Genetic variation in dopaminergic neuromodulation influences the ability to rapidly and flexibly adapt decisions
Open accessAbstract
The ability to rapidly and flexibly adapt decisions to available rewards is crucial for survival in dynamic environments. Reward-based decisions are guided by reward expectations that are updated based on prediction errors, and processing of these errors involves dopaminergic neuromodulation in the striatum. To test the hypothesis that the COMT gene Val 158 Met polymorphism leads to interindividual differences in reward-based learning, we used the neuromodulatory role of dopamine in signaling prediction errors. We show a behavioral advantage for the phylogenetically ancestral Val/Val genotype in an instrumental reversal learning task that requires rapid and flexible adaptation of decisions to changing reward contingencies in a dynamic environment. Implementing a reinforcement learning model with a dynamic learning rate to estimate prediction error and learning rate for each trial, we discovered that a higher and more flexible learning rate underlies the advantage of the Val/Val genotype. Model-based fMRI analysis revealed that greater and more differentiated striatal fMRI responses to prediction errors reflect this advantage on the neurobiological level. Learning rate-dependent changes in effective connectivity between the striatum and prefrontal cortex were greater in the Val/Val than Met/Met genotype, suggesting that the advantage results from a downstream effect of the prefrontal cortex that is presumably mediated by differences in dopamine metabolism. These results show a critical role of dopamine in processing the weight a particular prediction error has on the expectation updating for the next decision, thereby providing important insights into neurobiological mechanisms underlying the ability to rapidly and flexibly adapt decisions to changing reward contingencies.
Integrity signals
Registry
Cites
Statistics
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?
The Rescorla-Wagner model does not differentiate between learning from positive and negative reward prediction errors.
“In their behavioral modeling, Javadi et al. (2014a) used a Rescorla–Wagner model which does not differentiate between learning from positive and negative RPEs (Krugel et al., 2009; Rescorla and Wagner, 1972).”
From Cognitive flexibility in adolescence: Neural and behavioral mechanisms of reward prediction error processing in adaptive decision making during development (2015) · cites this paper as Krugel et al., 2009
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 · see citing works
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,263 · see citing works
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 · see citing works
Classical conditioning II: Current research and theory.
Black, A. H., & Prokasy, W. F. (1972) · Appleton-Century-Crofts eBooks · Cited by 4,449 · see citing works
Dopamine, learning and motivation.
Wise, R. A. (2004) · Nature Reviews Neuroscience · doi:10.1038/nrn1406 · Cited by 3,379 · cited 1× · see citing works
“Dopamine (DA) modulates synaptic efficacy in both the striatum and PFC (5) and is thus involved in different kinds of learning.”
BackgroundPsychophysiological and Modulatory Interactions in Neuroimaging
Friston, K. J., Buechel, C., Fink, G. R., Morris, J., Rolls, E., & Dolan, R. J. (1997) · NeuroImage · doi:10.1006/nimg.1997.0291 · Cited by 3,097 · see citing works
Individual 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 2,598 · see citing works
Effect of COMT Val 108/158 Met genotype on frontal lobe function and risk for schizophrenia
Egan, M. F., Goldberg, T. E., Kolachana, B. S., Callicott, J. H., Mazzanti, C. M., Straub, R. E., Goldman, D. A., & Weinberger, D. R. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.111134598 · Cited by 2,374 · see citing works
Dissociable 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 · see citing works
Learning the value of information in an uncertain world
Behrens, T. E. J., Woolrich, M. W., Walton, M. E., & Rushworth, M. F. S. (2007) · Nature Neuroscience · doi:10.1038/nn1954 · Cited by 2,053 · see citing works
Grace, A. A. (1991) · Neuroscience · doi:10.1016/0306-4522(91)90196-u · Cited by 1,831 · see citing works
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 · see citing works
The principal features and mechanisms of dopamine modulation in the prefrontal cortex
Seamans, J. K., & Yang, C. R. (2004) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2004.05.006 · Cited by 1,592 · see citing works
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 · see citing works
Individual choice behavior: A theoretical analysis.
