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      Body Mechanics, Optimality, and Sensory Feedback in the Human Control of Complex Objects

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          Abstract

          Humans are adept at a wide variety of motor skills, including the handling of complex objects and using tools. Advances to understand the control of voluntary goal-directed movements have focused on simple behaviors such as reaching, uncoupled to any additional object dynamics. Under these simplified conditions, basic elements of motor control, such as the roles of body mechanics, objective functions, and sensory feedback, have been characterized. However, these elements have mostly been examined in isolation, and the interactions between these elements have received less attention. This study examined a task with internal dynamics, inspired by the daily skill of transporting a cup of coffee, with additional expected or unexpected perturbations to probe the structure of the controller. Using optimal feedback control (OFC) as the basis, it proved necessary to endow the model of the body with mechanical impedance to generate the kinematic features observed in the human experimental data. The addition of mechanical impedance revealed that simulated movements were no longer sensitively dependent on the objective function, a highly debated cornerstone of optimal control. Further, feedforward replay of the control inputs was similarly successful in coping with perturbations as when feedback, or sensory information, was included. These findings suggest that when the control model incorporates a representation of the mechanical properties of the limb, that is, embodies its dynamics, the specific objective function and sensory feedback become less critical, and complex interactions with dynamic objects can be successfully managed.

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          Most cited references107

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          Estimating the Dimension of a Model

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            Bayes Factors

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              Neural population dynamics during reaching

              Most theories of motor cortex have assumed that neural activity represents movement parameters. This view derives from an analogous approach to primary visual cortex, where neural activity represents patterns of light. Yet it is unclear how well that analogy holds. Single-neuron responses in motor cortex appear strikingly complex, and there is marked disagreement regarding which movement parameters are represented. A better analogy might be with other motor systems, where a common principle is rhythmic neural activity. We found that motor cortex responses during reaching contain a brief but strong oscillatory component, something quite unexpected for a non-periodic behavior. Oscillation amplitude and phase followed naturally from the preparatory state, suggesting a mechanistic role for preparatory neural activity. These results demonstrate unexpected yet surprisingly simple structure in the population response. That underlying structure explains many of the confusing features of individual-neuron responses.
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                Author and article information

                Contributors
                Journal
                9426182
                20143
                Neural Comput
                Neural Comput
                Neural computation
                0899-7667
                1530-888X
                30 May 2023
                18 April 2023
                07 June 2023
                : 35
                : 5
                : 853-895
                Affiliations
                Department of Mechanical Engineering, Northern Arizona University, Flagstaff, AZ 86011, U.S.A.
                Department of Biology and Department of Electrical and Computer Engineering, Northeastern University, Boston, MA 02115, U.S.A.
                Department of Electrical and Computer Engineering and Institute for Experiential Robotics, Northeastern University, Boston, MA 02115, U.S.A.
                Department of Electrical and Computer Engineering, Northeastern University, Boston, MA 02115, U.S.A.
                Departments of Biology, Electrical and Computer Engineering, and Physics, Institute for Experiential Robotics, Northeastern University, Boston, MA 02115, U.S.A.
                Author notes

                Reza Sharif Razavian, Mohsen Sadeghi, and Salah Bazzi contributed equally.

                [*]

                Work completed when R. Sharif Razavian was with Northeastern University, Boston, MA 02115, U.S.A.

                Article
                NIHMS1904888
                10.1162/neco_a_01576
                10246336
                36944234
                ae784e66-99ae-45c0-b456-1bd6a3f04adf

                Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.

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