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      Congruent visual cues speed dynamic motor adaptation

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      Journal of Neurophysiology

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          Abstract

          We demonstrate that adaptation to novel dynamics is stronger when additional online visual cues that are congruent with the dynamics are presented during adaptation, compared with either a constant or incongruent visual cue. This effect was found regardless of whether a bidirectional or unidirectional velocity-dependent force field was presented to the participants. We propose that this effect might arise through the inclusion of this additional visual cue information within the state estimation process.

          Abstract

          Motor adaptation to novel dynamics occurs rapidly using sensed errors to update the current motor memory. This adaption is strongly driven by proprioceptive and visual signals that indicate errors in the motor memory. Here, we extend this previous work by investigating whether the presence of additional visual cues could increase the rate of motor adaptation, specifically when the visual motion cue is congruent with the dynamics. Six groups of participants performed reaching movements while grasping the handle of a robotic manipulandum. A visual cue (small red circle) was connected to the cursor (representing the hand position) via a thin red bar. After a baseline, a unidirectional (3 groups) or bidirectional (3 groups) velocity-dependent force field was applied during the reach. For each group, the movement of the red object relative to the cursor was either congruent with the force field dynamics, incongruent with the force field dynamics, or constant (fixed distance from the cursor). Participants adapted more to the unidirectional force fields than to the bidirectional force field groups. However, across both force fields, groups in which the visual cues matched the type of force field (congruent visual cue) exhibited higher final adaptation level at the end of learning than the control or incongruent conditions. In all groups, we observed that an additional congruent cue assisted the formation of the motor memory of the external dynamics. We then demonstrate that a state estimation-based model that integrates proprioceptive and visual information can successfully replicate the experimental data.

          NEW & NOTEWORTHY We demonstrate that adaptation to novel dynamics is stronger when additional online visual cues that are congruent with the dynamics are presented during adaptation, compared with either a constant or incongruent visual cue. This effect was found regardless of whether a bidirectional or unidirectional velocity-dependent force field was presented to the participants. We propose that this effect might arise through the inclusion of this additional visual cue information within the state estimation process.

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          The assessment and analysis of handedness: The Edinburgh inventory

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            A New Approach to Linear Filtering and Prediction Problems

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              Bayesian integration in sensorimotor learning.

              When we learn a new motor skill, such as playing an approaching tennis ball, both our sensors and the task possess variability. Our sensors provide imperfect information about the ball's velocity, so we can only estimate it. Combining information from multiple modalities can reduce the error in this estimate. On a longer time scale, not all velocities are a priori equally probable, and over the course of a match there will be a probability distribution of velocities. According to bayesian theory, an optimal estimate results from combining information about the distribution of velocities-the prior-with evidence from sensory feedback. As uncertainty increases, when playing in fog or at dusk, the system should increasingly rely on prior knowledge. To use a bayesian strategy, the brain would need to represent the prior distribution and the level of uncertainty in the sensory feedback. Here we control the statistical variations of a new sensorimotor task and manipulate the uncertainty of the sensory feedback. We show that subjects internally represent both the statistical distribution of the task and their sensory uncertainty, combining them in a manner consistent with a performance-optimizing bayesian process. The central nervous system therefore employs probabilistic models during sensorimotor learning.
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                Author and article information

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                Journal
                Journal of Neurophysiology
                Journal of Neurophysiology
                0022-3077
                1522-1598
                August 01 2023
                August 01 2023
                : 130
                : 2
                : 319-331
                Affiliations
                [1 ]Neuromuscular Diagnostics, Department of Sport and Health Sciences, Technical University of Munich, Munich, Germany
                [2 ]Physiology Section, Department of Integrative Medical Biology, Umeå University, Umeå, Sweden
                [3 ]Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University of Munich, Munich, Germany
                [4 ]Munich Data Science Institute (MDSI), Technical University of Munich, Munich, Germany
                Article
                10.1152/jn.00060.2023
                0e604f0b-cf95-47e6-8ac9-2408214c2b2f
                © 2023
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