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Brain Information Systems Lab
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The lab explores meta-intelligence, the mechanisms by which the brain changes how it learns.
AboutAbout the lab
Uncovering the computational and neural mechanisms of meta-intelligence
The Brain Information Systems Lab studies the brain's ability to reselect the way it learns, which the lab calls "meta-intelligence."
The lab investigates its computational principles and neural mechanisms by combining behavioral experiments, computational models, neural measurement, robotics, artificial intelligence, and neural intervention.
The lab also explores neurorehabilitation and new design principles for artificial systems.
On the lab's website, it is introduced under the name "HEBB'S Meta-Intelligence Science Lab."
Key terms
- Meta-intelligence
- A higher-level adaptive ability in which the brain evaluates its own learning state and selects and updates the memories, skills, and learning strategies it uses.
- Sense of agency
- The sense that one is the one generating one's own actions. It is thought to arise when the brain predicts movements and compares them with sensory input.
- Counterfactual thinking
- Thinking about outcomes that did not actually occur, such as "if I had chosen a different action, the result might have been better."
ResearchResearch themes
The lab views humans as hierarchical adaptive systems made up of the brain, body, and environment, and studies how the brain evaluates its own learning state and selects and updates the memories, skills, and learning strategies it uses. The lab calls this higher-level adaptive ability meta-intelligence, and aims to uncover its computational principles and neural mechanisms by integrating behavioral experiments, computational models, neural measurement, robotics, artificial intelligence, and neural intervention.
*Illustrative imageMeta-intelligence and meta-learning
The brain evaluates its learning state and the reliability of its memories, and changes how it learns in response to reward, punishment, error, and uncertainty. The lab studies skill selection and learning rate adjustment using behavioral experiments, computational models, brain stimulation, and other methods.
*Illustrative imageReward, decision-making, and motor control
The brain chooses actions by weighing effort, time, the possibility of failure, physical cost, and reward together. The lab views motor control as value-based decision-making and studies computational mechanisms such as reinforcement learning, motor exploration, and reward prediction error.
*Illustrative imageMotor memory and the embodied self
The sense of agency and the sense that one's body belongs to oneself are thought to arise when the brain predicts bodily movements and compares them with sensory input. The lab manipulates the relationship between body and sensation using virtual reality and robotics to investigate the underlying computational principles.
*Illustrative imageLearning based on emotion and counterfactuals
People are thought to learn not only from the outcomes they obtain but also from counterfactuals, with regret and relief serving as learning signals. The lab describes emotional decision-making with computational models and also studies the design of artificial intelligence that can learn flexibly.
MembersFaculty
The members of the Brain Information Systems Lab are faculty, doctoral students, master's students, and undergraduate students.As of 2026
- ProfessorJun Izawa
- Assistant ProfessorVictor Barradas
VoicesStudent voices
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Juggling Balls (81706016)” Richard Leonard / CC BY 2.0 (cropped)
- Meta-intelligence and meta-learning: “Transcranial magnetic stimulation” Eric Wassermann, M.D. / Public domain (cropped)
- Reward, decision-making, and motor control: “Archery butts at Berkhamsted and Hemel Hempstead Hockey Club, Cow Roast, Hertfordshire - June 2025” Mertbiol / CC0 (cropped)
- Motor memory and the embodied self: “HTC Vive Virtual Reality Headset and Controllers” PB / CC BY-SA 4.0 (cropped)
- Learning based on emotion and counterfactuals: “13-02-27-spielbank-wiesbaden-by-RalfR-093” Ralf Roletschek / CC BY-SA 3.0 (cropped)
- Related lab (生体情報処理研究室): “Neural network explain” TseKiChun / CC BY-SA 4.0 (cropped)
- Related lab (機械知能研究室): “Unraveling AI Complexity - A Comparative View of AI, Machine Learning, Deep Learning, and Generative AI” PopovaZhuhadar / CC BY-SA 4.0 (cropped)
- Related lab (身体知能研究室): “REEM-B humanoid robot” Facontidavide at English Wikipedia / Public domain (cropped)
- Related lab (新宅研究室): “MetalFatigue 001” National Institute of Standards and Technology / Public domain (cropped)
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