Abstract
Probabilistic reasoning in the brain has been studied primarily using simple two-choice tasks that are experimentally and theoretically tractable but have unclear relevance to more complex problems. Here we generalize principles from these studies using information geometry, which treats probability distributions (``beliefs'') as points on statistical manifolds whose geometry captures relationships between those beliefs. We introduce an ``information compass,'' a context-dependent, one-dimensional axis along which an arbitrarily complex belief is reinforced or repudiated. Projecting high-dimensional belief updates onto this axis casts even complex decisions into familiar forms of deliberation and commitment used for simpler decisions. Our framework provides a unified account of how biological and artificial systems can use geometric forms of probabilistic reasoning to make decisions across a range of tasks and domains.