Contextual Influences on the Category Construction of Geographic Scale Movement Patterns

我们生活的世界在所有尺度上都是动态的。在小范围内，想象穿过一个房间或开车去杂货店;在大范围内，想象飓风穿过一个州。因此，研究人类如何理解空间和时间之间的关系不仅是地理学的核心，也是认知科学和信息科学的核心。该项目的目标是双重的：开发一个研究框架，研究如何在地理尺度（MPGS）的运动模式的理解，并评估如何在地理信息科学中使用的形式主义能够捕捉人们如何理解MPGS。将认知和形式表征联系起来，使模型能够与人们对大规模空间过程的思考方式保持一致，从而增强人类和计算机之间的交流。宾夕法尼亚州立大学的亚历山大·克利佩尔博士将进行一系列实验，这些实验基于一种用于引出概念知识的分组范式（即，分类）。 参与者观看在不同时空阶段结束的大规模空间现象的动画。 例如，参与者将观看63个不同的动画，其中包括飓风接近海岸，登陆，最后向内陆移动。 然后，参与者将根据它们的相似性对这些动画进行分类。这些研究试图回答的问题是，跨不同领域的正式的、拓扑等价的表征（例如，无论移动的实体是飓风还是冰川）在认知上也是等同的。换句话说，动态特征的语义如何影响其认知概念化？语义是否改变个体拓扑关系的认知显著性？此外，该项目将解决规模效应的地理关键问题，并通过将运动模式的概念化与语言外化联系起来，为地理事件语言的正式基础做出贡献。为了实现这些目标，PI开发了软件来有效地收集和分析行为数据。 分析数据的软件将在宾夕法尼亚州立大学地理系GeoVISTA中心开发的现有地理视觉分析软件中开发，该项目将有助于从运动模式的形式特征的角度理解人类如何在地理尺度上概念化运动模式，例如飓风穿越半岛。连接时空信息的形式和认知表征对于开发高效的人机界面和空间认知模型至关重要。该项目将开发软件解决方案，用于实验设计和使用可视化分析方法对收集的数据进行有效分析。由于认知概念化是本体论和分类研究的核心，也是人类学等领域的核心，我们希望开发的研究框架和软件解决方案对大型研究社区有益。

The world we live in is dynamic on all scales. On a small scale, think of walking across a room or driving to the grocery store; on a large scale, imagine a hurricane crossing a state. Research that addresses how humans understand relationships between space and time is, therefore, central not only to geography, but also to cognitive and information sciences. The objectives of this project are two-fold: to develop a research framework for examining how movement patterns at the geographic scale (MPGS) are understood, and to evaluate how formalisms used in geographic information science are able to capture how people understand MPGS. Linking cognitive and formal characterizations enables models that align with how people think about large scale spatial processes, thereby enhancing communication at the interface between humans and computers. Dr. Alexander Klippel at the Pennsylvania State University will conduct a set of experiments that are based on a grouping paradigm that is used to elicit conceptual knowledge (i.e., categorization). Participants view animations of large-scale spatial phenomena ending at different spatio-temporal stages. For example, participants will look at 63 different animations of a hurricane approaching shore, making landfall, and finally moving far inland. Participants will then classify these animations based on their similarities. The question these studies are attempting to answer is whether formal, topologically equivalent characterizations across different domains (e.g., whether the moving entity is a hurricane or a glacier) are also equivalent cognitively. In other words, how does the semantics of dynamic features influence the cognitive conceptualizations thereof? Does semantics change the cognitive salience of individual topological relations? Additionally, the project will address the geographically critical question of scale effects and a contribution will be made to the formal underpinning of geographic event language by relating the conceptualizations of movement patterns to linguistic externalizations. To accomplish these goals, the PI has developed software to both collect and analyze behavioral data efficiently. The software to analyze the data will be developed within existing geovisual analytics software developed at the GeoVISTA Center in the Geography Department at the Pennsylvania State University.This project will contribute to the understanding of how humans conceptualize movement patterns at the geographic scale, such as hurricanes moving across a peninsula, from the perspective of how these movement patterns are characterized formally. Connecting formal and cognitive characterizations of spatio-temporal information is essential to develop efficient human-computer interfaces and models of spatial cognition. This project will develop software solutions for the design of experiments and the efficient analysis of the collected data using visual analytics approaches. As cognitive conceptualizations are core to research on ontologies and categorization, and also to fields such as anthropology, we expect both the developed research framework and the software solutions to be beneficial to a large research community.