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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
Summary
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.
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.
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