SGER: Extending Working Memory Functions by Presenting Bookmark and Result Sets as Temporal Visual Compositions

工作记忆是一种参与解决问题和做出决策的人类认知机制，已知其具有7 +/-2个单位的有限容量，其中每个单位的复杂性可以从单个二进制数字变化到对应于心理模型的组块。 研究进一步表明，人们对标记图像的反应比对没有伴随图像的文本元素的反应更好地形成心理模型。 信息时代是收集的时代。人们收集网页书签、文章、电子邮件、音频和视频。 数字图书馆收集文本和多媒体。 搜索引擎动态创建集合。 集合由记录集组成，通常以超链接列表的形式呈现，在大多数情况下是文本元素列表。依赖信息的任务，如买车或写研究论文，需要用户在元素之间建立概念联系。 我们需要发现如何表示信息元素的集合，以解释工作记忆的局限性，促进心理模型的形成，并支持用户任务。 构图是一种形成概念相关元素块的视觉技术。 在导航超媒体组合中，每个视觉信息元素充当到信息资源的导航链接。 PI的假设是，将书签和结果集呈现为标记的图像元素的组合将减轻对用户的工作记忆的要求。 PI的长期目标是发展对信息寻求的认知机制的理解，以便他们能够构建工具，使用户能够以支持知识构建所涉及的认知结构和过程的形式开发收藏。在该项目中，PI将收集初步数据，以确定通过将书签和结果集集合呈现为视觉组合来扩展工作记忆功能。 具体而言，它们将：通过发散和收敛思维任务评估书签和结果集表示;通过呈现带有标记图像的集合来支持心智模型的形成;通过将集合呈现为视觉混合标记图像的组合来扩展工作记忆容量。 为了达到这些目的，他们将利用认知科学的发现，以及信息检索，计算机图形学，视觉艺术和设计的技术。更广泛的影响：目前的方法来呈现书签和结果集作为文本列表不优化我们的能力，形成心理模型，并保持在工作记忆中。 学术界、政府和工业界的信息工作者和学生经常需要在解决问题、发现和写作等知识构建活动中连接多个想法来源。 由于信息工作者花费大量时间处理书签和结果集，结果是在整个知识经济中浪费了智力。 通过以优化心理模型和工作记忆能力的形式呈现集合，PI希望提高生产力并刺激广大互联网用户的创新。

Working memory is a human cognitive mechanism that is involved in solving problems and making decisions, which is known to have a limited capacity of 7 +/- 2 units, where the complexity of each unit can vary from a single binary digit to a chunk that corresponds to a mental model. It has further been shown that people form better mental models in response to labeled images than they do in response to textual elements without accompanying images. The information age is the age of collecting. People collect web page bookmarks, articles, email, audio, and video. Digital libraries collect texts and multimedia. Search engines create collections dynamically. Collections are comprised of sets of records, and are typically presented in the form of lists of hyperlinks, in most cases lists of textual elements. Information dependent tasks, such as buying a car or writing a research paper require users to make conceptual connections between elements. We need to discover how to represent collections of information elements to account for the limitations of working memory, promote the formation of mental models, and support user tasks. Composition is a visual technique for forming chunks of conceptually related elements. In a navigational hypermedia composition, each visual information element functions as a navigational link to an information resource. The PIs' hypothesis is that presenting bookmark and result sets as compositions of labeled image elements will mitigate the demands placed upon the user's working memory. The PIs' long-term goal is to develop an understanding of the cognitive mechanisms of information seeking, so that they will be able to build tools that enable users to develop collections in forms that support the cognitive structures and processes involved in knowledge building. In this project, the PIs will gather preliminary data to establish that working memory function is extended by presenting bookmark and result set collections as visual compositions. Specifically, they will: evaluate bookmark and result set representations with divergent as well as convergent thinking tasks; support the formation of mental models by presenting collections with labeled images; and extend working memory capacity by presenting collections as compositions of visually blended labeled images. To these ends, they will take advantage of findings from cognitive science, and techniques from information retrieval, computer graphics, visual art, and design.Broader Impacts: Current approaches to presenting bookmark and result sets as textual lists do not optimize our ability to form mental models, and maintain them in working memory. Information workers and students throughout academia, government, and industry often need to connect multiple idea sources, in the course of knowledge building activities such as problem solving, discovery, and writing. Because information workers spend so much time dealing with bookmark and result sets, the result is a waste of intellectual capacity across the knowledge economy. By presenting collections in forms that optimize use of mental model and working memory capabilities, the PIs expect to improve productivity and spur innovation for a broad population of internet users.