Miller Linda, Kraus Johannes, Babel Franziska, Baumann Martin
Department Human Factors, Institute of Psychology and Education, Ulm University, Ulm, Germany.
Front Psychol. 2021 Apr 12;12:592711. doi: 10.3389/fpsyg.2021.592711. eCollection 2021.
With service robots becoming more ubiquitous in social life, interaction design needs to adapt to novice users and the associated uncertainty in the first encounter with this technology in new emerging environments. Trust in robots is an essential psychological prerequisite to achieve safe and convenient cooperation between users and robots. This research focuses on psychological processes in which user dispositions and states affect trust in robots, which in turn is expected to impact the behavior and reactions in the interaction with robotic systems. In a laboratory experiment, the influence of propensity to trust in automation and negative attitudes toward robots on state anxiety, trust, and comfort distance toward a robot were explored. Participants were approached by a humanoid domestic robot two times and indicated their comfort distance and trust. The results favor the differentiation and interdependence of dispositional, initial, and dynamic learned trust layers. A mediation from the propensity to trust to initial learned trust by state anxiety provides an insight into the psychological processes through which personality traits might affect interindividual outcomes in human-robot interaction (HRI). The findings underline the meaningfulness of user characteristics as predictors for the initial approach to robots and the importance of considering users' individual learning history regarding technology and robots in particular.
随着服务机器人在社会生活中变得越来越普遍，交互设计需要适应新手用户以及在新兴环境中首次接触这项技术时的相关不确定性。对机器人的信任是实现用户与机器人之间安全便捷合作的重要心理前提。本研究聚焦于用户性格和状态影响对机器人信任的心理过程，而这种信任反过来又预计会影响与机器人系统交互时的行为和反应。在一项实验室实验中，探讨了自动化信任倾向和对机器人的负面态度对状态焦虑、对机器人的信任以及舒适距离的影响。参与者两次接触家用类人机器人，并表明他们的舒适距离和信任程度。结果支持了性格、初始和动态习得信任层的差异和相互依存关系。从信任倾向到初始习得信任通过状态焦虑的中介作用，为个性特征可能影响人机交互（HRI）中个体间结果的心理过程提供了见解。研究结果强调了用户特征作为预测首次接近机器人的重要性，以及考虑用户特别是关于技术和机器人的个人学习历史的重要性。
