CAREER: The social representation of the physical world

想象一下，走进一间拥挤的会议室，发现一个空座位，然后意识到上面有一件夹克，从这个简单的物体排列，我们可能会怀疑有人在使用椅子，暂时离开，并打算返回。我们从周围的物理环境中提取社会信息的情况是司空见惯的：我们在入口处挂绳子，以表明人们不应该走过，我们在树上做标记以指示远足路线，我们让办公室的门半开着以表明我们有空。该项目将使用计算建模和行为研究的结合来了解人们如何从物理环境中读取社会信号。描述人们如何从他们的物理环境中提取社会信息对于理解人类如何无缝协调和相互合作至关重要，即使在没有直接沟通或物理互动的情况下。这些结果也对在机器中实例化人类社会智能的目标产生了影响。例如，机器人导航已经取得了显著的进步，但这些算法还没有表现出物理环境中的社会信息，将其视为缺乏社会意义。拟议的项目将回答三个问题：物理世界的哪些方面包含人类的社会信息？我们如何推断是什么产生了这些信息？我们如何确定它的含义？为了回答这些问题，研究者将联合收割机结合社会推理的计算模型和物理推理的计算模型的进展，以确定这两个认知系统如何相互作用。具体来说，研究人员将建立计算模型，共同推理人们的精神状态如何导致不同的行为，以及这些行为如何在环境中留下可观察到的痕迹。模型的行为将与来自人类参与者的数据进行比较，以确定模型可以捕获和复制人类推理的程度。计算工具的开发将与生产高质量的开源图书馆，广泛访问所有学生感兴趣的计算认知科学的并行目标。软件将辅以广泛的教程，展示如何实现各种经典模型，由研究人员与本科生和研究生合作创建。该奖项反映了NSF的法定使命，并已被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

Imagine walking into a crowded conference room, spotting an empty seat, then realizing there is a jacket on it. From this simple arrangement of objects, we might suspect that someone was using the chair, left temporarily, and intends to return. Situations where we extract social information from our physical surroundings are commonplace: we hang rope across entrances to signal people should not walk through, we mark trees to indicate hiking trails, and we leave our office doors ajar to reveal we are available. This project will use a combination of computational modeling and behavioral studies to understand how people read social signals from their physical environment. Characterizing how people extract social information from their physical surroundings is critical for understanding how humans seamlessly coordinate and cooperate with each other, even in the absence of direct communication or physical interactions. The results also have implications for the goal of instantiating human social intelligence in machines. For example, there have been remarkable advances in robot navigation, but these algorithms do not yet represent the social information in physical environments, treating them as devoid of social meaning.The proposed project will answer three questions: What aspects of the physical world contain social information for humans? How do we infer what produced this information? And how do we determine what it means? To answer these questions, the investigator will combine advances in computational models of social reasoning and computational models of physical reasoning to determine how these two cognitive systems interact with each other. Specifically, the investigator will build computational models that jointly reason about how people’s mental states lead to different behaviors and how these behaviors leave observable traces in the environment. The model’s behavior will be compared with data from human participants to determine the extent to which the model can capture and replicate human reasoning. Computational tools will be developed with the parallel goals of producing high-quality open-source libraries that are widely accessible to all students interested in computational cognitive science. Software will be complemented with extensive tutorials showing how to implement various classical models, created by the investigator in collaboration with undergraduate and graduate students.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.