Humans have the remarkable cognitive capacity to rapidly adapt to changing environments. Central to this capacity is the ability to form high-level, abstract representations that take advantage of regularities in the world to support generalization¹. However, little is known about how these representations are encoded in populations of neurons, how they emerge through learning and how they relate to behaviour^2,3. Here we characterized the representational geometry of populations of neurons (single units) recorded in the hippocampus, amygdala, medial frontal cortex and ventral temporal cortex of neurosurgical patients performing an inferential reasoning task. We found that only the neural representations formed in the hippocampus simultaneously encode several task variables in an abstract, or disentangled, format. This representational geometry is uniquely observed after patients learn to perform inference, and consists of disentangled directly observable and discovered latent task variables. Learning to perform inference by trial and error or through verbal instructions led to the formation of hippocampal representations with similar geometric properties. The observed relation between representational format and inference behaviour suggests that abstract and disentangled representational geometries are important for complex cognition.

人类具有快速适应不断变化的环境的卓越认知能力。这种能力的核心是形成高级抽象表示的能力，这些表示利用世界的规律性来支持泛化¹ 。然而，人们对这些表征如何在神经元群体中编码、它们如何通过学习出现以及它们与行为有何关系^2,3知之甚少。在这里，我们描述了执行推理任务的神经外科患者的海马体、杏仁核、内侧额叶皮层和腹侧颞叶皮层中记录的神经元群体（单个单位）的代表性几何形状。我们发现，只有在海马体中形成的神经表征才能同时以抽象或解开的格式编码多个任务变量。这种表征几何学是在患者学会进行推理后独特地观察到的，并且由解开的直接可观察和发现的潜在任务变量组成。学习通过反复试验或通过口头指令进行推理，导致形成具有相似几何特性的海马表征。观察到的表征格式和推理行为之间的关系表明，抽象和解开的表征几何对于复杂认知很重要。