The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Temporal Difference Models and Reward-Related Learning in the Human Brain
Open accessAbstract
Temporal difference learning has been proposed as a model for Pavlovian conditioning, in which an animal learns to predict delivery of reward following presentation of a conditioned stimulus (CS). A key component of this model is a prediction error signal, which, before learning, responds at the time of presentation of reward but, after learning, shifts its response to the time of onset of the CS. In order to test for regions manifesting this signal profile, subjects were scanned using event-related fMRI while undergoing appetitive conditioning with a pleasant taste reward. Regression analyses revealed that responses in ventral striatum and orbitofrontal cortex were significantly correlated with this error signal, suggesting that, during appetitive conditioning, computations described by temporal difference learning are expressed in the human brain.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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 source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“First, the signed prediction error indicates whether an outcome is better or worse than expected, and thereby plays a crucial role in changing the value allocated to cues, thereby guiding future decisions [14–21].”
From Precision weighting of cortical unsigned prediction error signals benefits learning, is mediated by dopamine, and is impaired in psychosis (2021) · cites this paper as [17]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Many early fMRI studies into prediction errors used model-free accounts in Pavlovian paradigms and located prediction errors in striatal BOLD (Berns et al., 2001; O’Doherty, 2004; O’Doherty et al., 2003; Haruno et al., 2004).”
From Goals and Habits in the Brain (2013) · cites this paper as O’Doherty et al., 2003
In the citing paper
“These changes in striatum BOLD activity coincide with the process of learning how to predict rewards 15, 16 – an observation supported by electrophysiological properties of its dopaminergic afferents.”
From The social brain and reward: Social information processing in the human striatum (2014) · cites this paper as [15]
In the citing paper
“human fMRI studies have found that VS activity correlates with RPEs during tasks that feature exclusively Pavlovian reward associations [27,28]”
From Contributions of the striatum to learning, motivation, and performance: an associative account (2012) · cites this paper as [28]
In the citing paper
“Pupillary response dynamics are thought to reflect multiple different features, including sensory cues36,37, arousal/task engagement38,39, motor activity40,41, and motivation42,43.”
From Revealing abrupt transitions from goal-directed to habitual behavior (2026) · cites this paper as [37]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A neural substrate of prediction and reward
Schultz, W., Dayan, P., & Montague, P. R. (1997) · Science · doi:10.1126/science.275.5306.1593 · Cited by 9,388
Self-citationRescorla, R. A. (1972) · Medical Entomology and Zoology · Cited by 7,268
Learning to predict by the methods of temporal differences
Sutton, R. S. (1988) · Machine Learning · doi:10.1007/bf00115009 · Cited by 5,148
Predictive reward signal of dopamine neurons
Schultz, W. (1998) · Journal of Neurophysiology · doi:10.1152/jn.1998.80.1.1 · Cited by 4,564
Spatial registration and normalization of images
Friston, K. J., Ashburner, J., Frith, C., Poline, J.-B., Heather, J. D., & Frackowiak, R. S. J. (1995) · Human Brain Mapping · doi:10.1002/hbm.460030303 · Cited by 3,744
Self-citationPearce, J. M., & Hall, G. (1980) · Psychological Review · doi:10.1037/0033-295x.87.6.532 · Cited by 3,023
Anticipation of Increasing Monetary Reward Selectively Recruits Nucleus Accumbens
Knutson, B., Adams, C. M., Fong, G. W., & Hommer, D. (2001) · The Journal of Neuroscience · doi:10.1523/jneurosci.21-16-j0002.2001 · Cited by 2,244
Functional imaging of neural responses to expectancy and experience of monetary gains and losses
Breiter, H. C., Aharon, I., Kahneman, D., Dale, A., & Shizgal, P. (2001) · Neuron · doi:10.1016/s0896-6273(01)00303-8 · Cited by 1,404
Dissociation of reward anticipation and outcome with event-related fMRI
Knutson, B., Fong, G. W., Adams, C. M., Varner, J. L., & Hommer, D. (2001) · Neuroreport · doi:10.1097/00001756-200112040-00016 · Cited by 1,300
Tracking the Hemodynamic Responses to Reward and Punishment in the Striatum
Delgado, M. R., Nystrom, L. E., Fissell, C., Noll, D. C., & Fiez, J. A. (2000) · Journal of Neurophysiology · doi:10.1152/jn.2000.84.6.3072 · Cited by 1,116
Cited by
Sorted by citations
Items 501–600 of 1,002
Neuroeconomics: Opening the Gray Box
Camerer, C. F. (2008) · Neuron · doi:10.1016/j.neuron.2008.10.027 · Cited by 72
Ketamine perturbs perception of the flow of time in healthy volunteers
Coull, J. T., Morgan, H., Cambridge, V. C., Moore, J. W., Giorlando, F., Adapa, R., Corlett, P. R., & Fletcher, P. C. (2011) · Psychopharmacology · doi:10.1007/s00213-011-2346-9 · Cited by 73
Mutual benefits: Combining reinforcement learning with sequential sampling models
Miletić, S., Boag, R. J., & Forstmann, B. U. (2019) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2019.107261 · Cited by 70
Bray, S. (2009) · Frontiers in Human Neuroscience · doi:10.3389/neuro.09.032.2009 · Cited by 74
Computational models of reinforcement learning: the role of dopamine as a reward signal
Samson, R. D., Frank, M. J., & Fellous, J. (2010) · Cognitive Neurodynamics · doi:10.1007/s11571-010-9109-x · Cited by 75
Automatic and controlled processing in the corticocerebellar system
Ramnani, N. (2014) · Progress in Brain Research · doi:10.1016/b978-0-444-63356-9.00010-8 · Cited by 70
Neurobiology of decision-making: quo vadis?
