The DOI matches its Crossref record; 84 of the 84 references it cites were found in a registry or an index.
Complementary roles of basal ganglia and cerebellum in learning and motor control
Abstract
The classical notion that the basal ganglia and the cerebellum are dedicated to motor control has been challenged by the accumulation of evidence revealing their involvement in non-motor, cognitive functions. From a computational viewpoint, it has been suggested that the cerebellum, the basal ganglia, and the cerebral cortex are specialized for different types of learning: namely, supervised learning, reinforcement learning and unsupervised learning, respectively. This idea of learning-oriented specialization is helpful in understanding the complementary roles of the basal ganglia and the cerebellum in motor control and cognitive functions.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“In particular, learning in the cerebellum is driven by error signals (Doya, 2000; Hikosaka et al., 2002).”
From The Forward Model: A Unifying Theory for the Role of the Cerebellum in Motor Control and Sense of Agency (2021) · cites this paper as Doya, 2000
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
Emergence of simple-cell receptive field properties by learning a sparse code for natural images
Olshausen, B. A., & Field, D. J. (1996) · Nature · doi:10.1038/381607a0 · Cited by 5,694
Predictive reward signal of dopamine neurons
Schultz, W. (1998) · Journal of Neurophysiology · doi:10.1152/jn.1998.80.1.1 · Cited by 4,564
Functional architecture of basal ganglia circuits: neural substrates of parallel processing
Alexander, G. E., & Crutcher, M. D. (1990) · Trends in Neurosciences · doi:10.1016/0166-2236(90)90107-l · Cited by 4,131
Marr, D. (1969) · The Journal of Physiology · doi:10.1113/jphysiol.1969.sp008820 · Cited by 3,253
Internal models for motor control and trajectory planning.
Kawato, M. (1999) · Current Opinion in Neurobiology · doi:10.1016/s0959-4388(99)00028-8 · Cited by 2,685
A theory of cerebellar function
Albus, J. S. (1971) · Mathematical Biosciences · doi:10.1016/0025-5564(71)90051-4 · Cited by 2,539
Internal models in the cerebellum
Wolpert, D. M., Miall, R. C., & Kawato, M. (1998) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(98)01221-2 · Cited by 2,469
The cerebellum and neural control
Ito, M. (1984) · Cited by 2,314
Barto, A. (1998) · IFAC Proceedings Volumes · doi:10.1016/s1474-6670(17)38315-5 · Cited by 2,172
Research works printed but not linked: 1
- Hikosaka, 1983, Visual and oculomotor functions of monkey Macaca mulatta substantia nigra pars reticulata, J Physiol, 49, 1230
Cited by
Sorted by citations
Items 901–969 of 969
Coolidge, F. L. (2020) · Oxford University Press eBooks · doi:10.1093/oso/9780190940942.002.0003
Coolidge, F. L. (2020) · Oxford University Press eBooks · doi:10.1093/oso/9780190940942.002.0007
Self-organizing recruitment of compensatory areas maximizes residual motor performance post-strokePreprint
Lee, K., Barradas, V. R., & Schweighofer, N. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.28.601213
High cognitive violation of expectations is compromised in cerebellar ataxia
Daniel, L., Vakil, E., & Saban, W. (2025) · eLife · doi:10.7554/elife.105864.3
Liu, Y., Halfen, E. J., Yau, J. M., Fischer‐Baum, S., Kohler, P. J., Faseyitan, O. K., Coslett, H. B., & Medina, J. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.25.640230
High cognitive violation of expectations is compromised in cerebellar ataxiaPreprint
Leonardo Daniel, A., Vakil, E., & Saban, W. (2025) · eLife · doi:10.7554/elife.105864
Robertazzi, F., Vissani, M., & Falotico, E. (2025) · International Journal of Neural Systems · doi:10.1142/s0129065725500406
Thinking Like a Brain, Moving Like a Robot: A Formal Bridge Between Symbolic AI and Physical ActionPreprint
Barros, S. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5191875
Tonami, R., Aoki, S., & Abe, K. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.08.652969
Neurological Soft Signs in Children with Attention Deficit and Hyperactivity Disorder
Keleş, M., Uçar, H. N., & Çetin, F. H. (2025) · Çocuk ve Gençlik Ruh Sağlığı Dergisi / Turkish Journal of Child and Adolescent Mental Health · doi:10.4274/tjcamh.galenos.2024.53825
Estimation-uncertainty affects decisions with and without learning opportunities
Aberg, K. C., Antle, L., & Paz, R. (2025) · Nature Communications · doi:10.1038/s41467-025-61960-2
Tsurumi, T., Kato, A., Kumar, A., & Morita, K. (2025) · eLife · doi:10.7554/elife.104101.3
Postsynaptic integration and plasticity in the striatum
Yagishita, S. (2025) · Handbook of behavioral neuroscience · doi:10.1016/b978-0-443-29867-7.00021-9
Morita, K., & Kumar, A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.10.669522
A Computational Model of Action Specification in the Basal GangliaPreprint
Bartlett, M. E., Furlong, P. M., Stewart, T. C., & Orchard, J. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.12.669938
High Cognitive Violation of Expectations is Compromised in Cerebellar AtaxiaPreprint
Daniel, L., Vakil, E., & Saban, W. (2025) · eLife · doi:10.7554/elife.105864.2
Huang, M., Sun, Q., Ma, L., Tian, J., Gao, M., Jiang, Y., Zhou, Y., Cheng, Z., Liu, J., Zhang, Y., et al. (2025) · Behavioural Brain Research · doi:10.1016/j.bbr.2025.115852
Silbaugh, A. M., Koster, K. P., & Hansel, C. (2025) · eLife · doi:10.7554/elife.109183.1
Gutierrez, C. E., Liénard, J., Girard, B., Urakubo, H., & Doya, K. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.10.687760
Self-citationSilbaugh, A. M., Koster, K. P., & Hansel, C. (2025) · eLife · doi:10.7554/elife.109183.2
The Future of Research in Cognitive Robotics: Foundation Models or Developmental Cognitive Models?
