In the real world, uncertainty is omnipresent due to incomplete or noisy information. This makes inferring the state-of-the-world difficult. Furthermore, the state-of-the-world often changes over time, though with some regularity. This makes learning and decision-making challenging. Organisms have evolved to take advantage of environmental regularities, that allow organisms to acquire a model of the world and perform model-based inference to robustly make decisions and adjust behavior efficiently under uncertainty. Recent research has shed light on many aspects of model-based inference and its neural underpinnings. Here we review recent progress on hidden-state inference, state transition inference, and hierarchical inference processes.

在现实世界中，由于信息不完整或嘈杂，不确定性无处不在。这使得推断世界状态变得困难。此外，世界状态经常随着时间而变化，尽管有一定的规律性。这使得学习和决策具有挑战性。有机体已经进化到利用环境规律，这使得有机体能够获得世界模型并执行基于模型的推理，从而在不确定性下稳健地做出决策并有效地调整行为。最近的研究揭示了基于模型的推理及其神经基础的许多方面。在这里，我们回顾了隐藏状态推理、状态转换推理和分层推理过程的最新进展。