Real-world deployments of human--swarm teams depend on balancing operator workload to leverage human strengths without inducing overload. A key challenge is that swarm size is often dynamic: robots may join or leave the mission due to failures or redeployment, causing abrupt workload fluctuations. Understanding how such changes affect human workload and performance is critical for robust human--swarm interaction design. This paper investigates how the magnitude and direction of changes in swarm size influence operator workload. Drawing on the concept of workload history, we test three hypotheses: (1) workload remains elevated following decreases in swarm size, (2) small increases are more manageable than large jumps, and (3) sufficiently large changes override these effects by inducing a cognitive reset. We conducted two studies (N = 34) using a monitoring task with simulated drone swarms of varying sizes. By varying the swarm size between episodes, we measured perceived workload relative to swarm size changes. Results show that objective performance is largely unaffected by small changes in swarm size, while subjective workload is sensitive to both change direction and magnitude. Small increases preserve lower workload, whereas small decreases leave workload elevated, indicating workload residue; large changes in either direction attenuate these effects, suggesting a reset response. These findings offer actionable guidance for managing swarm-size transitions to support operator workload in dynamic human--swarm systems.

翻译：在人类-无人机集群团队的真实部署中，平衡操作员工作负荷以发挥人类优势而不引发超负荷至关重要。一个关键挑战在于集群规模通常是动态的：由于故障或重新部署，机器人可能加入或离开任务，导致工作负荷的突然波动。理解这种变化如何影响人类工作负荷与表现，对于设计稳健的人机交互系统至关重要。本文探究集群规模变化幅度和方向对操作员工作负荷的影响。基于工作负荷历史的概念，我们检验了三个假设：（1）当集群规模减少后，工作负荷会持续升高；（2）小幅增加比大幅跳跃更易管理；（3）足够大的变化会通过引发认知重置来覆盖这些影响。我们开展了两个实验（N=34），使用模拟不同规模无人机集群的监控任务。通过在任务段落间改变集群规模，我们测量了感知工作负荷相对于规模变化的关系。结果显示，客观表现在集群规模小幅变化下基本不受影响，而主观工作负荷对变化的方向和幅度均敏感。小幅增加可维持较低工作负荷，而小幅减少则导致工作负荷升高（表明存在工作负荷残留）；大幅变化（无论增减）则削弱了这些效应，提示存在重置反应。这些发现为管理动态人机系统中的集群规模过渡、支持操作员工作负荷提供了可操作的指导。