Cited by 229Neurotransmitter Receptor Influence on BehaviorNeural and Behavioral Psychology StudiesDecision-Making and Behavioral Economics

The DOI matches its Crossref record; 50 of the 50 references it cites were found in a registry or an index.

Genetic variation in dopaminergic neuromodulation influences the ability to rapidly and flexibly adapt decisions

Open access

Krugel, Lea K., Biele, Guido, Mohr, Peter N. C., Li, Shu-Chen, Heekeren, Hauke R.·2009·Proceedings of the National Academy of Sciences

DOI 10.1073/pnas.0905191106PMC PMC2760487

Open this paper in CoChat — free account

Abstract

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

Crossref · 10.1073/pnas.0905191106

Cites

50 of 50 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

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.”
Background
Psychophysiological 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

Show all 50 references

Sorted by citations

Items 101–200 of 229

A Healthy Fear of the Unknown: Perspectives on the Interpretation of Parameter Fits from Computational Models in Neuroscience
Nassar, M. R., & Gold, J. I. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003015 · Cited by 41
Noradrenergic and Cholinergic Modulation of Belief Updating
Jepma, M., Brown, S. B. R. E., Murphy, P. R., Koelewijn, S. C., de Vries, B., van den Maagdenberg, A. M., & Nieuwenhuis, S. (2018) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01317 · Cited by 39
A dynamic attentional control framework for understanding sleep deprivation effects on cognition
Whitney, P. J., Hinson, J. M., & Nusbaum, A. T. (2019) · Progress in brain research · doi:10.1016/bs.pbr.2019.03.015 · Cited by 42
Attraction Effect in Risky Choice Can Be Explained by Subjective Distance Between Choice Alternatives
Mohr, P. N. C., Heekeren, H. R., & Rieskamp, J. (2017) · Scientific Reports · doi:10.1038/s41598-017-06968-5 · Cited by 34
Self-citation
Individual Neurons in the Cingulate Cortex Encode Action Monitoring, Not Selection, during Adaptive Decision-Making
Li, Y., Nassar, M. R., Kable, J. W., & Gold, J. I. (2019) · Journal of Neuroscience · doi:10.1523/jneurosci.0159-19.2019 · Cited by 37
Prefrontal mechanisms of comorbidity from a transdiagnostic and ontogenic perspective
MacDonald, A. N., Goines, K., Novacek, D. M., & Walker, E. F. (2016) · Development and Psychopathology · doi:10.1017/s0954579416000742 · Cited by 40
Human inference reflects a normative balance of complexity and accuracy
Tavoni, G., Doi, T., Pizzica, C., Balasubramanian, V., & Gold, J. I. (2022) · Nature Human Behaviour · doi:10.1038/s41562-022-01357-z · Cited by 35
Changes in Endogenous Dopamine Induced by Methylphenidate Predict Functional Connectivity in Nonhuman Primates
Birn, R. M., Converse, A. K., Rajala, A. Z., Alexander, A. L., Block, W. F., McMillan, A. B., Christian, B. T., Filla, C., Murali, D., Hurley, S. A., et al. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.2513-18.2018 · Cited by 34
Social anxiety and dynamic social reinforcement learning in a volatile environment
Beltzer, M. L., Adams, S., Beling, P. A., & Teachman, B. A. (2019) · Clinical Psychological Science · doi:10.1177/2167702619858425 · Cited by 32
Distinct roles for dopamine clearance mechanisms in regulating behavioral flexibility
Korn, C., Akam, T., Jensen, K. H. R., Vagnoni, C., Huber, A., Tunbridge, E. M., & Walton, M. E. (2021) · Molecular Psychiatry · doi:10.1038/s41380-021-01194-y · Cited by 31
Dopamine D3 receptor gene variation: impact on electroconvulsive therapy response and ventral striatum responsiveness in depression
