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Psychological Research

, Volume 71, Issue 3, pp 240–251 | Cite as

Humans do not switch between path knowledge and landmarks when learning a new environment

  • Patrick FooEmail author
  • Andrew Duchon
  • William H. WarrenJr.
  • Michael J. Tarr
Original Article

Abstract

Using a metric shortcut paradigm, we have found that like honeybees (Dyer in Animal Behaviour 41:239–246, 1991), humans do not seem to build a metric “cognitive map” from path integration. Instead, observers take novel shortcuts based on visual landmarks whenever they are available and reliable (Foo, Warren, Duchon, & Tarr in Journal of Experimental Psychology-Learning Memory and Cognition 31(2):195–215, 2005). In the present experiment we examine whether humans, like ants (Wolf & Wehner in Journal of Experimental Biology 203:857–868, 2000), first use survey-type path knowledge, built up from path integration, and then subsequently shift to reliance on landmarks. In our study participants walked in an immersive virtual environment while head position and orientation were recorded. During training, participants learned two legs of a triangle with feedback: paths from Home to Red and Home to Blue. A configuration of colored posts surrounded the Red location. To test reliance on landmarks, these posts were covertly translated, rotated, or left unchanged during six probe trials. These probe trials were interspersed during the training procedure to measure changes over learning. Dependence on visual landmarks was immediate and sustained during training, and no significant learning effects were observed other than a decrease in hesitation time. Our results suggest that while humans have at least two distinct navigational strategies available to them, unlike ants, a computationally-simpler landmark strategy dominates during novel shortcut navigation.

Keywords

Test Trial Path Integration Probe Trial Head Mount Display Local Landmark 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This research was supported by the National Science Foundation (LIS IRI 9720327; 0214383). The authors would like to thank Amanda Forte, Nick Beem, and Jonathan Ring for help in collecting data.

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Copyright information

© Springer-Verlag 2006

Authors and Affiliations

  • Patrick Foo
    • 1
    Email author
  • Andrew Duchon
    • 2
  • William H. WarrenJr.
    • 2
  • Michael J. Tarr
    • 2
  1. 1.Department of Psychology, One University HeightsUniversity of North Carolina at AshevilleAshevilleUSA
  2. 2.Department of Cognitive and Linguistic SciencesBrown UniversityProvidenceUSA

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