A Comparison of Auditory and Visual Representations of System Confidence to Support Trust Specificity, Attention Management, and Joint Performance in Human-Machine Teams

Abstract

Breakdowns in human-robot teaming can result from trust miscalibration, i.e., a poor mapping of trust to a system’s capabilities, resulting in misuse or disuse of the technology. Trust miscalibration also negatively affects operators’ top-down attention allocation and monitoring of the system. This experiment assessed the efficacy of visual and auditory representations of a system’s confidence in its own abilities for supporting trust specificity, attention management and joint performance in the context of a UAV-supported target detection task. In contrast to earlier studies, neither visual nor auditory confidence information improved detection accuracy. Visual representations of confidence led to slower response times than auditory representations, likely due to resource competition with the visual target detection task. Finally, slower response times were observed when a UAV incorrectly detected a target. Results from this study can inform the design of visual and auditory representations of system confidence in human-machine teams with high attention demands.