VirtualReal Transfer in a Large-Scale Environment : Impact of Active Navigation as a Function of the Viewpoint Displacement Effect and Recall Tasks

Abstract EN

The purpose of this study was to examine the effect of navigation mode (passive versus active) on the virtual/real transfer of spatial learning, according to viewpoint displacement (ground: 1 m 75 versus aerial: 4 m) and as a function of the recall tasks used.

We hypothesize that active navigation during learning can enhance performances when route strategy is favored by egocentric match between learning (ground-level viewpoint) and recall (egocentric frame-based tasks).

Sixty-four subjects (32 men and 32 women) participated in the experiment.

Spatial learning consisted of route learning in a virtual district (four conditions: passive/ground, passive/aerial, active/ground, or active/aerial), evaluated by three tasks: wayfinding, sketch-mapping, and picture-sorting.

In the wayfinding task, subjects who were assigned the ground-level viewpoint in the virtual environment (VE) performed better than those with the aerial-level viewpoint, especially in combination with active navigation.

In the sketch-mapping task, aerial-level learning in the VE resulted in better performance than the ground-level condition, while active navigation was only beneficial in the ground-level condition.

The best performance in the picture-sorting task was obtained with the ground-level viewpoint, especially with active navigation.

This study confirmed the expected results that the benefit of active navigation was linked with egocentric frame-based situations.