Dynamic Causal Modelling of Hierarchical Planning

Abstract

Hierarchical planning (HP) is a strategy that optimizes the planning by storing the steps towards the goal (lower-level planning) into subgoals (higher-level planning). In the framework of model-based reinforcement learning, HP requires the computation through the transition value between higher-level hierarchies. Previous study identified the dmPFC, PMC and SPL were involved in the computation process of HP respectively. However, it is still unclear about how these regions interaction with each other to support the computation in HP, which could deepen our understanding about the implementation of plan algorithm in hierarchical environment. To address this question, we conducted an fMRI experiment using a virtual subway navigation task. We identified the activity of the dmPFC, premotor cortex (PMC) and superior parietal lobe (SPL) with general linear model (GLM) in HP. Then, Dynamic Causal Modelling (DCM) was performed to quantify the influence of the higher- and lower-planning on the connectivity between the brain areas identified by the GLM. The strongest modulation effect of the higher-level planning was found on the dmPFC→right PMC connection. Furthermore, using Parametric Empirical Bayes (PEB), we found the modulation of higher-level planning on the dmPFC→right PMC and right PMC→SPL connections could explain the individual difference of the response time. We conclude that the dmPFC-related connectivity takes the response to the higher-level planning, while the PMC acts as the bridge between the higher-level planning to behavior outcome.

Keywords

• Dynamic Causal Modelling (DCM)
• Parametric Empirical Bayes (PEB)
• fMRI
• neural architecture
• individual difference