Collaborative Research: Computational Behavioral Science: Modeling, Analysis, and Visualization of Social and Communicative Behavior

合作研究：计算行为科学：社交和交流行为的建模、分析和可视化

• 批准号: 1029035
• 负责人: David Forsyth
• 金额: $ 150万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1029035&HistoricalAwards=false
• 关键词: Collaborative; Research; Computational; Behavioral; Science

Computational Behavioral Science: Modeling, Analysis, and Visualization of Social and Communicative BehaviorLead PI/Institution: James M. Rehg, Georgia Institute of TechnologyThis Expedition will develop novel computational methods for measuring and analyzing the behavior of children and adults during face-to-face social interactions. Social behavior plays a key role in the acquisition of social and communicative skills during childhood. Children with developmental disorders, such as autism, face great challenges in acquiring these skills, resulting in substantial lifetime risks. Current best practices for evaluating behavior and assessing risk are based on direct observation by highly-trained specialists, and cannot be easily scaled to the large number of individuals who need evaluation and treatment. For example, autism affects 1 in 110 children in the U.S., with a lifetime cost of care of $3.2 million per person. By developing methods to automatically collect fine-grained behavioral data, this project will enable large-scale objective screening and more effective delivery and assessment of therapy. Going beyond the treatment of disorders, this technology will make it possible to automatically measure behavior over long periods of time for large numbers of individuals in a wide range of settings. Many disciplines, such as education, advertising, and customer relations, could benefit from a quantitative, data-drive approach to behavioral analysis. Human behavior is inherently multi-modal, and individuals use eye gaze, hand gestures, facial expressions, body posture, and tone of voice along with speech to convey engagement and regulate social interactions. This project will develop multiple sensing technologies, including vision, speech, and wearable sensors, to obtain a comprehensive, integrated portrait of expressed behavior. Cameras and microphones provide an inexpensive, noninvasive means for measuring eye, face, and body movements along with speech and nonspeech utterances. Wearable sensors can measure physiological variables such as heart-rate and skin conductivity, which contain important cues about levels of internal stress and arousal that are linked to expressed behavior. This project is developing unique capabilities for synchronizing multiple sensor streams, correlating these streams to measure behavioral variables such as affect and attention, and modeling extended interactions between two or more individuals. In addition, novel behavior visualization methods are being developed to enable real-time decision support for interventions and the effective use of repositories of behavioral data. Methods are also under development for reflecting the capture and analysis process to users of the technology.The long-term goal of this project is the creation of a new scientific discipline of computational behavioral science, which draws equally from computer science and psychology in order to transform the study of human behavior. A comprehensive education plan supports this goal through the creation of an interdisciplinary summer school for young researchers and the development of new courses in computational behavior. Outreach activities include significant and on-going collaborations with major autism research centers in Atlanta, Boston, Pittsburgh, Urbana-Champaign, and Los Angeles.

计算行为科学：社交和交流行为的建模、分析和可视化领导派/研究所：佐治亚理工学院的詹姆斯·M·雷格此次考察将开发新的计算方法，用于测量和分析儿童和成人在面对面社交互动中的行为。社会行为在儿童时期获得社交和沟通技能方面起着关键作用。患有发育障碍的儿童，如自闭症，在获得这些技能方面面临着巨大的挑战，导致巨大的终生风险。目前评估行为和评估风险的最佳做法是基于训练有素的专家的直接观察，不能轻易推广到需要评估和治疗的大量个人。例如，在美国，每110名儿童中就有1名患有自闭症，每人一生的护理费用为320万美元。通过开发自动收集细粒度行为数据的方法，该项目将能够进行大规模的客观筛查，并更有效地提供和评估治疗。这项技术超越了对疾病的治疗，将使在广泛的环境中自动测量大量个人在长时间内的行为成为可能。许多学科，如教育、广告和客户关系，都可以从行为分析的量化、数据驱动方法中受益。人类的行为本质上是多模式的，个体使用眼睛凝视、手势、面部表情、身体姿势和语气以及言语来传达参与和调节社交互动。该项目将开发多种传感技术，包括视觉、语音和可穿戴传感器，以获得对表达行为的全面、完整的描述。摄像头和麦克风为测量眼睛、面部和身体运动以及语音和非语音话语提供了一种廉价、非侵入性的手段。可穿戴传感器可以测量心率和皮肤电导率等生理变量，这些变量包含与表达行为有关的内部压力和唤醒水平的重要线索。该项目正在开发独特的功能，用于同步多个传感器流，将这些流关联起来测量行为变量，如情感和注意力，并对两个或更多个人之间的扩展交互进行建模。此外，正在开发新的行为可视化方法，以实现对干预措施的实时决策支持，并有效利用行为数据库。向技术使用者反映捕获和分析过程的方法也在开发中。该项目的长期目标是创建一门新的计算行为科学学科，它同样借鉴计算机科学和心理学，以改变对人类行为的研究。一项全面的教育计划通过为年轻研究人员创建跨学科暑期学校和开发计算行为方面的新课程来支持这一目标。外展活动包括与亚特兰大、波士顿、匹兹堡、厄巴纳-香槟和洛杉矶的主要自闭症研究中心进行重大和持续的合作。