In many real-life decisions, options are distributed in space and time, making it necessary to search sequentially through them, often without a chance to return to a rejected option. The optimal strategy in these tasks is to choose the first option that is above a threshold that depends on the current position in the sequence and reflects the expected value of the remaining options. Human behavior in optimal stopping tasks varies substantially but largely diverges from this optimal strategy. In the talk I will present a model we developed to describe how humans make decisions in optimal stopping problems. It assumes that humans decide whether to accept or reject an option based on a threshold that decreases linearly with each encountered option. The model can explain why human risk preferences seem to change across optimal stopping tasks and how humans adapt to different task environments.