Comparison of Performance Effects of Adaptive Automation Applied to Various Stages of Human-Machine System Information Processing

Abstract

Limitations in automation (expert system) capabilities and negative human performance consequences of automation in complex systems have led to the contention that use of computer assistance in high-level human-machine system information processing may be inappropriate. Adaptive automation (AA) has been explored as a solution to these problems; however, research has focused on the performance effects of dynamic control allocations of early sensory and information acquisition functions between human operators and computer controllers of complex systems. It has examined to a limited extent the human performance and workload effects of AA of cognitive tasks, such as decision-making, or of psychomotor functions such as response execution. This research compared the affects of AA applied to psychomotor tasks and cognitive tasks, including information monitoring, information analysis, decision-making, and action implementation, on overall human-machine system performance. Results demonstrated that operators are better able to adapt to AA when applied to lower level functions, such as information acquisition and action implementation, as compared to AA of information analysis and decision making tasks. The results also provided support for the use of AA, as compared to completely manual control.