AUTHOR=Feng Hui , Zeng Yi , Lu Enmeng TITLE=Brain-Inspired Affective Empathy Computational Model and Its Application on Altruistic Rescue Task JOURNAL=Frontiers in Computational Neuroscience VOLUME=16 YEAR=2022 URL=https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2022.784967 DOI=10.3389/fncom.2022.784967 ISSN=1662-5188 ABSTRACT=

Affective empathy is an indispensable ability for humans and other species' harmonious social lives, motivating altruistic behavior, such as consolation and aid-giving. How to build an affective empathy computational model has attracted extensive attention in recent years. Most affective empathy models focus on the recognition and simulation of facial expressions or emotional speech of humans, namely Affective Computing. However, these studies lack the guidance of neural mechanisms of affective empathy. From a neuroscience perspective, affective empathy is formed gradually during the individual development process: experiencing own emotion—forming the corresponding Mirror Neuron System (MNS)—understanding the emotions of others through the mirror mechanism. Inspired by this neural mechanism, we constructed a brain-inspired affective empathy computational model, this model contains two submodels: (1) We designed an Artificial Pain Model inspired by the Free Energy Principle (FEP) to the simulate pain generation process in living organisms. (2) We build an affective empathy spiking neural network (AE-SNN) that simulates the mirror mechanism of MNS and has self-other differentiation ability. We apply the brain-inspired affective empathy computational model to the pain empathy and altruistic rescue task to achieve the rescue of companions by intelligent agents. To the best of our knowledge, our study is the first one to reproduce the emergence process of mirror neurons and anti-mirror neurons in the SNN field. Compared with traditional affective empathy computational models, our model is more biologically plausible, and it provides a new perspective for achieving artificial affective empathy, which has special potential for the social robots field in the future.