The DOI matches its Crossref record; 116 of the 116 references it cites were found in a registry or an index.
Tonic dopamine: opportunity costs and the control of response vigor
Abstract
Rationale Dopamine neurotransmission has long been known to exert a powerful influence over the vigor, strength, or rate of responding. However, there exists no clear understanding of the computational foundation for this effect; predominant accounts of dopamine's computational function focus on a role for phasic dopamine in controlling the discrete selection between different actions and have nothing to say about response vigor or indeed the free-operant tasks in which it is typically measured.Objectives We seek to accommodate free-operant behavioral tasks within the realm of models of optimal control and thereby capture how dopaminergic and motivational manipulations affect response vigor.Methods We construct an average reward reinforcement learning model in which subjects choose both which action to perform and also the latency with which to perform it. Optimal control balances the costs of acting quickly against the benefits of getting reward earlier and thereby chooses a best response latency.Results In this framework, the long-run average rate of reward plays a key role as an opportunity cost and mediates motivational influences on rates and vigor of responding. We review evidence suggesting that the average reward rate is reported by tonic levels of dopamine putatively in the nucleus accumbens.Conclusions Our extension of reinforcement learning models to free-operant tasks unites psychologically and computationally inspired ideas about the role of tonic dopamine in striatum, explaining from a normative point of view why higher levels of dopamine might be associated with more vigorous responding.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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
“Tonic DA levels are largely independent of environmental triggers and may index an individual’s level of incentive-related drive (Niv et al, 2007; Willuhn, Wanat, Clark, & Phillips, 2010).”
From Incentive Motivation, Cognitive Control, and the Adolescent Brain: Is It Time for a Paradigm Shift? (2012) · cites this paper as Niv et al, 2007
In the citing paper
“Supportive evidence for this proposed interplay between tonic and phasic modes of DA activity and effects on behavior relies entirely on animal and computational models (Niv et al., 2007; Willuhn, 2010).”
From Incentive Motivation, Cognitive Control, and the Adolescent Brain: Is It Time for a Paradigm Shift? (2012) · cites this paper as Niv et al., 2007
In the citing paper
“These systems also interact as tonic DA activity regulates phasic signaling in an inhibitory fashion and phasic DA has been shown to enhance tonic activity (Niv, Daw et al. 2007).”
From Developmental imaging genetics: Linking dopamine function to adolescent behavior (2014) · cites this paper as Niv, Daw et al. 2007
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-citationPredictive reward signal of dopamine neurons
Schultz, W. (1998) · Journal of Neurophysiology · doi:10.1152/jn.1998.80.1.1 · Cited by 4,564
What is the role of dopamine in reward: hedonic impact, reward learning, or incentive salience?
Berridge, K. C., & Robinson, T. E. (1998) · Brain Research Reviews · doi:10.1016/s0165-0173(98)00019-8 · Cited by 4,127
(2012) · Springer eBooks · doi:10.1007/978-1-4419-1428-6_5086 · Cited by 3,601
Dopamine, learning and motivation.
Wise, R. A. (2004) · Nature Reviews Neuroscience · doi:10.1038/nrn1406 · Cited by 3,379 · cited 2×
“One possible explanation for these effects is that phasic signals transiently affect dopamine tone (Phillips and Wightman 2004; Roitman et al. 2004; Wise 2004), influencing vigor selection.”
Specious reward: A behavioral theory of impulsiveness and impulse control.
