Automation can be utilized to relieve humans of difficult and repetitive tasks in many domains, presenting the opportunity for safer and more efficient systems. This increase in automation has led to new supervisory roles for human operators where humans monitor feedback from autonomous systems and provide input when necessary. Optimizing these roles requires tools for evaluation of task complexity and resulting operator cognitive workload. Cognitive task analysis is a process for modeling the cognitive actions required of a human during a task. This work presents an enhanced version of this process: Cognitive Task Analysis and Workload Classification (CTAWC). The goal of developing CTAWC was to provide a standardized process to decompose cognitive tasks in enough depth to allow for precise identification of sources of cognitive workload. CTAWC has the following advantages over conventional CTA methodology:•Integrates standard terminology from existing taxonomies for task classification to describe expected operator cognitive workload during task performance.•Provides a framework to evaluate adequate cognitive depth when decomposing cognitive tasks.•Provides a standard model upon which to build an empirical study to evaluate task complexity.
Keywords: Autonomous; Human performance; Human-machine system; Pupillometry; Workstation.
© 2021 The Authors. Published by Elsevier B.V.