Identifying the structure of motion relations in the environment is critical for navigation, tracking, prediction, and pursuit. Yet, little is known about the mental and neural computations that allow the visual system to infer this structure online from a volatile stream of visual information. We propose online hierarchical Bayesian inference as a principled solution for how the brain might solve this complex perceptual task. We derive an online Expectation-Maximization algorithm that explains human percepts qualitatively and quantitatively for a diverse set of stimuli, covering classical psychophysics experiments, ambiguous motion scenes, and illusory motion displays. We thereby identify normative explanations for the origin of human motion structure perception and make testable predictions for future psychophysics experiments. The proposed online hierarchical inference model furthermore affords a neural network implementation which shares properties with motion-sensitive cortical areas and motivates targeted experiments to reveal the neural representations of latent structure.

识别环境中运动关系的结构对于导航、跟踪、预测和追踪至关重要。然而，人们对允许视觉系统从不稳定的视觉信息流中在线推断出这种结构的心理和神经计算知之甚少。我们建议将在线分层贝叶斯推理作为大脑如何解决这一复杂感知任务的原则性解决方案。我们推导出一种在线期望最大化算法，该算法定性和定量地解释人类对一组不同刺激的感知，涵盖经典心理物理学实验、模糊运动场景和虚幻运动显示。因此，我们确定了人类运动结构感知起源的规范解释，并为未来的心理物理学实验做出了可测试的预测。