Comparing attention theories utilizing static and dynamic function allocation methods operationalized with an expert system

This research examined two attention theories, Multiple Resource Theory and Neumann's view of attention, by using a secondary task methodology and a manipulation involving adaptive automation to determine if either theory was supported by the findings of this experimental study. The secondary task methodology used three tasks, with varying relationships to the primary task, to test predictions made by the attention theories. The adaptive automation manipulation involved asking participants to complete the primary task using static or dynamic function allocation methodologies. Although previous research found that dynamic task allocation should reduce attentional demands, the attention theories had different predictions about the trend of this reduction. From the adaptive automation manipulation, there was partial support for both theories, with detailed analyses showing more support for MRT. The secondary task manipulation revealed little support for either theory. In addition, there was not strong evidence showing a benefit of adaptive versus static function allocation. From these findings, task and display design recommendations were developed, as well as recommendations for improving the predictive power of the two attention theories. Also, measurement issues were highlighted, with specific recommendations given about how to improve the situation awareness and workload tools used in this study. Lastly, future research areas were suggested that could test the attention theory improvements suggested in this research, as well as utilizing the changes to the tools. This was an important question because it utilized attention theories to drive predictions about adaptive automation, whereas other research in this area simply made predictions based on previous experimental findings.