Learning by multiple human agents to perform a cooperative control task

Little has been known about the process of learning in a human population during continuous goal-directed tasks. Most models of human-like decision-making based multi-agent systems have been focused on learning by discrete feedback. Also, the multi-agent systems in engineering seldom involve the cognitive concept. In this study, we design a multi-agent system based task in which a group of persons controls a common actuator (i.e. a computer cursor) to achieve the task goal (i.e. target acquisition). The cursor movement provides human agents real-time visual feedback. Each subject is given the control capability of a specific cursor direction that is not informed a priori. The population vector, which has been developed to relate a population of motor neurons to arm direction, is used to drive the cursor movement. Learning occurs in the group of human agents as each agent becomes aware of their contributions (i.e. their controlling directions) to achieving the task goal, leveraging only continuous visual feedback of cursor position on the screen. Moreover, the system level learning is exhibited even though some agents failed to learn their true directions with high accuracy. The experimental results show that the multiple human agents can learn to gain the control of a cursor based on visual feedbacks. In addition, we examine how the learning occurs in the individual level and the collective level.