Human Manipulation Actions: Neurophysiological Validation of their Formal Characterization

人类操纵行为：其形式表征的神经生理学验证

• 批准号: 310431995
• 负责人: Professorin Dr. Ricarda Ines Schubotz
• 金额: --
• 依托单位: III. Physikalisches Institut - Biophysik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2016
• 资助国家: 德国
• 起止时间: 2015-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/310431995?language=en
• 关键词: Human; Manipulation; Actions; Neurophysiological; Validation

Human action is composed of chunks that are smoothly joined to form a continuous signal. Correspondingly, human observers as well as computers can decompose (segment) actions into characteristic phases. However, it remains unclear whether or not these phases are representative and relevant for action processing in the brain. To address this problem, we seek to quantify action phases asking whether purely data-driven action segmentation by computers is convergent with signatures of natural action segmentation as measured by human brain responses and behavior. In this project we focus on human object manipulation. Based on earlier works we will use machine vision methods to extract intrinsic properties of manipulations given by changes in object relations and trajectories. This way we can segment manipulations in an objective way, in the sense of being independent of overt human behavioral judgments. The first goal is to describe a large set of manipulations and to verify the hypothesis that the here-suggested data-driven segmentation into phases leads to a characteristic manipulation description that is independent of the individual way in which a manipulation has been performed.We then will assess to what degree brain activity and human behavior are linked to the derived phase structure. We address both conventional, overt human segmentation as well as covert segmentation by brain activity. Functional MRI BOLD attenuation is measured to assess the brain's expectation and surprise (i.e. information processing) during ongoing action observation, whereas Multi-Voxel Pattern Analysis will help to determine whether trajectory and object orientation are used by the brain to classify actions in the way the computational approach suggests. Thus, the common aim of the planned work is to arrive at an objective description of actions, and to biologically validate this approach to the characterization of human object manipulations.

人类行为由多个块组成，这些块平滑地连接在一起形成连续的信号。相应地，人类观察者和计算机可以将动作分解（分割）为特征阶段。然而，目前尚不清楚这些阶段是否具有代表性并与大脑中的动作处理相关。为了解决这个问题，我们寻求量化动作阶段，询问计算机纯粹数据驱动的动作分割是否与通过人脑反应和行为测量的自然动作分割的特征收敛。在这个项目中，我们专注于人体物体操纵。基于早期的工作，我们将使用机器视觉方法来提取由对象关系和轨迹变化给出的操作的内在属性。这样我们就可以客观地分割操纵，即独立于明显的人类行为判断。第一个目标是描述大量操作，并验证以下假设：这里建议的数据驱动分段会产生独立于执行操作的单独方式的特征操作描述。然后将评估大脑活动和人类行为与导出的相位结构的关联程度。我们解决传统的、公开的人类分割以及通过大脑活动进行的隐蔽分割。功能性 MRI BOLD 衰减的测量是为了评估大脑在持续动作观察过程中的期望和惊喜（即信息处理），而多体素模式分析将有助于确定大脑是否使用轨迹和物体方向来对动作进行分类。计算方法表明。因此，计划工作的共同目标是对行为进行客观描述，并从生物学上验证这种表征人类对象操作的方法。