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Selective Resetting Position and Heading Estimations While Driving in a Large-Scale Immersive Virtual Environment

  • Author(s) / Creator(s)

  • 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.

  • Date created
    2018-10-27
  • Subjects / Keywords
  • Type of Item
    Article (Published)
  • DOI
    https://doi.org/10.7939/R3CF9JP17
  • License
    Attribution-NonCommercial 4.0 International
  • Language
  • Source
    In the past five years, my student (Lei Zhang) and I tried to behaviorally measure people’ position (location) and heading (orientation) representations from their responses of pointing to distal buildings or replacing locations of proximal objects. Several colleagues asked me about the details of the methods. So I have uploaded the Matlab codes online to make the methods more accessible (see below). Please also see the attached papers for reference. Cheers! Weimin Method 1: calculate the homing error, position error, and heading error from the replaced objects locations of the path origin (O) and four proximal objects (X1 – X4) after walking an outbound path. Download the Matlab code here https://doi.org/10.7939/R3FT8F06G Related papers: Mou, W., & Zhang, L. (2014). Dissociating position and heading estimations: Rotated visual orientation cues perceived after walking reset headings but not positions. Cognition, 133(3), 553-571. Zhang, L., & Mou, W. (2017). Piloting systems reset path integration systems during position estimation. Journal of Experimental Psychology: Learning, Memory, and Cognition, 43(3), 472. Method 2: calculate participants’ position and heading estimates from their pointing to five distal buildings. Download the Matlab code here https://doi.org/10.7939/R3057D77Q Related paper: Zhang, L., & Mou, W. (2018). Selective resetting position and heading estimations while driving in a large-scale immersive virtual environment. Experimental Brain Research, accepted.