Dannlowski, U., Domschke, K., Birosova, E., Lawford, B. R., Young, R. M., Voisey, J., Morris, C. P., Suslow, T., Konrad, C., Kugel, H., et al. (2011) · The International Journal of Neuropsychopharmacology · doi:10.1017/s1461145711001659 · Cited by 33
With you or against you: Social orientation dependent learning signals guide actions made for others
Christopoulos, G. I., & King‐Casas, B. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.09.011 · Cited by 34
Learning environment-specific learning rates
Simoens, J., Verguts, T., & Braem, S. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011978 · Cited by 38
Animal expertise: mechanisms, ecology and evolution
Dukas, R. (2018) · Animal Behaviour · doi:10.1016/j.anbehav.2018.05.010 · Cited by 34
Dopamine DRD2/ANKK1 Taq1A and DAT1 VNTR polymorphisms are associated with a cognitive flexibility profile in pathological gamblers
Fagundo, A. B., Fernández‐Aranda, F., de la Torre, R., Verdejo‐García, A., Granero, R., Penelo, E., Gené, M., Barrot, C., Sánchez, C. G., Álvarez‐Moya, E. M., et al. (2014) · Journal of Psychopharmacology · doi:10.1177/0269881114551079 · Cited by 37
Neuromodulation and Neurophysiology on the Timescale of Learning and Decision-Making
Grossman, C. D., & Cohen, J. Y. (2022) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-092021-125059 · Cited by 30
Executive functions and selective attention are favored in middle-aged healthy women carriers of the Val/Val genotype of the catechol-O-methyltransferase gene: a behavioral genetic study
Solís-Ortiz, S., Pérez-Luque, E., Morado-Crespo, L., & Gutiérrez-Muñoz, M. (2010) · Behavioral and Brain Functions · doi:10.1186/1744-9081-6-67 · Cited by 28
Uncertainty deconstructed: conceptual analysis and state-of-the-art review of the ERP correlates of risk and ambiguity in decision-making
Botelho, C., Fernandes, C., Campos, C., Seixas, C., Pasion, R., Garcez, H., Ferreira‐Santos, F., Barbosa, F., Teixeira, J. M., & Paiva, T. O. (2023) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-023-01101-8 · Cited by 31
The stability flexibility tradeoff and the dark side of detail
Nassar, M. R., & Troiani, V. (2020) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-020-00848-8 · Cited by 30
Functional Polymorphism of the Mu-Opioid Receptor Gene (OPRM1) Influences Reinforcement Learning in Humans
Lee, M. R., Gallen, C. L., Zhang, X., Hodgkinson, C. A., Goldman, D., Stein, E. A., & Barr, C. S. (2011) · PLoS ONE · doi:10.1371/journal.pone.0024203 · Cited by 27
C957T polymorphism of the dopamine D2 receptor gene is associated with motor learning and heart rate
Huertas, E., Bühler, K., Echeverry‐Alzate, V., Giménez, T., & López‐Moreno, J. A. (2012) · Genes Brain & Behavior · doi:10.1111/j.1601-183x.2012.00793.x · Cited by 27
Commonly-occurring polymorphisms in the COMT, DRD1 and DRD2 genes influence different aspects of motor sequence learning in humans
Baetu, I., Burns, N. R., Urry, K., Barbante, G. G., & Pitcher, J. B. (2015) · Neurobiology of Learning and Memory · doi:10.1016/j.nlm.2015.09.009 · Cited by 26
COMT influences on prefrontal and striatal blood oxygenation level-dependent responses during working memory among individuals with schizophrenia, their siblings, and healthy controls
Ceaser, A. E., Csernansky, J. G., & Barch, D. M. (2012) · Cognitive Neuropsychiatry · doi:10.1080/13546805.2012.698100 · Cited by 26
Tolcapone-Enhanced Neurocognition in Healthy Adults: Neural Basis and Predictors
Bhakta, S. G., Light, G. A., Talledo, J., Balvaneda, B., Hughes, E., Álvarez, A., Rana, B. K., Young, J. W., & Swerdlow, N. R. (2017) · The International Journal of Neuropsychopharmacology · doi:10.1093/ijnp/pyx074 · Cited by 26
