Specialized Computations for Generalized World Modelling in Medial Prefrontal Cortex

Abstract

The medial prefrontal cortex is central to learning flexible internal models across diverse domains, yet the functional specialization enabling this remains unknown. We tested whether medial prefrontal specialization is representational (encoding domain-specific features) or computational (implementing domain-general computations). During fMRI, participants learned probabilistic features of virtual environments representing spatial, social, and sequential domain knowledge. Although each domain used different features, they shared the same feature-to-latent state mapping, matching their computational demands. The medial PFC showed no domain-specific feature representations. Instead, its neural patterns revealed a triad of specialized yet domain-invariant computations. Ventromedial PFC patterns reflected probabilistic inference, abstracting hidden probability distributions from observations and tracking trial-wise posterior task state changes within a low-dimensional latent space. Anteromedial PFC organized task states along orthogonal axes, tracking directional shifts within states and switches between different states, suggesting a global task coordinate system. Dorsomedial PFC patterns represented task dynamics, using predictive surprise to monitor validity of the current internal model and switch task policies. These results suggest a principled architecture in medial PFC where three general-purpose computations jointly enable learning world-models across diverse environments.