Proceedings of the Annual Meeting of the Cognitive Science Society

In an optimal stopping problem, people encounter a sequenceof options and are tasked with choosing the best one; once anoption is rejected, it is no longer available. Recent studies ofoptimal stopping suggest that people compare the current op-tion with an internal threshold and accept it when the optionexceeds the threshold. In contrast, we propose that humans de-cide to accept or reject an option based on an estimate of theprobability that a better option will be observed in the future.We develop a computational model that formalizes this idea,and compare the model to the optimal policy in two experi-ments. Our model provides a better account of the data thanthe optimal model. In particular, our model explains how thedistributional structure of option values affects stopping behav-ior, providing a step towards a more complete psychologicaltheory of optimal stopping.