On computational models of theory of mind and the imitative reinforcement learning in spiking neural networks

• Published in: Scientific reports Vol. 14; no. 1; pp. 1945 - 14
• Main Authors: Gorgan Mohammadi, Ashena, Ganjtabesh, Mohammad
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 23.01.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 631/378/116; 631/378/116/1925; 631/378/116/2396; Cognition; Computational neuroscience; Computer Simulation; Firing pattern; Humanities and Social Sciences; Learning; Learning behavior; multidisciplinary; Neural networks; Neural Networks, Computer; Observational learning; Reinforcement; Reinforcement, Psychology; Science; Science (multidisciplinary); Social discrimination learning; Social interactions; Theory of Mind
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-52299-7

Theory of Mind is referred to the ability of inferring other’s mental states, and it plays a crucial role in social cognition and learning. Biological evidences indicate that complex circuits are involved in this ability, including the mirror neuron system. The mirror neuron system influences imitation abilities and action understanding, leading to learn through observing others. To simulate this imitative learning behavior, a Theory-of-Mind-based Imitative Reinforcement Learning (ToM-based ImRL) framework is proposed. Employing the bio-inspired spiking neural networks and the mechanisms of the mirror neuron system, ToM-based ImRL is a bio-inspired computational model which enables an agent to effectively learn how to act in an interactive environment through observing an expert, inferring its goals, and imitating its behaviors. The aim of this paper is to review some computational attempts in modeling ToM and to explain the proposed ToM-based ImRL framework which is tested in the environment of River Raid game from Atari 2600 series.