Biologically Inspired Multimodal Integration: Interferences in a Human-Robot Interaction Game

This paper presents a biologically inspired approach to multimodal integration and decision-making in the context of human-robot interactions. More specifically, we address the principle of ideomotor compatibility by which observing the movements of others influences the quality of one's own performance. This fundamental human ability is likely to be linked with human imitation abilities, social interactions, the transfer of manual skills, and probably to mind reading. We present a robotic control model capable of integrating multimodal information, decision making, and replicating a stimulus-response compatibility task, originally designed to measure the effect of ideomotor compatibility on human behavior. The model consists of a neural network based on the dynamic field approach, which is known for its natural ability for stimulus enhancement as well as cooperative and competitive interactions within and across sensorimotor representations. Finally, we discuss how the capacity for ideomotor facilitation can provide the robot with human-like behavior, but at the expense of several disadvantages, such as hesitation and even mistakes.