Collaborative Research: Effector and Task Neural Representations of Hand-Object Interactions

这个项目的重点是了解人们如何学习、计划和执行手部动作来抓取和使用物体。例如，一个人如何举起一杯咖啡而不洒出来，或者一个工厂工人如何将十字螺丝刀与螺丝上的凹槽排列在一起？尽管我们日常地执行这些任务而不给它们太多的思考，但灵巧的操作是最复杂和最不了解的人类技能之一，并且仍然限制了机器人在工业中的应用。特别有趣的是理解大脑机制，使人们能够学习以一种方式操纵物体（例如，通过手柄举起杯子），然后以不同的方式应用该知识（例如，通过侧面举起同一个杯子）。研究人员正致力于在神经和行为层面建立一个全面的理论，研究人们如何学习和概括这些手-物相互作用。这一结果可能会激发出更加灵巧的新型机器人操纵器，它们具有类似人类的能力，可以将学习到的运动行为推广到新的环境中。这项工作也可能影响更灵巧的神经假肢的发展。该项目的其他更广泛的影响包括关于人机系统的社会和伦理影响的公开讲座和讨论，以及参与由亚利桑那科学中心主办的“科学咖啡馆”系列活动。研究人员之前的工作为学习如何操纵物体的两种情况提供了证据。在一个场景中，人们构建对象操作的高级（即任务级）表示，这允许他们将学习到的操作推广到不同的上下文中。例如，即使在将手指从物体接触面移开或添加到物体接触面上，或者当物体被对侧手臂操纵时，人们也能成功地操纵物体。在第二种情况下，人们建立一个效应级表示。在这种情况下，尽管新的环境需要不同的解决方案，它们仍然坚持产生相同的手指位置和力，例如旋转具有不对称质量分布的物体。促进或干扰习得的手-物互动泛化的神经机制是什么？在任务级的神经表征——实现泛化——可以在重复暴露于不同的操作环境后建立吗？这些问题代表了我们对熟练的物体操作的理解上的一个重大差距。这项合作研究的总体目标是阐明手-物交互的任务级和效应级表征的神经机制。这些研究将记录手指的位置和抓握物体时使用的力，以探索学习给定手-物体交互的环境的影响。脑电图（EEG）将用于确定成功和不成功的新背景概括的神经关联。量化这些行为变量和相应的大脑机制将有助于我们深入了解物体是如何在心理上表征的，以及这些表征是如何成为灵巧操作计划和执行的基础的。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This project focuses on understanding how people learn, plan, and execute hand movements to grasp and use objects. For example, how does a person lift a cup of coffee without spilling or a factory worker line up a Phillips-head screwdriver with the grooves on a screw? Although we perform these tasks routinely without giving them too much thought, dexterous manipulation is one of the most complex and least understood human skills and one that still limits the utility of robots in industry. Of particular interest is understanding the brain mechanisms that allow people to learn to manipulate an object one way (e.g., to lift a mug by the handle) and then apply that knowledge differently (e.g., to lift the same mug by its sides). The investigators are working towards a comprehensive theory, at both the neural and behavioral levels, of how people learn and generalize these hand-object interactions. The results may inspire new robotic manipulators that are more dexterous, with human-like ability to generalize a learned motor behavior to novel contexts. The work may also influence development of more dexterous neuroprosthetics. The project's other broader impacts include a public lecture and discussion on the social and ethical implications of human-robot systems and participation in the "Science Cafe" series hosted by the Arizona Science Center.Previous work by the investigators has provided evidence for two scenarios for learning how to manipulate objects. In one scenario, people build a high-level (i.e., task-level) representation of object manipulation, which allows them to generalize the learned manipulation to a different context. For example, people successfully manipulate an object even after a finger is removed from, or added to, the object's contact surface or when the object is manipulated by the contralateral arm. In a second scenario, people build an effector-level representation. In this case, they persist in generating the same finger placement and forces despite a new context that requires different solutions, as when an object with an asymmetric mass distribution is rotated. What are the neural mechanisms involved with promoting or interfering with generalization of learned hand-object interactions? Can neural representations at the task level - enabling generalization - be built following repeated exposure to a different manipulation context? These questions represent a significant gap in our understanding of skilled object manipulation. The overall goal of this collaborative research is to elucidate neural mechanisms underlying task- and effector-level representations of hand-object interactions. The studies will record finger position and forces utilized when grasping objects in order to probe the influence of the context in which a given hand-object interaction is learned. Electroencephalography (EEG) will be used to determine the neural correlates of successful and unsuccessful generalizations to new contexts. Quantification of these behavioral variables and the corresponding brain mechanisms will provide insights into how objects are mentally represented and how these representations underlie planning and execution of dexterous manipulation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.