title: When Optimal Feedback Control Is Not Enough: Feedforward Strategies Are Required for Optimal Control with Active Sensing.
abstract: 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.
authors.affiliation: Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, United Kingdom.|School of Sport, Exercise and Rehabilitation Sciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom.
authors.forename: Sang-Hoon
authors.initials: SH
authors.lastname: Yeo
authors.affiliation: Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, United Kingdom.|Department of Sport and Health Sciences, Technical University of Munich, Munich, Germany.
authors.forename: David W
authors.identifier: 0000-0001-9530-0820
authors.initials: DW
authors.lastname: Franklin
authors.affiliation: Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, United Kingdom.
authors.forename: Daniel M
authors.initials: DM
authors.lastname: Wolpert
mesh_terms: D000328:Adult; D001696:Biomechanical Phenomena; D019295:Computational Biology; D056228:Feedback, Sensory / Q000502:physiology*; D005260:Female; D006225:Hand / Q000502:physiology; D006801:Humans; D008297:Male; D009043:Motor Activity / Q000502:physiology*; D011597:Psychomotor Performance / Q000502:physiology*; D055815:Young Adult