Anatomical connection strength predicts dopaminergic drug effects on fronto-striatal function
van Schouwenburg, M. R., Zwiers, M. P., van der Schaaf, M. E., Geurts, D. E. M., Schellekens, A., Buitelaar, J. K., Verkes, R. J., & Cools, R. (2013) · Psychopharmacology · doi:10.1007/s00213-013-3000-5 · Cited by 25
Optimal structure of metaplasticity for adaptive learning
Khorsand, P., & Soltani, A. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005630 · Cited by 28
Dopamine and sense of agency: Determinants in personality and substance use
Render, A., & Jansen, P. (2019) · PLoS ONE · doi:10.1371/journal.pone.0214069 · Cited by 25
A Single Nucleotide Polymorphism of the Neuropeptide B/W Receptor-1 Gene Influences the Evaluation of Facial Expressions
Watanabe, N., Wada, M., Irukayama‐Tomobe, Y., Ogata, Y., Tsujino, N., Suzuki, M., Furutani, N., Sakurai, T., & Yamamoto, M. (2012) · PLoS ONE · doi:10.1371/journal.pone.0035390 · Cited by 24
The effects of catechol-O-methyl-transferase polymorphism Val158Met on functional connectivity in healthy young females: A resting EEG study
Lee, T., Yu, Y. W. ‐., Hong, C., Tsai, S., Wu, H., & Chen, T. (2011) · Brain Research · doi:10.1016/j.brainres.2010.12.073 · Cited by 26
Candidate-gene association study searching for genetic factors involved in migraine chronification
Louter, M., Fernández‐Morales, J., de Vries, B., Winsvold, B. S., Anttila, V., Fernández‐Cadenas, I., Vila‐Pueyo, M., Sintas, C., van Duijn, C. M., Cormand, B., et al. (2014) · Cephalalgia · doi:10.1177/0333102414547141 · Cited by 24
Association between genetic variation in the dopamine system and motor recovery after stroke
Kim, B.-R., Kim, H. Y., Chun, Y. I., Yun, Y., Kim, H.-T., Choi, D., & Lee, J. (2016) · Restorative Neurology and Neuroscience · doi:10.3233/rnn-160667 · Cited by 25
Individual differences in behavioral flexibility in a probabilistic reversal learning task: An fMRI study.
Waegeman, A., Declerck, C. H., Boone, C., Seurinck, R., & Parizel, P. M. (2014) · Journal of Neuroscience Psychology and Economics · doi:10.1037/npe0000026 · Cited by 23
Motivational deficits in schizophrenia relate to abnormalities in cortical learning rate signals
Hernaus, D., Xu, Z., Brown, E. C., Ruiz, R., Frank, M. J., Gold, J. M., & Waltz, J. A. (2018) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-018-0643-z · Cited by 21
Effects of Expressive Writing on Neural Processing During Learning
DiMenichi, B. C., Ceceli, A. O., Bhanji, J. P., & Tricomi, E. M. (2019) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2019.00389 · Cited by 24
The effect of mental schema evolution on mental workload measurement: an EEG study with simulated quadrotor UAV operation
Gu, H., Yao, Q., Chen, H., Ding, Z., Zhao, X., Liu, H., Feng, Y., Li, C., & Xiaoli Li, A. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac6828 · Cited by 20
Differential representation of feedback and decision in adolescents and adults
Javadi, A., Schmidt, D. H. K., & Smolka, M. N. (2014) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2014.01.021 · Cited by 18
Caudate nucleus reactivity predicts perceptual learning rate for visual feature conjunctions
Reavis, E. A., Frank, S., & Tse, P. U. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.01.051 · Cited by 19
Congruence of Inherent and Acquired Values Facilitates Reward-Based Decision-Making
Chien, S., Wiehler, A., Spezio, M., & Gläscher, J. P. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.3084-15.2016 · Cited by 19
Influence of Prenatal Methamphetamine Abuse on the Brain
