People Dynamically Update Trust When Interactively Teaching Robots

IF 4.2 Q2 ROBOTICS ACM Transactions on Human-Robot Interaction Pub Date : 2023-03-13 DOI:10.1145/3568162.3576962

V. B. Chi, B. Malle

引用次数: 5

Abstract

Human-robot trust research often measures people's trust in robots in individual scenarios. However, humans may update their trust dynamically as they continuously interact with a robot. In a well-powered study (n = 220), we investigate the trust updating process across a 15-trial interaction. In a novel paradigm, participants act in the role of teacher to a simulated robot on a smartphone-based platform, and we assess trust at multiple levels (momentary trust feelings, perceptions of trustworthiness, and intended reliance). Results reveal that people are highly sensitive to the robot's learning progress trial by trial: they take into account both previous-task performance, current-task difficulty, and cumulative learning across training. More integrative perceptions of robot trustworthiness steadily grow as people gather more evidence from observing robot performance, especially of faster-learning robots. Intended reliance on the robot in novel tasks increased only for faster-learning robots.

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交互式教学机器人时，人们动态更新信任

人-机器人信任研究经常测量人们在个别场景下对机器人的信任。然而，人类可能会随着与机器人的不断互动而动态地更新他们的信任。在一项强有力的研究(n = 220)中，我们调查了15个试验交互中的信任更新过程。在一个新颖的范例中，参与者在基于智能手机的平台上扮演模拟机器人的老师角色，我们从多个层面评估信任(瞬间信任感觉、可信度感知和预期依赖)。结果表明，人们对机器人一次又一次的学习进度非常敏感:他们会考虑之前任务的表现、当前任务的难度以及整个训练过程中的累积学习。随着人们从观察机器人的表现，尤其是学习速度更快的机器人中收集到越来越多的证据，人们对机器人可信度的综合认知也在稳步增长。只有学习速度更快的机器人在完成新任务时才会增加对机器人的预期依赖。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

ACM Transactions on Human-Robot Interaction Computer Science-Artificial Intelligence

CiteScore

7.70

自引率

5.90%

发文量

期刊介绍： ACM Transactions on Human-Robot Interaction (THRI) is a prestigious Gold Open Access journal that aspires to lead the field of human-robot interaction as a top-tier, peer-reviewed, interdisciplinary publication. The journal prioritizes articles that significantly contribute to the current state of the art, enhance overall knowledge, have a broad appeal, and are accessible to a diverse audience. Submissions are expected to meet a high scholarly standard, and authors are encouraged to ensure their research is well-presented, advancing the understanding of human-robot interaction, adding cutting-edge or general insights to the field, or challenging current perspectives in this research domain. THRI warmly invites well-crafted paper submissions from a variety of disciplines, encompassing robotics, computer science, engineering, design, and the behavioral and social sciences. The scholarly articles published in THRI may cover a range of topics such as the nature of human interactions with robots and robotic technologies, methods to enhance or enable novel forms of interaction, and the societal or organizational impacts of these interactions. The editorial team is also keen on receiving proposals for special issues that focus on specific technical challenges or that apply human-robot interaction research to further areas like social computing, consumer behavior, health, and education.

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