Multimodal Goal Representations and Feedback in Hierarchical Motor Control

The capabilities of human motor behavior build on the integration of multiple sensory modalities in goal representation and feedback processing. Here, we present a hierarchical neural network model of motor control to simulate these capabilities, based on the SURE REACH model. The model is able to integrate visual and proprioceptive goal representations, but, by now, relies only on proprioceptive feedback to control ongoing movements. Here, we extend the model to a neural network that processes both, proprioceptive and visual feedback. In simulated reaching experiments we demonstrate that visual feedback considerably enhances the accuracy of the original controller. Moreover, the ability to combine visual and proprioceptive goal representations, or to adjust behavior to task-specific constraints is not affected. Finally, we discuss the results, propose further enhancements, and outline the model’s relevance for other domains of human cognition.