The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
Temporally Extended Dopamine Responses to Perceptually Demanding Reward-Predictive Stimuli
Open accessAbstract
Midbrain dopamine neurons respond to reward-predictive stimuli. In the natural environment reward-predictive stimuli are often perceptually complicated. Thus, to discriminate one stimulus from another, elaborate sensory processing is necessary. Given that previous studies have used simpler types of reward-predictive stimuli, it has yet to be clear whether and, if so, how dopamine neurons obtain reward information from perceptually complicated stimuli. To investigate this, we recorded the activities of monkey dopamine neurons while they were performing discrimination between two coherent motion directions in random-dot motion stimuli. These coherent directions were paired with different magnitudes of reward. We found that dopamine neurons showed reward-predictive responses to random-dot motion stimuli. Moreover, dopamine neurons showed temporally extended activity correlated with changes in reward prediction (i.e., reward prediction error) from coarse to fine scales between initial motion detection and subsequent motion discrimination phases. Noticeably, dopamine reward-predictive responses became differential in a later phase than previously reported. This response pattern was consistent with the time course of processing required for the estimation of expected reward value that parallels the motion direction discrimination processing. The results demonstrate that dopamine neurons are able to reflect the reward value of perceptually complicated stimuli, and suggest that dopamine neurons use the moment-to-moment reward prediction associated with environmental stimuli to compute a reward prediction error.
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
“Dopamine neurons in medial and lateral, or dorsal and ventral, midbrain positions do show graded differences in responses to rewarding and aversive stimuli2,23,24,26,33.”
From Dopamine reward prediction-error signalling: A two-component response (2016) · cites this paper as [33]
Experimental — accuracy not yet measuredIn the citing paper
“True to the notion that dopamine neurons are involved in temporal prediction-error coding, the initial activation is sensitive to the time of stimulus occurrence and thus codes a temporal-event prediction error33.”
From Dopamine reward prediction-error signalling: A two-component response (2016) · cites this paper as [33]
In the citing paper
“However, on closer inspection, dopamine responses in such elaborate tasks constitute standard reward prediction-error signals2,33,59,66,69,75,151–153.”
From Dopamine reward prediction-error signalling: A two-component response (2016) · cites this paper as [33]
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
Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. (1994) · Cited by 15,411
Predictive reward signal of dopamine neurons
Schultz, W. (1998) · Journal of Neurophysiology · doi:10.1152/jn.1998.80.1.1 · Cited by 4,564
Self-citationDopamine, learning and motivation.
Wise, R. A. (2004) · Nature Reviews Neuroscience · doi:10.1038/nrn1406 · Cited by 3,379 · cited 1×
“Dopamine plays a central role in reward information processing (Schultz, 1998; Wise, 2004).”
BackgroundThe psychometric function: I. Fitting, sampling, and goodness of fit
Wichmann, F. A., & Hill, N. J. (2001) · Perception & Psychophysics · doi:10.3758/bf03194544 · Cited by 2,359
Discrete coding of reward probability and uncertainty by dopamine neurons
Fiorillo, C. D., Tobler, P. N., & Schultz, W. (2003) · Science · doi:10.1126/science.1077349 · Cited by 2,085
Self-citationThe analysis of visual motion: a comparison of neuronal and psychophysical performance
Britten, K., Shadlen, M., Newsome, W., & Movshon, J. (1992) · The Journal of Neuroscience · doi:10.1523/jneurosci.12-12-04745.1992 · Cited by 1,958
Neural Basis of a Perceptual Decision in the Parietal Cortex (Area LIP) of the Rhesus Monkey
Shadlen, M. N., & Newsome, W. T. (2001) · Journal of Neurophysiology · doi:10.1152/jn.2001.86.4.1916 · Cited by 1,755
Newsome, W., & Pare, E. (1988) · The Journal of Neuroscience · doi:10.1523/jneurosci.08-06-02201.1988 · Cited by 1,615
Haber, S. N., Fudge, J. L., & McFarland, N. R. (2000) · The Journal of Neuroscience · doi:10.1523/jneurosci.20-06-02369.2000 · Cited by 1,610
Roitman, J. D., & Shadlen, M. N. (2002) · The Journal of Neuroscience · doi:10.1523/jneurosci.22-21-09475.2002 · Cited by 1,589 · cited 5×
“This is consistent with a time course in which neural discrimination begins at 200 ms after RDM onset in the LIP area (Roitman and Shadlen, 2002).”
