Learning of Spatial Properties of a Large-scale Virtual City with an Interactive Map

Abstract

On the basis of embodied/-enacted theories of the mind, investigations of spatial cognition related to real world environments have become current research interests. How this perspective relates to acquiring spatial knowledge not by active exploration, but through map learning, however, remains unresolved. Therefore, we designed a large virtual city comprised of over 200 houses, suitable for active exploration or spatial learning based on a map. Here, we report our results after single and repeated 30-minute training sessions using an interactive city map. We tested subjects' knowledge of the orientation of houses towards cardinal north and of two houses relative to each other and the locations of two houses towards each other in a pointing task. Our results revealed that a single training session was sufficient to repeatedly view the majority of houses covering a large area of the city. However, repeated training sessions were necessary to improve the performance level and reveal significant differences between tasks. In contrast to a previous study in a real world city, performance in the two orientation tasks was better than in the pointing task. The lack of distance and the lack of angular difference effects onto task performance suggest the use of a global reference frame.Performance was positively correlated with a self-report on spatial abilities (FRS) in the absolute orientation and pointing task but not the relative orientation task. Overall, our results suggest that training with an interactive city map enhances abstract knowledge, not directly available from an embodied perspective.