Vernon, D. (2025) · Advanced Robotics Research · doi:10.1002/adrr.202500066 · Cited by 2
Lv, X., Liu, Q., & Wang, D. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.108455
Reward-driven adaptation of movements requires strong recurrent basal ganglia–cortical loops
Leblois, A., Boraud, T., & Hansel, D. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2515994122
Sala, R. W., Ayyaz, A., Léna, C., & Popa, D. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.09.693142 · Cited by 1
Bedömning av prosodisk förmåga hos vuxna med förvärvad hjärnskada.
Sundström, S., & Åhlund, P. (2010) · KTH Publication Database DiVA (KTH Royal Institute of Technology)
Watanabe, A., Sawamura, D., Nakazono, H., Tokikuni, Y., Miura, H., Sugawara, K., Fuyama, K., Tohyama, H., Yoshida, S., & Sakai, S. (2024) · Figshare · doi:10.6084/m9.figshare.26571055
稔, 塚. (2025) · The Brain & Neural Networks · doi:10.3902/jnns.32.177
The role of feedback for sensorimotor decisions under risk
Wolf, C., Belopolsky, A. V., & Lappe, M. (2026) · Journal of Vision · doi:10.1167/jov.26.1.13
Accelerating Teleoperation Skill Acquisition through Visuo-Haptic Replay
Ribeiro, F., Park, J., Asada, M., & Öztop, E. (2026) · IEEE/SICE International Symposium on System Integration (SII) · doi:10.1109/sii64115.2026.11404713
A Basal Ganglia–Inspired Reinforcement Learning Framework Using Dopamine-Modulated Plasticity
Rathore, A., Moyya, P. D., Rana, K., Kumar, A., Singh, A., Rana, J., Nikhil, N., & Kumar, S. S. (2026) · International Conference on Computational Intelligence in Data Science (ICCIDS) · doi:10.1109/iccids69108.2026.11407699
Morita, K., & Kumar, A. (2026) · Journal of Neuroscience · doi:10.1523/jneurosci.1762-25.2026
Lixenberg, A., Henshke, Y., Kreisel, T., Lottem, E., & Joshua, M. (2026) · Nature Communications · doi:10.1038/s41467-026-71046-2
Bases cérébrales de l’écriture manuscrite chez l’adulte et l’enfant : de l’orthographe au geste
Palmis, S. (2019) · theses.fr (ABES) · doi:10.70675/2cc924b9z2522z4b64zbf25ze5353dceaad5
Constans, A. (2019) · theses.fr (ABES) · doi:10.70675/93486891z6065z449cz992az7390677dca22
Effets de l’administration chronique de corticostérone sur le développement postnatal du cervelet
Hernandez, M. (2019) · theses.fr (ABES) · doi:10.70675/11108d29z1668z4c5fza0a1z1a48cad3879d
Bation, R. (2018) · theses.fr (ABES) · doi:10.70675/3c89240bzc242z45f4z9247z73969c8af558 · Cited by 1
Torres-Herraez, A. (2019) · theses.fr (ABES) · doi:10.70675/c2c08c6fzad3ez4051zaa90z0935f26ce422
Interactions between the cerebellum and the striatum in PRRT2 and ADCY5 dystonia
Ekmen, A. (2021) · theses.fr (ABES) · doi:10.70675/1290633fz622cz42b1z8953z676e0c712c77
Les effets du sommeil, du passage du temps et de l'activité sur l'apprentissage moteur
Truong, C. (2023) · theses.fr (ABES) · doi:10.70675/7f7b52d0z6f50z49b4zbe28z59fcc37f6bb1
Dual pathway architecture in songbirds enables robust sensorimotor learningPreprint
Sankar, R., Suryawanshi, A., Rougier, N. P., & Leblois, A. (2026) · eLife · doi:10.64898/2026.05.07.723469 · Cited by 2
Brain-AI convergence: Generative world models and hierarchical attention for human intelligence
Ohmae, S., & Ohmae, K. (2026) · Patterns · doi:10.1016/j.patter.2026.101593
Wang, H., & Max, L. (2026) · Neurobiology of Language · doi:10.1162/nol.a.291
Zhang, K., jianyu, L., Cao, C., & Zhang, K. (2026) · Heliyon · doi:10.1016/j.heliyon.2026.e45176
SORLDN2: A brain-inspired navigation decision-making framework for Mobile robots
Tan, Y., & Wang, D. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.116157
Early Longitudinal Brain Network Changes in Huntington's Disease Before Clinical Motor Onset