Ainslie, G. (1975) · Psychological Bulletin · doi:10.1037/h0076860 · Cited by 2,708
Herrnstein, R. J. (1970) · Journal of the Experimental Analysis of Behavior · doi:10.1901/jeab.1970.13-243 · Cited by 2,611
Daw, N. D., Niv, Y., & Dayan, P. (2005) · Nature Neuroscience · doi:10.1038/nn1560 · Cited by 2,533
Self-citation
Cited by
Sorted by citations
Items 401–500 of 1,000
Cognitive Control as a Multivariate Optimization Problem
Ritz, H., Leng, X., & Shenhav, A. (2022) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01822 · Cited by 57
Reaching and Grasping Movements in Parkinson’s Disease: A Review
Fasano, A., Mazzoni, A., & Falotico, E. (2022) · Journal of Parkinson s Disease · doi:10.3233/jpd-213082 · Cited by 48
Bustamante, L. A., Oshinowo, T., Lee, J. R., Tong, E., Burton, A. R., Shenhav, A., Cohen, J. D., & Daw, N. D. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2221510120 · Cited by 59
Self-citationYouths’ sensitivity to social media feedback: A computational account
Pinho, A. D. S., Izquierdo, V. C., Lindström, B., & van den Bos, W. (2024) · Science Advances · doi:10.1126/sciadv.adp8775 · Cited by 58
A Neurocomputational Model for Cocaine Addiction
Dezfouli, A., Piray, P., Keramati, M., Ekhtiari, H., Lucas, C., & Mokri, A. (2009) · Neural Computation · doi:10.1162/neco.2009.10-08-882 · Cited by 50
The influence of emotions on cognitive control: feelings and beliefs—where do they meet?
Harlé, K. M., Shenoy, P., & Paulus, M. P. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00508 · Cited by 56
Cognitive control and capacity for prospective memory in complex dynamic environments.
Boag, R. J., Strickland, L. J. G., Heathcote, A., Neal, A. F., & Loft, S. D. (2019) · Journal of Experimental Psychology General · doi:10.1037/xge0000599 · Cited by 52
Lohani, S., Martig, A. K., Underhill, S. M., Oranburg, T. D., Roberts, M. J., Rinaman, L., Amara, S., & Moghaddam, B. (2018) · Neuropsychopharmacology · doi:10.1038/s41386-018-0088-7 · Cited by 48
Auerbach, R. P., Pagliaccio, D., & Kirshenbaum, J. S. (2022) · Current topics in behavioral neurosciences · doi:10.1007/7854_2022_358 · Cited by 51
Rat Anterior Cingulate Cortex Continuously Signals Decision Variables in a Patch Foraging Task
Kane, G. A., James, M. H., Shenhav, A., Daw, N. D., Cohen, J. D., & Aston‐Jones, G. (2022) · Journal of Neuroscience · doi:10.1523/jneurosci.1940-21.2022 · Cited by 48
Self-citationNagano‐Saito, A., Cisek, P., Perna, A. S., Shirdel, F. Z., Benkelfat, C., Leyton, M., & Dagher, A. (2012) · Journal of Neurophysiology · doi:10.1152/jn.00592.2011 · Cited by 49
Striatonigral control of movement velocity in mice
Bartholomew, R. A., Li, H., Gaidis, E., Stackmann, M., Shoemaker, C. T., Rossi, M. A., & Yin, H. H. (2016) · European Journal of Neuroscience · doi:10.1111/ejn.13187 · Cited by 45
Shizgal, P. (2015) · Elsevier eBooks · doi:10.1016/b978-0-08-097086-8.55007-7 · Cited by 47
Reinforcement Learning and Tourette Syndrome
Palminteri, S., & Pessiglione, M. (2013) · International review of neurobiology · doi:10.1016/b978-0-12-411546-0.00005-6 · Cited by 47
Fischbach-Weiss, S., Reese, R. M., & Janak, P. H. (2017) · Neuroscience · doi:10.1016/j.neuroscience.2017.12.003 · Cited by 47
Dopamine and proximity in motivation and cognitive control
Westbrook, A., & Frank, M. J. (2018) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2017.12.011 · Cited by 47
Gallistel, C. R., Craig, A. R., & Shahan, T. A. (2019) · Psychological Review · doi:10.1037/rev0000163 · Cited by 46
Striatal dopamine integrates cost, benefit, and motivation
Eshel, N., Touponse, G. C., Wang, A. R., Osterman, A. K., Shank, A. N., Groome, A. M., Taniguchi, L., Pinto, D. F. C., Tucciarone, J., Bentzley, B. S., et al. (2023) · Neuron · doi:10.1016/j.neuron.2023.10.038 · Cited by 51
Advanced Reinforcement Learning
Daw, N. D. (2013) · Elsevier eBooks · doi:10.1016/b978-0-12-416008-8.00016-4 · Cited by 46
Self-citationVoon, V., & Dalley, J. W. (2015) · Current topics in behavioral neurosciences · doi:10.1007/7854_2015_5013 · Cited by 46
Action history influences subsequent movement via two distinct processes
Marinovic, W., Poh, E., de Rugy, A., & Carroll, T. J. (2017) · eLife · doi:10.7554/elife.26713 · Cited by 44
Catecholaminergic modulation of the avoidance of cognitive control.
