Predictability in complex object control

项目总结 操作复杂的物体或使用工具是日常生活的一个标志，由于运动而失去了手动灵活性 损伤会导致丧失独立性。当对象具有以下情况时，操作对象尤其具有挑战性 不受直接控制的内部动力。即使是看起来很简单的运送一杯咖啡的任务 具有内在的动态，人类需要预测、抢占和补偿以避免溢出。控制 这种基于精确内部模型的在线误差修正的复杂非线性系统应运而生 考虑到神经过程缓慢和感觉运动系统中无处不在的噪音，这是令人望而生畏的。因此，这是 研究验证了这样一种假设，即人类学会简化对象的交互，即进行交互 这是可预见的。端着一杯咖啡的任务是用手推车和钟摆系统建模的，该系统被渲染 在虚拟环境中，受试者通过机器人操作手与虚拟杯子交互。获得洞察力 在人类控制策略中，该提案开发了一种任务动态方法，为 通过将复杂的动态解析为执行和结果变量来进行假设检验，使用最少的 关于人类控制者的假设。八个实验测试了人类寻求的总体假设 解决方案是可预测的，通过关联手-对象运动，并使行为稳定和宽容 避免错误和风险以避免错误纠正和防止失败。AIM-1测试线性系统的内部动力学控制 并研究了人类如何选择初始条件来缓解扰动，他们如何抢占先机 不受欢迎的球摆动，它们如何利用间歇性接触来发展稳定的节奏，以及它们如何修改 该对象的属性有助于稳定的接触行为。为了检查学习，Aim-2放大了 任务的维度通过引入更多现实生活中的平面杯运动，这创建了一个指数 复杂性的增加。四个实验测试了引入新的动态挑战的任务目标，例如 有节奏的和离散的运动相结合，改变运动方向时复杂的球动力学， 对象属性的适配和修改，所有这些都是为了展示人类如何利用或覆盖内部 实现可预测性的动态。AIM-3通过定制设计的设备引入了任务的真实版本， 魔法桌。在比较了真实和虚拟的设置之后，魔术表被用来利用 创建新的灵敏指标以量化临床应用的运动功能的理论框架。 具体地说，我们评估了一组中风后患者运动障碍的严重程度和恢复情况。作为手册 灵巧性在许多神经障碍患者中受到损害，实验范式和它的 量化分析有望成为深入了解神经疾病的有用平台。

PROJECT SUMMARY Manipulation of complex objects or tool use is a hallmark of daily living, and loss of manual dexterity due to motor impairments lead to loss of independence. Manipulating objects is particularly challenging when the object has internal dynamics that is not directly controlled. Even the seemingly simple task of transporting a cup of coffee has intrinsic dynamics that humans need to predict, preempt, and compensate for to avoid spilling. Control of such complex nonlinear systems with online error corrections based on precise internal models appears daunting, given the slow neural processes and the ubiquitous noise in the sensorimotor system. Hence, this research tests the hypothesis that humans learn to simplify the object interactions, i.e., make the interactions predictable. The task of carrying a cup of coffee is modeled with a cart-and-pendulum system that is rendered in a virtual environment and subjects interact with the virtual cup via a robotic manipulandum. To gain insight into human control strategies, this proposal develops a task-dynamic approach that affords principled hypothesis-testing by parsing the complex dynamics into execution and result variables, with minimal assumptions about the human controller. Eight experiments test the overall hypothesis that humans seek solutions that are predictable, by correlating hand-object motions, and making the behavior stable and tolerant to error and risk to obviate error corrections and prevent failure. Aim-1 tests control of internal dynamics in linear movements and examines how humans choose initial conditions to mitigate perturbations, how they preempt undesired ball oscillations, how they exploit intermittent contact to develop a stable rhythm, and how they modify the object properties to facilitate stable contact behavior. To examine learning, Aim-2 scales up the dimensionality of the task by introducing more real-life planar cup movements, which creates an exponential increase in complexity. Four experiments test task goals that introduce new dynamic challenges, such as combination of rhythmic and discrete movements, complex ball dynamics when changing movement directions, adaptation and modification of object properties, all to show how humans either exploit or override internal dynamics to achieve predictability. Aim-3 introduces a real version of the task with a custom-designed device, the MAGIC Table. Following a comparison of the real and virtual set-ups, the MAGIC Table is used to leverage the theoretical framework to create novel sensitive metrics to quantify motor function for clinical applications. Specifically, we assess severity and recovery of motor impairment in a cohort of patients after stroke. As manual dexterity is compromised in many individuals with neurological disorders, the experimental paradigm and its quantitative analyses promise to become a useful platform to gain insights into neurological diseases.