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Related Experiment Video

Updated: Feb 2, 2026

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

Lei Zhang1, Weimin Mou2

  • 1Department of Psychology, University of Alberta, P217 Biological Sciences Bldg, Edmonton, AB, T6G 2E9, Canada. lz14@ualberta.ca.

Experimental Brain Research
|November 9, 2018
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Summary

Human navigation in virtual environments relies on both self-motion cues and landmarks. Driving participants used landmarks for position and self-motion for heading, selectively updating their spatial awareness based on available cues.

Keywords:
DrivingHeading estimationsLandmarksPosition estimationsSelf-motion cues

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Area of Science:

  • Cognitive Psychology
  • Virtual Reality Navigation
  • Spatial Cognition

Background:

  • Understanding human spatial orientation and navigation in large-scale environments is crucial.
  • Virtual environments offer controlled settings to investigate navigation strategies.
  • The interplay between self-motion cues and external landmarks in navigation is not fully understood.

Purpose of the Study:

  • To investigate how self-motion cues and landmarks interact in determining human position and heading estimations.
  • To examine selective updating of spatial representations based on environmental cues during virtual driving.
  • To differentiate the roles of proximal and distal landmarks in spatial updating.

Main Methods:

  • Two experiments were conducted using a driving simulator with a gaming wheel and pedals in a virtual city.
  • Participants learned building locations and then drove along streets without visual references.
  • Post-drive, either a displaced proximal tower or rotated distal scenes reappeared, and participants estimated building directions.

Main Results:

  • Participants used displaced proximal towers for position estimation but self-motion cues for heading estimation.
  • Rotated distal scenes were used for heading estimation.
  • Continuous path tracking instructions led to position estimates aligned with self-motion cues; otherwise, position estimates were undetermined.

Conclusions:

  • Self-motion cues enable continuous heading calculation via path integration, with position updates occurring on demand.
  • External landmarks (proximal or distal) can selectively reset or update path integration outputs for position or heading.
  • Navigation strategies dynamically integrate self-motion and landmark information based on availability and task demands.