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Abstract

Many types of intelligent behavior can be framed as a search problem, where an individual must explore a vast set of possible actions, while carefully balancing the exploration-exploitation dilemma. Under finite search horizons, optimal solutions are normally unobtainable. Yet humans and other animals regularly manage to gracefully solve these problems. How do they accomplish this? We propose an explanation based on two principles: generalization over features and uncertainty-guided exploration. Together these form a model that learns from past observations to generalize to similar options and eagerly seeks out uncertainty to gain more information about the search space. This model can be used to predict participants' search behavior in a complex multi-armed bandit task. The model's parameter estimates can also be used to gain meaningful insights into developmental differences in generalization and exploration. Furthermore, we can use our model to describe customers' purchasing decisions in a large-scale data set of 1.6 million online food delivery purchases. Finally, I will map out ongoing work investigating the neural principles of generalization, where we find that people form mental structures that allow them to search for rewards more efficiently, a form of neural representation learning.