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Cognitive problem-solving benefits from cognitive maps aiding navigation andplanning. Previous studies revealed that cognitive maps for physical spacenavigation involve hippocampal (HC) allocentric codes, while cognitive maps forabstract task space engage medial prefrontal cortex (mPFC) task-specific codes.Solving challenging cognitive tasks requires integrating these two types ofmaps. This is exemplified by spatial alternation tasks in multi-corridorsettings, where animals like rodents are rewarded upon executing an alternationpattern in maze corridors. Existing studies demonstrated the HC - mPFCcircuit's engagement in spatial alternation tasks and that its disruptionimpairs task performance. Yet, a comprehensive theory explaining how thiscircuit integrates task-related and spatial information is lacking. We advancea novel hierarchical active inference model clarifying how the HC - mPFCcircuit enables the resolution of spatial alternation tasks, by mergingphysical and task-space cognitive maps. Through a series of simulations, wedemonstrate that the model's dual layers acquire effective cognitive maps fornavigation within physical (HC map) and task (mPFC map) spaces, using abiologically-inspired approach: a clone-structured cognitive graph. The modelsolves spatial alternation tasks through reciprocal interactions between thetwo layers. Importantly, disrupting inter-layer communication impairs difficultdecisions, consistent with empirical findings. The same model showcases theability to switch between multiple alternation rules. However, inhibitingmessage transmission between the two layers results in perseverative behavior,consistent with empirical findings. In summary, our model provides amechanistic account of how the HC - mPFC circuit supports spatial alternationtasks and how its disruption impairs task performance.

Bridging Cognitive Maps: a Hierarchical Active Inference Model of Spatial Alternation Tasks and the Hippocampal-Prefrontal Circuit