Tomášková, A., Šlamberová, R., & Černá, M. (2020) · Epigenomes · doi:10.3390/epigenomes4030014 · Cited by 15
Measuring the role of neurotransmitters in investment decision: A proposed constructs
Khan, A., & Mubarik, M. S. (2020) · International Journal of Finance & Economics · doi:10.1002/ijfe.2150 · Cited by 21
The influence of COMT rs4680 on functional connectivity in healthy adults: A systematic review
Morris, K. A., Grace, S., Woods, W., Dean, B., & Rossell, S. L. (2020) · European Journal of Neuroscience · doi:10.1111/ejn.14748 · Cited by 18
Magnified effects of the COMT gene on white-matter microstructure in very old age
Papenberg, G., Lövdén, M., Laukka, E. J., Kalpouzos, G., Keller, L., Graff, C., Köhncke, Y., Li, T., Fratiglioni, L., & Bäckman, L. (2014) · Brain Structure and Function · doi:10.1007/s00429-014-0835-4 · Cited by 18
Learning at Variable Attentional Load Requires Cooperation of Working Memory, Meta-learning, and Attention-augmented Reinforcement Learning
Womelsdorf, T., Watson, M. R., & Tiesinga, P. H. E. (2021) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01780 · Cited by 20
The effects of life stress and neural learning signals on fluid intelligence
Friedel, E., Schlagenhauf, F., Beck, A., Dolan, R. J., Huys, Q. J. M., Rapp, M. A., & Heinz, A. (2014) · European Archives of Psychiatry and Clinical Neuroscience · doi:10.1007/s00406-014-0519-3 · Cited by 18
Neural Mechanisms of Decision-Making and the Personal Level
Shea, N. J. (2013) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780199579563.013.0062 · Cited by 16
Human creativity: Functions, mechanisms, and social conditioning
Dreu, C. K. W. D., Nijstad, B. A., & Baas, M. (2023) · Advances in experimental social psychology · doi:10.1016/bs.aesp.2023.11.004 · Cited by 16
Resting posterior minus frontal EEG slow oscillations is associated with extraversion and DRD2 genotype
Koehler, S., Wacker, J., Odorfer, T., Reif, A., Gallinat, J., Fallgatter, A. J., & Herrmann, M. J. (2011) · Biological Psychology · doi:10.1016/j.biopsycho.2011.05.006 · Cited by 15
The effects of methylphenidate on cerebral responses to conflict anticipation and unsigned prediction error in a stop-signal task
Manza, P., Hu, S., Ide, J. S., Farr, O. M., Zhang, S., Leung, H., & Li, C. R. (2016) · Journal of Psychopharmacology · doi:10.1177/0269881115625102 · Cited by 16
Variability in behavior that cognitive models do not explain can be linked to neuroimaging data
Gluth, S., & Rieskamp, J. (2016) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2016.04.012 · Cited by 16
Replication study implicates COMT val158met polymorphism as a modulator of probabilistic reward learning
Lancaster, T. M., Heerey, E. A., Mantripragada, K. K., & Linden, D. E. J. (2015) · Genes Brain & Behavior · doi:10.1111/gbb.12228 · Cited by 15
Imaging genetics: implications for research on variable antidepressant drug response
Rabl, U., Scharinger, C., Müller, M., & Pezawas, L. (2010) · Expert Review of Clinical Pharmacology · doi:10.1586/ecp.10.35 · Cited by 15
Accurate step-hold tracking of smoothly varying periodic and aperiodic probability
Ricci, M. R., & Gallistel, C. R. (2017) · Attention Perception & Psychophysics · doi:10.3758/s13414-017-1310-0 · Cited by 11
Genetic moderation of the association between regulatory focus and reward responsiveness: a proof-of-concept study
Davis, E., Hariri, A. R., Pizzagalli, D. A., & Strauman, T. J. (2013) · Biology of Mood & Anxiety Disorders · doi:10.1186/2045-5380-3-3 · Cited by 12
Cumulative Dopamine Genetic Score predicts behavioral and electrophysiological correlates of response inhibition via interactions with task demand
