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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. [ABSTRACT FROM AUTHOR]