Luce, R. D. (2005) · doi:10.1037/14396-000 · Cited by 1,157 · see citing works
Floresco, S. B., West, A. R., Ash, B., Moore, H., & Grace, A. A. (2003) · Nature Neuroscience · doi:10.1038/nn1103 · Cited by 1,106 · see citing works
Dopamine neurons report an error in the temporal prediction of reward during learning
Hollerman, J. R., & Schultz, W. (1998) · Nature Neuroscience · doi:10.1038/1124 · Cited by 1,103 · see citing works
Frank, M. J. (2005) · Journal of Cognitive Neuroscience · doi:10.1162/0898929052880093 · Cited by 925 · see citing works
Carr, D. B., & Sesack, S. R. (2000) · Journal of Neuroscience · doi:10.1523/jneurosci.20-10-03864.2000 · Cited by 880 · see citing works
Gogos, J. A., Morgan, M., Luine, V., Santha, M., Ogawa, S., Pfaff, D., & Karayiorgou, M. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.17.9991 · Cited by 864 · see citing works
Cools, R., Clark, L., Owen, A. M., & Robbins, T. W. (2002) · The Journal of Neuroscience · doi:10.1523/jneurosci.22-11-04563.2002 · Cited by 784 · see citing works
Bilder, R. M., Volavka, J., Lachman, H. M., & Grace, A. A. (2004) · Neuropsychopharmacology · doi:10.1038/sj.npp.1300542 · Cited by 769 · see citing works
Reinforcement learning: The Good, The Bad and The Ugly
Dayan, P., & Niv, Y. (2008) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2008.08.003 · Cited by 690 · see citing works
Genetic triple dissociation reveals multiple roles for dopamine in reinforcement learning
Frank, M. J., Moustafa, A. A., Haughey, H. M., Curran, T., & Hutchison, K. E. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0706111104 · Cited by 669 · see citing works
Dopamine-Mediated Stabilization of Delay-Period Activity in a Network Model of Prefrontal Cortex
Durstewitz, D., Seamans, J. K., & B, T. J. S. A. (2000) · Journal of Neurophysiology · doi:10.1152/jn.2000.83.3.1733 · Cited by 617 · see citing works
A Functional Polymorphism in the COMT Gene and Performance on a Test of Prefrontal Cognition
Malhotra, A. K., Kestler, L. J., Mazzanti, C. M., Bates, J. A., Goldberg, T. E., & Goldman, D. A. (2002) · American Journal of Psychiatry · doi:10.1176/appi.ajp.159.4.652 · Cited by 612 · see citing works
Hampton, A. N., Bossaerts, P., & O’Doherty, J. P. (2006) · The Journal of Neuroscience · doi:10.1523/jneurosci.1010-06.2006 · Cited by 498 · see citing works
Midbrain dopamine and prefrontal function in humans: interaction and modulation by COMT genotype
Meyer-Lindenberg, A., Kohn, P. D., Kolachana, B., Kippenhan, S., McInerney-Leo, A., Nussbaum, R., Weinberger, D. R., & Berman, K. F. (2005) · Nature Neuroscience · doi:10.1038/nn1438 · Cited by 428 · see citing works
Genetically Determined Differences in Learning from Errors
Klein, T. A., Neumann, J., Reuter, M., Hennig, J., von Cramon, D. Y., & Ullsperger, M. (2007) · Science · doi:10.1126/science.1145044 · Cited by 424 · see citing works
Roberts, A., De Salvia, M., Wilkinson, L., Collins, P., Muir, J., Everitt, B., & Robbins, T. (1994) · The Journal of Neuroscience · doi:10.1523/jneurosci.14-05-02531.1994 · Cited by 415 · see citing works
Human aging magnifies genetic effects on executive functioning and working memory
Nagel, I. E. (2008) · Frontiers in Human Neuroscience · doi:10.3389/neuro.09.001.2008 · Cited by 415 · see citing works
Barnett, J. H., Scoriels, L., & Munafò, M. R. (2008) · Biological Psychiatry · doi:10.1016/j.biopsych.2008.01.005 · Cited by 412 · see citing works
Schönberg, T., Daw, N. D., Joel, D., & O'Doherty, J. P. (2007) · The Journal of Neuroscience · doi:10.1523/jneurosci.2496-07.2007 · Cited by 390 · see citing works
Global variation in the frequencies of functionally different catechol-O-methyltransferase alleles.
