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Serotonin Selectively Modulates Reward Value in Human Decision-Making
Open accessAbstract
Establishing a function for the neuromodulator serotonin in human decision-making has proved remarkably difficult because if its complex role in reward and punishment processing. In a novel choice task where actions led concurrently and independently to the stochastic delivery of both money and pain, we studied the impact of decreased brain serotonin induced by acute dietary tryptophan depletion. Depletion selectively impaired both behavioral and neural representations of reward outcome value, and hence the effective exchange rate by which rewards and punishments were compared. This effect was computationally and anatomically distinct from a separate effect on increasing outcome-independent choice perseveration. Our results provide evidence for a surprising role for serotonin in reward processing, while illustrating its complex and multifarious effects.
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“ATD has been shown to affect measures of both ‘cold’ (rational and non-emotional), and ‘hot’ (social and emotional) cognition [13–18].”
From Chronic escitalopram in healthy volunteers has specific effects on reinforcement sensitivity: a double-blind, placebo-controlled semi-randomised study (2023) · cites this paper as [13]
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“Effects of ATD have also been seen on reinforcement behaviour [13], reversal learning [9, 14], learning and memory [15], affective and social cognition [9, 16, 17] and moral judgement [18].”
From Chronic escitalopram in healthy volunteers has specific effects on reinforcement sensitivity: a double-blind, placebo-controlled semi-randomised study (2023) · cites this paper as [13]
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“A previous study examining how reinforcement is influenced by serotonergic modulation showed that acute tryptophan depletion decreased reinforcement sensitivity, by impairing the representation of reward outcome value [13].”
From Chronic escitalopram in healthy volunteers has specific effects on reinforcement sensitivity: a double-blind, placebo-controlled semi-randomised study (2023) · cites this paper as [13]
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“However, three decision-making studies (Seymour et al., 2012; Roy et al., 2014; Eldar et al., 2016) that involved avoiding pain or shock examined prediction errors to successful and unsuccessful avoidance, and two found evidence for striatal prediction errors to successful avoidance of shock.”
From Active avoidance: Neural mechanisms and attenuation of pavlovian conditioned responding (2017) · cites this paper as Seymour et al., 2012
In the citing paper
“The vmPFC, rather than regions included in the NPS, correlates with spontaneous, clinical pain [52], and encodes the learned, behavioral avoidance value associated with painful stimulation [85],[86].”
From Distinct Brain Systems Mediate the Effects of Nociceptive Input and Self-Regulation on Pain (2015) · cites this paper as [85]
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“By contrast, vmPFC activity seems to reflect expected positive value in a variety of contexts and paradigms45, including reward learning, economic choice46, pain avoidance11, and extinction/extinction recall47.”
From Representation of aversive prediction errors in the human periaqueductal gray (2014) · cites this paper as [11]
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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
Self-citationCortical 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-citationDissociable Roles of Ventral and Dorsal Striatum in Instrumental Conditioning
O'Doherty, J., Dayan, P., Schultz, J., Deichmann, R., Friston, K., & Dolan, R. J. (2004) · Science · doi:10.1126/science.1094285 · Cited by 2,088
Self-citationThe use of analogue scales in rating subjective feelings
BOND, A., & LADER, M. (1974) · British Journal of Medical Psychology · doi:10.1111/j.2044-8341.1974.tb02285.x · Cited by 1,972
Mowrer, O. H. (1960) · doi:10.1037/10802-000 · Cited by 1,670
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-citationGonzalez, R. C., & Mowrer, O. H. (1962) · The American Journal of Psychology · doi:10.2307/1419634 · Cited by 1,584
Conditioning and Associative Learning
Hill, W. F., & Mackintosh, N. J. (1984) · The American Journal of Psychology · doi:10.2307/1422540 · Cited by 1,304
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
Brain Serotonin Content: Physiological Regulation by Plasma Neutral Amino Acids
Fernstrom, J. D., & Wurtman, R. J. (1972) · Science · doi:10.1126/science.178.4059.414 · Cited by 1,166
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Research works printed but not linked: 1
- Dickinson A Dearing MF (1979) in Mechanisms of learning and motivation: a memorial volume to Jerzy Konorski, Appetitive-aversive interactions and inhibitory processes, eds Dickinson A Boakes RA (Psychology Press, London), pp 203–231.
