A hierarchical active inference model of spatial alternation tasks and the hippocampal-prefrontal circuit

Toon Van de Maele, Bart Dhoedt, Tim Verbelen and Giovanni Pezzulo ()
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Toon Van de Maele: Ghent University - imec
Bart Dhoedt: Ghent University - imec
Tim Verbelen: VERSES Research Lab
Giovanni Pezzulo: National Research Council

Nature Communications, 2024, vol. 15, issue 1, 1-16

Abstract: Abstract Cognitive problem-solving benefits from cognitive maps aiding navigation and planning. Physical space navigation involves hippocampal (HC) allocentric codes, while abstract task space engages medial prefrontal cortex (mPFC) task-specific codes. Previous studies show that challenging tasks, like spatial alternation, require integrating these two types of maps. The disruption of the HC-mPFC circuit impairs performance. We propose a hierarchical active inference model clarifying how this circuit solves spatial interaction tasks by bridging physical and task-space maps. Simulations demonstrate that the model’s dual layers develop effective cognitive maps for physical and task space. The model solves spatial alternation tasks through reciprocal interactions between the two layers. Disrupting its communication impairs decision-making, which is consistent with empirical evidence. Additionally, the model adapts to switching between multiple alternation rules, providing a mechanistic explanation of how the HC-mPFC circuit supports spatial alternation tasks and the effects of disruption.

Date: 2024
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DOI: 10.1038/s41467-024-54257-3

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