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

协作研究：手-物体交互的效应器和任务神经表征

• 批准号: 1827752
• 负责人: Marco Santello
• 金额: $ 44.94万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1827752&HistoricalAwards=false
• 关键词: Collaborative; Research; Effector; Task; Neural

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.

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住和使用物体。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一把十字螺丝刀与螺丝上的凹槽排成一行？虽然我们经常执行这些任务，而没有过多考虑它们，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。特别感兴趣的是理解允许人们学习以一种方式操纵物体的大脑机制（例如，通过把手举起杯子），然后不同地应用该知识（例如，用它的两侧举起同一个杯子）。研究人员正在努力在神经和行为层面上建立一个全面的理论，以解释人们如何学习和概括这些手与物体的互动。这些结果可能会激发新的机器人操作器，它们更灵巧，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修复术的发展。该项目的其他更广泛的影响包括公开讲座和讨论人类机器人系统的社会和伦理影响，并参与亚利桑那科学中心主办的“科学咖啡馆”系列。研究人员以前的工作为学习如何操纵物体的两种情况提供了证据。在一种情况下，人们建立一个高级别的（即，任务级）表示对象操作，这允许他们将学习到的操作推广到不同的上下文。例如，即使在手指从对象的接触表面移除或添加到对象的接触表面之后，或者当对象被对侧手臂操纵时，人们也成功地操纵对象。在这种情况下，它们持续产生相同的手指放置和力，尽管新的上下文需要不同的解决方案，如旋转具有不对称质量分布的对象时。促进或干扰习得的手-物交互作用的泛化的神经机制是什么？在重复暴露于不同的操作环境之后，任务水平的神经表征--能够泛化--能够建立起来吗？这些问题代表了我们对熟练的对象操作的理解中的一个重大差距。这项合作研究的总体目标是阐明神经机制的任务和效应器水平的手-物体相互作用的表示。这些研究将记录手指的位置和抓握物体时所用的力，以探讨学习给定手-物体交互的背景的影响。脑电图（EEG）将被用来确定成功和不成功的概括到新的背景下的神经相关性。这些行为变量和相应的大脑机制的量化将提供深入了解对象是如何在精神上表示，以及这些表示是如何规划和执行灵巧的manipulation.This奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

Synergistic Organization of Neural Inputs from Spinal Motor Neurons to Extrinsic and Intrinsic Hand Muscles

• DOI: 10.1523/jneurosci.0419-21.2021
• 发表时间: 2021-06
• 期刊: The Journal of Neuroscience
• 作者: Simone Tanzarella;S. Muceli;M. Santello;D. Farina

Action observation facilitates anticipatory control of grasp for object mass but not weight distribution

动作观察有助于对物体质量的抓取进行预期控制，但不能控制重量分布

• DOI: 10.1016/j.neulet.2022.136549
• 发表时间: 2022
• 期刊: Neuroscience Letters
• 影响因子: 2.5
• 作者: Lee-Miller, Trevor;Gutterman, Jennifer;Chang, Jaymin;Gordon, Andrew M.
• 通讯作者: Gordon, Andrew M.

Pushing the boundaries of a physical approach for the study of sensorimotor control

突破物理方法研究感觉运动控制的界限

• DOI: 10.1016/j.plrev.2021.02.002
• 发表时间: 2021
• 期刊: Physics of Life Reviews
• 影响因子: 11.7
• 作者: Santello, Marco