Paulus, M. P. (2005) · Cognitive Brain Research · doi:10.1016/j.cogbrainres.2005.01.001 · Cited by 76
A common neural scale for the subjective pleasantness of different primary rewards
Fabian Grabenhorst, B., D’Souza, A., Parris, B. A., Rolls, E. T., & Passingham, R. E. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.03.043 · Cited by 72
Role of the Primate Ventral Tegmental Area in Reinforcement and Motivation
Arsenault, J. T., Rima, S., Stemmann, H., & Vanduffel, W. (2014) · Current Biology · doi:10.1016/j.cub.2014.04.044 · Cited by 70
Neuroanatomical Prediction of Anhedonia in Adolescents
Auerbach, R. P., Pisoni, A., Bondy, E., Kumar, P., Stewart, J. G., Yendiki, A., & Pizzagalli, D. A. (2017) · Neuropsychopharmacology · doi:10.1038/npp.2017.28 · Cited by 73
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
Boundary effects of expectation in human pain perception
Hird, E. J., Charalambous, C., El-Deredy, W., Jones, A. K. P., & Talmi, D. (2019) · Scientific Reports · doi:10.1038/s41598-019-45811-x · Cited by 68
All That Glitters … Dissociating Attention and Outcome Expectancy From Prediction Errors Signals
Roesch, M. R., Calu, D. J., Esber, G. R., & Schoenbaum, G. M. (2010) · Journal of Neurophysiology · doi:10.1152/jn.00173.2010 · Cited by 68
Hormonal and Genetic Influences on Processing Reward and Social Information
Caldú, X., & Dreher, J. (2007) · Annals of the New York Academy of Sciences · doi:10.1196/annals.1412.007 · Cited by 71
Zhou, F., Montag, C., Sariyska, R., Lachmann, B., Reuter, M., Weber, B., Trautner, P., Kendrick, K. M., Markett, S. A., & Becker, B. (2017) · Addiction Biology · doi:10.1111/adb.12570 · Cited by 77
Knowledge acquisition is governed by striatal prediction errors
Pine, A., Sadeh, N., Ben-Yakov, A., Dudai, Y., & Mendelsohn, A. (2018) · Nature Communications · doi:10.1038/s41467-018-03992-5 · Cited by 76
Impaired reward-related learning signals in remitted unmedicated patients with recurrent depression
Geugies, H., Mocking, R. J. T., Figueroa, C. A., Groot, P. F. C., Marsman, J.-B. C., Servaas, M. N., Steele, J. D., Schene, A. H., & Ruhé, H. G. (2019) · Brain · doi:10.1093/brain/awz167 · Cited by 67
The Role of the Parietal Cortex in the Representation of Task-Reward Associations
Wisniewski, D., Reverberi, C., Momennejad, I., Kahnt, T., & Haynes, J.-D. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.4882-14.2015 · Cited by 67
Model-based action planning involves cortico-cerebellar and basal ganglia networks
Fermin, A. S. R., Yoshida, T., Yoshimoto, J., Ito, M., Tanaka, S., & Doya, K. (2016) · Scientific Reports · doi:10.1038/srep31378 · Cited by 70
Apicella, P., Deffains, M., Ravel, S., & Legallet, E. (2009) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2009.06872.x · Cited by 68
Dopamine Increases a Value-Independent Gambling Propensity
Rigoli, F., Rutledge, R. B., Chew, B., Ousdal, O. T., Dayan, P., & Dolan, R. J. (2016) · Neuropsychopharmacology · doi:10.1038/npp.2016.68 · Cited by 63
Self-citationWatanabe, N., Sakagami, M., & Haruno, M. (2013) · The Journal of Neuroscience · doi:10.1523/jneurosci.3400-12.2013 · Cited by 65
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 64
Training Cognition in ADHD: Current Findings, Borrowed Concepts, and Future Directions
Rutledge, K. J., van den Bos, W., McClure, S. M., & Schweitzer, J. B. (2012) · Neurotherapeutics · doi:10.1007/s13311-012-0134-9 · Cited by 75
Behavioral and neural predictors of upcoming decisions
Cohen, M. X., & Ranganath, C. (2005) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/cabn.5.2.117 · Cited by 64
Dual-process theories and consciousness: the case for ‘Type Zero’ cognition: Table 1.
