In a busy city street, a pedestrian surrounded by distractions can pick out a single sign if it is relevant to their route. Artificial agents in outdoor Vision-and-Language Navigation (VLN) are also confronted with detecting supervisory signal on environment features and location in inputs. To boost the prominence of relevant features in transformer-based systems without costly preprocessing and pretraining, we take inspiration from priority maps - a mechanism described in neuropsychological studies. We implement a novel priority map module and pretrain on auxiliary tasks using low-sample datasets with high-level representations of routes and environment-related references to urban features. A hierarchical process of trajectory planning -with subsequent parameterised visual boost filtering on visual inputs and prediction of corresponding textual spans - addresses the core challenge of cross-modal alignment and feature-level localisation. The priority map module is integrated into a feature-location framework that doubles the task completion rates of standalone transformers and attains state-of-the-art performance for transformer-based systems on the Touchdown benchmark for VLN. We release code (https://github.com/JasonArmitage-res/PM-VLN) and data (https://zenodo.org/record/6891965.YtwoS3ZBxD8).