Evaluation of Haptic Interaction in Mirror Game by a Cerebellum Inspired Virtual Player

What effect does physical contact with a real or virtual playmate have on prediction behavior? In this study, we use the mirror game (MG) paradigm to observe the motion of human and virtual player (VP) in real-time with one leader and two followers. The main objective of this paper is to elucidate largely still a mystery called as use of haptic information between players in the mirror game with leader-follower (LF) modality. In particular, we propose a cerebellar architecture for the virtual follower with haptic interaction to help accomplish the human-like movement. We use Smith predictor to eliminate sensorimotor delay. A model predictive control method is developed by an adaptive feedback error learning algorithm (AFEL) to represent motor learning and motor adaptation of the cerebellum for the VP. Throughout this work, haptic interaction between followers is shown to enhance tracking performance using important mathematical metrics.