Palmatier, M., Kang, M. A., & Kidd, K. (1999) · Biological Psychiatry · doi:10.1016/s0006-3223(99)00098-0 · Cited by 385 · see citing works
Variation in dopamine genes influences responsivity of the human reward system
Dreher, J.-C., Kohn, P., Kolachana, B., Weinberger, D. R., & Berman, K. F. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0805517106 · Cited by 376 · see citing works
Matsumoto, M., Weickert, C. S., Akil, M., Lipska, B. K., Hyde, T. M., Herman, M. M., Kleinman, J. E., & Weinberger, D. R. (2003) · Neuroscience · doi:10.1016/s0306-4522(02)00556-0 · Cited by 365 · see citing works
Linking nucleus accumbens dopamine and blood oxygenation
Knutson, B., & Gibbs, S. E. B. (2007) · Psychopharmacology · doi:10.1007/s00213-006-0686-7 · Cited by 337 · see citing works
Catechol- <i>O</i> -Methyltransferase Genotype and Dopamine Regulation in the Human Brain
Akil, M., Kolachana, B. S., Rothmond, D. A., Hyde, T. M., Weinberger, D. R., & Kleinman, J. E. (2003) · The Journal of Neuroscience · doi:10.1523/jneurosci.23-06-02008.2003 · Cited by 325 · see citing works
Barnett, J. H., Jones, P. B., Robbins, T. W., & Müller, U. (2007) · Molecular Psychiatry · doi:10.1038/sj.mp.4001973 · Cited by 274 · see citing works
Moore, H., West, A. R., & Grace, A. (1999) · Biological Psychiatry · doi:10.1016/s0006-3223(99)00078-5 · Cited by 257 · see citing works
Gene–gene interaction associated with neural reward sensitivity
Yacubian, J., Sommer, T., Schroeder, K., Gläscher, J., Kalisch, R., Leuenberger, B., Braus, D. F., & Büchel, C. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0702029104 · Cited by 241 · see citing works
The dopamine transporter: A crucial component regulating dopamine transmission
Jaber, M., Jones, S. R., Bruno Giros, A., & Caron, M. G. (1997) · Movement Disorders · doi:10.1002/mds.870120502 · Cited by 232 · see citing works
Genetic evidence implicating DARPP-32 in human frontostriatal structure, function, and cognition
Meyer‐Lindenberg, A., Straub, R. E., Lipska, B. K., Verchinski, B. A., Goldberg, T. E., Callicott, J. H., Egan, M. F., Huffaker, S., Mattay, V. S., Kolachana, B. S., et al. (2007) · Journal of Clinical Investigation · doi:10.1172/jci30413 · Cited by 226 · see citing works
King, D. E., Zigmond, M. J., & Finlay, J. M. (1997) · Neuroscience · doi:10.1016/s0306-4522(96)00421-6 · Cited by 168 · see citing works
Retinotopic activation in response to subjective contours in primary visual cortex
Maertens, M. (2008) · Frontiers in Human Neuroscience · doi:10.3389/neuro.09.002.2008 · Cited by 155 · see citing works
Effect of aging on stimulus-reward association learning
Mell, T., Heekeren, H. R., Marschner, A., Wartenburger, I., Villringer, A., & Reischies, F. M. (2004) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2004.07.010 · Cited by 134 · see citing works
Self-citationVentral–striatal/nucleus–accumbens sensitivity to prediction errors during classification learning
Rodriguez, P. F., Aron, A. R., & Poldrack, R. A. (2006) · Human Brain Mapping · doi:10.1002/hbm.20186 · Cited by 122 · see citing works
Target‐Specific Glutamatergic Regulation of Dopamine Neurons in the Ventral Tegmental Area
Takahata, R., & Moghaddam, B. (2000) · Journal of Neurochemistry · doi:10.1046/j.1471-4159.2000.0751775.x · Cited by 101 · see citing works
Kolachana, B. S., Saunders, R. C., & Weinberger, D. R. (1995) · Neuroscience · doi:10.1016/0306-4522(95)00246-f · Cited by 91 · see citing works
Role of ventral striatum in reward-based decision making
Heekeren, H. R., Wartenburger, I., Marschner, A., Mell, T., Villringer, A., & Reischies, F. M. (2007) · Neuroreport · doi:10.1097/wnr.0b013e3281532bd7 · Cited by 65 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Frontal theta as a mechanism for cognitive control
Cavanagh, J. F., & Frank, M. J. (2014) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2014.04.012 · Cited by 2,726 · see citing works
Inverted-U–Shaped Dopamine Actions on Human Working Memory and Cognitive Control
Cools, R., & D'Esposito, M. (2011) · Biological Psychiatry · doi:10.1016/j.biopsych.2011.03.028 · Cited by 1,673 · see citing works
Rational regulation of learning dynamics by pupil-linked arousal systems
