Predictability in Complex Object Control

复杂对象控制的可预测性

• 批准号: 9055880
• 负责人: Dagmar Sternad
• 金额: $ 36.98万
• 依托单位: NORTHEASTERN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-24 至 2020-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9055880
• 关键词: Activities of Daily Living; Address; Aging; Biomechanics; California; Child; Clinical; Coffee; Complex; Coupled; Dystonia; Elderly; Environment; Experimental Psychology; Feedback; Freedom; Frequencies; Hand; Human; Individual; Information Theory; Learning; Manuals; Measurement; Measures; Modeling; Multi-Institutional Clinical Trial; Multiple Sclerosis; Noise; Nonlinear Dynamics; Performance; Physical therapy; Population; Process; Research; Research Personnel; Robot; Robotics; Signal Transduction; Solutions; Structure; System; Testing; Universities; Upper Extremity; Variant; base; clinically significant; computational neuroscience; dexterity; electric impedance; innovation; insight; nervous system disorder; neuroregulation; novel; preempt; public health relevance; relating to nervous system; research study; response; robot rehabilitation; theories; tool; virtual

 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.

 描述（由申请人提供）：复杂物体的操纵或工具的使用是许多日常生活活动的标志，但对手动灵活性的神经控制仍然很少 明白即使是看似简单的运送一杯咖啡而不洒出来的任务，也会产生复杂的相互作用，人类需要预测、抢先和补偿。基于内部模型预测这种复杂的非线性动力学似乎令人生畏。因此，这项研究测试了人类学习策略的假设，使互动可预测。携带一杯咖啡的任务是建模与车和钟摆系统，在虚拟环境中呈现和主题与虚拟杯通过机器人manipulhuiu互动。为了深入了解人类的控制策略，该建议开发了三个新的分析途径，基于经典的线性分析，信息理论和非线性动力学，可操作性的可预测性定量理论为基础的评估。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，因为它们不仅需要更低的力，而且由于更低的信号相关噪声而更可预测。三个实验研究了线性和非线性任务模型的瞬态和稳态性能。目标2考察了具有冗余的任务，提供了多种解决方案。可预测性是由所施加的力和物体动力学之间的互信息来操作的。三个实验测试了被试是否选择了那些具有最高互信息的策略。目标3应用收缩分析，一个理论框架，检查收敛，或稳定性，在状态空间的动力系统。两个实验研究受试者是否学习最大限度地收缩其轨迹的解决方案，特别是当面临扰动时。由于许多患有神经系统疾病的个体的手动灵活性受到损害，实验范式及其分析有望成为一个有用的平台，以深入了解神经系统疾病，如肌张力障碍，多发性硬化症，包括衰老。