Design and Validation of a Virtual Player for Studying Interpersonal Coordination in the Mirror Game

TL;DR

This paper presents a novel virtual player for the mirror game, using nonlinear control theory to imitate human motion and study interpersonal coordination, with potential applications in social neuroscience and clinical rehabilitation.

Contribution

It introduces a control architecture based on nonlinear feedback control and HKB oscillators for a virtual player that can lead or follow in the mirror game, validated through experiments.

Findings

The control algorithms effectively imitate human motion in the mirror game.

The virtual player can switch roles between leader and follower.

The approach outperforms existing designs in flexibility and realism.

Abstract

The mirror game has been recently proposed as a simple, yet powerful paradigm for studying interpersonal interactions. It has been suggested that a virtual partner able to play the game with human subjects can be an effective tool to affect the underlying neural processes needed to establish the necessary connections between the players, and also to provide new clinical interventions for the rehabilitation of patients suffering from social disorders. Inspired by the motor processes of the central nervous system (CNS) and the musculoskeletal system in the human body, in this paper we develop a novel interactive cognitive architecture based on nonlinear control theory to drive a virtual player (VP) to play the mirror game with a human player (HP) in different configurations. Specifically, we consider two cases: the former where the VP acts as leader and the latter where it acts as follower. The crucial problem is to design a feedback control architecture capable of imitating and following or leading a human player (HP) in a joint action task. Movement of the end-effector of the VP is modeled by means of a feedback controlled Haken-Kelso-Bunz (HKB) oscillator, which is coupled with the observed motion of the HP measured in real time. To this aim, two types of control algorithms (adaptive control and optimal control) are used and implemented on the HKB model so that the VP can generate human-like motion while satisfying certain kinematic constraints. A proof of convergence of the control algorithms is presented in the paper together with an extensive numerical and experimental validation of their effectiveness. A comparison with other existing designs is also discussed, showing the flexibility and the advantages of our control-based approach.