Nassar, M. R., Rumsey, K. M., Wilson, R. C., Parikh, K., Heasly, B., & Gold, J. I. (2012) · Nature Neuroscience · doi:10.1038/nn.3130 · Cited by 722 · see citing works
A Bayesian foundation for individual learning under uncertainty
Mathys, C. (2011) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2011.00039 · Cited by 655 · see citing works
Neural computations underlying arbitration between model-based and model-free learning
Lee, S. W., Shimojo, S., & O’Doherty, J. P. (2014) · Neuron · doi:10.1016/j.neuron.2013.11.028 · Cited by 642 · see citing works
Nassar, M. R., Wilson, R. C., Heasly, B. S., & Gold, J. I. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.0822-10.2010 · Cited by 564 · see citing works
Prefrontal cortical regulation of brainwide circuit dynamics and reward-related behavior
Ferenczi, E. A., Zalocusky, K. A., Liston, C., Grosenick, L., Warden, M. R., Amatya, D., Katovich, K., Mehta, H., Patenaude, B., Ramakrishnan, C., et al. (2016) · Science · doi:10.1126/science.aac9698 · Cited by 535 · see citing works
Psychopathology and the Human Connectome: Toward a Transdiagnostic Model of Risk For Mental Illness
Buckholtz, J. W., & Meyer‐Lindenberg, A. (2012) · Neuron · doi:10.1016/j.neuron.2012.06.002 · Cited by 433 · see citing works
Forming beliefs: Why valence matters
Sharot, T., & Garrett, N. (2016) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2015.11.002 · Cited by 345 · see citing works
Adaptive learning under expected and unexpected uncertainty
Soltani, A., & Izquierdo, A. (2019) · Nature reviews. Neuroscience · doi:10.1038/s41583-019-0180-y · Cited by 286 · see citing works
The drift diffusion model as the choice rule in reinforcement learning
Pedersen, M. L., Frank, M. J., & Biele, G. (2016) · Psychonomic Bulletin & Review · doi:10.3758/s13423-016-1199-y · Cited by 272 · see citing works
Self-citationRogers, R. D. (2010) · Neuropsychopharmacology · doi:10.1038/npp.2010.165 · Cited by 248 · see citing works
Hauser, T. U., Iannaccone, R., Walitza, S., Brandeis, D., & Brem, S. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2014.09.018 · Cited by 203 · see citing works
Dillon, D. G., Rosso, I. M., Pechtel, P., Killgore, W. D. S., Rauch, S. L., & Pizzagalli, D. A. (2014) · Depression and Anxiety · doi:10.1002/da.22202 · Cited by 209 · see citing works
Dopaminergic control of cognitive flexibility in humans and animals
Klanker, M., Feenstra, M., & Denys, D. (2013) · Frontiers in Neuroscience · doi:10.3389/fnins.2013.00201 · Cited by 209 · see citing works
Pisauro, M. A., Fouragnan, E. F., Retzler, C., & Philiastides, M. G. (2017) · Nature Communications · doi:10.1038/ncomms15808 · Cited by 217 · see citing works
Neurogenetics and Pharmacology of Learning, Motivation, and Cognition
Frank, M. J., & Fossella, J. (2010) · Neuropsychopharmacology · doi:10.1038/npp.2010.96 · Cited by 191 · see citing works
Boot, N. C., Baas, M., van Gaal, S., Cools, R., & Dreu, C. K. W. D. (2017) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2017.04.007 · Cited by 187 · see citing works
Dopaminergic Genes Predict Individual Differences in Susceptibility to Confirmation Bias
Doll, B. B., Hutchison, K. E., & Frank, M. J. (2011) · The Journal of Neuroscience · doi:10.1523/jneurosci.6486-10.2011 · Cited by 184 · see citing works
Ursini, G., Bollati, V., Fazio, L., Porcelli, A., Iacovelli, L., Catalani, A., Sinibaldi, L., Gelao, B., Romano, R., Rampino, A., et al. (2011) · The Journal of Neuroscience · doi:10.1523/jneurosci.6631-10.2011 · Cited by 188 · see citing works
Normative evidence accumulation in unpredictable environments
Glaze, C. M., Kable, J. W., & Gold, J. I. (2015) · eLife · doi:10.7554/elife.08825 · Cited by 190 · see citing works
The town bird and the country bird: problem solving and immunocompetence vary with urbanization
Audet, J., Ducatez, S., & Lefebvre, L. (2015) · Behavioral Ecology · doi:10.1093/beheco/arv201 · Cited by 177 · see citing works
Serotonin neurons modulate learning rate through uncertainty
Grossman, C. D., Bari, B. A., & Cohen, J. Y. (2022) · Current Biology · doi:10.1016/j.cub.2021.12.006 · Cited by 164 · see citing works
A Meta-Analysis of Decision-Making and Attention in Adults With ADHD
