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: 2151464
• 负责人: Vesna Novak
• 金额: $ 44.84万
• 依托单位: University of Cincinnati Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2151464&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.

该项目获得了有关人们如何对情感感知技术造成的错误做出反应的新基本知识。这样的技术分析了诸如心率，大脑活动和身体手势之类的测量值，以获得对用户心理和情感状态的估计；然后，他们采取行动以改善用户状态 - 例如，通过帮助完成任务。但是，由于通常难以解释测量值，因此感知感知的技术通常会犯错误。在工程和心理学研究人员的跨学科研究人员的带领下，该项目将研究用户如何对情感感知技术造成的不同类型的错误做出反应并弥补。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感动感知设备的最低可接受准确性以及开发人员最需要减少最严重的错误类型。这项研究的结果将在许多方面促进民族健康和福祉，因为情感感知的技术在多种应用中变得越来越普遍，例如检测驾驶员的嗜睡，飞行和资源管理的适应性自动化，适应学生的学习材料以及适应学生的康复运动。 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.对于用户对感性感知到的网络人类系统中的机器错误的反应知之甚少，前三个实验室研究将系统地改变四个关键特征：他们认识到用户心理状态的准确性，系统所采取的动作的幅度（变化对任务难度），错误对任务绩效的影响以及系统的交易过程的影响以及系统的决策过程。 OZ实验设计的向导将引起错误，其中该主题未知，机器响应实际上是由人类操作员模拟的。在此项目中，将询问用户如何改变多属性任务电池的难度，并且通过与用户想要的相反的方式引起错误。用户将不知道这种操作，并被告知这些错误实际上是由于信号处理和模式识别率较差。然后，最后一项研究将研究系统的状态识别准确性与其用户对系统的接受度之间的权衡。在所有四项研究中，结果指标将是客观的任务绩效以及NASA任务负载指数和内在动机清单报告的主观用户体验。这将为研究社区提供有关情感感知到的网络人类系统的不同特征的详细信息，这些系统如何影响用户使用此类系统的经验的客观和主观方面。这项奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响审查标准来评估通过评估来支持的。