BackgroundMethodResultInfluential
Cited by
Sorted by citations
Dopamine in motivational control: rewarding, aversive, and alerting
Bromberg-Martin, E. S., Matsumoto, M., & Hikosaka, O. (2010) · Neuron · doi:10.1016/j.neuron.2010.11.022 · Cited by 2,396
Neuronal Reward and Decision Signals: From Theories to Data
Schultz, W. (2015) · Physiological Reviews · doi:10.1152/physrev.00023.2014 · Cited by 1,126
Self-citationDopamine reward prediction-error signalling: A two-component response
Schultz, W. (2016) · Nature Reviews Neuroscience · doi:10.1038/nrn.2015.26 · Cited by 1,036
Self-citationUpdating dopamine reward signals
Schultz, W. (2013) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2012.11.012 · Cited by 651
Self-citationDopamine reward prediction error coding
Schultz, W. (2016) · Dialogues in Clinical Neuroscience · doi:10.31887/dcns.2016.18.1/wschultz · Cited by 597
Self-citationCollins, A. G. E., & Frank, M. J. (2014) · Psychological Review · doi:10.1037/a0037015 · Cited by 403
Predictive representations can link model-based reinforcement learning to model-free mechanisms
Russek, E. M., Momennejad, I., Botvinick, M., Gershman, S. J., & Daw, N. D. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005768 · Cited by 335
Cognitive Training for Impaired Neural Systems in Neuropsychiatric Illness
Vinogradov, S., Fisher, M., & de Villers‐Sidani, E. (2011) · Neuropsychopharmacology · doi:10.1038/npp.2011.251 · Cited by 345
Dynamic modulation of decision biases by brainstem arousal systems
de Gee, J. W., Colizoli, O., Kloosterman, N. A., Knapen, T. H. J., Nieuwenhuis, S., & Donner, T. H. (2017) · eLife · doi:10.7554/elife.23232 · Cited by 312
What, If, and When to Move: Basal Ganglia Circuits and Self-Paced Action Initiation
Klaus, A., Alves da Silva, J., & Costa, R. M. (2019) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-072116-031033 · Cited by 284
Reward functions of the basal ganglia
Schultz, W. (2016) · Journal of Neural Transmission · doi:10.1007/s00702-016-1510-0 · Cited by 264
Self-citationDopamine reward prediction errors reflect hidden-state inference across time
Starkweather, C. K., Babayan, B. M., Uchida, N., & Gershman, S. J. (2017) · Nature Neuroscience · doi:10.1038/nn.4520 · Cited by 206
Gershman, S. J., & Uchida, N. (2019) · Nature Reviews Neuroscience · doi:10.1038/s41583-019-0220-7 · Cited by 236
Mesolimbic dopamine signaling in acute and chronic pain
Taylor, A. M. W., Becker, S., Schweinhardt, P., & Cahill, C. M. (2016) · Pain · doi:10.1097/j.pain.0000000000000494 · Cited by 248
Midbrain dopamine neurons signal belief in choice accuracy during a perceptual decision
Lak, A., Nomoto, K., Keramati, M., Sakagami, M., & Kepecs, A. (2017) · Current Biology · doi:10.1016/j.cub.2017.02.026 · Cited by 170
Self-citationThe Basal Ganglia’s Contributions to Perceptual Decision Making
Ding, L., & Gold, J. I. (2013) · Neuron · doi:10.1016/j.neuron.2013.07.042 · Cited by 208
Rao, R. P. N. (2010) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2010.00146 · Cited by 196
Redgrave, P., Vautrelle, N., & Reynolds, J. N. J. (2011) · Neuroscience · doi:10.1016/j.neuroscience.2011.07.060 · Cited by 195
Model-based predictions for dopamine
Langdon, A. J., Sharpe, M. J., Schoenbaum, G., & Niv, Y. (2018) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2017.10.006 · Cited by 161
Schultz, W., Stauffer, W. R., & Lak, A. (2017) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2017.03.013 · Cited by 171
Self-citationLearning from Action: Reconsidering Movement Signaling in Midbrain Dopamine Neuron Activity
Coddington, L. T., & Dudman, J. T. (2019) · Neuron · doi:10.1016/j.neuron.2019.08.036 · Cited by 150
Anatomical and functional organization of the human substantia nigra and its connections
Zhang, Y., Larcher, K. M.-H., Mišić, B., & Dagher, A. (2017) · eLife · doi:10.7554/elife.26653 · Cited by 156
