Date of Award


Document Type


Degree Name

Doctor of Philosophy (PhD)



Committee Chair/Advisor

Richard Pak

Committee Member

Leo Gugerty

Committee Member

Cyrus Foroughi

Committee Member

Dustin Souders


Research in human-automation interaction has demonstrated that some individuals are more severely impacted by the negative effects of unreliable automation (i.e., exhibit lower performance) than others. A body of work has sought to explain this variability through individual differences, primarily investigating the role of working memory. However, not all studies have demonstrated a clear relationship between working memory capabilities and performance when using automation. Engle’s (2002) controlled attention theory of working memory posits that the relationship between working memory and other cognitive constructs such as fluid intelligence can be explained through a shared reliance on attention control. Studying the role of attention control compared to working memory might be a more parsimonious way of explaining differences in automation performance. The purpose of Study 1 was to investigate whether there was a relationship between attention control and decision accuracy with automation. Study 1 found that working memory was positively related to decision accuracy in the information automation condition but not the decision automation condition. This was due to the fact that those with lower attention control were disproportionately helped by high-level automation. The purpose of Study 2 was to compare the extent to which attention control and working memory predict variance in performance when using automation. Hierarchical regression models demonstrated that attention control to significantly predict explained variance in decision accuracy above and beyond working memory in most experimental conditions. This work marks a pivotal shift from a focus on working memory to the importance of attention in human-automation interaction.

Author ORCID Identifier




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