Plasticity of the minimal self in healthy aging - how virtual and real-life changes in sensorimotor experiences shape perception of body ownership, and agency

健康老龄化中最小自我的可塑性——感觉运动体验的虚拟和现实变化如何塑造对身体所有权和能动性的感知

• 批准号: 402779631
• 负责人: Professor Dr. Ben Godde
• 金额: --
• 依托单位: Arbeitsgruppe Cognitive Systems Laboratory
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2018
• 资助国家: 德国
• 起止时间: 2017-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/402779631?language=en
• 关键词: Plasticity; minimal; self; healthy; aging

The minimal self (MS) can be defined as the way we perceive ourselves in a specific situation, i.e. how body parts are perceived as belonging to one’s own body (body ownership), and which actions or events are produced by these body parts (agency). Recent developments in virtual / augmented reality techniques allow detailed parametric manipulation of action-sensation interactions and contingencies. Herewith, it might be an optimal method to investigate the embodiment of tools into the body schema (BS) and body image (BI), and the association between body-ownership and agency as well as their mutual dependency on action-related sensory feedback.Our project will contribute to the question of which functional mechanisms underlie the construction of the MS, and to which extent the MS is plastic and affected by sensorimotor experiences. Our proposed work program will apply an Augmented Reality (AR) approach to study short-term and long-term plasticity of the MS. First, we will focus on the analysis of short-term effects of tool-use trainings in AR. Then, we will examine the more long-term consequences of aging as a proxy for altered sensorimotor abilities and reduced involvement in and experience of specific sensory-motor skills in everyday activities. The impact on BS and BI, more specifically on perceived body ownership and agency, will be investigated for both types of plasticity, including the analysis of biosignal data (EEG). In three experiments, participants will learn to control a virtual tool, consisting of a hand-like tool mounted to a stick, held by the subject’s real hand in AR, to select and grasp target objects among distracting objects and to fit them into a target whole adjusted to the shape of the respective object.We will further complement the traditional hypothesis-driven analysis on averaged data with a more dynamically data-driven perspective: For each single trial of the virtual tool-use task, we process the related multimodal data streams and derive an online prediction of the level of body ownership plasticity. Such an online prediction of BO plasticity can be used for giving feedback to the user, or for optimizing training duration and intensity by terminating a training episode after an individual time period. For this purpose, we will calculate features from recorded kinematic data (from motion tracking and myoelectric signals) and neural data (from high-density EEG), as well as behavioral features.By studying the MS with adult and aging samples, we can learn more about how the mature human brain maintains a sense of MS while adapting to changes to its natural bodily boundaries. By systematically studying the MS in humans, we can then build upon this knowledge when building artificial systems and robots designed to develop a MS. We argue that the question of the extent to which having a body with clear physical boundaries is necessary for the MS, is indeed highly relevant for humans as well as for future robots.

最小自我(MS)可以被定义为我们在特定情况下感知自己的方式，即身体部位如何被感知为属于自己的身体(身体所有权)，以及这些身体部位(机构)产生了哪些行动或事件。虚拟/增强现实技术的最新发展允许对动作-感觉、交互和意外情况进行详细的参数操作。因此，研究工具在身体图式(BS)和身体意象(BI)中的体现，以及身体所有权和代理之间的联系，以及它们对与动作相关的感觉反馈的相互依赖，可能是一种最佳的方法。我们的项目将有助于解决以下问题：MS的构建基础是什么功能机制，MS在多大程度上是可塑性的，并受到感觉运动经验的影响。我们建议的工作计划将应用增强现实(AR)方法来研究MS的短期和长期可塑性。首先，我们将重点分析AR中工具使用培训的短期效果。然后，我们将研究衰老作为感觉运动能力改变和日常活动中特定感觉运动技能参与和体验减少的代理的更长期的后果。对BS和BI的影响，更具体地说是对感知的身体所有权和代理的影响，将针对这两种类型的可塑性进行调查，包括生物信号数据(EEG)的分析。在三个实验中，参与者将学习如何控制一个虚拟工具，该工具由安装在棍子上的手形工具组成，由受试者的真实手在AR中握住，从分散注意力的对象中选择和抓住目标对象，并将它们放入根据各个对象的形状调整的目标整体。我们将以更动态的数据驱动的观点进一步补充传统的假设驱动的平均数据分析：对于虚拟工具使用任务的每一次尝试，我们处理相关的多通道数据流，并得出身体所有权可塑性水平的在线预测。这种对BO可塑性的在线预测可用于向用户提供反馈，或用于通过在单个时间段后终止训练片段来优化训练持续时间和强度。为此，我们将根据记录的运动学数据(来自运动跟踪和肌电信号)和神经数据(来自高密度脑电)以及行为特征来计算特征。通过使用成人和衰老样本研究多发性硬化症，我们可以更多地了解成熟的人脑如何在适应自然身体边界变化的同时保持多发性硬化症的感觉。通过系统地研究人类的多发性硬化症，我们可以在构建旨在发展多发性硬化症的人工系统和机器人时建立这些知识。我们认为，多发性硬化症需要在多大程度上拥有一个具有明确物理边界的身体，这一问题确实与人类和未来的机器人高度相关。