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      Priors Engaged in Long-Latency Responses to Mechanical Perturbations Suggest a Rapid Update in State Estimation

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      PLoS Computational Biology
      Public Library of Science

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          Abstract

          In every motor task, our brain must handle external forces acting on the body. For example, riding a bike on cobblestones or skating on irregular surface requires us to appropriately respond to external perturbations. In these situations, motor predictions cannot help anticipate the motion of the body induced by external factors, and direct use of delayed sensory feedback will tend to generate instability. Here, we show that to solve this problem the motor system uses a rapid sensory prediction to correct the estimated state of the limb. We used a postural task with mechanical perturbations to address whether sensory predictions were engaged in upper-limb corrective movements. Subjects altered their initial motor response in ∼60 ms, depending on the expected perturbation profile, suggesting the use of an internal model, or prior, in this corrective process. Further, we found trial-to-trial changes in corrective responses indicating a rapid update of these perturbation priors. We used a computational model based on Kalman filtering to show that the response modulation was compatible with a rapid correction of the estimated state engaged in the feedback response. Such a process may allow us to handle external disturbances encountered in virtually every physical activity, which is likely an important feature of skilled motor behaviour.

          Author Summary

          It is commonly assumed that the brain uses internal estimates of the state of the body to adjust motor commands and perform successful movements. A problem arises when external disturbances deviate the limb from the ongoing task. In such cases, the estimated state of the body must be corrected based on sensory feedback. Because neural transmission delays can destabilize feedback control, an important challenge for motor systems is to correct the estimated state as quickly as possible. In this paper, we tested whether such a rapid correction is performed following mechanical loads applied to the upper limb. Our results indicate that long latency responses (∼50–100 ms) exhibit knowledge of the relationship between the delayed sensed joint displacement and the current state of the limb at the onset of the motor response. Importantly, this knowledge can be adjusted from one perturbation response to the next, should a distinct perturbation profile be experienced. These results suggest that a correction of state estimation is performed within the limb rapid-feedback pathways, allowing fast and stable feedback control.

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          Most cited references41

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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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            An internal model for sensorimotor integration.

            On the basis of computational studies it has been proposed that the central nervous system internally simulates the dynamic behavior of the motor system in planning, control, and learning; the existence and use of such an internal model is still under debate. A sensorimotor integration task was investigated in which participants estimated the location of one of their hands at the end of movements made in the dark and under externally imposed forces. The temporal propagation of errors in this task was analyzed within the theoretical framework of optimal state estimation. These results provide direct support for the existence of an internal model.
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              Learning of action through adaptive combination of motor primitives.

              Understanding how the brain constructs movements remains a fundamental challenge in neuroscience. The brain may control complex movements through flexible combination of motor primitives, where each primitive is an element of computation in the sensorimotor map that transforms desired limb trajectories into motor commands. Theoretical studies have shown that a system's ability to learn action depends on the shape of its primitives. Using a time-series analysis of error patterns, here we show that humans learn the dynamics of reaching movements through a flexible combination of primitives that have gaussian-like tuning functions encoding hand velocity. The wide tuning of the inferred primitives predicts limitations on the brain's ability to represent viscous dynamics. We find close agreement between the predicted limitations and the subjects' adaptation to new force fields. The mathematical properties of the derived primitives resemble the tuning curves of Purkinje cells in the cerebellum. The activity of these cells may encode primitives that underlie the learning of dynamics.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, USA )
                1553-734X
                1553-7358
                August 2013
                August 2013
                15 August 2013
                : 9
                : 8
                : e1003177
                Affiliations
                [1 ]Centre for Neuroscience Studies, Queen's University, Kingston, Canada
                [2 ]Department of Biomedical and Molecular Sciences, Queen's University, Kingston, Canada
                University College London, United Kingdom
                Author notes

                SHS is associated with BKIN-Technologies (which commercializes the robotic apparatus that was utilized) and was supported by a GlaxoSmithKlein chair in Neuroscience.

                Conceived and designed the experiments: FC SHS. Performed the experiments: FC. Analyzed the data: FC SHS. Contributed reagents/materials/analysis tools: FC. Wrote the paper: FC SHS.

                Article
                PCOMPBIOL-D-13-00520
                10.1371/journal.pcbi.1003177
                3744400
                23966846
                afe20c9d-14f5-401c-9d39-9bf072396f76
                Copyright @ 2013

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 28 March 2013
                : 19 June 2013
                Page count
                Pages: 14
                Funding
                This work was supported by the National Sciences and Engineering Research Council of Canada (NSERC). FC was supported by a postdoctoral fellowship from the Canadian Institute for Health Research (CIHR). SHS was supported by a GlaxoSmithKlein chair in Neuroscience. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology
                Neuroscience
                Computational Neuroscience
                Motor Systems
                Neurophysiology

                Quantitative & Systems biology
                Quantitative & Systems biology

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