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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.
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“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
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
Classical conditioning II: Current research and theory.
Black, A. H., & Prokasy, W. F. (1972) · Appleton-Century-Crofts eBooks · Cited by 4,449
Dopamine, learning and motivation.
Wise, R. A. (2004) · Nature Reviews Neuroscience · doi:10.1038/nrn1406 · Cited by 3,379 · cited 1×
“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
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 2,538
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 2,416
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
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
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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
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
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
A Bayesian foundation for individual learning under uncertainty
Mathys, C. (2011) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2011.00039 · Cited by 655
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
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 607
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
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 449
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
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
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 295
Self-citationRogers, R. D. (2010) · Neuropsychopharmacology · doi:10.1038/npp.2010.165 · Cited by 262
Hauser, T. U., Iannaccone, R., Walitza, S., Brandeis, D., & Brem, S. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2014.09.018 · Cited by 203
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
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
Pisauro, M. A., Fouragnan, E. F., Retzler, C., & Philiastides, M. G. (2017) · Nature Communications · doi:10.1038/ncomms15808 · Cited by 229
Neurogenetics and Pharmacology of Learning, Motivation, and Cognition
Frank, M. J., & Fossella, J. (2010) · Neuropsychopharmacology · doi:10.1038/npp.2010.96 · Cited by 195
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 198
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
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
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
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
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
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
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
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 157
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 164
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
Cavanagh, J. F. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.02.007 · Cited by 154
Farrell, S., Tunbridge, E. M., Braeutigam, S., & Harrison, P. J. (2012) · Biological Psychiatry · doi:10.1016/j.biopsych.2011.12.023 · Cited by 150
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
de Visser, L. (2011) · Frontiers in Neuroscience · doi:10.3389/fnins.2011.00109 · Cited by 157
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
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 151
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 145
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
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
“In a study involving the performance of a probabilistic RL task in conjunction with event-related MRI, Krugel et al. (2009) found that the Val/Val genotype of the Val158Met polymorphism was associated with a higher and more flexible learning rate.”
BackgroundResultInfluentialTemporal 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 131
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 126
Connectomic Intermediate Phenotypes for Psychiatric Disorders
Fornito, A., & Bullmore, E. T. (2012) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2012.00032 · Cited by 121
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 123
Gatzke‐Kopp, L. M. (2010) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2010.09.013 · Cited by 119
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
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
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 114
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 114
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 110
Reiter, A. M. F., Heinze, H., Schlagenhauf, F., & Deserno, L. (2016) · Neuropsychopharmacology · doi:10.1038/npp.2016.95 · Cited by 104
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
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 105
Foti, D., & Hajcak, G. (2012) · Psychophysiology · doi:10.1111/j.1469-8986.2011.01343.x · Cited by 109
Learning and motivation in the human striatum
Shohamy, D. (2011) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2011.05.009 · Cited by 100
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 106
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 106
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 105
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
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
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 97
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
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
Herrero, E. M., & Marco‐Pallarés, J. (2013) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00516 · Cited by 96
Computational models of adaptive behavior and prefrontal cortex
Soltani, A., & Koechlin, É. (2021) · Neuropsychopharmacology · doi:10.1038/s41386-021-01123-1 · Cited by 86
Kamhi, J. F., & Traniello, J. F. A. (2013) · Brain Behavior and Evolution · doi:10.1159/000356091 · Cited by 85
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 85
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 84
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
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
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 74
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
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
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
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
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 61
Decision neuroscience: neuroeconomics
Smith, D. V., & Huettel, S. A. (2010) · Wiley Interdisciplinary Reviews Cognitive Science · doi:10.1002/wcs.73 · Cited by 67
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
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
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 65
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 71
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 65
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
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 61
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
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 58
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 59
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 58
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
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 52
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
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 54
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 47
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 48
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
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 48
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 42
Queirazza, F., Fouragnan, E. F., Steele, J. D., Cavanagh, J., & Philiastides, M. G. (2019) · Science Advances · doi:10.1126/sciadv.aav4962 · Cited by 52
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Metadata
- 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
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