Mowinckel, A. M., Pedersen, M. L., Eilertsen, E., & Biele, G. (2015) · Journal of Attention Disorders · doi:10.1177/1087054714558872 · Cited by 158 · see citing works
Self-citationThe Neural Basis of Following Advice
Biele, G., Rieskamp, J., Krugel, L. K., & Heekeren, H. R. (2011) · PLoS Biology · doi:10.1371/journal.pbio.1001089 · Cited by 157 · see citing works
Self-citationFrontostriatal Involvement in Task Switching Depends on Genetic Differences in D2 Receptor Density
Stelzel, C., Basten, U., Montag, C., Reuter, M., & Fiebach, C. J. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.1062-10.2010 · Cited by 153 · see citing works
A matched filter hypothesis for cognitive control
Chrysikou, E. G., Weber, M. J., & Thompson‐Schill, S. L. (2013) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2013.10.021 · Cited by 150 · see citing works
Effects of Direct Social Experience on Trust Decisions and Neural Reward Circuitry
Fareri, D. S., Chang, L. J., & Delgado, M. R. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2012.00148 · Cited by 147 · see citing works
Cavanagh, J. F. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.02.007 · Cited by 148 · see citing works
Farrell, S., Tunbridge, E. M., Braeutigam, S., & Harrison, P. J. (2012) · Biological Psychiatry · doi:10.1016/j.biopsych.2011.12.023 · Cited by 144 · see citing works
Costs of memory: lessons from ‘mini’ brains
Burns, J. G., Foucaud, J., & Mery, F. (2010) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2010.2488 · Cited by 144 · see citing works
de Visser, L. (2011) · Frontiers in Neuroscience · doi:10.3389/fnins.2011.00109 · Cited by 140 · see citing works
Decision neuroscience and consumer decision making
Yoon, C., Gonzalez, R., Bechara, A., Berns, G. S., Dagher, A., Dubé, L., Huettel, S. A., Kable, J. W., Liberzon, I., Plaßmann, H., et al. (2012) · Marketing Letters · doi:10.1007/s11002-012-9188-z · Cited by 135 · see citing works
The emotional Stroop task and posttraumatic stress disorder: A meta-analysis
Cisler, J. M., Wolitzky-Taylor, K. B., Adams, T. G., Babson, K. A., Badour, C. L., & Willems, J. L. (2011) · Clinical Psychology Review · doi:10.1016/j.cpr.2011.03.007 · Cited by 134 · see citing works
Different Varieties of Uncertainty in Human Decision-Making
Bland, A. R., & Schaefer, A. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2012.00085 · Cited by 126 · see citing works
Peciña, M., Mickey, B. J., Love, T., Wang, H., Langenecker, S. A., Hodgkinson, C., Shen, P.-H., Villafuerte, S., Hsu, D., Weisenbach, S. L., et al. (2013) · Cortex · doi:10.1016/j.cortex.2012.01.010 · Cited by 123 · see citing works
The neural underpinnings of cognitive flexibility and their disruption in psychotic illness
Waltz, J. A. (2017) · Neuroscience · doi:10.1016/j.neuroscience.2016.06.005 · Cited by 122 · see citing works
Temporal dynamics of prediction error processing during reward-based decision making
Philiastides, M. G., Biele, G., Vavatzanidis, N., Kazzer, P., & Heekeren, H. R. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.05.052 · Cited by 120 · see citing works
Self-citationPearson‐Fuhrhop, K. M., Minton, B. R., Acevedo, D., Shahbaba, B., & Cramer, S. C. (2013) · PLoS ONE · doi:10.1371/journal.pone.0061197 · Cited by 119 · see citing works
Connectomic Intermediate Phenotypes for Psychiatric Disorders
Fornito, A., & Bullmore, E. T. (2012) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2012.00032 · Cited by 116 · see citing works
Heinz, A., Murray, G. K., Schlagenhauf, F., Sterzer, P., Grace, A. A., & Waltz, J. A. (2018) · Schizophrenia Bulletin · doi:10.1093/schbul/sby154 · Cited by 118 · see citing works
Gatzke‐Kopp, L. M. (2010) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2010.09.013 · Cited by 115 · see citing works
Ziegler, S., Pedersen, M. L., Mowinckel, A. M., & Biele, G. (2016) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2016.09.002 · Cited by 110 · see citing works
Self-citationA Mixture of Delta-Rules Approximation to Bayesian Inference in Change-Point Problems
Wilson, R. C., Nassar, M. R., & Gold, J. I. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003150 · Cited by 103 · see citing works
The perception of probability.
