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

Abstract

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.

Acknowledgements

The authors would also like to thank undergraduate computer science student, Jeanelle Tanhueco, for developing the interface used for the object identification task.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Shree Frazier

Shree Frazier earned a PhD in Industrial Engineering from Purdue University in 2022 specializing in Human Factors.

Sara A. McComb

Sara A. McComb is a professor in the School of Nursing at Purdue University. She earned a PhD in Industrial Engineering from Purdue University in 1998.

Zachary Hass

Zachary Hass is an assistant professor with a joint appointment in the Schools of Nursing and Industrial Engineering. He earned a PhD in Statistics from Purdue University in 2017.

Brandon J. Pitts

Brandon J. Pitts is an assistant professor in the School of Industrial Engineering and a faculty associate with the Center on Aging and the Life Course (CALC) at Purdue University. He earned a PhD in Industrial and Operations Engineering from the University of Michigan in 2016.