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Spatial inference without a cognitive map: the role of higher-order path integration.

Youcef Bouchekioua1,2, Aaron P Blaisdell3, Yutaka Kosaki4

  • 1Department of Psychology, Keio University, Tokyo, 108-8345, Japan.

Biological Reviews of the Cambridge Philosophical Society
|September 17, 2020
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Summary

This study introduces a new model for spatial navigation, combining path integration with associative learning. This higher-order path integration (HOPI) model explains how animals achieve complex navigation without a cognitive map.

Keywords:
cognitive mapgoal-directed behaviourhead-direction vectorsinferencepath integrationvector learning

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Animal Behavior

Background:

  • The cognitive map model is standard for efficient route inference.
  • Path integration, relying on vestibular stimuli and pace counting, is considered less flexible.
  • Distinguishing between cognitive maps and path integration in complex navigation has been experimentally challenging.

Purpose of the Study:

  • To propose a new model of spatial learning that integrates path integration with higher-order associative learning.
  • To explain novel route taking, detours, and shortcuts without relying on a cognitive map.
  • To resolve the long-standing debate on mechanisms underlying complex spatial navigation.

Main Methods:

  • Development of a novel computational model: higher-order path integration (HOPI).
  • Demonstration of the HOPI model's ability to account for spatial inferences like novel detours and shortcuts.
  • Analysis of vector-based navigational strategies utilizing associative processes.

Main Results:

  • The HOPI model successfully explains novel route taking, a behavior not explained by traditional associative learning.
  • The model demonstrates how complex spatial inferences can be achieved without a cognitive map.
  • Evidence suggests a phylogenetically ancient, vector-based strategy is sufficient for complex spatial inferences.

Conclusions:

  • Spatial navigation can be explained by a combination of path integration and higher-order associative learning.
  • The proposed HOPI model offers a mechanistic explanation for novel route taking.
  • A sophisticated vector-based navigational strategy, supported by associative processes, can account for complex spatial behaviors.