Shea, N. J., & Frith, C. (2016) · Neuroscience of Consciousness · doi:10.1093/nc/niw005 · Cited by 68
Scholl, J., & Klein-Flügge, M. C. (2017) · Behavioural Brain Research · doi:10.1016/j.bbr.2017.09.050 · Cited by 69
Serotonin Affects Association of Aversive Outcomes to Past Actions
Tanaka, S., Shishida, K., Schweighofer, N., Okamoto, Y., Yamawaki, S., & Doya, K. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.2799-09.2009 · Cited by 62
Opioid Receptors in the Nucleus Accumbens Regulate Attentional Learning in the Blocking Paradigm
Iordanova, M. D., McNally, G. P., & Westbrook, R. F. (2006) · Journal of Neuroscience · doi:10.1523/jneurosci.4679-05.2006 · Cited by 63
Neural mechanisms and computations underlying stress effects on learning and memory
Lukšys, G., & Sandi, C. (2011) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2011.03.003 · Cited by 65
Measuring Beliefs and Rewards: A Neuroeconomic Approach*
Caplin, A., Dean, M. R. W., Glimcher, P. W., & Rutledge, R. B. (2010) · The Quarterly Journal of Economics · doi:10.1162/qjec.2010.125.3.923 · Cited by 64
Unexpected but Incidental Positive Outcomes Predict Real-World Gambling
Otto, A. R., Fleming, S., & Glimcher, P. (2016) · Psychology Science · doi:10.1177/0956797615618366 · Cited by 58
The Role of Feedback in Learning and Motivation
Tricomi, E. M., & DePasque, S. (2016) · Advances in motivation and achievement · doi:10.1108/s0749-742320160000019015 · Cited by 73
Kim, D., Park, G. Y., O’Doherty, J. P., & Lee, S. W. (2019) · Nature Communications · doi:10.1038/s41467-019-13632-1 · Cited by 64
Self-citationLearning by doing: an fMRI study of feedback-related brain activations
Marco‐Pallarés, J., Müller, S., & Münte, T. F. (2007) · Neuroreport · doi:10.1097/wnr.0b013e3282e9a58c · Cited by 66
Fabian Grabenhorst, B., & Rolls, E. T. (2010) · Journal of Neurophysiology · doi:10.1152/jn.00352.2010 · Cited by 62
Policy Adjustment in a Dynamic Economic Game
Li, J., McClure, S. M., King‐Casas, B., & Montague, P. R. (2006) · PLoS ONE · doi:10.1371/journal.pone.0000103 · Cited by 63
Theoretical and Empirical Studies of Learning
Niv, Y., & Montague, P. R. (2009) · Elsevier eBooks · doi:10.1016/b978-0-12-374176-9.00022-1 · Cited by 65
Greater insula white matter fiber connectivity in women recovered from anorexia nervosa
Shott, M. E., Pryor, T. L., Yang, T. T., & Frank, G. K. W. (2016) · Neuropsychopharmacology · doi:10.1038/npp.2015.172 · Cited by 62
The role of executive function in shaping reinforcement learning
Rmus, M., McDougle, S. D., & Collins, A. G. E. (2020) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2020.10.003 · Cited by 61
Neuroimaging of Cognition: Past, Present, and Future
Dolan, R. J. (2008) · Neuron · doi:10.1016/j.neuron.2008.10.038 · Cited by 67
Self-citationSelection and inhibition mechanisms for human voluntary action decisions
Zhang, J., Hughes, L. E., & Rowe, J. B. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.06.058 · Cited by 62
Greening, S. G., Finger, E. C., & Mitchell, D. G. V. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.09.017 · Cited by 63
Affective reactivity during adolescence: Associations with age, puberty and testosterone
Vijayakumar, N., Pfeifer, J. H., Flournoy, J. C., Hernandez, L. M., & Dapretto, M. (2019) · Cortex · doi:10.1016/j.cortex.2019.04.024 · Cited by 72
Neural Computations Underlying Causal Structure Learning
Tomov, M. S., Dorfman, H. M., & Gershman, S. J. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.3336-17.2018 · Cited by 61
Modular deep reinforcement learning from reward and punishment for robot navigation
Wang, J., Elfwing, S., & Uchibe, E. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.12.001 · Cited by 62
Piray, P., Ly, V., Roelofs, K., Cools, R., & Toni, I. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.1394-18.2018 · Cited by 61
Attention Deficit Hyperactivity Disorder
