, M. J., Rolls, E. T., & Mora, F. (1976) · Experimental Neurology · doi:10.1016/0014-4886(76)90189-8 · Cited by 252
The organization of midbrain projections to the ventral striatum in the primate
Lynd‐Balta, E., & Haber, S. N. (1994) · Neuroscience · doi:10.1016/0306-4522(94)90181-3 · Cited by 169
The Role of Prefrontal Dopamine D1 Receptors in the Neural Mechanisms of Associative Learning
Puig, M. V., & Miller, E. K. (2012) · Neuron · doi:10.1016/j.neuron.2012.04.018 · Cited by 155
The primate amygdala combines information about space and value
Peck, C. J., Lau, B., & Salzman, C. D. (2013) · Nature Neuroscience · doi:10.1038/nn.3328 · Cited by 115
EXPERIMENTAL TESTS OF A STOCHASTIC DECISION THEORYPreprint
Davidson, D., & Marschak, J. (1958) · RePEc: Research Papers in Economics · Cited by 110
Multiple Timescales of Memory in Lateral Habenula and Dopamine Neurons
Bromberg-Martin, E. S., Matsumoto, M., Nakahara, H., & Hikosaka, O. (2010) · Neuron · doi:10.1016/j.neuron.2010.06.031 · Cited by 97
Hypothalamic neuron involvement in integration of reward, aversion, and cue signals
Ono, T., Nakamura, K., Nishijo, H., & Fukuda, M. (1986) · Journal of Neurophysiology · doi:10.1152/jn.1986.56.1.63 · Cited by 87
Bidirectional Modulation of Substantia Nigra Activity by Motivational State
Rossi, M. A., Fan, D., Barter, J. W., & Yin, H. H. (2013) · PLoS ONE · doi:10.1371/journal.pone.0071598 · Cited by 43
An, X., Bandler, R., �ng�r, D., & Price, J. L. (1998) · The Journal of Comparative Neurology · doi:10.1002/(sici)1096-9861(19981130)401:4
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Schultz, W. (2016) · Nature Reviews Neuroscience · doi:10.1038/nrn.2015.26 · Cited by 1,036
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Schultz, W. (2016) · Dialogues in Clinical Neuroscience · doi:10.31887/dcns.2016.18.1/wschultz · Cited by 597
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Metadata
- Authors
- Lak, A., Stauffer, W. R., & Schultz, W.
- Affiliations
- University of Cambridge (GB)
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2014-01-22
- Volume
- 111
- Issue
- 6
- Pages
- 2343-2348
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC3926061
- OpenAlex
- W2010297727
- Cited by
- 257
- References
- 51
- Citations by year
- 2026: 8 · 2025: 19 · 2024: 17 · 2023: 23 · 2022: 16 · 2021: 24 · 2020: 15 · 2019: 22 · 2018: 24 · 2017: 26 · 2016: 26 · 2015: 24 · 2014: 13
- FWCI
- 13.55
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Licence
- other-oa
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- Wellcome Trust (095495)
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Keywords
- dopamine, prediction error, subjective value, economic choice, reward magnitude, risk, reward type, single-unit recording, primate decision making, value integration, reinforcement learning
- MeSH terms
- Animals, Dopamine, Macaca mulatta, Male, Neurons, Probability, Reward
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