Human Performance with Vocal Cueing of Automation State Changes in an Adaptive System

Abstract

Previous research on cueing of control mode changes in adaptive systems has focused on the use of visual cues with some work on complex auditory cues. Research has not explored the use of vocal cues for automation state changes and human performance implications. This study investigated vocal cues as feedback on adaptive robotic system states for supporting operator performance when responding to control mode transitions compared with visual (icons) and auditory (earcons) feedback. Thirty-two participants performed a virtual reality simulation of a telerobot-assisted underwater mine disposal task. Modal cues were presented with task phase changes and robot control mode changes. Cue type and complexity (length of messages) were varied between and within subjects, respectively. Operator performance was evaluated in terms of time-to-task completion, workload, and situation awareness. Results demonstrated vocal cues to be superior to simple visual icons and no cueing for performance. Earcons did not produce worse performance ratings than vocal cues for complex messages. The results of this study are applicable to the design of future automated systems.