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.

人类的行动是由平稳连接的块组成的，形成一个连续的信号。相应地，人类观察者以及计算机可以将动作分解(分段)为特征阶段。然而，目前还不清楚这些阶段是否具有代表性，是否与大脑中的动作处理相关。为了解决这个问题，我们试图量化动作阶段，询问由计算机进行的纯数据驱动的动作分割是否与通过人脑反应和行为衡量的自然动作分割的签名收敛。在这个项目中，我们主要关注人类对象的操作。在前人工作的基础上，我们将使用机器视觉方法来提取对象关系和轨迹变化所给出的操作的内在属性。通过这种方式，我们可以以一种客观的方式分割操作，从独立于公开的人类行为判断的意义上说。第一个目标是描述一大组操作，并验证这样一个假设，即这里建议的数据驱动的阶段分割导致独立于操作执行的个体方式的特征操作描述。然后，我们将评估大脑活动和人类行为与导出的阶段结构的联系程度。我们既解决了传统的、公开的人体分割问题，也解决了大脑活动的隐蔽分割问题。功能磁共振成像BOLD衰减的测量是为了评估大脑在正在进行的动作观察过程中的预期和惊喜(即信息处理)，而多体素模式分析将有助于确定大脑是否按照计算方法所建议的方式使用轨迹和对象方向来分类动作。因此，计划工作的共同目标是达到对行为的客观描述，并从生物学上验证这种方法对人类对象操纵的表征。