The hippocampus enables abstract structure learning without reward

Abstract

Statistical learning (SL) allows organisms to infer latent structure from sensory input without instruction, feedback, or reward, yet how the brain accomplishes such abstract, unsupervised learning remains unknown. Here we show that mice, like humans, rapidly acquire multiple forms of statistical structure, including event frequency, sequence identity, and abstract structural rules, and that the hippocampus is essential for this capacity. Pupil dynamics provided a cross-species, implicit readout of expectation formation, revealing spontaneous sensitivity to these regularities during passive listening. In mice, pharmacological and temporally precise optogenetic inactivation of dorsal CA1 abolished all learning-related pupil signatures without affecting baseline pupil size, target-evoked responses, or task performance, demonstrating a causal requirement for the hippocampus in forming and updating internal models of sensory structure. High-density recordings further revealed that dCA1 ensembles track evolving statistical contexts while dynamically reorganising population activity into subspaces that separately encode sensory features and abstract rules, enabling generalisation across distinct but structurally equivalent sequences. Together, these results identify the hippocampus as a critical neural substrate for latent abstract structure learning and offer a mechanistic account of how internal models emerge from unsupervised experience.