SoCS: Modeling Agency and Intentions in Dynamic Environments as a Precursor to Efficient Human-Computer Interaction

人们能够识别戏剧性的场景，并将角色和意图归因于感知到的角色，即使是在非常简单的线索面前。 任何一个卡通观众都可以证明，两个动画形状足以描述一个场景，涉及温柔的恋人，残酷的欺凌，紧张的对抗和令人毛骨悚然的逃生。这些关于能动性和意向性的基本概念是我们对世界的社会认知的基础。 它们提供了主体和客体之间的第一个区别，描绘了世界的哪些元素可以以目标为导向的目的移动，并提供了描述因果关系的原始结构。 广泛的实验室实验已经描述了许多基本属性，这些属性在受控刺激下产生这些感知。 然而，只有有限的尝试来量化这些过程，并没有尝试看看这些相同的属性是否适用于现实世界的活动模式。这个项目模拟了我们人类在动态现实世界环境中感知代理，意图和目标导向行为的能力。 使用现成的实时定位系统，人们和物体的运动被记录下来，因为他们从事非结构化的活动和分阶段的群体游戏。 利用这些经验数据和从心理物理学数据中得出的理论，构建了量化，解释和预测现实世界的社会和目标导向行为的计算模型。 这项工作的好处包括：（1）行为研究中使用的建模工具，（2）心理物理学研究的现实世界基础，以及（3）社会和故意行为的计算模型，将增强人机和人机界面。

People recognize dramatic situations and attribute roles and intentions to perceived characters, even when presented with extremely simple cues. As any cartoon viewer can attest, two animated shapes are sufficient to describe a scene involving tender lovers, brutal bullies, tense confrontations and hair-raising escapes. These basic notions of agency and intentionality are foundational to our social perception of the world. They provide the first discriminations between agents and objects, delineate which elements of the world can move with goal-directed purpose, and provide the primitive structure for describing cause and effect. Extensive laboratory experiments have described many of the basic properties that produce these perceptions on controlled stimuli. However there have been only limited attempts to quantify these processes and no attempts to see if these same properties hold on real-world activity patterns.This project models our human ability to perceive agency, intentionality, and goal-directed behavior in dynamic real-world environments. Using off-the-shelf real-time localization systems, the movements of people and objects are recorded as they engage in unstructured activity and staged group games. Drawing on both this empirical data and theories drawn from the psychophysical data, computational models are constructed that quantify, explain, and predict real-world social and goal-directed behavior. The benefits of this work include: (1) modeling tools for use within behavioral studies, (2) a real-world grounding for psychophysical studies, and (3) a computational model of social and intentional behavior that would enhance human-computer and human-robot interfaces.