Matthews, M., Nigg, J. T., & Fair, D. A. (2013) · Current topics in behavioral neurosciences · doi:10.1007/7854_2013_249 · Cited by 80
The neuroscience of investing: fMRI of the reward system
Peterson, R. L. (2005) · Brain Research Bulletin · doi:10.1016/j.brainresbull.2005.06.015 · Cited by 65
Bebko, G. M., Bertocci, M. A., Fournier, J. C. J., Hinze, A. K., Bonar, L. K., Almeida, J., Perlman, S. B., Versace, A., Schirda, C., Travis, M. J., et al. (2013) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2013.2870 · Cited by 61
Gottfried, J. A., Small, D. M., & Zald, D. H. (2006) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780198565741.003.0006 · Cited by 62
Environmental morale and motivation
Frey, B. S., & Stutzer, A. (2008) · Cambridge University Press eBooks · doi:10.1017/cbo9780511490118.017 · Cited by 62
How Administration of the Beta-Blocker Propranolol Before Extinction can Prevent the Return of Fear
Kroes, M. C. W., Tona, K., den Ouden, H. E. M., Vogel, S., van Wingen, G. A., & Fernández, G. (2015) · Neuropsychopharmacology · doi:10.1038/npp.2015.315 · Cited by 64
The neuroscience of active learning and direct instruction
Dubinsky, J. M., & Hamid, A. (2024) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2024.105737 · Cited by 121
Retest reliability of reward-related BOLD signals
Fließbach, K., Rohe, T., Linder, N. S., Trautner, P., Elger, C. E., & Weber, B. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.01.036 · Cited by 60
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
Lee, J. C., Nopoulos, P., & Tomblin, J. B. (2013) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2013.07.011 · Cited by 56
Within- and cross-participant classifiers reveal different neural coding of information
Clithero, J. A., Smith, D. V., Carter, R., & Huettel, S. A. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.03.057 · Cited by 65
Formal Learning Theory Dissociates Brain Regions with Different Temporal Integration
Gläscher, J. P., & Büchel, C. (2005) · Neuron · doi:10.1016/j.neuron.2005.06.008 · Cited by 59
Decision-making: from neuroscience to neuroeconomics—an overview
Serra, D. (2021) · Theory and Decision · doi:10.1007/s11238-021-09830-3 · Cited by 63
Ventral striatal signal changes represent missed opportunities and predict future choice
Büchel, C., Brassen, S., Yacubian, J., Kalisch, R., & Sommer, T. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.05.031 · Cited by 58
Ge, T., Feng, J., Fabian Grabenhorst, B., & Rolls, E. T. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.08.047 · Cited by 59
Scholl, J., Kolling, N., Nelissen, N., Browning, M., Rushworth, M. F. S., & Harmer, C. J. (2017) · PLOS Biology · doi:10.1371/journal.pbio.2000756 · Cited by 58
Suppression of Striatal Prediction Errors by the Prefrontal Cortex in Placebo Hypoalgesia
Schenk, L. A., Sprenger, C., Onat, S., Colloca, L., & Büchel, C. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.1101-17.2017 · Cited by 58
Bayesian Learning Models of Pain: A Call to Action
Tabor, A., & Burr, C. (2019) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2018.10.006 · Cited by 57
A cortical circuit mechanism for structural knowledge-based flexible sensorimotor decision-making
Liu, Y., Xin, Y., & Xu, N.-L. (2021) · Neuron · doi:10.1016/j.neuron.2021.04.014 · Cited by 57
Striatal outcome processing in healthy aging
Cox, K. M., Aizenstein, H. J., & Fiez, J. A. (2008) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/cabn.8.3.304 · Cited by 57
CNTRICS Final Task Selection: Long-Term Memory
Ragland, J. D., Cools, R., Frank, M. J., Pizzagalli, D. A., Preston, A. R., Ranganath, C., & Wagner, A. D. (2008) · Schizophrenia Bulletin · doi:10.1093/schbul/sbn134 · Cited by 58
de Boer, L., Axelsson, J., Riklund, K., Nyberg, L. H., Dayan, P., Bäckman, L., & Guitart‐Masip, M. (2017) · eLife · doi:10.7554/elife.26424 · Cited by 61
Self-citationReinforcement Learning in Autism Spectrum Disorder