Leocadi, M., Hobbs, N. Z., Farag, M., Murphy, M., Fayer, K., Thackeray, O., Knights, H. D. J., Rowe, J. B., Robbins, T. W., Sahakian, B. J., et al. (2026) · Movement Disorders · doi:10.1002/mds.70461
Cortico-subcortical multi-head self-attention as a substrate for cognitive performancePreprint
Babu, V. K., Granier, A., Proix, T., Balvočius, T., Woodman, M., Saudargienė, A., Jirsa, V., & Senn, W. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.08.06.743205 · Cited by 1
Yaghoubi, N., Shah, H. P., Berman, B. D., & Dexheimer, B. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-45068-6.00015-9
Cognitive neuroscience: Unraveling brain function
Sinha, J. K., & Ghosh, S. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-36544-7.00006-9
Foundations of neuropsychology and neurosciences
Ghosh, S., & Sinha, J. K. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-36544-7.00010-0
Nardi, F., Arora, A., Faisal, A. A., & Haar, S. (2026) · iScience · doi:10.1016/j.isci.2026.117523
A Geometry-Aware Bio-Inspired Sparse Reservoir With Online Learning
Borzı̀, A. (2026) · Neural Computation · doi:10.1162/neco.a.1578
Understanding Creativity Across Brains and Machines Through the GEEC Framework
Owen, L. L. W., Peng, Z., & Guzik, E. E. (2026) · Knowledge · doi:10.3390/knowledge6040025
Optimizing learning for control of human-machine interfaces
Nagarajan, S. S., & Khanna, P. (2026) · Neuron · doi:10.1016/j.neuron.2026.09.023
Meirhaeghe, N. (2026) · Journal of Neuroscience · doi:10.1523/jneurosci.1809-25.2026
Effort-based decision-making: A value-guided dissection and arousal-focused synthesis
Clairis, N., & Tobler, P. N. (2026) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2026.107062
Experiments in time:exploring the components of motor timing behaviour in dyslexia
Birkett, E. E. (2014) · Aston Publications Explorer (Aston University) · doi:10.48780/publications.aston.ac.uk.00021406
Faisabilité d’une alimentation orale chez les patients en état de conscience altérée
Maudoux, A., Breuskin, I., Gosseries, O., Caroline Schnakers, A., & Vanhaudenhuyse, A. (2011) · Springer eBooks · doi:10.1007/978-2-8178-0127-8_12
Context-specificity in Implicit Sequence Learning: Evidence for Episodic Representations
D’Angelo, M. C. (2013) · MacSphere (McMaster University) · doi:10.71548/13172
Handlungsfehler und Handlungsüberwachung
Hommel, B., & Nattkemper, D. (2011) · Springer-Lehrbuch · doi:10.1007/978-3-642-12858-5_9
Metadata
- Authors
- Doya, K.
- Affiliations
- Advanced Telecommunications Research Institute International (JP)
- Corresponding author
- Doya, K
- ORCID
- Doya, K
- Venue
- Current Opinion in Neurobiology
- Publisher
- Elsevier BV
- Published
- 2000-12-01
- Volume
- 10
- Issue
- 6
- Pages
- 732-739
- ISSN
- 0959-4388 (ISSN-L)
- OpenAlex
- W2060052647
- Cited by
- 964
- References
- 84
- Citations by year
- 2026: 32 · 2025: 64 · 2024: 37 · 2023: 44 · 2022: 57 · 2021: 53 · 2020: 61 · 2019: 59 · 2018: 50 · 2017: 50 · 2016: 59 · 2015: 53 · 2014: 39 · 2013: 40 · 2012: 36
- FWCI
- 1.23
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Current Opinion in Neurobiology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Health Sciences › Medicine › Clinical Neurology › Deep Brain Stimulation and Movement Disorders
- Keywords
- basal ganglia, cerebellum, motor control, learning, supervised learning, reinforcement learning, unsupervised learning, cognitive function, cerebral cortex, computational neuroscience
- MeSH terms
- Animals, Basal Ganglia, Cerebellum, Humans, Learning, Motor Neurons, Movement
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