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

Hanspeter A Mallot1, Stephan Lancier2

  • 1Institute for Neurobiology, University of Tübingen, Tübingen, Germany. hanspeter.mallot@uni-tuebingen.de.

Biological Cybernetics
|February 27, 2018
PubMed
Summary
This summary is machine-generated.

Humans use depth cues from landmark configurations for accurate place recognition. A depth-based model, unlike a view-based one, accurately predicts human spatial memory and navigation performance.

Keywords:
Maximum likelihood modelPlace recognitionSpatial cognition

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Area of Science:

  • Cognitive Psychology
  • Neuroscience
  • Spatial Navigation

Background:

  • Human place recognition is fundamental for navigation.
  • Understanding the sensory cues used in spatial memory is crucial.

Purpose of the Study:

  • To investigate human place recognition using a configuration of four visual landmarks.
  • To compare behavioral data with computational models of place recognition.

Main Methods:

  • A behavioral experiment involving navigation and incidental learning of a turning point.
  • Testing participants' ability to return to the turning point in a landmark-only environment.
  • Comparing results to maximum likelihood models using view-based and depth-based cues.

Main Results:

  • The depth-based model showed good qualitative agreement with human performance.
  • The depth-based model accurately reproduced landmark configuration-dependent biases and error distributions.
  • Effects of approach direction were also replicated by the depth-based model.

Conclusions:

  • Depth and bearing of landmarks at a target location form a crucial place code.
  • Egocentric working memory, including a local map-like representation, is vital for human place recognition.
  • Depth-based cues are more critical than view-based cues for this type of spatial memory task.