Learning to Control Flexible Objects Using Error-Tolerant Movement Strategies

描述（由申请人提供）：人类在进行日常生活活动时必须操纵和运输各种物体。简单的刚性物体很容易响应人类的控制动作，通常没有运动约束。相比之下，柔性对象包含不能直接控制的元素，并对操作操作构成独特的约束。例如，杯子里的咖啡只能通过操纵杯子来控制，如果动作不当，咖啡可能会洒出来。迄今为止，大多数研究都集中在具有刚性对象的任务上，这些任务具有明确定义的最佳运动模式，导致单一目标；然而，许多日常任务并没有精确定义的最佳策略——许多策略会导致期望的目标。重要的是，与其他策略相比，某些策略对控制错误的敏感性可能较低，而这些策略可能是首选策略。扩展先前的研究，我们将容错运动策略定义为最小化运动可变性对任务性能的影响。在柔性物体的控制中，容忍度尤为重要，因为在柔性物体中，违反能量约束可能导致稳定性丧失，并带来意想不到的后果，例如溢出咖啡。柔性对象控制的关键问题包括个体是否对误差容忍度敏感以及容忍度如何随着学习而变化。因此，本研究的目标是开发一种量化柔性物体连续控制中的容错分析方法，并研究人类在搬运柔性物体时是否学习了容错策略。将追求三个具体目标：1)开发一个基于搬运具有能量约束的柔性物体任务的实验范式，创建该任务的数学模型，并使用机器人设备实现该任务；2)将现有的TNC（公差、噪声、协变）分析方法扩展到连续运输任务；3)人类在正常和受干扰的运输任务中学习容错运动策略。这项研究结合了理论和实验工作，以解决我们对人类如何在运动可变性和扰动的存在下操纵柔性物体的基本理解中的重要空白。此外，考虑到老年人的运动变异性增加，宽容运动策略的发展可能是老年人运动控制的关键特征。因此，这项研究将为未来对老年人的研究奠定基础，这些研究将探讨老年人移动缓慢是否作为增加耐受性的战略反应，而不仅仅是由于生理限制。

DESCRIPTION (provided by applicant): Humans must manipulate and transport a variety of objects when performing daily life activities. Simple rigid objects readily respond to human control actions and usually have no movement constraints. In contrast, flexible objects contain elements that cannot be directly controlled and pose unique constraints on manipulative actions. For example, coffee in a cup can only be controlled by manipulating the cup, and the coffee may spill if the movements are inappropriate. Most studies to date have focused on tasks with rigid objects that have a well-defined optimal movement pattern that leads to a single goal; however, many everyday tasks do not have precisely defined optimal strategies - many strategies lead to the desired goal. Importantly, some strategies may have less sensitivity to control errors compared to others, and these may be preferred. Extending previous research, we define an error-tolerant movement strategy as one that minimizes the effects of movement variability on task performance. Tolerance is particularly important in the control of flexible objects, where violations of energetic constraints can lead to a loss of stability with undesired consequences, such as spilling coffee. Critical unanswered questions for flexible object control include whether individuals are sensitive to error tolerance and how tolerance changes with learning. Accordingly, the goal of this research is to develop an analysis approach to quantify error tolerance in the continuous control of flexible objects, and examine whether humans learn error-tolerant strategies when transporting flexible objects. Three specific aims will be pursued: 1) develop an experimental paradigm based on the task of transporting a flexible object with energetic constraints, create a mathematical model of the task, and implement the task using a robotic device; 2) extend an existing analysis approach called TNC (Tolerance, Noise, Covariation) to the continuous transportation task; 3) show that humans learn error tolerant movement strategies for the transportation task during normal and perturbed task performances. This research combines theoretical and experimental work to address important gaps in our basic understanding of how humans manipulate flexible objects in the presence of movement variability and perturbations. Additionally, given that older adults have increased variability in their movements, the development of tolerant movement strategies may be a key feature of movement control in older adults. Hence, this study will lay the groundwork for future studies in older adults, which will explore whether older adults move slower as a strategic response to increase tolerance rather than simply due to physiological limitations. PUBLIC HEALTH RELEVANCE: When performing everyday activities such as carrying a cup of coffee, humans should move in a way that minimizes the chance that the coffee will be spilled if the cup is unexpectedly disturbed. This research proposes that humans naturally learn such error-tolerant movement strategies. It may explain why older adults move slower, which may be a strategy to increase error tolerance due to their increased movement variability.