On the process of automation transition in multitask human-machine systems

This paper examined the effects of different forms of automation invocation, the reconfiguration of the task display when, automated, and the influence of the specific modality that warned of the manual/automation transition on operator performance. Thirty-two experienced pilots engaged in a multiple-task situation consisting of tracking, monitoring, and fuel management subtasks, representative of typical aviation demands. Automation of the tracking task could be invoked in four different ways: 1) system-initiated automation (SIA); 2) pilot command by negation (PCN); 3) pilot command by initiation (PCI); and 4) pilotinitiated automation (PIA). Pilots were warned of the mode change between manual and automated controls by either a visual, an aural, or a combined visual and aural cue. The display of the subtask while in automation was reduced in size and placed either in a central or peripheral location. Results indicated that SIA had a differential effect on tracking performance as compared to all other forms of automation invocation. The respective location of the automated display had it effects in the fuel management subtask, whereas monitoring capability remained stable across manipulations. A significant three-way interaction between invocation procedure, display location, and warning modality illustrated the selective disadvantage of the visual warning combined with the central location under the PCI procedure on tracking response. Measures of -subjective response suggested that visual warning modality was slightly more taxing than either an auditory or a combined auditory Ind visual warning. Pilots also experienced elevated fatigue when the system initiated the automation. These results confirm that both performance and subjective perception of multitask demand are greater when the system controls the option to automate. A qualitative model is presented, which provides an approach for the integrated assessment of human performance with adaptive systems.