Route selection in non-Euclidean virtual environments

Abstract

The way people choose routes through unfamiliar environments provides clues about the underlying representation they use. One way to test the nature of observers' representation is to manipulate the structure of the scene as they move through it and measure which aspects of performance are significantly affected and which are not. We recorded the routes that participants took in virtual mazes to reach previously-viewed targets. The mazes were either physically realizable or impossible (the latter contained 'wormholes' that altered the layout of the scene without any visible change at that moment). We found that participants could usually find the shortest route between remembered objects even in physically impossible environments, despite the gross failures in pointing that an earlier study showed are evident in the physically impossible environment (Muryy A, Glennerster A. Pointing errors in non-metric virtual environments. Lecture Notes in Computer Science, 11034. pp. 43-57). In the physically impossible conditions, the choice made at a junction was influenced to a greater extent by whether that choice had, in the past, led to the discovery of a target (compared to a shortest-distance prediction). In the physically realizable mazes, on the other hand, junction choices were determined more by the shortest distance to the target. This pattern of results is compatible with the idea of a graph-like representation of space that can include information about previous success or failure for traversing each edge and also information about the distance between nodes. Our results suggest that complexity of the maze may dictate which of these is more important in influencing navigational choices.

Spatial exploration strategies in childhood; exploration behaviours are predictive of navigation success

“…We were also interested in individual differences. Even in adulthood there are individual differences in the use of different types of spatial knowledge (referred to as navigation strategies) and some adults do not draw on configural knowledge or change navigation strategies dependent on the complexity of the environment (Muryy & Glennerster, 2020). Weisberg and Newcombe (2016) report three types of navigation strategies in adults, imprecise navigators, non-integrators and integrators, the latter two categories reflecting the use of route knowledge and configural knowledge respectively.…”