Movement is the only way we have of interacting with the world, whether foraging for food or attracting a waiter's attention. Therefore, the purpose of the human brain is to use sensory signals to determine future actions.
Despite the infinite number of possible movements that could be used to achieve a task, humans and other animals show highly stereotypical trajectories.
Humans exhibit an enormous repertoire of motor behavior that enables us to interact with many different objects in a variety of different environments.
Our work on internal predictive models—neural systems that simulate the behavior of a natural process—has shown that they are a vital theoretical component used to solve fundamental problems in sensorimotor control.
Both decision making and motor control require acting on streams of noisy evidence. Thus both rely on inference, termination rules, time constraints, and value/effort costs.
We develop robotic interfaces critical for studying the control of movement.