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: 1029679
• 负责人: James Rehg
• 金额: $ 325.07万
• 依托单位: Georgia Tech Research Corporation
• 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 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1029679&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：社会和交际行为的建模、分析和可视化领导PI/机构：James M. Rehg，佐治亚理工学院这次考察将开发新的计算方法来测量和分析儿童和成人在面对面的社会互动中的行为。社会行为在儿童时期社会和沟通技能的习得中起着关键作用。患有发育障碍（如自闭症）的儿童在获得这些技能方面面临巨大挑战，从而导致重大的终生风险。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，不容易扩展到需要评估和治疗的大量个体。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，该项目将实现大规模的客观筛查，并更有效地提供和评估治疗。除了治疗疾病之外，这项技术还可以在很长一段时间内自动测量各种环境下大量个体的行为。许多学科，如教育、广告和客户关系，都可以从定量的、数据驱动的行为分析方法中受益。人类的行为本质上是多模态的，个体使用眼神、手势、面部表情、身体姿势和语音语调来传达参与和调节社会互动。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，以获得表达行为的全面、综合画像。照相机和麦克风提供了一种廉价的、非侵入性的方法来测量眼睛、面部和身体的运动，以及语言和非语言的话语。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相关的内部压力和觉醒水平的重要线索。该项目正在开发独特的功能，用于同步多个传感器流，将这些流关联起来以测量行为变量，如影响和注意力，并为两个或更多个体之间的扩展交互建模。此外，正在开发新的行为可视化方法，以便为干预措施提供实时决策支持，并有效利用行为数据存储库。还在开发向技术用户反映捕获和分析过程的方法。该项目的长期目标是创建一门新的计算行为科学学科，它从计算机科学和心理学中汲取知识，以改变人类行为的研究。一项全面的教育计划通过为年轻研究人员创建跨学科的暑期学校和开发计算行为方面的新课程来支持这一目标。外联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要和持续的合作。