Despite the infinite number of possible movements that could be used to achieve a task, humans and other animals show highly stereotypical trajectories.
We have investigated the computational mechanisms that transform a task into a particular motor action. We hypothesized that a particular type of stochasticity, signal-dependent motor noise, is a key constraint when planning an action. Motor noise leads to variability of our actions, such as the spread of darts when we try to hit the bullseye. We developed a theory based on the physiological principle that neural control signals are corrupted by noise whose variance increases with the size of the control signal and that planning aims to minimize the deleterious effects of such noise. Based on this principle we developed an optimal control theory of motor planning for goal-directed eye and arm movements. This theory provides a simple and powerful unifying perspective for movement control (Harris and Wolpert, 1998; Harris and Wolpert, 2006). This idea formed a key component of the theory of Optimal Feedback Control (OFC) developed by others in which planning consists of setting time-varying feedback gains that minimize the cost of a movement, which is typically a tradeoff between accuracy and effort.