Enge, S., Sach, M., Reif, A., Lesch, K., Miller, R. E., & Fleischhauer, M. (2019) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-019-00752-w · Cited by 12
Risky decision-making following prefrontal D1 receptor manipulation
Beyer, D. K. E., Horn, L., Klinker, N., & Freund, N. (2021) · Translational Neuroscience · doi:10.1515/tnsci-2020-0187 · Cited by 10
Efficiency of the Brain Network Is Associated with the Mental Workload with Developed Mental Schema
Gu, H., Chen, H., Yao, Q., He, W., Wang, S., Yang, C., Li, J., Liu, H., Xiaoli Li, A., Zhao, X., et al. (2023) · Brain Sciences · doi:10.3390/brainsci13030373 · Cited by 9
Testing the convergent validity, domain generality, and temporal stability of selected measures of people’s tendency to explore
Anvari, F., Billinger, S., Analytis, P. P., Franco, V. R., & Marchiori, D. (2024) · Nature Communications · doi:10.1038/s41467-024-51685-z · Cited by 9
Mediodorsal thalamus nucleus–medial prefrontal cortex circuitry regulates cost–benefit decision-making selections
Ding, T.-H., Hu, Y.-Y., Li, J., Sun, C., & Ma, C. (2024) · Cerebral Cortex · doi:10.1093/cercor/bhae476 · Cited by 8
Further perceptions of probability: In defence of associative models.
Forsgren, M., Juslin, P., & van den Berg, R. (2023) · Psychological Review · doi:10.1037/rev0000410 · Cited by 8
The Role of Dopaminergic Genes in Probabilistic Reinforcement Learning in Schizophrenia Spectrum Disorders
Frydecka, D., Misiak, B., Piotrowski, P., Bielawski, T., Pawlak, E., Kłosińska, E., Krefft, M., Noaimy, K. A., Rymaszewska, J., Moustafa, A. A., et al. (2021) · Brain Sciences · doi:10.3390/brainsci12010007 · Cited by 9
The aging investor: Insights from neuroeconomicsPreprint
Mohr, P. N. C., & Heekeren, H. R. (2012) · RePEc: Research Papers in Economics · Cited by 9
Self-citation
A preliminary study suggests that nicotine and prefrontal dopamine affect cortico‐striatal areas in smokers with performance feedback
Lee, M. R., Gallen, C. L., Ross, T. J., Kurup, P. K., Salmeron, B. J., Hodgkinson, C. A., Goldman, D. A., Stein, E. A., & Enoch, M. (2013) · Genes Brain & Behavior · doi:10.1111/gbb.12027 · Cited by 8
When is the Time for a Change? Decomposing Dynamic Learning Rates
Fischer, A. G., & Ullsperger, M. (2014) · Neuron · doi:10.1016/j.neuron.2014.10.050 · Cited by 8
Active reinforcement learning versus action bias and hysteresis: control with a mixture of experts and nonexperts
Colas, J. T., O’Doherty, J. P., & Grafton, S. T. (2024) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1011950 · Cited by 6
Beta and High Gamma Oscillations in the Cortico-striatal Network Reflect Reward Certainty on a Probabilistic Reversal Learning Task
Koloski, M. F., Salimi, M., Hulyalkar, S., Tang, T., Barnes, S. A., Mishra, J., & Ramanathan, D. (2025) · Journal of Neuroscience · doi:10.1523/jneurosci.0858-25.2025 · Cited by 7
Consumer Neuroscience and Neuromarketing
Weber, B. (2016) · Studies in neuroscience, psychology and behavioral economics · doi:10.1007/978-3-642-35923-1_17 · Cited by 6
Dopaminergic Genetic Polymorphisms Predict Rule-based Category Learning
Byrne, K. A., Davis, T. H., & Worthy, D. A. (2016) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00942 · Cited by 6
Polymorphisms that affect GABA neurotransmission predict processing of aversive prediction errors in humans
Baetu, I., Pitcher, J. B., Cohen‐Woods, S., Lancer, B. H., Beu, N., Foreman, L. M., Taylor, P. D., & Burns, N. R. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.04.058 · Cited by 6
Neural systems supporting and affecting economically relevant behavior