Kim, K. M., Baratta, M. V., Yang, A., Lee, D., Boyden, E. S., & Fiorillo, C. D. (2012) · PLoS ONE · doi:10.1371/journal.pone.0033612 · Cited by 151
The medial prefrontal cortex shapes dopamine reward prediction errors under state uncertainty
Starkweather, C. K., Gershman, S. J., & Uchida, N. (2018) · Neuron · doi:10.1016/j.neuron.2018.03.036 · Cited by 136
Development and function of the midbrain dopamine system: what we know and what we need to
Bissonette, G. B., & Roesch, M. R. (2015) · Genes Brain & Behavior · doi:10.1111/gbb.12257 · Cited by 141
Ito, M., & Doya, K. (2011) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2011.04.001 · Cited by 141
Sub-second Dopamine and Serotonin Signaling in Human Striatum during Perceptual Decision-Making
Bang, D., Kishida, K. T., Lohrenz, T. M., White, J. P., Laxton, A. W., Tatter, S. B., Fleming, S. M., & Montague, P. R. (2020) · Neuron · doi:10.1016/j.neuron.2020.09.015 · Cited by 130
Distinct Representations of Cognitive and Motivational Signals in Midbrain Dopamine Neurons
Matsumoto, M., & Takada, M. (2013) · Neuron · doi:10.1016/j.neuron.2013.07.002 · Cited by 120
Dopaminergic Reward Signals Selectively Decrease fMRI Activity in Primate Visual Cortex
Arsenault, J. T., Nelissen, K., Jarraya, B., & Vanduffel, W. (2013) · Neuron · doi:10.1016/j.neuron.2013.01.008 · Cited by 122
Dopamine neurons code subjective sensory experience and uncertainty of perceptual decisions
de Lafuente, V., & Romo, R. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1117636108 · Cited by 117
How mechanisms of perceptual decision-making affect the psychometric function
Gold, J. I., & Ding, L. (2012) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2012.05.008 · Cited by 120
Human Scalp Potentials Reflect a Mixture of Decision-Related Signals during Perceptual Choices
Philiastides, M. G., Heekeren, H. R., & Sajda, P. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.3012-14.2014 · Cited by 118
Midbrain dopamine neurons signal aversion in a reward-context-dependent manner
Matsumoto, H., Tian, J., Uchida, N., & Watabe‐Uchida, M. (2016) · eLife · doi:10.7554/elife.17328 · Cited by 121
Fiorillo, C. D., Song, M. R., & Yun, S. R. (2013) · The Journal of Neuroscience · doi:10.1523/jneurosci.3883-12.2013 · Cited by 98
Multiplicity of control in the basal ganglia: computational roles of striatal subregions
Bornstein, A. M., & Daw, N. D. (2011) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2011.02.009 · Cited by 106
Tsutsui‐Kimura, I., Matsumoto, H., Akiti, K., Yamada, M. M., Uchida, N., & Watabe‐Uchida, M. (2020) · eLife · doi:10.7554/elife.62390 · Cited by 92
Winstanley, C. A., Cocker, P. J., & Rogers, R. D. (2011) · Neuropsychopharmacology · doi:10.1038/npp.2010.230 · Cited by 101
van Kempen, J., Loughnane, G. M., Newman, D. P., Kelly, S. P., Thiele, A., O’Connell, R. G., & Bellgrove, M. A. (2019) · eLife · doi:10.7554/elife.42541 · Cited by 92
Dopamine neurons learn relative chosen value from probabilistic rewards
Lak, A., Stauffer, W. R., & Schultz, W. (2016) · eLife · doi:10.7554/elife.18044 · Cited by 88
Self-citationThe role of learning-related dopamine signals in addiction vulnerability
Huys, Q. J. M., Tobler, P. N., Hasler, G., & Flagel, S. B. (2014) · Progress in brain research · doi:10.1016/b978-0-444-63425-2.00003-9 · Cited by 92
Recent advances in understanding the role of phasic dopamine activity
Schultz, W. (2019) · F1000Research · doi:10.12688/f1000research.19793.1 · Cited by 94
Self-citationIn Pursuit of Healthy Aging: Effects of Nutrition on Brain Function
Melzer, T. M., Manosso, L. M., Yau, S., Gil‐Mohapel, J., & Brocardo, P. S. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22095026 · Cited by 97
Hill-Bowen, L. D., Riedel, M. C., Poudel, R., Salo, T., Flannery, J. S., Camilleri, J. A., Eickhoff, S. B., Laird, A. R., & Sutherland, M. T. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2021.08.010 · Cited by 72
