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

最小自我（MS）可以定义为我们在特定情况下感知自己的方式，即如何感知身体部位属于自己的身体（身体所有权），以及这些身体部位产生哪些行动或事件（代理）。虚拟/增强现实技术的最新发展允许对动作-感觉交互和偶然性进行详细的参数操作。因此，研究工具在身体图式（BS）和身体形象（BI）中的体现，以及身体所有权和代理之间的关系及其对动作相关感觉反馈的相互依赖，可能是一种最佳方法。我们的项目将有助于研究哪些功能机制是MS构建的基础，以及MS在多大程度上是可塑的，并受感觉运动体验的影响。我们提出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工具使用训练的短期效果。然后，我们将研究衰老的更长期后果，作为日常活动中感觉运动能力改变和特定感觉运动技能参与和体验减少的代表。对BS和BI的影响，更具体地说是对感知身体所有权和代理的影响，将研究这两种类型的可塑性，包括生物信号数据（EEG）的分析。在三个实验中，参与者将学习控制一个虚拟工具，包括一个安装在棍子上的手状工具，由受试者的真实手在AR中握住，在分散的物体中选择和抓住目标物体，并将它们放入调整到相应物体形状的目标整体中。我们将以更动态的数据驱动视角进一步补充传统的平均数据假设驱动分析：对于虚拟工具使用任务的每一次试验，我们处理相关的多模态数据流，并得出身体所有权可塑性水平的在线预测。这种BO可塑性的在线预测可用于向用户提供反馈，或通过在单个时间段后终止训练集来优化训练时间和强度。为此，我们将从记录的运动学数据（来自运动跟踪和肌电信号）和神经数据（来自高密度脑电图）以及行为特征中计算特征。通过研究成人和老年样本的质谱，我们可以更多地了解成熟的人类大脑如何在适应自然身体边界变化的同时保持质谱感。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开发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，对于多发性硬化症来说，具有明确物理边界的身体在多大程度上是必要的，这一问题确实与人类以及未来的机器人高度相关。

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.