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，人们对心理和神经计算知之甚少，而这些计算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。我们提出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解决方案。我们推导出一种在线期望最大化算法，可以定性和定量地解释人类对各种刺激的感知，涵盖经典的心理物理学实验、模糊的运动场景和虚幻的运动显示。因此，我们确定了人类运动结构感知起源的规范性解释，并为未来的心理物理学实验做出了可检验的预测。所提出的在线分层推理模型还提供了一种神经网络实现，该神经网络实现与运动敏感皮层区域共享属性，并激发有针对性的实验来揭示潜在结构的神经表示。