A Cognitive Model for Guiding Automation

A variety of systems and exist for managing human-machine team throughput and effectiveness. One example is autonomous managers (AMs), software that dynamically reallocates tasks to individual members of a team based on their workload and performance. Cognitive models can inform these technologies by projecting performance into the future and enabling “what-if” analyses. For example, would removing a task from an individual whose current performance is low cause them to improve? Conversely, can a team member who is currently performing well handle even more work without dropping performance? In the present study, we develop and validate a cognitive model built in the Adaptive Control of Thought – Rational (ACT-R) cognitive architecture in a novel empirical paradigm: The Intelligence, Surveillance, and Reconnaissance Multi-attribute Task Battery (ISR-MATB). In this task, participants engage in a mock ISR task in which they must integrate information from several subtasks to arrive at a decision about a situation. These tasks include searching visual displays, listening for audio chatter, making decisions based on multiple cues, and remaining vigilant for signals. The tasks are based upon analogous laboratory psychology tasks to improve empirical rigor. Eight participants completed the task under two 30-minute conditions: easy and difficult. The difficult task required searching more complex stimuli in the audio and visual domain than in the easy condition. In addition, subjective workload ratings (NASA-TLX) were collected. We describe the preliminary behavioral and self-report results, as well as the ACT-R model’s fit to the behavioral data. Further, we describe a new method for workload visualization and task decomposition using model-based analyses.