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Cognitive graphs: The geometry of spatial knowledge

认知图：空间知识的几何

基本信息

批准号：
1848903
负责人：
Elizabeth Chrastil
金额：
$ 46.13万
依托单位：
University of California-Irvine
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-08-15 至 2024-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1848903&HistoricalAwards=false
关键词：
Cognitive graphs geometry spatial knowledge

项目摘要

Being oriented in a new environment and finding and remembering the locations of resources are critical skills for human existence. When exploring a new city, tourists often wander through the streets, with the goal of visiting individual sites and taking in specific views. Through this wandering, they gain an understanding of the network of the city streets. When returning to their hotel, they are unlikely to retrace their steps. They also will not take a straight-line shortcut. Instead, they will draw on what they have learned about their environment to determine a path home using the city streets. How are tourists able to find their way back, based on experience alone, yet unable to determine a straight-line shortcut? This project addresses the challenging problem in the study of human navigation: What is the underlying geometric structure of our navigational knowledge? Is it truly a cognitive map, as is commonly assumed? Is it something coarser and more fluid? We propose that the underlying structure is a graph?a series of connections between places in a network, much like city streets. The overarching goal of this proposal is to test hypotheses about graph knowledge and its properties using walking virtual reality (VR) methods. Outcomes of this research have the potential to impact other fields, including robotics, neuroscience, mathematics, and geography. The interdisciplinary research proposed here will establish a deep understanding of the theory and cognitive mechanisms of spatial orientation, with far-reaching impacts. Greater knowledge of the basic properties of human navigational systems will lead to improved and more effective electronic navigation and GPS systems, self-driving vehicles, emergency response training, and transportation signage. Dr. Chrastil is committed to training the next generation of scientists and increasing the participation of underrepresented groups in STEM fields. As part of this proposal, Dr. Chrastil will develop a workshop on research methods for virtual reality to train researchers on this exciting technique. She actively participates as a teacher and mentor for programs that encourage participation of women and girls in science.This project specifically tests the hypothesis that the most likely structure of navigational knowledge is a labeled graph, which incorporates local metric information but is not globally consistent across the environment. We have previously conducted an initial test of the idea of a cognitive graph, demonstrating that graph spatial knowledge is used preferentially rather than simply learning routes between locations (Chrastil & Warren, 2014). However, a systematic test of labeled graph knowledge has not been conducted. Little is known about how graph knowledge is constructed during learning or whether labeled graphs are used across all individuals and situations. Here, we propose a framework detailing the theoretical contributors to graph knowledge. We will also test the relationship between graph knowledge and other spatial knowledge taxonomies. In a series of experiments, we will use fully-immersive virtual reality to test hypotheses regarding the nature of spatial knowledge. First, we will characterize the nature of cognitive graphs by pitting labeled graph knowledge against other types of spatial knowledge and testing a primary prediction of labeled graph knowledge. Next, we will test the constraints on labeled graph knowledge, such as generalizability across contexts, its relationship to other taxonomies, and spatial preference. Finally, we will examine the mechanisms of learning labeled graph knowledge, in particular the deployment of attention and active decision making. This research cuts across levels of analysis by contrasting local and global levels of spatial understanding and by pitting labeled graphs against other spatial systems. The results of the proposed work will provide critical insight into the fundamental formation of human spatial knowledge and will contribute to the fields of robotics and neuroscience.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.

在新的环境中寻找和记住资源的位置是人类生存的关键技能。当探索一个新的城市时，游客经常在街道上漫步，目的是参观各个景点并欣赏特定的景色。通过这种漫游，他们了解了城市街道的网络。当他们回到酒店时，他们不太可能原路返回。他们也不会走直线捷径。相反，他们将利用他们对环境的了解来确定一条使用城市街道回家的道路。旅游者如何能凭经验找到回去的路，却无法确定一条直线捷径？该项目解决了人类导航研究中的挑战性问题：我们导航知识的基本几何结构是什么？它真的像人们通常认为的那样是一幅认知地图吗？是更粗糙更流畅的东西吗？我们建议，底层结构是一个图？网络中各个地方之间的一系列联系，就像城市街道一样。该提案的总体目标是使用步行虚拟现实（VR）方法来测试关于图知识及其属性的假设。这项研究的结果有可能影响其他领域，包括机器人技术，神经科学，数学和地理。本文提出的跨学科研究将对空间定向的理论和认知机制建立深刻的理解，具有深远的影响。更多地了解人类导航系统的基本特性将导致改进和更有效的电子导航和GPS系统，自动驾驶车辆，应急响应培训和交通标志。Chrastil博士致力于培养下一代科学家，并增加STEM领域代表性不足的群体的参与。作为该提案的一部分，Chrastil博士将开发一个关于虚拟现实研究方法的研讨会，以培训研究人员掌握这项令人兴奋的技术。她积极参与作为教师和导师的计划，鼓励妇女和女孩在science.This项目的参与专门测试的假设，最有可能的导航知识结构是一个标记的图，其中包含本地度量信息，但不是全球一致的整个环境。我们之前已经对认知图的概念进行了初步测试，证明了图形空间知识优先使用，而不是简单地学习位置之间的路线（Chrastil Warren，2014）。然而，标记的图形知识的系统测试还没有进行。关于图知识在学习过程中是如何构建的，或者标记图是否在所有个人和情况下使用，我们知之甚少。在这里，我们提出了一个框架，详细介绍了理论贡献者的图形知识。我们还将测试图形知识和其他空间知识分类之间的关系。在一系列实验中，我们将使用完全沉浸式虚拟现实来测试关于空间知识性质的假设。首先，我们将通过将标记图知识与其他类型的空间知识进行对比并测试标记图知识的主要预测来表征认知图的性质。接下来，我们将测试标记图知识的约束，例如跨上下文的泛化能力，它与其他分类法的关系以及空间偏好。最后，我们将研究学习标记图知识的机制，特别是注意力的部署和主动决策。本研究通过对比局部和全局空间理解水平以及将标记图与其他空间系统进行对比来跨越分析水平。拟议工作的结果将为人类空间知识的基本形成提供重要的见解，并将有助于机器人和神经科学领域。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。