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Neural and neurochemical basis of reinforcement-guided decision making
Khani, A., & Rainer, G. (2016) · Journal of Neurophysiology · doi:10.1152/jn.01113.2015 · Cited by 38
Serotonin depletion impairs both Pavlovian and instrumental reversal learning in healthy humans
Kanen, J. W., Apergis-Schoute, A. M., Yellowlees, R., Arntz, F. E., van der Flier, F. E., Price, A., Cardinal, R. N., Christmas, D. M., Clark, L., Sahakian, B. J., et al. (2021) · Molecular Psychiatry · doi:10.1038/s41380-021-01240-9 · Cited by 34
Bloem, B., Huda, R., Amemori, K., Abate, A., Krishna, G., Wilson, A. L., Carter, C. W., Sur, M., & Graybiel, A. M. (2022) · Nature Communications · doi:10.1038/s41467-022-28983-5 · Cited by 33
Social brains on drugs: tools for neuromodulation in social neuroscience
Crockett, M. J., & Fehr, E. (2013) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nst113 · Cited by 32
The control of tonic pain by active relief learning
Zhang, S., Mano, H., Lee, M., Yoshida, W., Kawato, M., Robbins, T. W., & Seymour, B. (2018) · eLife · doi:10.7554/elife.31949 · Cited by 37
Self-citationJepma, M., Roy, M., Ramlakhan, K., van Velzen, M., & Dahan, A. (2022) · eLife · doi:10.7554/elife.74149 · Cited by 28
A Role for Serotonin in Modulating Opposing Drive and Brake Circuits of Impulsivity
Desrochers, S. S., Spring, M. G., & Nautiyal, K. M. (2022) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2022.791749 · Cited by 29
The Manager’s Notepad: Working Memory, Exploration, and Performance
Martinez, D. L., Brusoni, S., Tata, A., & Zollo, M. (2019) · Journal of Management Studies · doi:10.1111/joms.12528 · Cited by 30
Oxytocin modulates neurocomputational mechanisms underlying prosocial reinforcement learning
Martins, D., Lockwood, P. L., Cutler, J., Moran, R., & Paloyelis, Y. (2022) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2022.102253 · Cited by 26
Luo, Q., Kanen, J. W., Bari, A., Skandali, N., Langley, C., Knudsen, G. M., Alsiö, J., Phillips, B. U., Sahakian, B. J., Cardinal, R. N., et al. (2023) · Neuropsychopharmacology · doi:10.1038/s41386-023-01762-6 · Cited by 26
Pharmacology of Economic and Social Decision Making
Crockett, M. J., & Fehr, E. (2013) · Elsevier eBooks · doi:10.1016/b978-0-12-416008-8.00014-0 · Cited by 29
Computational psychopharmacology: a translational and pragmatic approach
Robbins, T. W., & Cardinal, R. N. (2019) · Psychopharmacology · doi:10.1007/s00213-019-05302-3 · Cited by 25
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Epstein, J., & Silbersweig, D. (2015) · Journal of Neuropsychiatry · doi:10.1176/appi.neuropsych.13120370 · Cited by 27
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Jamal, M., Ameno, K., Miki, T., Tanaka, N., Ito, A., Ono, J., Takakura, A., Kumihashi, M., & Kinoshita, H. (2015) · NeuroToxicology · doi:10.1016/j.neuro.2015.12.011 · Cited by 26
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When is a loss a loss? Excitatory and inhibitory processes in loss-related decision-making
Seymour, B., Maruyama, M., & Martino, B. D. (2015) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2015.09.003 · Cited by 24
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Brusoni, S., Martinez, D. L., Canessa, N., & Zollo, M. (2019) · Industrial and Corporate Change · doi:10.1093/icc/dtz070 · Cited by 26
Retinal functioning and reward processing in schizophrenia
Demmin, D. L., Mote, J., Abel, D. B., Thompson, J. L., & Silverstein, S. M. (2019) · Schizophrenia Research · doi:10.1016/j.schres.2019.06.019 · Cited by 21
Lan, D. C. L., & Browning, M. D. (2022) · Computational Psychiatry · doi:10.5334/cpsy.83 · Cited by 17
Impaired Learning From Negative Feedback in Stimulant Use Disorder: Dopaminergic Modulation
Lim, T. V., Cardinal, R. N., Bullmore, E. T., Robbins, T. W., & Ersche, K. D. (2021) · The International Journal of Neuropsychopharmacology · doi:10.1093/ijnp/pyab041 · Cited by 18
Ger, Y., Nachmani, E., Wolf, L., & Shahar, N. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011678 · Cited by 19
Amino acid challenge and depletion techniques in human functional neuroimaging studies: an overview
Biskup, C. S., Gaber, T. J., Helmbold, K., Bubenzer‐Busch, S., & Zepf, F. D. (2015) · Amino Acids · doi:10.1007/s00726-015-1919-z · Cited by 19
