Issues in Human–Automation Interaction Modeling: Presumptive Aspects of Frameworks of Types and Levels of Automation

Abstract

The current cognitive engineering literature includes a broad range of models of human–automation interaction (HAI) in complex systems. Some of these models characterize types and levels of automation (LOAs) and relate different LOAs to implications for human performance, workload, and situation awareness as bases for systems design. However, some have suggested that the LOAs approach has overlooked key issues that need to be considered during the design process. Others are simply unsatisfied with the current state of the art in modeling HAI. In this paper, I argue that abandoning an existing framework with some utility for design makes little sense unless the cognitive engineering community can provide the broader design community with other sound alternatives. On this basis, I summarize issues with existing definitions of LOAs, including (a) presumptions of human behavior with automation and (b) imprecision in defining behavioral constructs for assessment of automation. I propose steps for advances in LOA frameworks. I provide evidence of the need for precision in defining behavior in use of automation as well as a need for descriptive models of human performance with LOAs. I also provide a survey of other classes of HAI models, offering insights into ways to achieve descriptive formulations of taxonomies of LOAs to support conceptual and detailed systems design. The ultimate objective of this line of research is reliable models for predicting human and system performance to serve as a basis for design.