AbstractPerceptual decisions should depend on sensory evidence. However, such decisions are also influenced by past choices and outcomes. These choice history biases may reflect advantageous strategies to exploit temporal regularities of natural environments. However, it is unclear whether and how observers can adapt their choice history biases to different temporal regularities, to exploit the multitude of temporal correlations that exist in nature. Here, we show that mice adapt their perceptual choice history biases to different temporal regularities. This adaptation is well captured by a normative reinforcement learning algorithm with multi-trial belief states, comprising both current trial sensory and previous trial memory states. We demonstrate that striatal dopamine tracks predictions of the model and behavior, pointing towards the involvement of dopamine in forming adaptive history biases. Our results reveal the adaptive nature of perceptual choice history biases, and shed light on their underlying computational principles and neural implementation.