The goal of this study was to assess the performance and workload effects of applying adaptive automation (AA) to four stages of human-machine system information processing (information acquisition, information analysis, decision-making, and action implementation) and facilitating dynamic function allocations (DFAs) through two levels of computer authority (suggestion and mandate). The research was to provide insight into any interaction between these aspects of AA design. It was hypothesized that higher level automation, such as information analysis and decision making, would be more compatible with computer mandated allocations, while lower levels, such as information acquisition and action implementation, would be more effective under partial human control (computer suggestion and human veto). Results demonstrated that the effectiveness of AA is dependent upon both the type of automation presented to an operator and the type of invocation authority designed into the system. Performance with AA of information acquisition was superior to performance under decision automation. When using automated assistance, human acceptance of computer suggestions was superior to computer mandates. The results of this study may serve as an applicable guide for AA design in future complex systems.
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