Bayesian Decision Theory and Navigation

Spatial navigation is a complex cognitive activity that depends on perception, action, memory, reasoning, and problem solving. 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 self-motion (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. Possible limitations 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 examine cue combination in spatial navigation from a Bayesian perspective and present the fundamental principles of Bayesian decision theory. We show that a complete Bayesian decision model with an explicit loss function can explain a discrepancy between optimal cue weights and empirical cues weights observed by Chen, McNamara, Kelly and Wolbers (2017) and that the use of informative priors to represent cue bias can explain the incongruity between heading variability and heading direction observed by Zhao and Warren (2015b). We conclude that Bayesian decision theory offers a productive theoretical framework for investigating human spatial navigation and believe that it will lead to a deeper understanding of navigational behaviors.