Complementary neural correlates of motivation in dopaminergic and noradrenergic neurons of monkeys
Bouret, S., Ravel, S., & Richmond, B. J. (2012) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2012.00040 · Cited by 73
Components and characteristics of the dopamine reward utility signal
Stauffer, W. R., Lak, A., Kobayashi, S., & Schultz, W. (2015) · The Journal of Comparative Neurology · doi:10.1002/cne.23880 · Cited by 65
Self-citationReward Contexts Extend Dopamine Signals to Unrewarded Stimuli
Kobayashi, S., & Schultz, W. (2014) · Current Biology · doi:10.1016/j.cub.2013.10.061 · Cited by 63
Self-citationSense, memory, and decision-making in the somatosensory cortical network
Romo, R., Lemus, L., & de Lafuente, V. (2012) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2012.08.002 · Cited by 62
Ongoing, rational calibration of reward-driven perceptual biases
Fan, Y., Gold, J. I., & Ding, L. (2018) · eLife · doi:10.7554/elife.36018 · Cited by 64
Context-Dependent Multiplexing by Individual VTA Dopamine Neurons
Kremer, Y., Flakowski, J., Rohner, C., & Lüscher, C. (2020) · Journal of Neuroscience · doi:10.1523/jneurosci.0502-20.2020 · Cited by 57
Wang, Y., Toyoshima, O., Kunimatsu, J., Yamada, H., & Matsumoto, M. (2021) · eLife · doi:10.7554/elife.63166 · Cited by 59
Dopamine neurons encode errors in predicting movement trigger occurrence
Pasquereau, B., & Turner, R. S. (2014) · Journal of Neurophysiology · doi:10.1152/jn.00401.2014 · Cited by 54
Park, J., & Moghaddam, B. (2017) · eLife · doi:10.7554/elife.30056 · Cited by 55
Dopamine-associated cached values are not sufficient as the basis for action selection
Hollon, N. G., Arnold, M. M., Gan, J. O., Walton, M. E., & Phillips, P. E. M. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1419770111 · Cited by 55
Reinforcement Learning and Causal Models
Gershman, S. J. (2017) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780199399550.013.20 · Cited by 51
Evidence accumulation for value computation in the prefrontal cortex during decision making
Lin, Z., Nie, C., Zhang, Y., Yang, C., & Yang, T. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2019077117 · Cited by 51
Stark, E. A., Vuust, P., & Kringelbach, M. L. (2018) · Progress in brain research · doi:10.1016/bs.pbr.2018.03.019 · Cited by 50
Takakuwa, N., Kato, R., Redgrave, P., & Isa, T. (2017) · eLife · doi:10.7554/elife.24459 · Cited by 48
Baudonnat, M., Huber, A., David, V., & Walton, M. E. (2013) · Frontiers in Neuroscience · doi:10.3389/fnins.2013.00175 · Cited by 43
Advanced Reinforcement Learning
Daw, N. D. (2013) · Elsevier eBooks · doi:10.1016/b978-0-12-416008-8.00016-4 · Cited by 46
A dopamine mechanism for reward maximization
Schultz, W. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2316658121 · Cited by 47
Self-citationA neural mechanism for conserved value computations integrating information and rewards
Bromberg-Martin, E. S., Feng, Y.-Y., Ogasawara, T., White, J. K., Zhang, K., & Monosov, I. E. (2024) · Nature Neuroscience · doi:10.1038/s41593-023-01511-4 · Cited by 43
High reward enhances perceptual learning
Zhang, P., Hou, F., Yan, F.-F., Xi, J., Lin, B.-R., Zhao, J., Yang, J., Ge, C., Zhang, M.-Y., He, Q., et al. (2018) · Journal of Vision · doi:10.1167/18.8.11 · Cited by 39
Carmichael, K., Sullivan, B. T., Lopez, E., Sun, L., & Cai, H. (2021) · Ageing and Neurodegenerative Diseases · doi:10.20517/and.2021.07 · Cited by 48
Persichetti, A. S., Aguirre, G. K., & Thompson‐Schill, S. L. (2014) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00760 · Cited by 36
Dopamine Modulation of Motor and Sensory Cortical Plasticity among Vertebrates
Macêdo-Lima, M., & Remage‐Healey, L. (2021) · Integrative and Comparative Biology · doi:10.1093/icb/icab019 · Cited by 38