Braeutigam, S. (2012) · Neuroscience and Neuroeconomics · doi:10.2147/nan.s25038 · Cited by 6
Genetic Variation in the Catechol-O-Methyl Transferase Val108/158Met Is Linked to the Caudate and Posterior Cingulate Cortex Volume in Healthy Subjects: Voxel-Based Morphometry Analysis of Brain Magnetic Resonance Imaging
Watanabe, K., Kakeda, S., Yoshimura, R., Ide, S., Hayashi, K., Katsuki, A., Umene‐Nakano, W., Watanabe, R., Abe, O., & Korogi, Y. (2015) · PLoS ONE · doi:10.1371/journal.pone.0142862 · Cited by 4
Paradoxical effects of drugs on cognitive function: the neuropsychopharmacology of the dopamine and other neurotransmitter systems
Cools, R., Aarts, E., & Mehta, M. A. (2011) · Cambridge University Press eBooks · doi:10.1017/cbo9780511978098.025 · Cited by 4
Human Learning About Causation
Baetu, I., & Baker, A. G. (2016) · Wiley eBooks · doi:10.1002/9781118650813.ch15 · Cited by 4
Orbitofrontal and Thalamic Influences on Striatal Involvement in Human Reversal LearningPreprint
Bell, T., Langdon, A., Lindner, M., Lloyd, W. K., & Christakou, A. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/246371 · Cited by 4
Dopamine regulates the exploration-exploitation trade-off in ratsPreprint
Cinotti, F., Fresno, V., Aklil, N., Coutureau, É., Girard, B., Marchand, A. R., & Khamassi, M. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/482802 · Cited by 4
The complexity dividend: when sophisticated inference mattersPreprint
Tavoni, G., Balasubramanian, V., & Gold, J. I. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/563346 · Cited by 4
Learning at variable attentional load requires cooperation between working memory, meta-learning and attention-augmented reinforcement learningPreprint
Womelsdorf, T., Watson, M. R., & Tiesinga, P. H. E. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.09.27.315432 · Cited by 4
Human complex exploration strategies are extended via noradrenaline-modulated heuristicsPreprint
Dubois, M., Habicht, J., Michely, J., Moran, R., Dolan, R. J., & Hauser, T. U. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.02.20.958025 · Cited by 4
Neural Diversity and Decisions
Thoelen, G., & Zak, P. J. (2024) · Adaptive Human Behavior and Physiology · doi:10.1007/s40750-024-00237-2 · Cited by 3
Navigating Uncertainty: The Role of Mood and Confidence in Decision-Making Flexibility and Performance
Lavín, C., García, R., & Fuentes, M. (2024) · Behavioral Sciences · doi:10.3390/bs14121144 · Cited by 3
Association between executive functions and COMT Val108/158Met polymorphism among healthy younger and older adults: A preliminary study
Apa, Z. L., Gilsoul, J., Dideberg, V., & Collette, F. (2024) · PLoS ONE · doi:10.1371/journal.pone.0303343 · Cited by 3
Early Salience Signals Predict Interindividual Asymmetry in Decision Accuracy Across Rewarding and Punishing Contexts
Westwood, S., & Philiastides, M. G. (2024) · Human Brain Mapping · doi:10.1002/hbm.70072 · Cited by 4
Balancing safety and efficiency in human decision makingPreprint
Mahajan, P., Tong, S., Lee, S. W., & Seymour, B. (2024) · eLife · doi:10.7554/elife.101371.1 · Cited by 3
Balancing safety and efficiency in human decision-making
Mahajan, P., Tong, S., Lee, S. W., & Seymour, B. (2024) · eLife · doi:10.7554/elife.101371.3 · Cited by 7
Gate-controlled neuromodulatory optical synaptic transistor for adaptive learning and energy-accuracy balance
Yun, J. M., Kim, Y. B., Choi, M. J., & Kang, S. J. (2026) · Materials Horizons · doi:10.1039/d5mh02224j · Cited by 2
Neurotransmitters and the Behavior of Individual Investors: Exploratory and Confirmatory Factor Analysis
