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.

该项目获得了关于人们如何对影响感知技术所犯错误做出反应的新的基础知识。这些技术分析心率、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;然后采取行动改善用户的状态-例如，通过帮助完成任务。然而，由于测量结果通常很难解释，影响感知技术经常出错。该项目由工程学和心理学的跨学科研究人员领导，将研究用户如何对影响感知技术所造成的不同类型的错误做出反应和补偿。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，以及开发人员最需要减少哪些类型的错误。研究结果将在许多方面促进国民健康和福祉，因为影响感知技术在各种应用中变得越来越普遍，例如检测驾驶员的困倦，飞行和资源管理中的自适应自动化，学习材料的适应学生，以及康复练习的适应患者。该团队将开发人的因素和人机交互方面的新的跨学科课程，并将对多个群体进行关于网络人类系统的推广，包括K-12和社区大学的学生和教师，遍布怀俄明州。该项目的结构是一系列的四个实验室研究，涉及人类受试者，所有使用一套生理传感器和NASA多属性任务电池。由于对用户对情感感知网络人类系统中机器错误的反应知之甚少，前三项实验室研究将系统地改变四个关键特征：他们识别用户心理状态的准确性，系统采取的行动的大小（任务难度的变化），错误对任务性能的影响，以及系统决策过程的透明度。这些错误将由一个绿野仙踪实验设计引起，在这个实验中，受试者不知道机器的反应实际上是由人类操作员模拟的。在这个项目中，用户将被问到他们想如何改变多属性任务组合的难度，错误将通过做用户想要的相反的事情来诱导。用户将不知道这种操作，并将被告知错误实际上是由于信号处理和模式识别不良。最后一项研究将探讨系统的状态识别精度和用户友好性之间的权衡，用户接受的系统。在所有四项研究中，结果测量将是客观的任务表现以及主观的用户体验报告与美国宇航局的任务负荷指数和内在动机清单。这将为研究界提供有关影响感知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的详细信息。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。