II-EN: Laboratory for Research on Deep Structures of Collaboration

II-EN：协作深层结构研究实验室

• 批准号: 1205539
• 负责人: Laura Dabbish
• 金额: $ 50万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-07-01 至 2017-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1205539&HistoricalAwards=false
• 关键词: II; EN; Laboratory; Research; Deep

Our society has greater need than ever before for people to collaborate effectively, whether they be teams of employees, patients and their doctors, large virtual communities, neighborhoods or family groups. Collaboration is involved in many societal problems including energy use, law enforcement, disaster relief, and scientific research. Researchers in human-computer interaction and computer-supported cooperative work have an impressive record of developing technology to support interpersonal communication and group interaction in a range of problem areas such as medical team coordination, disruption of family schedules, multi-tasking and interruptions in the workplace, energy use in households, collaborative intelligence analysis, virtual organization, and collaborative learning. Despite such efforts, the deep structure of collaboration is not well understood. The goal of this project is to acquire instrumentation that will enable the PI and her colleagues to leverage advances in other disciplines such as neuroscience and engineering to advance our knowledge of how details of brain and behavior are related to the underlying meaning, processes, and outcomes of collaboration, that is, its deep structure.Intellectual MeritThe requested research infrastructure will allow future projects to include a focus on the fine details of human behavior, including brain responses, facial expressions, body positions, gestures, and movements. The PI argues that in-depth analysis of behavior is necessary for understanding the mechanics of face-to-face interaction, for identifying those aspects of face-to-face interaction that are key to successful remote collaboration, for increasing the benefits of social media online, and for programming intelligent collaborative systems. Availability for interaction in the home environment, for example, might be indicated by a combination of posture and facial expression; detailed knowledge of these indicators could then help us design systems that deliver notifications to families at appropriate times. Similarly, remote experts collaborating around shared visualizations may benefit from camera systems that understand when partners have a shared understanding, as evidenced by their facial expressions and movements. Human-robot interaction might be refined by a fine-grained analysis of human-human interactions such as handoffs and greetings. And home health care and remote health delivery could be improved with better feedback about patient emotion and cognition during and after consultations. The infrastructure to be acquired with this funding will provide a platform for exploring ways to achieve these goals.Broader ImpactThe ability to understand the deep structures underlying collaboration has the potential to radically transform science and engineering in general, by furthering our ability to analyze contributors to effective team performance across many domains, and by laying the foundation for new collaborative interfaces that support interaction through gesture, facial expression, eye gaze and brain activity. More specifically, there is a potential for direct broader impact in the domains of health and education. In the domain of health, the new techniques will enhance ongoing work at the PI's institution on chronic illness management and home-health care, supporting better collaboration between parents and children with chronic illnesses, and patients and therapists through high fidelity patient observation. In education, computer-supported and collaborative learning will advance by computer tutors understanding student attention and knowledge sharing in groups. The instrumentation will also have local impact in the CMU HCI Institute, allowing faculty and student projects to include a focus on understanding the dynamics of collaboration and on the development of new tools to enhance people's ability to collaborate in diverse settings.

我们的社会比以往任何时候都更需要人们进行有效的协作，无论他们是员工团队，患者和他们的医生，大型虚拟社区，社区还是家庭团体。 合作涉及许多社会问题，包括能源使用，执法，救灾和科学研究。 人机交互和计算机支持的合作工作的研究人员有一个令人印象深刻的记录，开发技术，以支持人际沟通和群体互动的一系列问题领域，如医疗团队协调，家庭时间表的中断，工作场所的多任务和中断，家庭能源使用，协作智能分析，虚拟组织和协作学习。 尽管作出了这些努力，但合作的深层结构并没有得到很好的理解。 该项目的目标是获得仪器，使PI和她的同事能够利用其他学科的进步，如神经科学和工程学，以推进我们对大脑和行为的细节如何与合作的潜在意义，过程和结果相关的知识，即，它的深层结构。智力优点所要求的研究基础设施将允许未来的项目包括对人类行为的精细细节的关注，包括大脑的反应，面部表情、身体姿势、手势和动作。 PI认为，深入的行为分析是必要的，以了解面对面互动的机制，识别那些方面的面对面互动是成功的远程协作的关键，增加社交媒体在线的好处，并编程智能协作系统。 例如，在家庭环境中进行交互的可用性可能通过姿势和面部表情的组合来表示;对这些指标的详细了解可以帮助我们设计在适当的时候向家庭发送通知的系统。 类似地，围绕共享的可视化进行协作的远程专家可以受益于相机系统，该相机系统理解合作伙伴何时具有共享的理解，如通过他们的面部表情和动作所证明的。 人机交互可以通过对诸如问候和问候之类的人机交互的细粒度分析来细化。 家庭医疗保健和远程医疗服务可以通过在咨询期间和之后更好地反馈患者的情绪和认知来改善。 通过这笔资金收购的基础设施将为探索实现这些目标的方法提供一个平台。更广泛的影响理解协作背后的深层结构的能力有可能从根本上改变科学和工程学，通过进一步提高我们在许多领域分析有效团队绩效贡献者的能力，并为新的协作界面奠定基础，这些界面支持通过手势、面部表情、眼睛注视和大脑活动进行交互。 更具体地说，在卫生和教育领域有可能产生更广泛的直接影响。 在健康领域，新技术将加强PI机构在慢性病管理和家庭保健方面正在进行的工作，通过高保真的患者观察支持父母和慢性病儿童以及患者和治疗师之间更好的合作。 在教育方面，计算机支持和协作学习将通过计算机教师了解学生的注意力和小组知识共享来推进。 该仪器也将在CMU HCI研究所产生当地影响，使教师和学生的项目包括重点了解协作的动态和新工具的开发，以提高人们在不同环境中协作的能力。