Inference as a fundamental process in behavior.

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.