Froböse, M. I., Swart, J. C., Cook, J. L., Geurts, D. E. M., den Ouden, H. E. M., & Cools, R. (2018) · Journal of Experimental Psychology General · doi:10.1037/xge0000523 · Cited by 53
Motivational modulation of bradykinesia in Parkinson’s disease off and on dopaminergic medication
Kojović, M., Mir, P., Trender‐Gerhard, I., Schneider, S. A., Pareés, I., Edwards, M. J., Bhatia, K. P., & Jahanshahi, M. (2014) · Journal of Neurology · doi:10.1007/s00415-014-7315-x · Cited by 45
Automated pose estimation in primates
Hayden, B. Y., Park, H. S., & Zimmermann, J. (2021) · American Journal of Primatology · doi:10.1002/ajp.23348 · Cited by 46
Stimulation of the vagus nerve reduces learning in a go/no-go reinforcement learning task
Kühnel, A., Teckentrup, V., Neuser, M. P., Huys, Q. J. M., Burrasch, C., Walter, M., & Kroemer, N. B. (2020) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2020.03.023 · Cited by 43
Iigaya, K. (2016) · eLife · doi:10.7554/elife.18073 · Cited by 45
Context-sensitive valuation and learning
Hunter, L. E., & Daw, N. D. (2021) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2021.05.001 · Cited by 44
Self-citationDopamine release in nucleus accumbens during rewarded task switching measured by [¹¹C]raclopride
Jonasson, L. S., Axelsson, J., Riklund, K., Braver, T. S., Ögren, M., Bäckman, L., & Nyberg, L. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.05.047 · Cited by 42
Kucinski, A. J., Albin, R. L., Lustig, C., & Sarter, M. F. (2015) · Behavioural Brain Research · doi:10.1016/j.bbr.2015.01.012 · Cited by 42
Galea, J. M., Ruge, D., Buijink, A. W. G., Bestmann, S., & Rothwell, J. C. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.1294-12.2013 · Cited by 41
Chowdhury, R., Guitart‐Masip, M., Lambert, C., Dolan, R. J., & Düzel, E. (2013) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2013.03.030 · Cited by 42
Köhncke, Y., Papenberg, G., Jonasson, L. S., Karalija, N., Wåhlin, A., Salami, A., Andersson, M., Axelsson, J., Nyberg, L. H., Riklund, K., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.07.036 · Cited by 48
The Effect of Acute Aerobic Exercise on Divergent and Convergent Thinking and Its Influence by Mood
Aga, K., Inamura, M., Chen, C., Hagiwara, K., Yamashita, R., Hirotsu, M., Seki, T., Takao, A., Fujii, Y., Matsubara, T., et al. (2021) · Brain Sciences · doi:10.3390/brainsci11050546 · Cited by 45
Decision neuroscience and neuroeconomics: Recent progress and ongoing challenges
Dennison, J. B., Sazhin, D., & Smith, D. V. (2022) · Wiley Interdisciplinary Reviews Cognitive Science · doi:10.1002/wcs.1589 · Cited by 50
Features of compulsive checking behavior mediated by nucleus accumbens and orbital frontal cortex
Dvorkin‐Gheva, A., Silva, C., McMurran, T., Bisnaire, L., Foster, J. A., & Szechtman, H. (2010) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2010.07398.x · Cited by 41
Lau, B., Monteiro, T., & Paton, J. J. (2017) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2017.08.015 · Cited by 41
Dopamine promotes instrumental motivation, but reduces reward-related vigour
Grogan, J. P., Sandhu, T., Hu, M. T., & Manohar, S. (2020) · eLife · doi:10.7554/elife.58321 · Cited by 38
Tremblay, A.-M., Desmond, R. C., Poulos, C. X., & Zack, M. H. (2010) · Addiction Biology · doi:10.1111/j.1369-1600.2010.00208.x · Cited by 41
The Role of Value Systems in Decision Making
Dayan, P. (2008) · The MIT Press eBooks · doi:10.7551/mitpress/7735.003.0006 · Cited by 52
Self-citationDopamine Promotes Cognitive Effort by Biasing the Benefits Versus Costs of Cognitive WorkPreprint