Gallistel, C. R., Krishan, M., Liu, Y., Miller, R., & Latham, P. E. (2014) · Psychological Review · doi:10.1037/a0035232 · Cited by 107 · see citing works
Neurogenetics of depression: A focus on reward processing and stress sensitivity
Bogdan, R., Nikolova, Y. S., & Pizzagalli, D. A. (2012) · Neurobiology of Disease · doi:10.1016/j.nbd.2012.05.007 · Cited by 105 · see citing works
Variability in Dopamine Genes Dissociates Model-Based and Model-Free Reinforcement Learning
Doll, B. B., Bath, K. G., Daw, N. D., & Frank, M. J. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.1901-15.2016 · Cited by 105 · see citing works
Reiter, A. M. F., Heinze, H., Schlagenhauf, F., & Deserno, L. (2016) · Neuropsychopharmacology · doi:10.1038/npp.2016.95 · Cited by 103 · see citing works
Diederen, K. M. J., Spencer, T., Vestergaard, M. D., Fletcher, P. C., & Schultz, W. (2016) · Neuron · doi:10.1016/j.neuron.2016.04.019 · Cited by 102 · see citing works
Schlagenhauf, F., Rapp, M. A., Huys, Q. J. M., Beck, A., Wüstenberg, T., Deserno, L., Buchholz, H., Kalbitzer, J., Buchert, R., Bauer, M., et al. (2012) · Human Brain Mapping · doi:10.1002/hbm.22000 · Cited by 103 · see citing works
Foti, D., & Hajcak, G. (2012) · Psychophysiology · doi:10.1111/j.1469-8986.2011.01343.x · Cited by 104 · see citing works
Learning and motivation in the human striatum
Daphna Shohamy, A. (2011) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2011.05.009 · Cited by 100 · see citing works
Metaplasticity as a Neural Substrate for Adaptive Learning and Choice under Uncertainty
Farashahi, S. G., Donahue, C. H., Khorsand, P., Seo, H., Lee, D., & Soltani, A. (2017) · Neuron · doi:10.1016/j.neuron.2017.03.044 · Cited by 98 · see citing works
Visual Decision-Making in an Uncertain and Dynamic World
Gold, J. I., & Stocker, A. A. (2017) · Annual Review of Vision Science · doi:10.1146/annurev-vision-111815-114511 · Cited by 100 · see citing works
Dopamine blockade impairs the exploration-exploitation trade-off in rats
Cinotti, F., Fresno, V., Aklil, N., Coutureau, É., Girard, B., Marchand, A. R., & Khamassi, M. (2019) · Scientific Reports · doi:10.1038/s41598-019-43245-z · Cited by 94 · see citing works
Catecholaminergic Regulation of Learning Rate in a Dynamic Environment
Jepma, M., Murphy, P. R., Nassar, M. R., Rangel-Gomez, M., Meeter, M., & Nieuwenhuis, S. (2016) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005171 · Cited by 91 · see citing works
Enhanced flexibility of place discrimination learning by targeting striatal cholinergic interneurons
Okada, K., Nishizawa, K., Fukabori, R., Kai, N., Shiota, A., Ueda, M., Tsutsui, Y., Sakata, S., Matsushita, N., & Kobayashi, K. (2014) · Nature Communications · doi:10.1038/ncomms4778 · Cited by 90 · see citing works
Social stress reactivity alters reward and punishment learning
Cavanagh, J. F., Frank, M. J., & Allen, J. J. B. (2010) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsq041 · Cited by 90 · see citing works
Global reward state affects learning and activity in raphe nucleus and anterior insula in monkeys
Wittmann, M. K., Fouragnan, E. F., Folloni, D., Klein-Flügge, M. C., Chau, B. K. H., Khamassi, M., & Rushworth, M. F. S. (2020) · Nature Communications · doi:10.1038/s41467-020-17343-w · Cited by 88 · see citing works
Vinckier, F., Gaillard, R., Palminteri, S., Rigoux, L., Salvador, A., Fornito, A., Adapa, R., Krebs, M. O., Pessiglione, M., & Fletcher, P. C. (2016) · Molecular Psychiatry · doi:10.1038/mp.2015.73 · Cited by 87 · see citing works
Herrero, E. M., & Marco‐Pallarés, J. (2013) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00516 · Cited by 86 · see citing works
Computational models of adaptive behavior and prefrontal cortex
Soltani, A., & Koechlin, É. (2021) · Neuropsychopharmacology · doi:10.1038/s41386-021-01123-1 · Cited by 85 · see citing works
Kamhi, J. F., & Traniello, J. F. A. (2013) · Brain Behavior and Evolution · doi:10.1159/000356091 · Cited by 81 · see citing works
Reiter, A. M. F., Deserno, L., Kallert, T. W., Heinze, H., Heinz, A., & Schlagenhauf, F. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.4322-15.2016 · Cited by 83 · see citing works
Glaze, C. M., Filipowicz, A. L. S., Kable, J. W., Balasubramanian, V., & Gold, J. I. (2018) · Nature Human Behaviour · doi:10.1038/s41562-018-0297-4 · Cited by 80 · see citing works
Human complex exploration strategies are enriched by noradrenaline-modulated heuristics
Dubois, M., Habicht, J., Michely, J., Moran, R., Dolan, R. J., & Hauser, T. U. (2021) · eLife · doi:10.7554/elife.59907 · Cited by 76 · see citing works
Dissociable reward and timing signals in human midbrain and ventral striatum
Klein-Flügge, M. C., Hunt, L. T., Bach, D. R., Dolan, R. J., & Behrens, T. E. J. (2011) · Neuron · doi:10.1016/j.neuron.2011.08.024 · Cited by 75 · see citing works
Distinct manifestations of executive dysfunction in aged rats
Beas, B. S., Setlow, B., & Bizon, J. L. (2013) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2013.03.019 · Cited by 73 · see citing works
Scaling prediction errors to reward variability benefits error-driven learning in humans
Diederen, K. M. J., & Schultz, W. (2015) · Journal of Neurophysiology · doi:10.1152/jn.00483.2015 · Cited by 69 · see citing works
Balancing new against old information: the role of puzzlement surprise in learning
Faraji, M., Preuschoff, K., & Gerstner, W. (2018) · Neural Computation · doi:10.1162/neco_a_01025 · Cited by 68 · see citing works
Dissociable forms of uncertainty-driven representational change across the human brain
Nassar, M. R., McGuire, J. T., Ritz, H., & Kable, J. W. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.1713-18.2018 · Cited by 65 · see citing works
Linking neurogenetics and individual differences in language learning: the dopamine hypothesis.
