title: Learning optimal adaptation strategies in unpredictable motor tasks.
abstract: Picking up an empty milk carton that we believe to be full is a familiar example of adaptive control, because the adaptation process of estimating the carton's weight must proceed simultaneously with the control process of moving the carton to a desired location. Here we show that the motor system initially generates highly variable behavior in such unpredictable tasks but eventually converges to stereotyped patterns of adaptive responses predicted by a simple optimality principle. These results suggest that adaptation can become specifically tuned to identify task-specific parameters in an optimal manner.
authors.affiliation: Bernstein Center for Computational Neuroscience, Albert Ludwig University, 79104 Freiburg, Germany. dab54@cam.ac.uk
authors.forename: Daniel A
authors.initials: DA
authors.lastname: Braun
authors.forename: Ad
authors.initials: A
authors.lastname: Aertsen
authors.forename: Daniel M
authors.initials: DM
authors.lastname: Wolpert
authors.forename: Carsten
authors.initials: C
authors.lastname: Mehring
mesh_terms: D000222:Adaptation, Physiological / Q000502:physiology*; D003198:Computer Simulation; D005246:Feedback / Q000502:physiology; D006801:Humans; D007858:Learning / Q000502:physiology*; D008954:Models, Biological; D009048:Motor Skills / Q000502:physiology*; D009068:Movement / Q000502:physiology*; D011237:Predictive Value of Tests; D012399:Rotation; D013647:Task Performance and Analysis