Westbrook, A., van den Bosch, R., Määttä, J. I., Hofmans, L., Papadopetraki, D., Cools, R., & Frank, M. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/778134 · Cited by 41
Fröber, K., & Dreisbach, G. (2020) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-020-00825-1 · Cited by 40
What Is Mental Effort: A Clinical Perspective
Wolpe, N., Holton, R., & Fletcher, P. C. (2024) · Biological Psychiatry · doi:10.1016/j.biopsych.2024.01.022 · Cited by 49
Dopamine modulates processing speed in the human mesolimbic system
Eckart, C., & Bunzeck, N. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.11.001 · Cited by 38
The Effect of Motivation on Movement: A Study of Bradykinesia in Parkinson’s Disease
Shiner, T., Seymour, B., Symmonds, M., Dayan, P., Bhatia, K. P., & Dolan, R. J. (2012) · PLoS ONE · doi:10.1371/journal.pone.0047138 · Cited by 40
Self-citationSalamone, J. D., Correa, M., Farrar, A. M., Nunes, E. J., & Collins, L. E. (2010) · Future Neurology · doi:10.2217/fnl.10.19 · Cited by 40
The Psychopharmacology of Obsessive-Compulsive Disorder: A Preclinical Roadmap
Szechtman, H., Harvey, B. H., Woody, E. Z., & Hoffman, K. L. (2019) · Pharmacological Reviews · doi:10.1124/pr.119.017772 · Cited by 41
Informing deep neural networks by multiscale principles of neuromodulatory systems
Mei, J., Muller, E. B., & Ramaswamy, S. (2022) · Trends in Neurosciences · doi:10.1016/j.tins.2021.12.008 · Cited by 47
Wang, J., Carnicella, S., Ahmadiantehrani, S., He, D., Barak, S., Kharazia, V., Hamida, S. B., Zapata, A. G., Shippenberg, T. S., & Dorit Ron, A. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.3909-10.2010 · Cited by 39
Nicotinic alteration of decision-making
Naudé, J., Dongelmans, M., & Faure, P. (2015) · Neuropharmacology · doi:10.1016/j.neuropharm.2014.11.021 · Cited by 39
Preservation of Eye Movements in Parkinson's Disease Is Stimulus- and Task-Specific
Fooken, J., Patel, P., Jones, C. B., McKeown, M. J., & Spering, M. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.1690-21.2021 · Cited by 41
A neural substrate of sex-dependent modulation of motivation
Cox, J., Minerva, A. R., Fleming, W., Zimmerman, C. A., Hayes, C. L., Zorowitz, S., Bandi, A., Ornelas, S., McMannon, B., Parker, N. F., et al. (2023) · Nature Neuroscience · doi:10.1038/s41593-022-01229-9 · Cited by 38
Altered nucleus accumbens functional connectivity precedes apathy in Parkinson’s disease
Morris, L., Harrison, S. J., Melzer, T. R., Dalrymple‐Alford, J. C., Anderson, T., MacAskill, M. R., & Heron, C. J. L. (2023) · Brain · doi:10.1093/brain/awad113 · Cited by 40
Serotonin's many meanings elude simple theories
Dayan, P., & Huys, Q. J. M. (2015) · eLife · doi:10.7554/elife.07390 · Cited by 41
Self-citationThe influence of contextual reward statistics on risk preference
Rigoli, F., Rutledge, R. B., Dayan, P., & Dolan, R. J. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.12.016 · Cited by 39
Self-citationA computational reward learning account of social media engagementPreprint
Lindström, B., Bellander, M., Schultner, D. T., Chang, A., Tobler, P. N., & Amodio, D. M. (2019) · doi:10.31234/osf.io/78mh5 · Cited by 39
O’Doherty, J. P. (2012) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2012.08074.x · Cited by 37
Smith, R. S., Alkozei, A., Killgore, W. D. S., & Lane, R. D. (2017) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2017.09.011 · Cited by 39
The “Effort Elephant” in the Room: What Is Effort, Anyway?
