Causal event structure shapes convergent reactivation signatures in human cortex and language model

Authors

Zhang, Z., Chen, Y., Liu, Q., Zhao, X., Ding, X., Fei, X., Du, J., Bao, Y., Gao, J., Huang, X., Cai, B., Liang, X., Qin, B., Liu, T.

Abstract

Humans adapt to changing environments by understanding the latent causal structure of events, and modern large language models (LLMs) increasingly exhibit analogous abilities. However, elucidating how causal knowledge is represented and integrated in the human brain and in LLMs poses a substantial challenge for cognitive science. To address this, we combine topological analysis of representational manifolds with neuroscientific analyses of reactivation. We find that event-level causal relations modulate temporally structured neural reactivation in the human brain, particularly in the angular gyrus, precuneus and prefrontal cortex. By contrast, LLMs express these relations through positionally structured representational reactivation. Despite these differences, the resulting reactivation patterns exhibit similar intrinsic manifold structure across the two systems. Together, these findings suggest that the human brain and LLM share a common principle for organizing causal knowledge.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 08 Oct 2026.

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