Jeremy R. Manning, Timothy F. Lew, Ningcheng Li, Robert Sekuler, Michael J. Kahana

In an unfamiliar environment, searching for and navigating to a target requires that spatial information be acquired, stored, processed, and retrieved. In a study encompassing all of these processes, participants acted as taxicab drivers who learned to pick up and deliver passengers in a series of small virtual towns. We used data from these experiments to refine and validate MAGELLAN, a cognitive map-based model of spatial learning and wayfinding. MAGELLAN accounts for the shapes of participants' spatial learning curves, which measure their experience-based improvement in navigational efficiency in unfamiliar environments. The model also predicts the ease (or difficulty) with which different environments are learned and, within a given environment, which landmarks will be easy (or difficult) to localize from memory. Using just 2 free parameters, MAGELLAN provides a useful account of how participants' cognitive maps evolve over time with experience, and how participants use the information stored in their cognitive maps to navigate and explore efficiently. (PsycINFO Database Record (c) 2014 APA, all rights reserved).