CHS: Small: Guiding future design of affect-aware cyber-human systems through the investigation of human reactions to machine errors

CHS：小型：通过研究人类对机器错误的反应来指导情感感知网络人类系统的未来设计

• 批准号: 2007908
• 负责人: Vesna Novak
• 金额: $ 44.84万
• 依托单位: University of Wyoming
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2007908&HistoricalAwards=false
• 关键词: CHS; Small; Guiding; future; design

This project obtains new fundamental knowledge about how people react to errors made by affect-aware technologies. Such technologies analyze measurements such as heart rate, brain activity and body gestures to obtain an estimate of a user's mental and emotional state; they then take actions to improve the user's state - for example, by helping with a task. However, since the measurements are often hard to interpret, affect-aware technologies often make mistakes. Led by an interdisciplinary group of researchers in engineering and psychology, this project will examine how users react to and compensate for different types of errors made by affect-aware technologies. This will help guide future design of such technologies, as it will help researchers and developers identify what the minimal acceptable accuracy of an affect-aware device is and what types of errors most critically need to be reduced by developers. Results of the research will advance national health and well-being in many ways, as affect-aware technologies are becoming increasingly common for diverse applications such as detecting drowsiness in drivers, adaptive automation in flight and resource management, adaptation of learning material to students, and adaptation of rehabilitation exercises to patients. The team will develop new interdisciplinary courses in human factors and human-computer interaction, and will perform outreach about cyber-human systems to multiple groups including K-12 and community college students and teachers all around Wyoming.The project is structured as a series of four lab studies involving human subjects, all using a set of physiological sensors and the NASA Multi-Attribute Task Battery. As little is known about user reactions to machine errors in affect-aware cyber-human systems, the first three lab studies will systematically vary four critical characteristics: the accuracy with which they recognize the user's psychological state, the magnitude of the actions (changes to task difficulty) taken by the system, the impact that an error has on task performance, and the transparency of the system's decision-making process. The errors will be induced with a Wizard of Oz experiment design in which, unknown to the subject, the machine responses are actually simulated by a human operator. In this project, the user will be asked how they would like to change the difficulty of the Multi-Attribute Task Battery, and errors will be induced by doing the opposite of what the user wants. Users will be unaware of this manipulation, and will be told that the errors are actually due to poor signal processing and pattern recognition. The last study will then examine the trade-off between a system's state recognition accuracy and its user-friendliness with regard to user acceptance of the system. In all four studies, the outcome measures will be objective task performance as well as subjective user experience reported with the NASA Task Load Index and Intrinsic Motivation Inventory. This will provide the research community with detailed information about how different characteristics of affect-aware cyber-human systems influence both objective and subjective aspects of users' experiences with such systems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这个项目获得了关于人们如何应对情感感知技术所犯错误的新的基础知识。这类技术分析心率、大脑活动和身体姿势等测量数据，以获得对用户精神和情绪状态的估计；然后它们采取行动改善用户的状态--例如，通过帮助完成一项任务。然而，由于测量结果往往很难解释，感知影响的技术往往会出错。该项目由工程学和心理学领域的一个跨学科研究小组领导，将研究用户对情感感知技术所犯的不同类型错误的反应和补偿。这将有助于指导此类技术的未来设计，因为它将帮助研究人员和开发人员确定影响感知设备的最低可接受精度是什么，以及开发人员最关键需要减少哪些类型的错误。这项研究的结果将在许多方面促进国民健康和福祉，因为感知影响的技术正变得越来越普遍，用于不同的应用，如检测司机的昏昏欲睡，飞行和资源管理的自适应自动化，为学生调整学习材料，以及根据患者调整康复练习。该团队将开发关于人的因素和人机交互的新的跨学科课程，并将向包括K-12和怀俄明州各地的社区大学学生和教师在内的多个群体开展关于网络-人类系统的拓展。该项目由四个实验室研究组成，涉及人类受试者，所有这些研究都使用了一套生理传感器和NASA多属性任务组。由于人们对感知影响的网络人类系统中机器错误的用户反应知之甚少，前三项实验室研究将系统性地改变四个关键特征：他们识别用户心理状态的准确性、系统采取的行动(任务难度的变化)的幅度、错误对任务绩效的影响以及系统决策过程的透明度。错误将通过Oz实验设计向导来诱导，在该实验设计中，受试者未知的机器响应实际上是由操作员模拟的。在这个项目中，用户将被问到他们想要如何改变多属性任务电池的难度，如果做了与用户想要的相反的事情，就会导致错误。用户将不知道这种操作，并将被告知错误实际上是由于糟糕的信号处理和模式识别。最后一项研究将审查系统的状态识别准确性与用户对系统的接受程度之间的权衡。在所有四项研究中，结果衡量标准将是客观的任务表现以及NASA任务负荷指数和内在动机清单报告的主观用户体验。这将为研究界提供详细的信息，说明感知影响的网络人类系统的不同特征如何影响用户使用此类系统的客观和主观方面。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。