When Optimal Feedback Control Is Not Enough: Feedforward Strategies Are Required for Optimal Control with Active Sensing

Movement planning is thought to be primarily determined by motor costs such as inaccuracy and effort. Solving for the optimal plan that minimizes these costs typically leads to specifying a time-varying feedback controller which both generates the movement and can optimally correct for errors that arise within a movement. However, the quality of the sensory feedback during a movement can depend substantially on the generated movement. We show that by incorporating such state-dependent sensory feedback, the optimal solution incorporates active sensing and is no longer a pure feedback process but includes a significant feedforward component. To examine whether people take into account such state-dependency in sensory feedback we asked people to make movements in which we controlled the reliability of sensory feedback. We made the visibility of the hand state-dependent, such that the visibility was proportional to the component of hand velocity in a particular direction. Subjects gradually adapted to such a sensory perturbation by making curved hand movements. In particular, they appeared to control the late visibility of the movement matching predictions of the optimal controller with state-dependent sensory noise. Our results show that trajectory planning is not only sensitive to motor costs but takes sensory costs into account and argues for optimal control of movement in which feedforward commands can play a significant role.Author Summary: The dominant theory of how movements are planned suggests that a task specifies a motor cost in terms of effort and accuracy and the motor system chooses a movement to minimize this cost. The pre-eminent theory is that this results an optimal feedback controller that both generates the movement and can correct online for disturbances that arise due to factors such as noise. However, current formulations of the optimal feedback control problem have assumed that sensory information has noise independent of the action, or noise that simply scales with movement speed. In reality, visual and proprioceptive noise of limb position vary substantially with the state of the body. We show that including such variation in sensory feedback qualitatively changes the optimal solution so that feedback control alone is no longer optimal for the task and an additional feedforward controller must be used. To test this idea, we develop a novel motor learning paradigm in which we can vary the sensory noise on visual feedback of the limb and show that human participants adapt to such a perturbation to produce complex reach paths which are well accounted for by our model.