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首页> 外文期刊>Journal of Neurophysiology >Kistemaker, D.A.a b , Wong, J.D.c , Gribble, P.L.a The cost of moving optimally: Kinematic path selection
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Kistemaker, D.A.a b , Wong, J.D.c , Gribble, P.L.a The cost of moving optimally: Kinematic path selection

机译：Kistemaker，D.A. a b，Wong，J.D.c，Gribble，P.L.a最佳移动成本：运动路径选择

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It is currently unclear whether the brain plans movement kinematics explicitly or whether movement paths arise implicitly through optimization of a cost function that takes into account control and/or dynamic variables. Several cost functions are proposed in the literature that are very different in nature (e.g., control effort, torque change, and jerk), yet each can predict common movement characteristics. We set out to disentangle predictions of the different variables using a combination of modeling and empirical studies. Subjects performed goal-directed arm movements in a force field (FF) in combination with visual perturbations of seen hand position. This FF was designed to have distinct optimal movements for muscle-input and dynamic costs while leaving kinematic cost unchanged. Visual perturbations in turn changed the kinematic cost but left the dynamic and muscle-input costs unchanged. An optimally controlled, physiologically realistic arm model was used to predict movements under the various cost variables. Experimental results were not consistent with a cost function containing any of the control and dynamic costs investigated. Movement patterns of all experimental conditions were adequately predicted by a kinematic cost function comprising both visually and somatosensory perceived jerk. The present study provides clear behavioral evidence that the brain solves kinematic and mechanical redundancy in separate steps: in a first step, movement kinematics are planned; and in a second, separate step, muscle activation patterns are generated.
机译：目前尚不清楚大脑是明确计划运动运动还是通过考虑控制和/或动态变量的成本函数优化来隐式出现运动路径。文献中提出了几种成本函数，它们在本质上是非常不同的（例如，控制作用力，转矩变化和冲击），但每个函数都可以预测共同的运动特性。我们开始结合建模和实证研究来解开对不同变量的预测。受试者在力场（FF）中进行了目标定向的手臂运动，并结合了所看到的手部位置的视觉干扰。该FF被设计为针对肌肉输入和动态成本具有独特的最佳运动，同时保持运动成本不变。视觉干扰反过来改变了运动学成本，但保持了动态和肌肉投入成本不变。在各种成本变量下，使用最优控制的，生理上逼真的手臂模型来预测运动。实验结果与包含所研究的任何控制成本和动态成本的成本函数不一致。所有运动条件的运动模式都可以通过运动成本函数适当地预测，该运动成本函数包括视觉上和体感上的混蛋。本研究提供了明确的行为证据，表明大脑在单独的步骤中解决了运动学和机械冗余：第一步，计划运动学;第二步，运动学。然后在第二个单独的步骤中生成肌肉激活模式。

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