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Place recognition from distant landmarks: human performance and maximum likelihood model

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Abstract

We present a simple behavioral experiment on human place recognition from a configuration of four visual landmarks. Participants were asked to navigate several paths, all involving a turn at one specific point, and while doing so incidentally learned the position of that turning point. In the test phase, they were asked to return to the turning point in a reduced environment leaving only the four landmarks visible. Results are compared to two versions of a maximum likelihood model of place recognition using either view-based or depth-based cues for place recognition. Only the depth-based model is in good qualitative agreement with the data. In particular, it reproduces landmark configuration-dependent effects of systematic bias and statistical error distribution as well as effects of approach direction. The model is based on a place code (depth and bearing of the landmarks at target location) and an egocentric working memory of surrounding space including current landmark position in a local, map-like representation. We argue that these elements are crucial for human place recognition.

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Notes

  1. Thanks to Andrew Glennerster reviewer for pointing out this possibility.

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Acknowledgements

We grateful to Marie Admard, Marcel Dorer, Isa-Maria Gross, Amanda Link, and Niklas Schulze for help with the data collection.

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Authors and Affiliations

  1. Institute for Neurobiology, University of Tübingen, Tübingen, Germany

    Hanspeter A. Mallot

  2. Wettstetten, Germany

    Stephan Lancier

Authors
  1. Hanspeter A. Mallot
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  2. Stephan Lancier
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Corresponding author

Correspondence to Hanspeter A. Mallot.

Additional information

Communicated by J. Leo van Hemmen.

The work described in this paper was carried out at the Department of Biology of the University of Tübingen. Additional support was obtained from the German Federal Ministry of Education and Research (BMBF) within the Bernstein Center of Computational Neuroscience Tübingen (Grant: FKZ 01GQ1002A).

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Mallot, H.A., Lancier, S. Place recognition from distant landmarks: human performance and maximum likelihood model. Biol Cybern 112, 291–303 (2018). https://doi.org/10.1007/s00422-018-0751-4

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