Wong, P. C. M., Morgan-Short, K., Ettlinger, M., & Zheng, J. (2012) · Cortex · doi:10.1016/j.cortex.2012.03.017 · Cited by 67 · see citing works
Zanella, S., Doi, A., Garcia, A. J., Elsen, F. P., Kirsch, S., Wei, A. D., & Ramirez, J. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.3644-12.2014 · Cited by 62 · see citing works
Decision neuroscience: neuroeconomics
Smith, D. V., & Huettel, S. A. (2010) · Wiley Interdisciplinary Reviews Cognitive Science · doi:10.1002/wcs.73 · Cited by 62 · see citing works
Fouragnan, E., Queirazza, F., Retzler, C., Mullinger, K. J., & Philiastides, M. G. (2017) · Scientific Reports · doi:10.1038/s41598-017-04507-w · Cited by 61 · see citing works
Nicotinic receptors in the ventral tegmental area promote uncertainty-seeking
Naudé, J., Tolu, S., Dongelmans, M., Torquet, N., Valverde, S., Rodriguez, G., Pons, S., Maskos, U., Mourot, A., Marti, F., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4223 · Cited by 61 · see citing works
Altered Medial Frontal Feedback Learning Signals in Anorexia Nervosa
Bernardoni, F., Geisler, D., King, J. A., Javadi, A., Ritschel, F., Murr, J., Reiter, A. M. F., Roessner, V., Smolka, M. N., Kiebel, S. J., et al. (2017) · Biological Psychiatry · doi:10.1016/j.biopsych.2017.07.024 · Cited by 61 · see citing works
Mowinckel, A. M., Alnæs, D., Pedersen, M. L., Ziegler, S., Fredriksen, M., Kaufmann, T., Sonuga-Barke, E. J. S., Endestad, T., Westlye, L. T., & Biele, G. (2017) · NeuroImage Clinical · doi:10.1016/j.nicl.2017.03.008 · Cited by 62 · see citing works
Self-citationAllostatic load and mental health: a latent class analysis of physiological dysregulation
Carbone, J. T. (2020) · Stress · doi:10.1080/10253890.2020.1813711 · Cited by 59 · see citing works
Deserno, L., Beck, A., Huys, Q. J. M., Lorenz, R. C., Buchert, R., Buchholz, H., Plotkin, M., Kumakara, Y., Cumming, P. D., Heinze, H., et al. (2014) · European Journal of Neuroscience · doi:10.1111/ejn.12802 · Cited by 57 · see citing works
The Ultimate Intra-/Extra-Dimensional Attentional Set-Shifting Task for Mice
Scheggia, D., Bebensee, A., Weinberger, D. R., & Papaleo, F. (2013) · Biological Psychiatry · doi:10.1016/j.biopsych.2013.05.021 · Cited by 58 · see citing works
Increased anterior cingulate cortex response precedes behavioural adaptation in anorexia nervosa
Geisler, D., Ritschel, F., King, J. A., Bernardoni, F., Seidel, M., Boehm, I., Runge, F., Goschke, T., Roessner, V., Smolka, M. N., et al. (2017) · Scientific Reports · doi:10.1038/srep42066 · Cited by 58 · see citing works
Electrophysiological correlates of decision making under varying levels of uncertainty
Bland, A. R., & Schaefer, A. (2011) · Brain Research · doi:10.1016/j.brainres.2011.08.031 · Cited by 54 · see citing works
Differential Influence of Levodopa on Reward-Based Learning in Parkinson's Disease
Graef, S., Biele, G., Krugel, L. K., Marzinzik, F., Wahl, M., Wotka, J., Klostermann, F., & Heekeren, H. R. (2010) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2010.00169 · Cited by 54 · see citing works
Self-citationDrug addiction: An affective-cognitive disorder in need of a cure
Fattore, L., & Diana, M. (2016) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2016.04.006 · Cited by 55 · see citing works
Galea, J. M., Bestmann, S., Beigi, M., Jahanshahi, M., & Rothwell, J. C. (2012) · The Journal of Neuroscience · doi:10.1523/jneurosci.3621-11.2012 · Cited by 52 · see citing works
COMT val158met predicts reward responsiveness in humans
Lancaster, T. M., Linden, D. E. J., & Heerey, E. A. (2012) · Genes Brain & Behavior · doi:10.1111/j.1601-183x.2012.00838.x · Cited by 51 · see citing works
Favaro, A., Clementi, M., Manara, R., Bosello, R., Forzan, M., Bruson, A., Tenconi, E., Degortes, D., Titton, F., Salle, F. D., et al. (2013) · Journal of Psychiatry and Neuroscience · doi:10.1503/jpn.120068 · Cited by 52 · see citing works
