Compensation for Changing Motor Uncertainty

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.Author Summary: To plan effective movements of the limbs, the human motor system must keep track of certain parameters: Obvious examples are the lengths and masses of to-be-controlled limb segments. In addition, the nervous system tracks its own motor outcome noise, which is important for selecting among movement plans where there are substantial costs associated with movement inaccuracies (e.g., reaching past a glass of red wine on a cluttered dinner table). Here, we introduce a change in motor noise in a reaching task: reaches were perturbed unpredictably by activating a reflex that introduced unplanned horizontal arm motion at the ends of reaches. We show that the motor system updates an internal model of the overall increase in motor noise induced by this reflex perturbation, but fails to represent the anisotropic component of the noise. This result is consistent with current theories of motor planning and control in which reach magnitudes and directions are represented independently, because a system that updates only a circular representation of recent motor errors is equivalent to a system that monitors only the magnitudes of recent errors, and ignores their directions.