Researcher

Matthew Botvinick

Also known as Matt Botvinick

Matthew Botvinick is an AI researcher whose work links reinforcement learning with human learning and cognitive control. After medical and doctoral training, he held faculty posts at Penn and Princeton and took a 2015 sabbatical from Princeton to study DeepMind's machine-learning work. His research spans hierarchical reinforcement learning, computational models of cognitive control, and a 2018 model of meta-learning in the prefrontal cortex. From 2023 to 2026, he studied for a J.D. at Yale Law School and served as a Senior Fellow; in October 2026, he published AI and Political Freedom, a book about AI's possible effects on democracy. As of October 2026, he leads Anthropic's AI and Rule of Law team after serving as Senior Director of Research at Google DeepMind.

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Timeline

  1. 2001

    A medical rotation and CMU PhD

    During a psychiatry rotation at Cornell medical school, an adviser introduces him to neural-network research. He completes a doctorate in psychology and cognitive neuroscience at Carnegie Mellon's Dietrich College in 2001 while finishing a psychiatry residency.

  2. 2013

    Hierarchical reinforcement learning

    In 2013, he coauthors a chapter on hierarchical reinforcement learning and task decomposition in humans. It reviews neural correlates of hierarchical RL and considers how people identify useful subgoals.

  3. 2014

    A cognitive control review

    In 2014, he coauthors a review with Jonathan D. Cohen on computational modeling in cognitive control. It frames control as the optimal parameterization of task processing.

  4. 2015

    A Princeton sabbatical

    After faculty posts at the University of Pennsylvania and Princeton, he takes a sabbatical from Princeton in 2015 to study DeepMind's machine-learning work.

  5. 2018

    Meta-reinforcement learning

    In 2018, he coauthors a Nature Neuroscience study that models the prefrontal cortex as a meta-reinforcement-learning system. Its recurrent neural network, trained with deep reinforcement learning, adapts across tasks with different rules.

  6. 2023–2026

    A Yale law fellowship

    From 2023 to 2026, he studies for a J.D. at Yale Law School and serves as a Senior Fellow there.

  7. October 2026

    AI and Political Freedom

    In October 2026, he publishes AI and Political Freedom: The Risk to Democracy and How to Respond with Princeton University Press. The book examines how AI may destabilize democratic institutions and aid leaders with authoritarian aims.

  8. As of October 2026

    AI and the rule of law

    In March 2026, he joins Anthropic as a founding hire to lead its work on AI and the rule of law. As of October 2026, he is an AI researcher who leads Anthropic's AI and Rule of Law team after serving as Senior Director of Research at Google DeepMind.

Career

  1. Since 2026AnthropicAI and Rule of Law team lead
  2. Until 2026Google DeepMindSabbatical, Senior Director of Research
  3. 2023–2026Yale UniversitySenior Fellow
  4. University of PennsylvaniaFaculty
  5. Princeton UniversityFaculty

Education

  1. 2026Yale UniversityJ.D.
  2. 2001Carnegie Mellon Universitydoctorate in psychology and cognitive neuroscience
  3. Cornell UniversityPsychiatry rotation

Sources and credits

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  1. Thinking Outside the Brain cmu.edu. Checked 21 October 2026.
  2. Divide and conquer: Hierarchical reinforcement learning and task decomposition in humans collaborate.princeton.edu. Checked 21 October 2026.
  3. The computational and neural basis of cognitive control: Charted territory and new frontiers collaborate.princeton.edu. Checked 21 October 2026.
  4. Prefrontal cortex as a meta-reinforcement learning system deepmind.google. Checked 21 October 2026.
  5. Matthew Botvinick — AI and Political Freedom mattbotvinick.com. Checked 21 October 2026.
  6. AI and Political Freedom: The Risk to Democracy and How to Respond - Google Books books.google.com. Checked 21 October 2026.
  7. Introducing The Anthropic Institute anthropic.com. Checked 21 October 2026.