Effects of serotonin and dopamine depletion on neural prediction computations during social learning
Frey, A.-L., & McCabe, C. (2020) · Neuropsychopharmacology · doi:10.1038/s41386-020-0678-z · Cited by 21
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Vlaev, I., Crockett, M. J., Clark, L., Müller, U., & Robbins, T. W. (2017) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-016-0496-2 · Cited by 17
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Liu, L., Artigas, S. O., Ulrich, A., Tardu, J., Mohr, P. N. C., Wilms, B., Koletzko, B. V., Schmid, S. M., & Park, S. Q. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.117951 · Cited by 15
Avoidance Behavioral Difference in Acquisition and Extinction of Pain-Related Fear
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Differential Associations of Dopamine and Serotonin With Reward and Punishment Processes in Humans
Mkrtchian, A., Qiu, Z., Abir, Y., Erdmann, T., Dercon, Q., Sedlinská, T., Browning, M. D., Costello, H., & Huys, Q. J. M. (2025) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2025.0839 · Cited by 16
Yamamori, Y., Robinson, O. J., & Roiser, J. P. (2023) · eLife · doi:10.7554/elife.87720 · Cited by 26
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Spring, M. G., & Nautiyal, K. M. (2024) · Journal of Neuroscience · doi:10.1523/jneurosci.0602-24.2024 · Cited by 13
Boddington, R., Dunlop, C. A. G., Garnham, L. C., Ryding, S., Abbey‐Lee, R. N., Kreshchenko, A., & Løvlie, H. (2020) · Animal Cognition · doi:10.1007/s10071-020-01394-z · Cited by 13
Chen, X., Kareem, A., Xu, G., Wang, H., Sun, Y., & Hu, L. (2021) · Wind Energy · doi:10.1002/we.2623 · Cited by 13
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Steding, J., Ritschel, F., Boehm, I., Geisler, D., King, J. A., Roessner, V., Smolka, M. N., Zepf, F. D., & Ehrlich, S. (2022) · Psychological Medicine · doi:10.1017/s0033291721005493 · Cited by 13
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Feng, Y.-Y., Bromberg-Martin, E. S., & Monosov, I. E. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.114341 · Cited by 18
Humby, T., Smith, G. E., Small, R., Davies, W., Carter, J., Bentley, C. A., Winstanley, C. A., Rogers, R. D., & Wilkinson, L. S. (2020) · Psychopharmacology · doi:10.1007/s00213-020-05496-x · Cited by 11
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Wang, J., Elfwing, S., & Uchibe, E. (2018) · IEEE International Conference on Development and Learning (ICDL) · doi:10.1109/devlrn.2018.8761044 · Cited by 12
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Mkrtchian, A., Valton, V., & Roiser, J. P. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.06.30.450026 · Cited by 12
Self-citationEnhanced behavioural and neural sensitivity to punishments in chronic pain and fatigue
Mancini, F., Mahajan, P., Guttesen, A. Á. V., Onysk, J., Scholtes, I., Shenker, N., Lee, M., & Seymour, B. (2024) · Brain · doi:10.1093/brain/awae408 · Cited by 14
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Tuzsus, D., Brands, A. M. A., Pappas, I., & Peters, J. (2024) · Computational Brain & Behavior · doi:10.1007/s42113-024-00202-y · Cited by 10
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Asher, D. E., Craig, A. B., Zaldivar, A., Brewer, A. A., & Krichmar, J. L. (2013) · Frontiers in Integrative Neuroscience · doi:10.3389/fnint.2013.00078 · Cited by 11
Knytl, P., & Opitz, B. (2018) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-018-00665-0 · Cited by 10
The relationship between reward and punishment processing and the 5-HT1A receptor as shown by PET
Faulkner, P., Selvaraj, S., Pine, A., Howes, O. D., & Roiser, J. P. (2014) · Psychopharmacology · doi:10.1007/s00213-013-3426-9 · Cited by 10
Self-citationSSRIs modulate asymmetric learning from reward and punishmentPreprint
Michely, J., Eldar, E., Erdman, A., Martin, I. M., & Dolan, R. J. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.21.108266 · Cited by 9
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Zazzo, A. D., Gregorio, C. D., Spelta, S., & Demircan, S. (2024) · European Journal of Ophthalmology · doi:10.1177/11206721241305661 · Cited by 9
Maddox, W. T., Gorlick, M. A., Koslov, S. R., McGeary, J. E., Knopik, V. S., & Beevers, C. G. (2015) · Cerebral Cortex · doi:10.1093/cercor/bhv309 · Cited by 10
The dynamics of pain avoidance: the exploration–exploitation dilemma
