Dynamic Sequential Interactions of Spatial Uncertainties Explain Human Navigation Strategies, Errors, and Variability

Human spatial navigation involves integrating visual cues about our motion and position relative to landmarks with internal signals from self-motion to form a sense of location and direction. However, navigating in the dark or trying to return to a starting point in an environment reveals the uncertainty of these multisensory inferences. Previous studies have revealed many navigational behaviors, including beaconing and path integration, and puzzling patterns of errors and variability in navigation. Ideal observer accounts of navigation have found evidence for perceptual cue integration, but some studies have reported single cues often dominating homing responses. However, purely perceptual accounts do not explicitly account for internal representations, motor planning, and the sequentiality of perception and action. Here, we present an ideal actor model of goal-directed navigation in terms of path planning in the framework of optimal control under uncertainty. This model explicitly accounts for state estimation and learning (Where am I? Where is my goal?) and planning and control (Where should I go? How do I get there?) while taking uncertainty in perception, action, and representation into account. Through simulation of five different triangle-completion experiments from three different laboratories with a single set of biologically plausible parameters, we demonstrate that the observed patterns of navigation are caused by the continuous and dynamic interaction of these three uncertainties. Contrary to ideal observer models, which attribute human endpoint variability to perceptual cue combination processes only, our ideal actor model provides a unifying account of a wide range of phenomena while considering variability in perception, action, and internal representations jointly. Importantly, these findings highlight how dynamic interactions of spatial uncertainties profoundly shape goal-directed navigation behavior and how active vision results from shaping uncertainties along the navigation trajectory, impacting cognitive maps, route planning, movement execution, and ultimately observed behavioral variability.