There are inherent difficulties in designing an effective Human–Machine Interface (HMI) for a first-of-its-kind system. Many leading cognitive research methods rely upon experts with prior experiences using the system and/or some type of existing mockups or working prototype of the HMI, and neither of these resources are available for such a new system. Further, these methods are time consuming and incompatible with more rapid and iterative systems development models (e.g., Agile/Scrum). To address these challenges, we developed a Wargame-Augmented Knowledge Elicitation (WAKE) method to identify information requirements and underlying assumptions in operator decision making concurrently with operational concepts. The developed WAKE method incorporates naturalistic observations of operator decision making in a wargaming scenario with freeze-probe queries and structured analytic techniques to identify and prioritize information requirements for a novel HMI. An overview of the method, required apparatus, and associated analytical techniques is provided. Outcomes, lessons learned, and topics for future research resulting from two different applications of the WAKE method are also discussed.

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Recently, there has been rapid growth in different methods of providing input into a computer system. Increased familiarity with the unique button layouts of game controllers and direct manipulation touchscreen devices has afforded an opportunity to evaluate how these new input methods can increase efficiency of computer systems that involve the display of complex, densely-packed information. Here, alternative button layouts (through various game controllers) and touchscreen devices were used to navigate through a simulated workflow in a submarine contact management task. Task completion time and errors were recorded, and results show that only some of the alternative input devices sped up the time it takes to cycle through a commonly used workflow. Implications for integration of these devices with alreadyexisting systems are discussed along with future research directions.

Traditional team training is conducted by physically co-locating students and accessing a common system. This method of training involves bringing the entire team to where the system is located. This can be made more efficient by allowing students to remotely access these systems from their duty station. However, the effects of conducting training in this distributed manner have not been investigated. This study identifies task and cognitive performance decrements of distributed teams. Teams of three people were trained on a complex, dynamic task and performed several scenarios. The key variable of interest was whether teams were seated next to each other (co-located) or were in separate spaces (distributed). Task performance metrics were collected, along with measures of workload, communications, and team cohesion. Results demonstrate that all teams had similar mission outcomes, but distributed teams reported higher team workload, lower group integration, and increased communications. This suggests that the act of distributing a team will impact training, but these impacts may not all be negative. Improvements to distributed training environments should be developed to increase performance and reduce workload in distributed teams.

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