Abstract：
How humans coordinate digit forces to perform dexterous manipulation is not well understood. This gap is due to the use of tasks devoid of dexterity requirements and/or the use of analytical techniques that cannot isolate the roles that digit forces play in preventing object slip and controlling object position and orientation (pose). In our recent work, we used a dexterous manipulation task and decomposed digit forces into FG, the internal force that prevents object slip, and FM, the force responsible for object pose control. Unlike FG, FM was modulated from object lift onset to hold, suggesting their different sensitivity to sensory feedback acquired during object lift. However, the extent to which FG and FM can be controlled independently remains to be determined. Importantly, how FG and FM change as a function of object property is mathematically indeterminate and therefore requires active modulation. To address this gap, we systematically changed either object mass or external torque. The FM normal component responsible for object orientation control was modulated to changes in object torque but not mass. In contrast, FG was distinctly modulated to changes in object mass and torque. These findings point to a differential sensitivity of FG and FM to task requirements and provide novel insights into the neural control of dexterous manipulation. Importantly, our results indicate that the proposed digit force decomposition has the potential to capture important differences in how sensory inputs are processed and integrated to simultaneously ensure grasp stability and dexterous object pose control.
摘要：
人类如何协调手指力来执行灵巧的操作还没有得到很好的理解。这种差距是由于使用了缺乏灵活性要求的任务和/或使用了无法隔离手指力在防止物体滑动和控制物体位置和方向（姿势）方面所起的作用的分析技术。在我们最近的工作中,我们使用了灵巧的操作任务，并将手指力分解为FG，防止物体滑动的内力，FM,负责物体姿势控制的力。不像FG,FM从物体抬起开始调制到保持，表明他们对物体提升过程中获得的感觉反馈的不同敏感性。然而,FG和FM可以独立控制的程度仍有待确定。重要的是,FG和FM如何作为对象属性的函数变化在数学上是不确定的,因此需要有源调制。为了解决这个差距，我们系统地改变了物体质量或外部扭矩。负责物体方向控制的FM法向分量被调制为物体扭矩的变化，而不是质量的变化。相比之下,FG被明显地调制以适应物体质量和扭矩的变化。这些发现指出了FG和FM对任务要求的不同敏感性，并为灵巧操作的神经控制提供了新的见解。重要的是,我们的结果表明，所提出的数字力分解有可能捕获感官输入如何处理和整合的重要差异，以同时确保把握稳定性和灵巧的物体姿势控制。