Thomson, K. S., & Oppenheimer, D. M. (2022) · Perspectives on Psychological Science · doi:10.1177/17456916211064896 · Cited by 46
Dopamine Modulates the Neural Representation of Subjective Value of Food in Hungry Subjects
Medic, N., Ziauddeen, H., Vestergaard, M. D., Henning, E., Schultz, W., Farooqi, I. S., & Fletcher, P. C. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.2051-14.2014 · Cited by 38
Schizophrenia: A Computational Reinforcement Learning Perspective
Frank, M. J. (2008) · Schizophrenia Bulletin · doi:10.1093/schbul/sbn123 · Cited by 38
Fiore, V. G., Sperati, V., Mannella, F., Mirolli, M., GURNEY, K. N., Friston, K. J., Dolan, R. J., & Baldassarre, G. (2014) · Frontiers in Psychology · doi:10.3389/fpsyg.2014.00124 · Cited by 38
Melancholy, anhedonia, apathy: the search for separable behaviors and neural circuits in depression
Post, R. J., & Warden, M. R. (2018) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2018.02.018 · Cited by 39
Sequence Effect in Parkinson’s Disease Is Related to Motor Energetic Cost
Tinaz, S., Pillai, A. S., & Hallett, M. (2016) · Frontiers in Neurology · doi:10.3389/fneur.2016.00083 · Cited by 37
A Formal Valuation Framework for Emotions and Their Control
Huys, Q. J. M., & Renz, D. (2017) · Biological Psychiatry · doi:10.1016/j.biopsych.2017.07.003 · Cited by 39
Wolf, C., & Lappe, M. (2021) · Cognitive Neurodynamics · doi:10.1007/s11571-020-09661-y · Cited by 38
Reinforcement learning links spontaneous cortical dopamine impulses to reward
Foo, C., Lozada, A. F., Aljadeff, J., Li, Y., Wang, J. W., Slesinger, P. A., & Kleinfeld, D. (2021) · Current Biology · doi:10.1016/j.cub.2021.06.069 · Cited by 36
Niv, Y. (2013) · Nature · doi:10.1038/500533a · Cited by 38
Self-citationRole of dopamine and clinical heterogeneity in cognitive dysfunction in Parkinson's disease
Cools, R., Tichelaar, J. G., Helmich, R. C. G., Bloem, B. R., Esselink, R. A. J., Smulders, K., & Timmer, M. H. M. (2022) · Progress in brain research · doi:10.1016/bs.pbr.2022.01.012 · Cited by 38
The roles of the amygdala in the affective regulation of body, brain, and behaviour
Mirolli, M., Mannella, F., & Baldassarre, G. (2010) · Connection Science · doi:10.1080/09540091003682553 · Cited by 36
Disordered Decision Making: A Cognitive Framework for Apathy and Impulsivity in Huntington’s Disease
Morris, L., O'Callaghan, C., & Le Heron, C. (2022) · Movement Disorders · doi:10.1002/mds.29013 · Cited by 36
Neuromodulated Synaptic Plasticity on the SpiNNaker Neuromorphic System
Mikaitis, M., García, G. P., Knight, J. C., & Furber, S. B. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00105 · Cited by 35
Jones, S., Neville, V., Higgs, L., Paul, E. S., Dayan, P., Robinson, E. S. J., & Mendl, M. (2018) · Scientific Reports · doi:10.1038/s41598-018-30571-x · Cited by 35
Self-citationA Neuroeconomic Framework for Creative Cognition
Lin, H., & Vartanian, O. A. (2018) · Perspectives on Psychological Science · doi:10.1177/1745691618794945 · Cited by 43
Parkinson Disease: Translating Insights from Molecular Mechanisms to Neuroprotection
Pirooznia, S. K., Rosenthal, L. S., Dawson, V. L., & Dawson, T. M. (2021) · Pharmacological Reviews · doi:10.1124/pharmrev.120.000189 · Cited by 36
