The Moderating Effects of Task Complexity and Age on the Relationship between Automation Use and Cognitive Workload

In recent decades, automation has become increasingly integrated into knowledge work environments, i.e., jobs that require specialized skills for task completion. Some research has shown that automation may reduce cognitive workload. However, other work reports that increasing automation does not always lower cognitive workload. These conflicting results suggest that further investigation is needed to identify factors that influence the relationship between automation use and cognitive workload. Therefore, this study aimed to investigate how two potential moderators, task complexity and age, influence that relationship. A total of 24 younger and 24 middle-aged adults performed an object identification task that resembled a baggage scanning procedure conducted by airport security personnel. The task was manipulated by varying the level of automation (4 increasing levels) that provided support in identifying objects of interest as well as the complexity of task (two types: component and coordinative). In addition, participants completed a pattern identification and sorting memory task, which helped emulate a knowledge work environment. Performance (i.e., object identification accuracy, average completion time, pattern identification accuracy, and sorting memory task accuracy) and subjective workload (NASA-RTLX score) measures were recorded. Participants also rated the usability of the automation. Overall, results showed that while the use of automation was associated with reduced cognitive workload, both types of task complexity negatively affected this relationship such that increased complexity was associated with a decrease in accuracy. Age did not moderate the relationship between automation and cognitive workload, but there were qualitative differences in terms of how the two age groups perceived the utility and usability of the automated systems. Knowledge generated from this research has implications for the design of future human-automation systems and can be used to inform interface design that is tailored to the needs and preferences of different users and use cases.