Schuetze, M., Rohr, C. S., Dewey, D., McCrimmon, A. W., & Bray, S. (2017) · Frontiers in Psychology · doi:10.3389/fpsyg.2017.02035 · Cited by 60
Foundations of Neuroeconomics: From Philosophy to Practice
Clithero, J. A., Tankersley, D., & Huettel, S. A. (2008) · PLoS Biology · doi:10.1371/journal.pbio.0060298 · Cited by 58
Tanabe, J., Regner, M. F., Sakai, J. T., Martínez, D., & Gowin, J. L. (2019) · British Journal of Radiology · doi:10.1259/bjr.20180942 · Cited by 58
Reward-Related Learning via Multiple Memory Systems
Delgado, M. R., & Dickerson, K. C. (2012) · Biological Psychiatry · doi:10.1016/j.biopsych.2012.01.023 · Cited by 61
Météreau, É., & Dreher, J. (2014) · Cortex · doi:10.1016/j.cortex.2014.08.012 · Cited by 60
Prévost, C., McCabe, J. A., Jessup, R. K., Bossaerts, P. L., & O’Doherty, J. P. (2011) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2011.07686.x · Cited by 61
Self-citationPositive mood enhances reward-related neural activity
Young, C. B., & Nusslock, R. (2016) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsw012 · Cited by 62
Shih, Y.-W., Tsai, H.-Y., Lin, F.-S., Lin, Y., Chiang, C.-Y., Lu, Z., & Tseng, M. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.2154-18.2018 · Cited by 61
Kosson, D. S., Budhani, S., Nakic, M., Chen, G., Saad, Z. S., Vythilingam, M., Pine, D. S., & Blair, J. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2005.07.060 · Cited by 58
Over a Decade of Neuroeconomics: What Have We Learned?
Konovalov, A., & Krajbich, I. M. (2016) · Organizational Research Methods · doi:10.1177/1094428116644502 · Cited by 55
Forgetting in Reinforcement Learning Links Sustained Dopamine Signals to Motivation
Kato, A., & Morita, K. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005145 · Cited by 59
Skvortsova, V., Degos, B., Welter, M., Vidailhet, M., & Pessiglione, M. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.2081-16.2017 · Cited by 53
Wimmer, G. E., Li, J. K., Gorgolewski, K. J., & Poldrack, R. A. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.0075-18.2018 · Cited by 57
Striatum in stimulus–response learning via feedback and in decision making
Hiebert, N. M., Vo, A., Hampshire, A., Owen, A. M., Seergobin, K. N., & MacDonald, P. A. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.07.013 · Cited by 56
Peer influence: neural mechanisms underlying in-group conformity
Stallen, M., Smidts, A., & Sanfey, A. G. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00050 · Cited by 63
Myers, C. E., Moustafa, A. A., Sheynin, J., VanMeenen, K. M., Gilbertson, M. W., Orr, S. P., Beck, K. D., Pang, K. C. H., & Servatius, R. J. (2013) · PLoS ONE · doi:10.1371/journal.pone.0072508 · Cited by 56
Insights from the application of computational neuroimaging to social neuroscience
Dunne, S., & O’Doherty, J. P. (2013) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2013.02.007 · Cited by 56
Self-citationReward-based spatial learning in unmedicated adults with obsessive-compulsive disorder
Marsh, R., Tau, G. Z., Wang, Z., Huo, Y., Liu, G., Hao, X., Packard, M. G., Peterson, B. S., & Simpson, H. B. (2015) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2014.13121700 · Cited by 53
Mattfeld, A. T., & Stark, C. E. L. (2015) · Hippocampus · doi:10.1002/hipo.22411 · Cited by 53
The virtual bodily self: Mentalisation of the body as revealed in anosognosia for hemiplegia
Fotopoulou, A. (2014) · Consciousness and Cognition · doi:10.1016/j.concog.2014.09.018 · Cited by 55
Neurobiological successor features for spatial navigation
de Cothi, W., & Barry, C. (2020) · Hippocampus · doi:10.1002/hipo.23246 · Cited by 62
Boehme, R., Deserno, L., Gleich, T., Katthagen, T., Pankow, A., Behr, J., Buchert, R., Roiser, J. P., Heinz, A., & Schlagenhauf, F. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.0805-15.2015 · Cited by 56
Impairment of probabilistic reward-based learning in schizophrenia.