Differential modulation of cognitive control networks by monetary reward and punishment
Cubillo, A., Makwana, A., & Hare, T. A. (2019) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsz006 · Cited by 38
The Role of Dopamine in Motor Flexibility
Bestmann, S., Ruge, D., Rothwell, J. C., & Galea, J. M. (2014) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00706 · Cited by 34
Bromocriptine Does Not Alter Speed–Accuracy Tradeoff
Winkel, J., van Maanen, L., Ratcliff, R., van der Schaaf, M. E., van Schouwenburg, M. R., Cools, R., & Forstmann, B. U. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2012.00126 · Cited by 34
A Biologically Inspired Computational Model of Basal Ganglia in Action Selection
Baston, C., & Ursino, M. (2015) · Computational Intelligence and Neuroscience · doi:10.1155/2015/187417 · Cited by 33
Han, S., Cui, Q., Wang, X., Chen, Y., Li, D., Li, L., Guo, X., Fan, Y., Guo, J., Sheng, W., et al. (2020) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2020.109865 · Cited by 32
Hofmans, L., Papadopetraki, D., van den Bosch, R., Määttä, J. I., Froböse, M. I., Zandbelt, B. B., Westbrook, A., Verkes, R., & Cools, R. (2020) · Neuropsychopharmacology · doi:10.1038/s41386-020-00834-1 · Cited by 37
A Computational Analysis of Aberrant Delay Discounting in Psychiatric Disorders
Story, G. W., Moutoussis, M., & Dolan, R. J. (2016) · Frontiers in Psychology · doi:10.3389/fpsyg.2015.01948 · Cited by 35
Toward a Unifying Account of Dopamine’s Role in Cost-Benefit Decision Making
Soutschek, A., Jetter, A., & Tobler, P. N. (2022) · Biological Psychiatry Global Open Science · doi:10.1016/j.bpsgos.2022.02.010 · Cited by 33
Dopamine regulates decision thresholds in human reinforcement learning in males
Chakroun, K., Wiehler, A., Wagner, B., Mathar, D., Ganzer, F., van Eimeren, T., Sommer, T., & Peters, J. (2023) · Nature Communications · doi:10.1038/s41467-023-41130-y · Cited by 36
Lee, S., Kim, D. J., Svenkeson, D., Parras, G. A., Oishi, M., & McKeown, M. J. (2015) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2015.00005 · Cited by 33
From apathy to addiction: Insights from neurology and psychiatry
Kirschner, M., Rabinowitz, A. G., Singer, N., & Dagher, A. (2020) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2020.109926 · Cited by 37
What is bipolar disorder? A disease model of dysregulated energy expenditure
Mansur, R. B., Lee, Y. L., McIntyre, R. S., & Brietzke, E. M. (2020) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2020.04.006 · Cited by 33
Combined model-free and model-sensitive reinforcement learning in non-human primates
Miranda, B., Malalasekera, W. M. N., Behrens, T. E. J., Dayan, P., & Kennerley, S. W. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007944 · Cited by 33
Self-citationNeuro-cognitive processes as mediators of psychological treatment effects
Reiter, A. M. F., Atiya, N. A. A., Berwian, I. M., & Huys, Q. J. M. (2021) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2021.02.007 · Cited by 36
Filla, I., Bailey, M. R., Schipani, E. S., Winiger, V., Mezias, C., Balsam, P., & Simpson, E. (2018) · Neuropsychopharmacology · doi:10.1038/s41386-018-0159-9 · Cited by 30
Strategy selection as rational metareasoningPreprint
