解码自我运动：视觉-前庭因果推断的洞见。

文献信息

中文摘要

自我运动知觉依赖于视觉光流与前庭信号的整合，然而这些线索常常是嘈杂的、不可靠的或彼此不一致的。因此，一个核心挑战不仅在于如何整合它们，还在于它们是否应当被整合。贝叶斯因果推断为这一问题提供了一个框架，提出大脑会推断视觉与前庭线索是否来自同一原因，并据此调整其整合。行为学发现，包括依赖于差异的知觉偏差、依赖于可靠性的线索重新加权以及依赖于经验的重校准，与该框架相容，但单独来看并不能诊断因果推断，也可能源于其他整合或决策机制。在神经层面，人类和非人灵长类的研究已识别出一个分布式的自我运动网络，涉及MSTd、VIP、VPS/PIVC以及额叶和顶叶联合区。然而，当前证据主要涉及多感觉汇聚、线索加权和冲突敏感性，而非因果结构的直接神经表征。在此，我们从三个相互关联的视角综述视觉-前庭因果推断：计算框架及其领域特异性假设、行为模型证据的强度与局限，以及候选神经基质和环路机制。最后，我们强调理解自然主义自我运动过程中因果推断的主要开放问题和未来方向。

英文摘要

Self-motion perception relies on the integration of visual optic flow and vestibular signals, yet these cues are often noisy, unreliable, or mutually inconsistent. A central challenge is therefore not only how to integrate them, but also whether they should be integrated at all. Bayesian causal inference provides a framework for this problem by proposing that the brain infers whether visual and vestibular cues arise from a common cause and adjusts their integration accordingly. Behavioral findings, including disparity-dependent perceptual biases, reliability-dependent cue reweighting, and experience-dependent recalibration, are compatible with this framework, but are not diagnostic of causal inference in isolation and may also arise from alternative integration or decision mechanisms. At the neural level, studies in humans and nonhuman primates have identified a distributed self-motion network involving MSTd, VIP, VPS/PIVC, and frontal and parietal association areas. However, current evidence mainly concerns multisensory convergence, cue weighting, and conflict sensitivity rather than a direct neural representation of causal structure. Here, we review visual-vestibular causal inference from three linked perspectives: the computational framework and its domain-specific assumptions, the strength and limitations of the behavioral model evidence, and the candidate neural substrates and circuit mechanisms. We conclude by highlighting major open questions and future directions for understanding causal inference during naturalistic self-motion.