Long-horizon associative learning as a unifying framework for statistical learning across scales.

Sensory inputs are rich with temporal patterns that unfold across multiple timescales. Uncovering these regularities is essential for anticipating future events and navigating the environment efficiently. Numerous models have been proposed to account for learning at specific temporal scales; however, they are often designed in isolation and rely on narrowly tuned statistical measures, limiting their generalizability to other paradigms. In contrast, humans typically learn without prior knowledge of the underlying structure or the relevant timescale at which regularities occur. Here, we present a unifying account of statistical learning that spans a wide range of temporal dependencies, from adjacent and nonadjacent transitions to complex network structures. This model, long-horizon associative learning, offers a biologically grounded implementation of the successor representation, or equivalently, the free energy minimization model. Reanalyzing data from 11 previously published studies, we show that a single neural mechanism captures both local statistical regularities and higher-order structural properties. This mechanism rests on graded temporal overlap of associative traces and is governed by a single free parameter (β). This initial domain-general associative learning process, emerging from the graded structure of associations, may later scaffold to higher-level operations such as grouping, categorization, rule abstraction, and memory formation. Overall, this framework offers a conceptual synthesis that bridges disparate strands of the statistical learning literature and reframes apparent paradigm-specific effects as different expressions of a common underlying computation.