Mini-max feedback control as a computational theory of sensorimotor control in the presence of structural uncertainty

Ueyama, Yuki

doi:10.3389/fncom.2014.00119

Cited by 18 publications

(14 citation statements)

References 63 publications

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“…Recent work examining sensorimotor control have suggested robust control as a framework for explaining and interpreting human behaviour [76][77][78]. Robust control would predict higher feedback gains with increased internal model uncertainties, fitting well our current experimental results.…”

Section: Discussionsupporting

confidence: 86%

Feedback Gains modulate with Motor Memory Uncertainty

Franklin

2021

Neurons, Behavior, Data Analysis, and Theory

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A sudden change in dynamics produces large errors leading to increases in muscle co-contraction and feedback gains during early adaptation. We previously proposed that internal model uncertainty drives these changes, whereby the sensorimotor system reacts to the change in dynamics by up regulating stiffness and feedback gains to reduce the effect of model errors. However, these feedback gain increases have also been suggested to represent part of the adaptation mechanism. Here, we investigate this by examining changes in visuomotor feedback gains during gradual or abrupt force field adaptation. Participants grasped a robotic manipulandum and reached while a curl force field was introduced gradually or abruptly. Abrupt introduction of dynamics elicited large initial increases in kinematic error, muscle co-contraction and visuomotor feedback gains, while gradual introduction showed little initial change in these measures despite evidence of adaptation. After adaptation had plateaued,there was a change in the co-contraction and visuomotor feedback gains relative to null field movements, but no differences (apart from the final muscle ac-

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Section: Discussionsupporting

confidence: 86%

Feedback Gains modulate with Motor Memory Uncertainty

Franklin

2021

Neurons, Behavior, Data Analysis, and Theory

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“…In general, the derivation for H ϱ -control is based on matrix recurrences that extend the framework of LQG to the cases where disturbances are not Gaussian. Details about these recurrences are provided in the references above, and an application to human reaching movements without experimental validation was proposed previously by Ueyama (2014). A detailed description of the derivation is beyond the scope of the present paper, but access to working code for replication can be found here: http://modeldb.yale.edu/258846.…”

Section: Modelmentioning

confidence: 99%

Robust Control in Human Reaching Movements: A Model-Free Strategy to Compensate for Unpredictable Disturbances

2019

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Current models of motor learning suggest that multiple timescales support adaptation to changes in visual or mechanical properties of the environment. These models capture patterns of learning and memory across a broad range of tasks, yet do not consider the possibility that rapid changes in behavior may occur without adaptation. Such changes in behavior may be desirable when facing transient disturbances, or when unpredictable changes in visual or mechanical properties of the task make it difficult to form an accurate model of the perturbation. Whether humans can modulate control strategies without an accurate model of the perturbation remains unknown. Here we frame this question in the context of robust control (H ϱ-control), a control strategy that specifically considers unpredictable disturbances by increasing initial movement speed and feedback gains. Correspondingly, we demonstrate in two human reaching experiments including males and females that the occurrence of a single unpredictable disturbance led to an increase in movement speed and in the gain of rapid feedback responses to mechanical disturbances on subsequent movements. This strategy reduced perturbation-related motion regardless of the direction of the perturbation. Furthermore, we found that changes in the control strategy were associated with co-contraction, which amplified the gain of muscle responses to both lengthening and shortening perturbations. These results have important implications for studies on motor adaptation because they highlight that trial-by-trial changes in limb motion also reflected changes in control strategies dissociable from error-based adaptation.

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“…Recently, it has been shown that the computational mechanism in M1 can be justified due to the optimal feedback control [91,92]. In this study, at the motor command production level, the NPC uses the nature of the feedback control by minimizing output error and MSs' effort (Eq 10).…”

Section: Physiological Evidence Of the Proposed Modelmentioning

confidence: 99%

How does the CNS control arm reaching movements? Introducing a hierarchical nonlinear predictive control organization based on the idea of muscle synergies

Dehghani

Bahrami

2020

PLoS ONE

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In this study, we introduce a hierarchical and modular computational model to explain how the CNS (Central Nervous System) controls arm reaching movement (ARM) in the frontal plane and under different conditions. The proposed hierarchical organization was established at three levels: 1) motor planning, 2) command production, and 3) motor execution. Since in this work we are not discussing motion learning, no learning procedure was considered in the model. Previous models mainly assume that the motor planning level produces the desired trajectories of the joints and feeds it to the next level to be tracked. In the proposed model, the motion control is described based on a regulatory control policy, that is, the output of the motor planning level is a step function defining the initial and final desired position of the hand. For the command production level, a nonlinear predictive model was developed to explain how the time-invariant muscle synergies (MSs) are recruited. We used the same computational model to explain the arm reaching motion for a combined ARM task. The combined ARM is defined as two successive ARM such that it starts from point A and reaches to point C via point B. To develop the model, kinematic and kinetic data from six subjects were recorded and analyzed during ARM task performance. The subjects used a robotic manipulator while moving their hand in the frontal plane. The EMG data of 15 muscles were also recorded. The MSs used in the model were extracted from the recorded EMG data. The proposed model explains two aspects of the motor control system by a novel computational approach: 1) the CNS reduces the dimension of the control space using the notion of MSs and thereby, avoids immense computational loads; 2) at the level of motor planning, the CNS generates the desired position of the hand at the starting, via and the final points, and this amounts to a regulatory and non-tracking structure.

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Mini-max feedback control as a computational theory of sensorimotor control in the presence of structural uncertainty

Cited by 18 publications

References 63 publications

Feedback Gains modulate with Motor Memory Uncertainty

Feedback Gains modulate with Motor Memory Uncertainty

Robust Control in Human Reaching Movements: A Model-Free Strategy to Compensate for Unpredictable Disturbances

How does the CNS control arm reaching movements? Introducing a hierarchical nonlinear predictive control organization based on the idea of muscle synergies

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