Navigation is a complex and dynamic task that engages multiple brain systems for perception, cognition, planning and decision making, and motor control. Most studies of navigation use sparse environments, simple tasks, and reduced models, but these studies cannot fully assess brain systems that mediate active navigation in the natural world. To address this Gallant and researchers performed human fMRI experiments involving active navigation in a virtual world. Participants first learned to navigate through a large virtual city containing hundreds of distinct roads, buildings and landmarks. After learning to criterion participants performed a taxi driver task in the MRI scanner while brain activity was recorded. Banded ridge regression was then used to create high-dimensional voxelwise encoding models separately for every subject, and model prediction accuracy and generalization was tested using a separate data set. They fit voxelwise encoding models to capture the representation of 38 separate high-dimensional feature spaces, and then hierarchically clustered the model weight vectors to identify the cortical networks underlying naturalistic navigation. Results show that naturalistic navigation is supported by a large, complex network in the cerebral cortex that spans 11 distinct regions in the visual, parietal, and prefrontal cortices. These regions are organized into broadly distributed functional gradients that reflect a continuous transformation from perception to motor responses. These diverse functional regions work together to support the perception-planning-action loop. Note that although Gallant will be focusing on navigation in this talk, the methods that underpin these studies can be applied to many different problem domains and across species, and his laboratory has developed a large suite of open source tools and tutorials to facilitate adoption of this approach. Therefore, he hopes that this talk will be of interest to a broad audience.