Selective resetting position and heading estimations while driving in a large-scale immersive virtual environment.

Abstract

Two experiments investigated how self-motion cues and landmarks interact in determining a human's position and heading estimations while driving in a large-scale virtual environment by controlling a gaming wheel and pedals. In an immersive virtual city, participants learned the locations of five buildings in the presence of two proximal towers and four distal scenes. Then participants drove two streets without viewing these buildings, towers, or scenes. When they finished driving, either one tower with displacement to the testing position or the scenes that had been rotated reappeared. Participants pointed in the directions of the five buildings. The least squares fitting method was used to calculate participants' estimated positions and headings. The results showed that when the displaced proximal tower reappeared, participants used this tower to determine their positions, but used self-motion cues to determine their headings. When the rotated distal scenes reappeared, participants used these scenes to determine their headings. If they were instructed to continuously keep track of the origin of the path while driving, their position estimates followed self-motion cues, whereas if they were not given instructions, their position estimates were undetermined. These findings suggest that when people drive in a large-scale environment, relying on self-motion cues, path integration calculates headings continuously but calculates positions only when they are required; relying on the displaced proximal landmark or the rotated distal scenes, piloting selectively resets the position or heading representations produced by path integration.