Weiler, J. A., Bellebaum, C., Brüne, M., Juckel, G., & Daum, I. (2009) · Neuropsychology · doi:10.1037/a0016166 · Cited by 55
Mechanisms of Reward Circuit Dysfunction in Psychiatric Illness
Pujara, M. S., & Koenigs, M. R. (2013) · The Neuroscientist · doi:10.1177/1073858413499407 · Cited by 51
Neural processes mediating contextual influences on human choice behaviour
Rigoli, F., Friston, K. J., & Dolan, R. J. (2016) · Nature Communications · doi:10.1038/ncomms12416 · Cited by 52
Self-citationAnnual Research Review: Developmental computational psychiatry
Hauser, T. U., Will, G., Dubois, M., & Dolan, R. J. (2018) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.12964 · Cited by 55
Self-citationGeerts, H., Wikswo, J. P., van der Graaf, P. H., Bai, J. P. F., Gaiteri, C., Bennett, D. A., Swalley, S. E., Schuck, E. L., Kaddurah‐Daouk, R., Tsaioun, K., et al. (2019) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12478 · Cited by 49
Differential brain activation according to chronic social reward frustration
Siegrist, J., Menrath, I., Stöcker, T., Klein, M., Kellermann, T., Shah, N. J., Zilles, K., & Schneider, F. W. (2005) · Neuroreport · doi:10.1097/01.wnr.0000186601.50996.f7 · Cited by 52
Neural Mechanisms of Placebo Anxiolysis
Meyer, B., Yuen, K. S. L., Ertl, M., Polomac, N., Mulert, C., Büchel, C., & Kalisch, R. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.4793-14.2015 · Cited by 50
How Glitter Relates to Gold: Similarity-Dependent Reward Prediction Errors in the Human Striatum
Kahnt, T., Park, S. Q., Burke, C. J., & Tobler, P. N. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.2383-12.2012 · Cited by 51
A hedonically complex odor mixture produces an attentional capture effect in the brain
Fabian Grabenhorst, B., Rolls, E. T., & Margot, C. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.12.023 · Cited by 55
Metadata
- Authors
- O'Doherty, J. P., Dayan, P., Friston, K., Critchley, H., & Dolan, R. J.
- Affiliations
- Gatsby Computational Neuroscience Unit (GB)
- UCL Queen Square Institute of Neurology (GB)
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2003-04
- Volume
- 38
- Issue
- 2
- Pages
- 329-337
- ISSN
- 0896-6273 (ISSN-L)
- OpenAlex
- W2007414406
- Cited by
- 1,522
- References
- 36
- Citations by year
- 2026: 51 · 2025: 50 · 2024: 51 · 2023: 46 · 2022: 53 · 2021: 55 · 2020: 54 · 2019: 64 · 2018: 87 · 2017: 65 · 2016: 97 · 2015: 71 · 2014: 70 · 2013: 93 · 2012: 97
- FWCI
- 17.96
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Neuron (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Max Planck Digital Library (submittedVersion, other-oa)
- MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Funders
- Wellcome Trust
- Gatsby Charitable Foundation
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Keywords
- temporal difference learning, prediction error signal, classical conditioning, appetitive conditioning, reward prediction, ventral striatum, orbitofrontal cortex, conditioned stimulus, human brain
- MeSH terms
- Adolescent, Adult, Brain, Brain Mapping, Conditioning, Classical, Corpus Striatum, Female, Frontal Lobe, Humans, Learning, Magnetic Resonance Imaging, Male, Reference Values, Reflex, Pupillary, Reward, Taste, Time Perception
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
