In the real world, complex dynamic scenes often arise from the composition of simpler parts. The visual system exploits this structure by hierarchically decomposing dynamic scenes: When we see a person walking on a train or an animal running in a herd, we recognize the individual’s movement as nested within a reference frame that is, itself, moving. Despite its ubiquity, surprisingly little is understood about the computations underlying hierarchical motion perception. To address this gap, we developed a class of stimuli that grant tight control over statistical relations among object velocities in dynamic scenes. We first demonstrate that structured motion stimuli benefit human multiple object tracking performance. Computational analysis revealed that the performance gain is best explained by human participants making use of motion relations during tracking. A second experiment, using a motion prediction task, reinforced this conclusion and provided fine-grained information about how the visual system flexibly exploits motion structure.

在现实世界中，复杂的动态场景通常是由较简单的部分组成的。视觉系统通过分层分解动态场景来利用这种结构：当我们看到一个人在火车上行走或一群动物在奔跑时，我们将个体的运动识别为嵌套在本身就是运动的参考框架中。尽管它无处不在，但关于分层运动感知的计算却很少了解。为了解决这一差距，我们开发了一种刺激，可以对动态场景中物体速度之间的统计关系进行严格控制。我们首先证明结构化运动刺激有益于人类多对象跟踪性能。计算分析表明，性能提高最好由人类参与者在跟踪过程中利用运动关系来解释。使用运动预测任务的第二个实验加强了这一结论，并提供了有关视觉系统如何灵活利用运动结构的细粒度信息。