Spatial navigation is a complex cognitive activity that depends on perception, action, memory, reasoning, and problem solving. People rely on their navigational skills every day, from activities as common as getting from home to work and back again, to less routine endeavors, such as finding a tourist destination in an unfamiliar city. Effective navigation depends on the ability to combine information from multiple spatial cues to estimate one's position and the locations of goals. Spatial cues include landmarks, and other visible features of the environment, and body-based cues generated by selfmotion (vestibular, proprioceptive, & efferent information). A number of projects have investigated the extent to which visual cues and body-based cues are combined optimally according to statistical principles. Although several studies have documented optimal or near-optimal cue combination, other studies have shown that navigators sometimes fail to combine cues optimally or even at all. Possible limitations of all of these investigations are that they have not accounted for navigators' prior experiences with or assumptions about the task environment and have not tested complete decision models. We show that some apparent violations of optimal cue combination can be explained by incorporating informative Bayesian priors or appropriate loss functions in decision-theoretic models, and propose novel lines of research on human spatial navigation that are motivated by Bayesian decision theory.