Human Performance in Competitive and Collaborative Human-Machine Teams

In the modern world, there are important tasks that have become too complex for a single unaided individual to manage. Some safety-critical tasks are conducted by teams to improve task performance and minimize risk of error. These teams have traditionally consisted of human operators, yet nowadays AI and machine systems are incorporated into team environments to improve performance and capacity. We used a computerized task, modeled after a classic arcade game, to investigate the performance of human-machine and human-human teams. We manipulated the group conditions between team members; sometimes they were incentivised to collaborate, sometimes compete, and sometimes to work separately. We evaluated players’ performance in the main task (game play) and also measured the cognitive workload they experienced. We compared workload and game performance between different team types (human-human vs. human-machine) and different group conditions (competitive, collaborate, independent). Adapting workload capacity analysis to human-machine teams, we found performance under both team types and all group conditions suffered a performance efficiency cost. However, we observed a reduced cost in collaborative over competitive teams within human-human pairings but this effect was diminished when playing with a machine partner. The implications of workload capacity analysis as a powerful tool for human-machine team performance measurement are discussed.