Compensation for Changing Motor Uncertainty

Abstract

When movement outcome differs consistently from the intended movement, errors are used to correct subsequent movements (e.g., adaptation to displacing prisms or force fields) by updating an internal model of motor and/or sensory systems. Here, we examine changes to an internal model of the motor system under changes in the variance structure of movement errors lacking an overall bias. We introduced a horizontal visuomotor perturbation to change the statistical distribution of movement errors anisotropically, while monetary gains/losses were awarded based on movement outcomes. We derive predictions for simulated movement planners, each differing in its internal model of the motor system. We find that humans optimally respond to the overall change in error magnitude, but ignore the anisotropy of the error distribution. Through comparison with simulated movement planners, we found that aimpoints corresponded quantitatively to an ideal movement planner that updates a strictly isotropic (circular) internal model of the error distribution. Aimpoints were planned in a manner that ignored the direction-dependence of error magnitudes, despite the continuous availability of unambiguous information regarding the anisotropic distribution of actual motor errors.

Energy margins in dynamic object manipulation

“…On the other hand, subjects with greater trial-to-trial variability may have chosen a larger safety margin to accommodate the greater uncertainty associated with more variable movements. This agrees with previous work suggesting that variability plays a central role in movement control such that the motor system optimizes movements to minimize the effects of variability on task goals (Chu WT, Sternad D, Sanger TD, unpublished observations;Cohen and Sternad 2009;Gepshtein et al 2007;Harris and Wolpert 1998;Hudson et al 2010;Sternad et al 2011;Trommershäuser et al 2005). The novelty of the present findings is that they span a period in which significant learning has occurred, and they support the hypothesis that individuals shape their control strategies in accordance with their variability.…”

Predictability and Robustness in the Manipulation of Dynamically Complex Objects

“…This follows previous work suggesting that variability plays a central role in movement control such that the motor system optimizes movements to minimize the effects of variability on task goals (Harris and Wolpert 1998; Trommershäuser et al 2005; Gepshtein et al 2007; Cohen and Sternad 2009; Hudson et al 2010; Sternad et al 2011; Chu et al 2013). Specifically, individuals with greater trial-to-trial variability should choose a larger energy margin, and vice versa.…”

Motor learning reveals the existence of multiple codes for movement planning

“…The best linear transformations converting three vectors derived from the three IREDs on each side of the ring (each defining a coordinate frame) into the fingertip location at the metal nub were computed (Hudson et al 2010). This allowed us to precisely locate the fingertip, as opposed to simply the IREDs, in space.…”