Morales, A. M., Jones, S. A., Ehlers, A., Lavine, J. B., & Nagel, B. J. (2018) · Neuropsychopharmacology · doi:10.1038/s41386-018-0087-8 · Cited by 51 · see citing works
Watanabe, K., Kakeda, S., Yoshimura, R., Abe, O., Ide, S., Hayashi, K., Katsuki, A., Umene‐Nakano, W., Watanabe, R., Nakamura, J., et al. (2015) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2015.07.024 · Cited by 48 · see citing works
Adolescents Adapt More Slowly than Adults to Varying Reward Contingencies
Javadi, A., Schmidt, D. H. K., & Smolka, M. N. (2014) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00677 · Cited by 45 · see citing works
Independence of Valence and Reward in Emotional Word Processing: Electrophysiological Evidence
Kaltwasser, L., Riès, S., Sommer, W., Knight, R. T., & Willems, R. M. (2013) · Frontiers in Psychology · doi:10.3389/fpsyg.2013.00168 · Cited by 48 · see citing works
Wallace, D. L., Vytlacil, J. J., Nomura, E. M., Gibbs, S. E. B., & D’Esposito, M. (2011) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2011.00032 · Cited by 46 · see citing works
Developmental Changes in Learning: Computational Mechanisms and Social Influences
Bolenz, F., Reiter, A. M. F., & Eppinger, B. (2017) · Frontiers in Psychology · doi:10.3389/fpsyg.2017.02048 · Cited by 38 · see citing works
Queirazza, F., Fouragnan, E. F., Steele, J. D., Cavanagh, J., & Philiastides, M. G. (2019) · Science Advances · doi:10.1126/sciadv.aav4962 · Cited by 47 · see citing works
Catecholaminergic modulation of meta-learning
Cook, J. L., Swart, J. C., Froböse, M. I., Diaconescu, A. O., Geurts, D. E. M., den Ouden, H. E. M., & Cools, R. (2019) · eLife · doi:10.7554/elife.51439 · Cited by 48 · see citing works
Models of Dynamic Belief Updating in Psychosis—A Review Across Different Computational Approaches
Katthagen, T., Fromm, S. P., Wieland, L., & Schlagenhauf, F. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.814111 · Cited by 43 · see citing works
Surguladze, S., Raduà, J., El‐Hage, W., Gohier, B., Sato, J. R., Kronhaus, D. M., Proitsi, P., Powell, J. F., & Phillips, M. L. (2012) · Translational Psychiatry · doi:10.1038/tp.2011.69 · Cited by 43 · see citing works
Investigating dopamine and glucocorticoid systems as underlying mechanisms of anhedonia
Lamontagne, S. J., Melendez, S. I., & Olmstead, M. C. (2018) · Psychopharmacology · doi:10.1007/s00213-018-5007-4 · Cited by 42 · see citing works
Klucken, T., Kruse, O., Wehrum‐Osinsky, S., Hennig, J., Schweckendiek, J., & Stark, R. M. (2014) · Human Brain Mapping · doi:10.1002/hbm.22688 · Cited by 40 · see citing works
- Authors
- Krugel, L. K., Biele, G., Mohr, P. N. C., Li, S.-C., & Heekeren, H. R.
- Affiliations
- Max Planck Institute for Human Development (DE)
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2009-10-20
- Volume
- 106
- Issue
- 42
- Pages
- 17951-17956
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC2760487
- OpenAlex
- W2156601568
- Cited by
- 229
- References
- 50
- Citations by year
- 2026: 3 · 2025: 10 · 2024: 13 · 2023: 6 · 2022: 5 · 2021: 8 · 2020: 12 · 2019: 14 · 2018: 15 · 2017: 16 · 2016: 22 · 2015: 14 · 2014: 18 · 2013: 19 · 2012: 22
- FWCI
- 7.46
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Max Planck Institute for Plasma Physics (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- 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
- COMT Val158Met polymorphism, dopaminergic modulation, reward-based decision-making, prediction error, learning rate, striatum, prefrontal cortex, effective connectivity, reinforcement learning models, dopamine metabolism
- MeSH terms
- Adult, Brain, Catechol O-Methyltransferase, Dopamine, Female, Genotype, Homozygote, Humans, Learning, Magnetic Resonance Imaging, Male, Models, Neurological, Reinforcement, Psychology, Reward, Genetic Variation, Amino Acid Substitution, Polymorphism, Single Nucleotide, Young Adult
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