Multiplexing signals in reinforcement learning with internal models and dopamine
Nakahara, H. (2014) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2014.01.001 · Cited by 32
Tanaka, S., O’Doherty, J. P., & Sakagami, M. (2019) · Nature Communications · doi:10.1038/s41467-019-11334-2 · Cited by 29
Self-citationSarno, S., de Lafuente, V., Romo, R., & Parga, N. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1712479114 · Cited by 32
The neuroscience of perceptual categorization in pigeons: A mechanistic hypothesis
Güntürkün, O., Koenen, C., Iovine, F., Garland, A., & Pusch, R. (2018) · Learning & Behavior · doi:10.3758/s13420-018-0321-6 · Cited by 30
Pando‐Naude, V., Matthews, T. E., Højlund, A., Jakobsen, S., Østergaard, K., Johnsen, E., Garza‐Villarreal, E. A., Witek, M. A. G., Penhune, V., & Vuust, P. (2024) · European Journal of Neuroscience · doi:10.1111/ejn.16128 · Cited by 29
Sensory regulation of dopaminergic cell activity: Phenomenology, circuitry and function
Overton, P. G., Vautrelle, N., & Redgrave, P. (2014) · Neuroscience · doi:10.1016/j.neuroscience.2014.01.023 · Cited by 31
Multiple brain networks contribute to the acquisition of bias in perceptual decision-making
Chen, M.-Y., Jimura, K., White, C. N., Maddox, W. T., & Poldrack, R. A. (2015) · Frontiers in Neuroscience · doi:10.3389/fnins.2015.00063 · Cited by 29
Herold, C., Joshi, I., Chehadi, O., Hollmann, M., & Güntürkün, O. (2012) · PLoS ONE · doi:10.1371/journal.pone.0036484 · Cited by 28
Models of neuromodulation for computational psychiatry
Iglesias, S., Tomiello, S., Schneebeli, M., & Stephan, K. E. (2016) · Wiley Interdisciplinary Reviews Cognitive Science · doi:10.1002/wcs.1420 · Cited by 28
Gil-Lievana, E., Ramírez-Mejía, G., Urrego-Morales, O., Luis-Islas, J., Gutiérrez, R., & Bermúdez‐Rattoni, F. (2022) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2022.823220 · Cited by 28
Finan, P. H., Remeniuk, B., & Dunn, K. E. (2017) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2017.07.029 · Cited by 27
Langdon, A., Song, M., & Niv, Y. (2019) · Behavioural Processes · doi:10.1016/j.beproc.2019.103891 · Cited by 25
Learning to represent reward structure: A key to adapting to complex environments
Nakahara, H., & Hikosaka, O. (2012) · Neuroscience Research · doi:10.1016/j.neures.2012.09.007 · Cited by 30
Colizoli, O., de Gee, J. W., van der Zwaag, W., & Donner, T. H. (2021) · Human Brain Mapping · doi:10.1002/hbm.25719 · Cited by 34
Visual cognition of birds and its underlying neural mechanism: A review
Niu, X., Jiang, Z., Peng, Y., Huang, S., Wang, Z., & Shi, L. (2022) · Avian Research · doi:10.1016/j.avrs.2022.100023 · Cited by 25
Jahn, C. I., Varazzani, C., Sallet, J., Walton, M. E., & Bouret, S. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa089 · Cited by 24
Sarno, S., Beirán, M., Falcó-Roget, J., Diaz-deLeon, G., Rossi-Pool, R., Romo, R., & Parga, N. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2113311119 · Cited by 22
Reward eliminates retrieval-induced forgetting
Imai, H., Kim, D., Sasaki, Y., & Watanabe, T. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1404469111 · Cited by 21
Self-citationReward prediction error in learning-related behaviors
Yujun, D., Song, D. F., Ni, J., Qing, H., & Quan, Z. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1171612 · Cited by 26
Hasirci, A. S., Maldonado‐Devincci, A. M., Beattie, M. C., O’Buckley, T. K., & Morrow, A. L. (2017) · Alcoholism Clinical and Experimental Research · doi:10.1111/acer.13300 · Cited by 19
Rubin, J. E., Vich, C., Clapp, M., Noneman, K. K., & Verstynen, T. (2020) · European Journal of Neuroscience · doi:10.1111/ejn.14745 · Cited by 24
Reward and value coding by dopamine neurons in non-human primates
Alikaya, A., Rack-Wildner, M., & Stauffer, W. R. (2017) · Journal of Neural Transmission · doi:10.1007/s00702-017-1793-9 · Cited by 20
