A biologically inspired hierarchical control model of frontoparietal motor planning for attentional inhibitory control

Goal-directed movement relies on the coordinated motor planning and execution, with the frontoparietal network playing a central role in high-level planning. Although inhibitory control, specifically attentional inhibition, has been strongly linked to prefrontal cortex (PFC) function, its specific contribution to motor planning remains unclear. Here, we propose a biologically inspired computational model of frontoparietal dynamics that incorporates inhibitory control mechanisms to support motor planning. The model features structured connectivity between the PFC and posterior parietal cortex and is validated through robotic arm simulations and obstacle-avoidance experiments. Parameter analysis confirms that inhibitory control is essential for successful task completion. Perturbation analyses further demonstrate that isolating the planning layer can release the inherent disturbance resistance of the execution layer, aligning with human behavioral data. Furthermore, the model enables complex-task decoupling and sequencing, exhibiting superior generalizability compared with conventional proportional controllers. Grounded in the attractor hypothesis, this study provides a plausible account of how attentional inhibitory control operates within motor planning, offering valuable insights for the development of brain-inspired control architectures.