Nazaripour, M., & Zakizadeh, B. (2025) · Faṣlnāmah-ʹi payām-i hājir. · doi:10.61186/ijf.2024.419806.1435 · Cited by 2
Novel Developments in Cognitive fMRI
Huettel, S. A. (2015) · Biological magnetic resonance · doi:10.1007/978-1-4899-7591-1_19 · Cited by 2
Construction and Validation of Neurotransmitters Scale
Ahmad, M., Tahir, A., & Sohail, N. (2018) · Global Social Sciences Review · doi:10.31703/gssr.2018(iii-iv).16 · Cited by 2
Increased dopamine availability magnifies nicotine effects on cognitive control: A pilot study
Ahrens, S., Laux, J., Müller, C., & Thiel, C. M. (2020) · Journal of Psychopharmacology · doi:10.1177/0269881120907989 · Cited by 2
The Role of Frontostriatal Systems in Instructed Reinforcement Learning: Evidence From Genetic and Experimentally-Induced Variation
Tardiff, N., Graves, K. N., & Thompson‐Schill, S. L. (2018) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00472 · Cited by 2
Computing the Uncontrollable: Insights from Computational Modelling of Learning and Choice in Depression
Chase, H. W. (2021) · Current Behavioral Neuroscience Reports · doi:10.1007/s40473-021-00228-7 · Cited by 2
Balancing New Against Old Information: The Role of Surprise in LearningPreprint
Faraji, M., Preuschoff, K., & Gerstner, W. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.05642 · Cited by 2
Further Perceptions of Probability: In Defence of Associative Models – A Commentary on Gallistel et al. 2014Preprint
Forsgren, M., Juslin, P., & van den Berg, R. (2019) · bioRxiv · doi:10.1101/2020.01.30.927558 · Cited by 2
The Dynamics of Feedback-based Learning is Modulated by Working Memory CapacityPreprint
Humann, J., Fischer, A. G., & Ullsperger, M. (2020) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/8qzu2 · Cited by 2
Balancing safety and efficiency in human decision-makingPreprint
Mahajan, P., Tong, S., Lee, S. W., & Seymour, B. (2024) · eLife · doi:10.7554/elife.101371 · Cited by 2
Balancing safety and efficiency in human decision makingPreprint
Mahajan, P., Tong, S., Lee, S. W., & Seymour, B. (2025) · eLife · doi:10.7554/elife.101371.2 · Cited by 2
Dissecting how psychopathic traits are linked to learning in different contexts: A multilevel computational and electrophysiological approach
Driessen, J. M. A., Diaconescu, A. O., Atanassova, D. V., Buitelaar, J. K., Kessels, R. P. C., Glennon, J. C., & Brazil, I. A. (2025) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-025-01295-z · Cited by 1
Subcortical neural basis of malevolent creativity
Gao, Z., Liu, X., Teng, J., & Hao, N. (2025) · iScience · doi:10.1016/j.isci.2025.113733 · Cited by 1
Learning and Using Different Task Rule Updating Strategies Require Extensive Practice
Xu, S., Wen, T., Egner, T., Verguts, T., & Braem, S. (2026) · Cognitive Science · doi:10.1111/cogs.70262 · Cited by 1
Culture as a Response to Uncertainty
Tobler, P. N., & Christopoulos, G. I. (2016) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780199357376.013.6 · Cited by 1
THE SOLANUM NIGRUM COMPLEX IN WESTERN KENYA
Matasyoh, L. G., & Mwaura, M. H. (2014) · International Journal of Plant Animal and Environmental Sciences · Cited by 1

Authors: Krugel, L. K., Biele, G., Mohr, P. N. C., Li, S.-C., & Heekeren, H. R.
Affiliations: Max Planck Institute for Human Development (DE)
ORCID: Biele, Guido
Mohr, Peter N. C.
Li, Shu-Chen
Heekeren, Hauke R.
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
DOI: 10.1073/pnas.0905191106
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

Genetic variation in dopaminergic neuromodulation influences the ability to rapidly and flexibly adapt decisions

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks