With the advancement of artificial intelligence, intelligent robots are increasingly integrated into the workforce, raising critical questions about how tasks should be allocated between humans and robots. According to mind perception theory, robots are attributed with moderate agency but low experience, leading to disparities in the types of tasks they can effectively undertake. These differences shape judgments of responsibility and influence task allocation. Despite their lower perceived agency and experience, robots' computational and storage advantages highlight the emerging trend of human-robot teams. Moreover, collaboration extends beyond individuals to group forms (human-human, human-robot, robot-robot), raising questions about collective mind and its implications for responsibility and task allocation.
This research employs four behavioral experiments. Studies 1 (N = 100) and 2 (N = 100) use contextual decision-making tasks to test whether individuals accept work allocations to humans or robots under monetary and moral scenarios. Studies 3 (N = 100) and 4 (N = 100) further examine whether collaborative teams influence acceptance of work allocation, comparing human-human, human-robot, and robot-robot teams within the same contexts.
The results show: (1) in both monetary and moral tasks, loss-related work is more readily allocated to robots, while benefit-related work is assigned to humans; (2) The differentiated mind of humans and robots lead to differences in the responsibilities they can assume, ultimately resulting in the allocation of tasks with distinct gain-loss works; (3) collaborative teams exhibit a collective mind; and (4) collective mind declines progressively from human-human to human-robot to robot-robot teams, with corresponding reductions in accepted monetary and moral responsibilities.
Grounded in mind perception theory, this study clarifies the psychological mechanisms driving acceptance of task allocation across entity types and teams, offering insights for the rational integration of intelligent robots into the division of labor.