Toward optimizing the design of virtual environments for route learning: empirically assessing the effects of changing levels of realism on memory

Abstract

Broadly, this paper is about designing memorable 3D geovisualizations for spatial knowledge acquisition during (virtual) navigation. Navigation is a fundamentally important task, and even though most people navigate every day, many find it difficult in unfamiliar environments. When people get lost in an unfamiliar environment, or are unable to remember a route that they took, they might feel anxiety, disappointment and frustration; and in real world, such incidents can be costly, and at times, life-threatening. Therefore, in this paper, we study the design decisions in terms of visual realism in a city model, propose a visualization design optimized for route learning, implement and empirically evaluate this design. The evaluation features a navigational route learning task, where we measure short- and long-term recall accuracy of 42 participants with varying spatial abilities and memory capacity. Our findings provide unique empirical evidence on how design choices affect memory in route learning with geovirtual environments, contributing toward empirically verified design guidelines for digital cities.