Various types of user data such as heart rate, age, eye tracking data, body mass index, conversational data and more can be used to model a user. Intelligent systems can user these models to recognize and adapt. For example, by recognizing if someone is bored or scared while playing games based on a model of their engagement and using that to adapt the difficulty of a game [1]. However, this survey will focus on modelling user decision-making and reasoning. It will thus answer the question: "How do intelligent systems acquire and use user data to model decision-making and reasoning, and how are these models applied for recognition and adaptation?" To answer these questions, a systematic literature review was conducted. Using the key concepts Intelligent Systems, Recognition, Adaptation, User Modelling, and Human Decision-Making and Reasoning, queries have been formulated for 4 different databases. Papers have been screened and extracted using the PRISMA guidelines [2], resulting in the usage of 52 articles. The results reveal that intelligent systems are dominated by recommender systems. They use user preferences to recommend items in all sorts of domains. These user preferences can be modelled using other models such as user demographics, behaviour, emotions and characteristics. Besides making the decision-making process easier and thereby improving user experience, they also deal with other challenges such as fairness, privacy, usability and prediction accuracy. While being less prevalent, other intelligent systems such as decision support systems, assistance systems and social robots sometimes serve even more important purposes and have the potential of solving diverse problems. However, their underutilization suggests an opportunity for research in this area.