Feng, H., Zeng, Y., & Lu, E. (2022).
Frontiers in computational neuroscience,
16, 784967.
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.
Here are some thoughts:
This article is significant because it highlights a growing effort to imbue machines with complex human-like experiences and behaviors, such as pain and altruism—traits that are deeply rooted in human psychology and evolution. By attempting to program pain, researchers are not merely simulating a sensory reaction but exploring how discomfort or negative feedback might influence learning, decision-making, and self-preservation in AI systems.
This has profound psychological implications, as it touches on how emotions and aversive experiences shape behavior and consciousness in humans. Similarly, programming altruism raises questions about the nature of empathy, cooperation, and moral reasoning—core areas of interest in social and cognitive psychology. Understanding how these traits can be modeled in AI helps psychologists explore the boundaries of machine autonomy, ethical behavior, and the potential consequences of creating entities that mimic human emotional and moral capacities. The broader implication is that this research challenges traditional psychological concepts of mind, consciousness, and ethics, while also prompting critical discussions about how such AI systems might interact with and influence human societies in the future.