A Hierarchical Emotion Regulated Sensorimotor Model: Case Studies

TL;DR

This paper introduces a hierarchical sensorimotor model influenced by emotion regulation, utilizing a novel RNNPB network to demonstrate emotion-based behavior regulation and recognition in cognitive agents.

Contribution

It presents a new hierarchical model integrating emotion regulation into sensorimotor behavior, implemented via a novel RNNPB network for case studies.

Findings

The RNNPB model effectively simulates emotion-regulated sensorimotor behaviors.

The model demonstrates emotion recognition capabilities based on internal status.

Case studies validate the hierarchical emotion regulation framework.

Abstract

Inspired by the hierarchical cognitive architecture and the perception-action model (PAM), we propose that the internal status acts as a kind of common-coding representation which affects, mediates and even regulates the sensorimotor behaviours. These regulation can be depicted in the Bayesian framework, that is why cognitive agents are able to generate behaviours with subtle differences according to their emotion or recognize the emotion by perception. A novel recurrent neural network called recurrent neural network with parametric bias units (RNNPB) runs in three modes, constructing a two-level emotion regulated learning model, was further applied to testify this theory in two different cases.