Impact of Cognitive Load on Human Trust in Hybrid Human-Robot Collaboration

作     者：Guo, Hao Wu, Bangan Li, Qi Ding, Zhen Jiang, Feng Yi, Chunzhi 

作者机构：School of Computer Science and Technology Harbin Institute of Technology Harbin China School of Management Harbin Institute of Technology Harbin China School of Mechatronics Engineering Harbin Institute of Technology Harbin China College of Computer and Control Engineering Northeast Forestry University Harbin China School of Artificial Intelligence Nanjing University of Information Science & Technology Nanjing China School of Medicine and Health Harbin Institute of Technology Harbin China 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2024年

核心收录：

主　　题：Man machine systems 

摘      要：Human trust plays a crucial role in the effectiveness of human-robot collaboration. Despite its significance, the development and maintenance of an optimal trust level are obstructed by the complex nature of influencing factors and their mechanisms. This study investigates the effects of cognitive load on human trust within the context of a hybrid human-robot collaboration task. An experiment is conducted where the humans and the robot, acting as team members, collaboratively construct pyramids with differentiated levels of task complexity. Our findings reveal that cognitive load exerts diverse impacts on human trust in the robot. Notably, there is an increase in human trust under conditions of high cognitive load. Furthermore, the rewards for performance are substantially higher in tasks with high cognitive load compared to those with low cognitive load, and a significant correlation exists between human trust and the failure risk of performance in tasks with low and medium cognitive load. By integrating interdependent task steps, this research emphasizes the unique dynamics of hybrid human-robot collaboration scenarios. The insights gained not only contribute to understanding how cognitive load influences trust but also assist developers in optimizing collaborative target selection and designing more effective human-robot interfaces in such environments. Copyright © 2024, The Authors. All rights reserved.