Predictability in Complex Object Control

 描述(申请人提供)：操作复杂的物体或使用工具是许多日常生活活动的标志，但神经控制的手动灵活性仍然很少 明白了。即使是看似简单的任务--运送一杯咖啡而不洒出来--也会产生复杂的相互作用力，人类需要预测、抢占和补偿。基于内部模型来预测如此复杂的非线性动力学似乎令人望而生畏。因此，这项研究测试了一种假设，即人类学习使互动变得可预测的策略。端着一杯咖啡的任务是用手推车和摆系统模拟的，该系统在虚拟环境中渲染，受试者通过机器人操作对话与虚拟杯子互动。为了深入了解人类的控制策略，该建议基于经典的线性分析、信息论和非线性动力学开发了三种新的分析方法，使基于定量理论的评估具有可预测性。目标1应用经典的频率响应分析并测试人类调谐到共振模式的假设，因为它们不仅需要较低的力，而且由于较低的信号依赖噪声而更容易预测。三个实验考察了线性和非线性任务模型的暂态和稳态性能。目标2检查了具有冗余的任务，提供了多种解决方案。可预测性是通过作用力和物体动力学之间的互信息来实现的。三个实验测试了受试者是否选择了互信息最高的策略。目标3应用收缩分析，这是一个检查动力系统状态空间中的收敛或稳定性的理论框架。两个实验考察了受试者是否学会了最大限度地收缩其轨迹的解决方案，特别是在面对扰动时。由于许多神经疾病患者的手动灵活性受到影响，实验范式及其分析有望成为一个有用的平台，以深入了解神经疾病，如肌张力障碍、多发性硬化症，包括衰老。

 DESCRIPTION (provided by applicant): Manipulation of complex objects or tool use is a hallmark of many activities of daily living, but neural control of manual dexterity is still little understood. Even the seemingly simple task of transporting a cup of coffee without spilling creates complex interaction forces that humans need to predict, preempt, and compensate for. Prediction of such complex nonlinear dynamics based on an internal model appears daunting. Hence, this research tests the hypothesis that humans learn strategies that 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 three new analysis avenues based on classical linear analysis, information theory, and nonlinear dynamics that operationalize predictability for quantitative theory-based assessment. Aim 1 applies classical frequency response analysis and tests the hypothesis that humans tune into resonance modes as they not only require lower forces, but also more predictable due to lower signal-dependent noise. Three experiments examine transient and steady-state performance with the linear and nonlinear task model. Aim 2 examines tasks with redundancy that offers a manifold of solutions. Predictability is operationalized by the mutual information between the applied force and object dynamics. Three experiments test whether subjects choose those strategies with the highest mutual information. Aim 3 applies contraction analysis, a theoretical framework that examines convergence, or stability, in the state space of the dynamical system. Two experiments examine whether subjects learn solutions that maximize contraction of their trajectories, especially when confronted with perturbations. As manual dexterity is compromised in many individuals with neurological disorders, the experimental paradigm and its analyses promise to become a useful platform to gain insights into neurological diseases, such as dystonia, multiple sclerosis, including aging.