modelling social decision making using reinforcement learning

In recent years, there have been a significant increase in the use of reinforcement learning (RL)models in cognitive science. However, the increased use of relatively complex computational approaches has led to potential misconceptions and misinterpretations. Here, we present a comprehensive framework for investigating social decision-making using a reinforcement learning approach.We discuss common problems in its application and offer practical suggestions. Our goal is to provide simple and scalable explanations of new and practical methods and guidelines for using reinforcement learning models and extracting sub-goals and obtaining collective information and knowledge for decision making.Also, the purpose of this article is to better understand the nature of decision-making processes using the reinforcement learning approach. For this purpose, the topics of why decision-making takes time, the role of emotions in decision-making, the neural pathways of decision-making, the role of self-motivated thoughts, and how reward and punishment decisions are made in the brain are explained. A review of the studies conducted on these topics shows that decision-making is a time-consuming process and a person cannot make more than one decision at a time. Emotions are a fundamental component in regulating interactions between environmental conditions and the human decision-making process, and through emotional systems, valuable tacit and explicit knowledge is provided for quick and rational decisions. Finally, decisions are cognitive in nature, and therefore the findings of cognitive science can help strengthen decision-making theories for a more complete understanding of people's choice process and provide more complete and realistic views of decision-making behaviors.