Impact of Cognitive Load on Human Trust in Hybrid Human-Robot Collaboration
Hao Guo, Bangan Wu, Qi Li, Zhen Ding, Feng Jiang, Chunzhi Yi

TL;DR
This study explores how cognitive load affects human trust in hybrid human-robot collaboration, revealing that higher cognitive load can increase trust and influence performance rewards, with implications for interface design.
Contribution
It provides novel insights into the impact of cognitive load on trust dynamics in hybrid collaboration, emphasizing the importance of task complexity and interdependence.
Findings
Higher cognitive load increases human trust in robots.
Performance rewards are higher under high cognitive load.
Trust correlates with failure risk in low and medium load tasks.
Abstract
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…
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Taxonomy
TopicsHuman-Automation Interaction and Safety