Krypotos, A., Crombez, G., & Vlaeyen, J. W. S. (2024) · Pain · doi:10.1097/j.pain.0000000000003222 · Cited by 8
Wang, D., Si, W., & Luo, Y. (2019) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2019.2953944 · Cited by 8
Faulkner, P., Mancinelli, F., Lockwood, P. L., Matarín, M., Dolan, R. J., Wood, N. W., Dayan, P., & Roiser, J. P. (2016) · The International Journal of Neuropsychopharmacology · doi:10.1093/ijnp/pyw075 · Cited by 9
Self-citationGao, X., Liang, Q., Wu, G., She, Y., Sui, N., & Chen, H. (2018) · Appetite · doi:10.1016/j.appet.2018.04.024 · Cited by 8
Lin, S., Chi, M. H., Lee, I. H., Chen, K. C., Tai, Y. C., Yao, W. J., Chiu, N.-T., Yang, D.-Y., Lin, C., Chen, P. S., et al. (2020) · CNS Spectrums · doi:10.1017/s1092852920001133 · Cited by 7
Zanchi, D., Meyer‐Gerspach, A. C., Suenderhauf, C., Janach, K., le Roux, C. W., Haller, S., Drewe, J., Beglinger, C., Wölnerhanssen, B. K., & Borgwardt, S. (2016) · Scientific Reports · doi:10.1038/srep35727 · Cited by 7
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Vadovičová, K., & Gasparotti, R. (2014) · ScienceOpen Research · doi:10.14293/s2199-1006.1.sor-life.aekzpz.v1 · Cited by 18
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Costa, V. D., Kakalios, L. C., & Averbeck, B. B. (2016) · Behavioral Neuroscience · doi:10.1037/bne0000162 · Cited by 7
Colas, J. T., O’Doherty, J. P., & Grafton, S. T. (2024) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1011950 · Cited by 6
Stolz, C., Pickering, A. D., & Mueller, E. M. (2022) · Psychophysiology · doi:10.1111/psyp.14235 · Cited by 8
Carvalheiro, J., & Philiastides, M. G. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.113589 · Cited by 11
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Hervig, M. E.-S., Zühlsdorff, K., Olesen, S. F., Phillips, B. U., Božič, T., Dalley, J. W., Cardinal, R. N., Alsiö, J., & Robbins, T. W. (2024) · Psychopharmacology · doi:10.1007/s00213-024-06586-w · Cited by 7
Sazhin, D., Dachs, A., & Smith, D. V. (2025) · European Journal of Neuroscience · doi:10.1111/ejn.70081 · Cited by 8
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Phillips, B. U., & Robbins, T. W. (2020) · Handbook of behavioral neuroscience · doi:10.1016/b978-0-444-64125-0.00031-1 · Cited by 5
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Millroth, P., Juslin, P., Eriksson, E., & Ågren, T. (2017) · Behavioral Neuroscience · doi:10.1037/bne0000209 · Cited by 4
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Muirhead, C. S., Guerra, S., Fox, B. W., Schroeder, F. C., & Srinivasan, J. (2025) · eNeuro · doi:10.1523/eneuro.0127-25.2025 · Cited by 3
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Iigaya, K., Fonseca, M. S., Murakami, M., Mainen, Z. F., & Dayan, P. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/215400 · Cited by 2
Self-citation
Metadata
- Authors
- Seymour, B., Daw, N. D., Roiser, J. P., Dayan, P., & Dolan, R.
- Affiliations
- Wellcome Centre for Human Neuroimaging (GB)
- Gatsby Computational Neuroscience Unit (GB)
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2012-04-25
- Volume
- 32
- Issue
- 17
- Pages
- 5833-5842
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC5321452
- OpenAlex
- W2083058253
- Cited by
- 252
- References
- 61
- Citations by year
- 2026: 8 · 2025: 16 · 2024: 20 · 2023: 25 · 2022: 15 · 2021: 17 · 2020: 19 · 2019: 13 · 2018: 20 · 2017: 17 · 2016: 17 · 2015: 17 · 2014: 22 · 2013: 19 · 2012: 7
- FWCI
- 8.57
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Neuroscience (publishedVersion, cc-by-nc-sa)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Max Planck Digital Library (submittedVersion, other-oa)
- Cambridge University Engineering Department Publications Database (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Funders
- Wellcome Trust (091593, 079866)
- 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
- serotonin, decision-making, reward value, tryptophan depletion, monetary rewards, pain, punishment, reward processing, computational modeling of choice
- MeSH terms
- Brain, Brain Mapping, Conditioning, Operant, Decision Making, Double-Blind Method, Electric Stimulation, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Oxygen, Pain, Pain Measurement, Probability, Punishment, Surveys and Questionnaires, Reward, Serotonin, Statistics as Topic, Tryptophan, Dietary Supplements
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