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

我们的社会比以往任何时候都更需要人们有效地合作，无论他们是员工团队、病人和医生、大型虚拟社区、邻里还是家庭团体。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协同工作领域的研究人员在开发技术以支持人际沟通和群体交互方面取得了令人印象深刻的记录，这些技术用于解决一系列问题领域，如医疗团队协调、家庭日程的中断、工作场所的多任务和中断、家庭的能源使用、协作智能分析、虚拟组织和协作学习。尽管做出了这样的努力，但人们对合作的深层结构并没有很好地理解。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，来推进我们对大脑和行为细节如何与合作的潜在意义、过程和结果相关的知识，也就是说，它的深层结构。所要求的研究基础设施将使未来的项目包括关注人类行为的细节，包括大脑反应、面部表情、身体姿势、手势和动作。PI认为，深入的行为分析对于理解面对面互动的机制，识别面对面互动的那些方面是成功远程协作的关键，对于增加在线社交媒体的好处，以及为智能协作系统编程是必要的。例如，家庭环境中互动的可用性可以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时间向家庭发送通知的系统。同样，远程专家围绕共享可视化进行协作，可能会受益于摄像头系统，该系统可以通过合作伙伴的面部表情和动作来了解他们是否有共同的理解。人机交互可以通过对人机交互（如交接和问候）的细粒度分析来改进。在会诊期间和会诊后，更好地反馈患者的情绪和认知，可以改善家庭医疗保健和远程医疗服务。这笔资金将获得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深层结构的能力有可能从根本上改变科学和工程，通过进一步提高我们分析跨多个领域有效团队绩效的贡献者的能力，并通过为新的协作界面奠定基础，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，在保健和教育领域有可能产生更广泛的直接影响。在卫生领域，这些新技术将加强私人计划所在机构正在进行的慢性病管理和家庭保健工作，通过对患者的高保真观察，支持慢性病患者与父母、患者与治疗师之间更好地合作。在教育方面，计算机支持和协作学习将通过计算机导师了解学生的注意力和小组知识共享而得到推进。该仪器还将对CMU HCI研究所产生本地影响，使教师和学生的项目包括关注理解合作的动态和开发新工具，以提高人们在不同环境下的合作能力。

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.