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），以及哪些行为或事件是由这些身体部位产生的（代理）。虚拟/增强现实技术的最新发展允许对动作-感觉交互和意外事件进行详细的参数化操纵。因此，这可能是一个最佳的方法来研究工具的体现到身体图式（BS）和身体意象（BI），和身体所有权和代理之间的关联，以及它们之间的相互依赖的动作相关的感官feedbacks. The我们的项目将有助于问题的功能机制的基础上的MS的建设，以及在何种程度上的MS是可塑性和受影响的感觉运动经验。我们提出的工作计划将采用增强现实（AR）的方法来研究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，我们将研究衰老的长期后果，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。对BS和BI的影响，更具体地说，对感知的身体所有权和代理，将调查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，参与者将学习控制虚拟工具，包括安装在棍子上的手状工具，由受试者的真实的手在AR中握住，从分散注意力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。我们将进一步补充传统的假设驱动的平均数据分析，以更动态的数据驱动视角：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，并获得身体所有权可塑性水平的在线预测。BO可塑性的这种在线预测可以用于向用户提供反馈，或者用于通过在单独的时间段之后终止训练片段来优化训练持续时间和强度。为此，我们将从记录的运动数据（来自运动跟踪和肌电信号）和神经数据（来自高密度EEG）以及行为特征中计算特征。通过研究成年和老年样本的MS，我们可以更多地了解成熟的人类大脑如何保持MS感，同时适应其自然身体边界的变化。通过系统地研究人类的MS，我们可以建立在这个知识的基础上，当建立人工系统和机器人设计开发MS。我们认为，在何种程度上有一个明确的物理边界的身体是必要的MS的问题，确实是高度相关的人类以及未来的机器人。

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.