Contributions of the Basal Ganglia to Visual Perceptual Decisions
Ding, L. (2023) · Annual Review of Vision Science · doi:10.1146/annurev-vision-111022-123804 · Cited by 17
Cortical regulation of dopaminergic neurons: role of the midbrain superior colliculus
Bertram, C., Dahan, L., Boorman, L. W., Harris, S., Vautrelle, N., Leriche, M., Redgrave, P., & Overton, P. G. (2013) · Journal of Neurophysiology · doi:10.1152/jn.00329.2013 · Cited by 18
Psychological and Neuroscientific Connections with Reinforcement Learning
Shah, A. (2012) · Adaptation, learning, and optimization · doi:10.1007/978-3-642-27645-3_16 · Cited by 17
Burkhardt, J. M., & Adermark, L. (2014) · Neurochemistry International · doi:10.1016/j.neuint.2014.07.006 · Cited by 18
Tomassini, A., Pollak, T. A., Edwards, M. J., & Bestmann, S. (2019) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2019.02.003 · Cited by 16
Blank, H., Biele, G., Heekeren, H. R., & Philiastides, M. G. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.4151-12.2013 · Cited by 23
Portella, A. K., Papantoni, A., Joseph, A. T., Chen, L., Lee, R. S., Silveira, P. P., Dubé, L., & Carnell, S. (2021) · Scientific Reports · doi:10.1038/s41598-021-02797-9 · Cited by 17
Fan, Y., Doi, T., Gold, J. I., & Ding, L. (2023) · Journal of Neuroscience · doi:10.1523/jneurosci.0902-23.2023 · Cited by 21
Stimulus-Dependent Adjustment of Reward Prediction Error in the Midbrain
Takemura, H., Samejima, K., Vogels, R., Sakagami, M., & Okuda, J. (2011) · PLoS ONE · doi:10.1371/journal.pone.0028337 · Cited by 17
Self-citationSaga, Y., Hashimoto, M., Tremblay, L., Tanji, J., & Hoshi, E. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.1187-13.2013 · Cited by 17
Dopamine activity on the perceptual salience for recognition memory
Osorio‐Gómez, D., Guzmán-Ramos, K. R., & Bermúdez‐Rattoni, F. (2022) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2022.963739 · Cited by 14
Signals of anticipation of reward and of mean reward rates in the human brain
Viviani, R., Dommes, L., Bosch, J., Steffens, M., Paul, A., Schneider, K. L., Stingl, J., & Beschoner, P. (2020) · Scientific Reports · doi:10.1038/s41598-020-61257-y · Cited by 14
Pasquereau, B., Tremblay, L., & Turner, R. S. (2018) · Neuroscience · doi:10.1016/j.neuroscience.2018.12.016 · Cited by 12
Metadata
- Authors
- Nomoto, K., Schultz, W., Watanabe, T., & Sakagami, M.
- Affiliations
- Tamagawa University (JP)
- Boston University (US)
- Corresponding author
- Sakagami, Masamichi
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2010-08-11
- Volume
- 30
- Issue
- 32
- Pages
- 10692-10702
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC3297489
- OpenAlex
- W2120222754
- Cited by
- 158
- References
- 41
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 7 · 2023: 9 · 2022: 7 · 2021: 11 · 2020: 11 · 2019: 10 · 2018: 14 · 2017: 16 · 2016: 14 · 2015: 5 · 2014: 16 · 2013: 13 · 2012: 10
- FWCI
- 4.26
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Neuroscience (publishedVersion, cc-by-nc-sa)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Wellcome Trust (095495)
- National Institutes of Health
- National Eye Institute (R01EY015980, R01EY019466)
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- dopaminergic neurons, reward prediction error, reward prediction, random dot motion, motion direction discrimination, midbrain dopamine
- MeSH terms
- Acoustic Stimulation, Analysis of Variance, Animals, Behavior, Animal, Conditioning, Operant, Discrimination, Psychological, Dopamine, Functional Laterality, Macaca fascicularis, Magnetic Resonance Imaging, Male, Mesencephalon, Motion Perception, Neurons, Orientation, Photic Stimulation, Predictive Value of Tests, Reward, Saccades, Time Factors, Tyrosine 3-Monooxygenase, Feedback, Physiological
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