How do we navigate a deeply structured world? Why are you reading this sentence first – and did you actually look at the fifth word? This review offers some answers by appealing to active inference based on deep temporal models. It builds on previous formulations of active inference to simulate behavioural and electrophysiological responses under hierarchical generative models of state transitions. Inverting these models corresponds to sequential inference, such that the state at any hierarchical level entails a sequence of transitions in the level below. The deep temporal aspect of these models means that evidence is accumulated over nested time scales, enabling inferences about narratives (i.e., temporal scenes). We illustrate this behaviour with Bayesian belief updating – and neuronal process theories – to simulate the epistemic foraging seen in reading. These simulations reproduce perisaccadic delay period activity and local field potentials seen empirically. Finally, we exploit the deep structure of these models to simulate responses to local (e.g., font type) and global (e.g., semantic) violations; reproducing mismatch negativity and P300 responses respectively.

我们如何驾驭一个深度结构化的世界？你为什么要先读这句话——你真的看过第五个词吗？这篇评论通过呼吁基于深度时间模型的主动推理提供了一些答案。它建立在先前的主动推理公式的基础上，以模拟状态转换的分层生成模型下的行为和电生理反应。反转这些模型对应于顺序推理，因此任何层次级别的状态都需要下一级的一系列转换。这些模型的深层时间方面意味着证据是在嵌套的时间尺度上积累的，从而能够推断出叙事（即时间场景）。我们用贝叶斯信念更新和神经过程理论来说明这种行为，以模拟阅读中看到的认知觅食。这些模拟再现了 perisaccadic 延迟期活动和经验上看到的局部场电位。最后，我们利用这些模型的深层结构来模拟对局部（例如，字体类型）和全局（例如，语义）违规的响应；分别再现错配消极性和 P300 反应。