Towards robustness to fluctuated perceptual patterns by a deterministic predictive coding model in a task of imitative synchronization with human movement patterns

Abstract

The current paper presents how performance of a particular deterministic dynamical neural network model in predictive coding scheme differ when it is trained for a set of prototypical movement patterns using their modulated teaching samples from when it is trained using unmodulated teaching samples. Multiple timescale neural network (MTRNN) trained with or without modulated patterns was applied in a simple numerical experiment for a task of imitative synchronization by inferencing the internal states by the error regression, and the results suggest that the scheme of training with modulated patterns can outperform the scheme of training without them. In our second experiment, our network was tested with naturally fluctuated movement patterns in an imitative interaction between a robot and different human subjects, and the results showed that a network trained with fluctuated patterns could achieve generalization in learning, and mutual imitation by synchronization was obtained.