Botvinick, Matthew

Hierarchical motor control in mammals and machines

Merel, J., Botvinick, M., & Wayne, G. (2019) · Nature Communications

Reinforcement Learning in RoboticsMotor Control and Adaptation

Cited by 274

Deep reinforcement learning and its neuroscientific implications

Botvinick, M., Wang, J. X., Dabney, W., Miller, K. J., & Kurth-Nelson, Z. (2020) · Neuron

Reinforcement Learning in RoboticsNeural and Behavioral Psychology Studies

Cited by 269

Darla Improving zero-shot transfer in reinforcement learning

Higgins, I., Pal, A., Rusu, A. A., Matthey, L., Burgess, C. P., Pritzel, A., Botvinick, M., Blundell, C., & Lerchner, A. (2017) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 219

Stabilizing Transformers for Reinforcement Learning

Parisotto, E., Song, H. F., Rae, J. W., Pascanu, R., Gulcehre, C., Jayakumar, S. M., Jaderberg, M., Kaufman, R. L., Clark, A., Noury, S., et al. (2019) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 199

Probing the neural basis of body ownership

Botvinick, M. (2004) · Science

Multisensory perception and integrationVirtual Reality Applications and Impacts

Cited by 181

How the brain represents the body: insights from neurophysiology and psychology.

Graziano, M. S. A., & Botvinick, M. M. (2002) · Common Mechanisms in Perception and Action

Motor Control and AdaptationMultisensory perception and integration

Cited by 174

Unsupervised deep learning identifies semantic disentanglement in single inferotemporal face patch neurons

Higgins, I., Chang, L., Langston, V., Hassabis, D., Summerfield, C., Tsao, D., & Botvinick, M. (2021) · Nature Communications

Face Recognition and PerceptionFace recognition and analysis

Cited by 162

Pain in the ACC?

Wager, T. D., Atlas, L. Y., Botvinick, M. M., Chang, L. J., Coghill, R. C., Davis, K. D., Iannetti, G. D., Poldrack, R. A., Shackman, A. J., & Yarkoni, T. (2016) · Proceedings of the National Academy of Sciences

Pain Mechanisms and TreatmentsNeural and Behavioral Psychology Studies

Cited by 162

Intuitive physics learning in a deep-learning model inspired by developmental psychology

Piloto, L. S., Weinstein, A., Battaglia, P., & Botvinick, M. (2022) · Nature Human Behaviour

Child and Animal Learning DevelopmentReinforcement Learning in Robotics

Cited by 118

Reduced model-based decision-making in schizophrenia.

Culbreth, A. J., Westbrook, A., Daw, N. D., Botvinick, M., & Barch, D. M. (2016) · Journal of Abnormal Psychology

Schizophrenia research and treatmentNeural and Behavioral Psychology Studies

Cited by 104

Human-centred mechanism design with Democratic AI

Koster, R., Balaguer, J., Tacchetti, A., Weinstein, A., Zhu, T., Hauser, O., Williams, D., Campbell-Gillingham, L., Thacker, P., Botvinick, M., et al. (2022) · Nature Human Behaviour

Game Theory and Voting SystemsEthics and Social Impacts of AI

Cited by 84

Environmental drivers of systematicity and generalization in a situated agent

Hill, F., Lampinen, A., Schneider, R., Clark, S., Botvinick, M., McClelland, J. L., & Santoro, A. (2019) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 81

How should the advancement of large language models affect the practice of science?

Binz, M., Alaniz, S., Roskies, A., Aczel, B., Bergstrom, C. T., Allen, C., Schad, D., Wulff, D., West, J. D., Zhang, Q., et al. (2025) · Proceedings of the National Academy of Sciences

Artificial Intelligence ApplicationsEthics and Social Impacts of AI

Cited by 74

Causal Reasoning from Meta-reinforcement Learning

Dasgupta, I., Wang, J., Chiappa, S., Mitrovic, J., Ortega, P., Raposo, D., Hughes, E., Battaglia, P., Botvinick, M., & Kurth-Nelson, Z. (2019) · arXiv

Reinforcement Learning in RoboticsBayesian Modeling and Causal Inference

Cited by 71

A virtual rodent predicts the structure of neural activity across behaviours

Aldarondo, D., Merel, J., Marshall, J. D., Hasenclever, L., Klibaite, U., Gellis, A., Tassa, Y., Wayne, G., Botvinick, M., & Ölveczky, B. P. (2024) · Nature

Motor Control and AdaptationNeural dynamics and brain function

Cited by 70

Meta-learning of Sequential Strategies

Ortega, P. A., Wang, J. X., Rowland, M., Genewein, T., Kurth-Nelson, Z., Pascanu, R., Heess, N., Veness, J., Pritzel, A., Sprechmann, P., et al. (2019) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 47

Scaffolding cooperation in human groups with deep reinforcement learning

McKee, K. R., Tacchetti, A., Bakker, M. A., Balaguer, J., Campbell-Gillingham, L., Everett, R., & Botvinick, M. (2023) · Nature Human Behaviour

Evolutionary Game Theory and CooperationExperimental Behavioral Economics Studies

Cited by 43

Meta-learned models of cognition

Binz, M., Dasgupta, I., Jagadish, A. K., Botvinick, M., Wang, J. X., & Schulz, E. (2024) · Behavioral and Brain Sciences

Cognitive Science and Education ResearchBayesian Modeling and Causal Inference

Cited by 38

Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution

Zador, A., Escola, S., Richards, B., Ölveczky, B., Bengio, Y., Boahen, K., Botvinick, M., Chklovskii, D., Churchland, A., Clopath, C., et al. (2022) · arXiv

Embodied and Extended CognitionPsychiatry, Mental Health, Neuroscience

Cited by 34

Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agents

Wang, J. X., King, M., Porcel, N., Kurth-Nelson, Z., Zhu, T., Deck, C., Choy, P., Cassin, M., Reynolds, M., Song, F., et al. (2021) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 10

A Unified Theory of Dual-Process Control

Moskovitz, T., Miller, K., Sahani, M., & Botvinick, M. M. (2022) · arXiv

Neural and Behavioral Psychology StudiesDecision-Making and Behavioral Economics

Cited by 2