title: Learning to Predict and Control the Physics of Our Movements.
abstract: When we hold an object in our hand, the mass of the object alters the physics of our arm, changing the relationship between motor commands that our brain sends to our arm muscles and the resulting motion of our hand. If the object is unfamiliar to us, our first movement will exhibit an error, producing a trajectory that is different from the one we had intended. This experience of error initiates learning in our brain, making it so that on the very next attempt our motor commands partially compensate for the unfamiliar physics, resulting in smaller errors. With further practice, the compensation becomes more complete, and our brain forms a model that predicts the physics of the object. This model is a motor memory that frees us from having to relearn the physics the next time that we encounter the object. The mechanism by which the brain transforms sensory prediction errors into corrective motor commands is the basis for how we learn the physics of objects with which we interact. The cerebellum and the motor cortex appear to be critical for our ability to learn physics, allowing us to use tools that extend our capabilities, making us masters of our environment.
authors.affiliation: Laboratory for Computational Motor Control, Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, Maryland 21205 shadmehr@jhu.edu.
authors.forename: Reza
authors.identifier: 0000-0002-7686-2569
authors.initials: R
authors.lastname: Shadmehr
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000818:Animals; D001921:Brain / Q000166:cytology / Q000000981:diagnostic imaging / Q000502:physiology*; D006225:Hand / Q000502:physiology; D006801:Humans; D007858:Learning / Q000502:physiology*; D008959:Models, Neurological; D009068:Movement / Q000502:physiology*; D059906:Neuroimaging; D009474:Neurons / Q000502:physiology*