Lieder, F., & Griffiths, T. L. (2016) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/6yugk · Cited by 48
Pauli, W. M., Hazy, T. E., & O'Reilly, R. C. (2012) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00155 · Cited by 32
Toschi, C., Hervig, M. E.-S., Moazen, P., Parker, M. G., Dalley, J. W., Gether, U., & Robbins, T. W. (2021) · Psychopharmacology · doi:10.1007/s00213-021-05883-y · Cited by 33
Dopamine Controls the Neural Dynamics of Memory Signals and Retrieval Accuracy
Apitz, T., & Bunzeck, N. (2013) · Neuropsychopharmacology · doi:10.1038/npp.2013.141 · Cited by 30
Thorndike's Law 2.0: Dopamine and the Regulation of Thrift
Beeler, J. A. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2012.00116 · Cited by 40
Humans Integrate Monetary and Liquid Incentives to Motivate Cognitive Task Performance
Yee, D. M., Krug, M. K., Allen, A. Z., & Braver, T. S. (2016) · Frontiers in Psychology · doi:10.3389/fpsyg.2015.02037 · Cited by 35
Porchet, R. I., Boekhoudt, L., Studer, B., Gandamaneni, P. K., Rani, N., Binnamangala, S., Müller, U., & Clark, L. (2013) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2013.00138 · Cited by 31
McDougle, S. D., Butcher, P. A., Parvin, D. E., Mushtaq, F., Niv, Y., Ivry, R. B., & Taylor, J. A. (2019) · Current Biology · doi:10.1016/j.cub.2019.04.011 · Cited by 31
Self-citationRational 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
Goal-Directed Behavior and Instrumental Devaluation: A Neural System-Level Computational Model
Mannella, F., Mirolli, M., & Baldassarre, G. (2016) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2016.00181 · Cited by 33
Sarno, S., de Lafuente, V., Romo, R., & Parga, N. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1712479114 · Cited by 32
Metadata
- Authors
- Niv, Y., Daw, N. D., Joel, D., & Dayan, P.
- Affiliations
- Hebrew University of Jerusalem (IL)
- Gatsby Computational Neuroscience Unit (GB)
- Tel Aviv University (IL)
- Corresponding author
- Niv, Yael
- Venue
- Psychopharmacology
- Published
- 2006-10-10
- Volume
- 191
- Issue
- 3
- Pages
- 507-520
- ISSN
- 0033-3158 (ISSN-L)
- 1432-2072
- OpenAlex
- W2156952192
- Semantic Scholar
- e1625352c7b6e275ec9a63984a5af25a98032671
- Cited by
- 1,133
- References
- 116
- Citations by year
- 2026: 49 · 2025: 65 · 2024: 75 · 2023: 59 · 2022: 76 · 2021: 80 · 2020: 64 · 2019: 63 · 2018: 69 · 2017: 69 · 2016: 84 · 2015: 52 · 2014: 62 · 2013: 61 · 2012: 44
- FWCI
- 9.35
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Psychopharmacology (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Funders
- Gatsby Charitable Foundation
- Hebrew University of Jerusalem
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Keywords
- tonic dopamine, response vigor, opportunity cost, optimal control, average-reward reinforcement learning, response latency, nucleus accumbens, striatum, free-operant behavior, reward rate, motivation, phasic dopamine
- MeSH terms
- Animals, Brain, Choice Behavior, Conditioning, Operant, Dopamine, Models, Psychological, Motivation, Reaction Time, Reinforcement, Psychology, Reinforcement Schedule, Reward, Neurotransmitter Agents, Rats
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