Prof. Huan Luo & Fang Fang & Dr. Aming Li :Compressive learning scaffolds higher-order network structure to enhance human knowledge acquisition

Abstract

Humans naturally seek knowledge, yet integrating vast, fragmented information remains challenging. Traditionally, knowledge acquisition has relied on random walks within network—an unguided and inefficient process. Here, we introduce “compressive learning”, a conceptual framework that embeds higher-order structural features—specifically node-degree inhomogeneity—into pre-learning trajectories to scaffold more efficient learning. We show that scale-free networks, owing to their pronounced degree inhomogeneity, are more compressible and more learnable than other network types. Critically, pre-learning paths that highlight this inhomogeneous higher-order structure facilitate subsequent network learning. Magnetoencephalography (MEG) recordings reveal that compressive pre-learning enhances structured neural representations in the dorsal anterior cingulate cortex (ACC). Two-stage computational modeling indicates that compressive learning constructs a network skeleton defined by higher-order structure that efficiently accommodates new inputs. Together, our results highlight the central role of higher-order network structure in human learning and offer a strategic approach to effectively integrating fragmented information into a coherent knowledge framework.

Original Link

https://www.nature.com/articles/s41467-026-75843-7