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Updated: Sep 4, 2025

Author Spotlight: Investigating the Effects of Mind-Body-Movement Practices on Brain Function

Author Spotlight: Investigating the Effects of Mind-Body-Movement Practices on Brain Function

Published on: January 26, 2024

Predictive maps in rats and humans for spatial navigation.

William de Cothi¹, Nils Nyberg², Eva-Maria Griesbauer²

¹Department of Cell and Developmental Biology, University College London, London, UK; Institute of Behavioral Neuroscience, Department of Experimental Psychology, Division of Psychology and Language Sciences, University College London, London, UK.

Current Biology : CB

|July 21, 2022

Summary

This summary is machine-generated.

Humans and rats exhibit similar spatial navigation strategies, mirroring reinforcement learning (RL) agents with predictive maps. This comparative approach reveals shared mechanisms underlying navigation in dynamic environments.

Keywords:

artificial intelligence cognitive map decision making detour hippocampus planning reinforcement learning shortcut spatial navigation successor representation

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Area of Science:

Comparative Cognition
Computational Neuroscience
Animal Behavior

Background:

Traditional navigation studies focus on single species with task-specific designs.
Understanding spatial navigation requires a framework that integrates diverse agents.
Dynamic environments necessitate adaptive navigation strategies.

Purpose of the Study:

To develop a unified experimental and analytic framework for studying spatial navigation.
To compare navigation dynamics across humans, rats, and reinforcement learning (RL) agents.
To identify shared mechanisms underlying adaptive navigation in changing environments.

Main Methods:

Development of a novel open-field navigation task ('Tartarus maze') with dynamic obstructions.
Integration of behavioral data from humans and rats with simulated RL agents.
Analysis of trajectory data to compare navigation strategies, including successor representation and model-based RL.

Main Results:

Human and rat navigation trajectories showed remarkable similarity in the Tartarus maze.
Both species' behavior closely resembled RL agents employing a successor representation (predictive mapping).
Human trajectories also aligned with model-based RL agents using tree-search planning.

Conclusions:

Comparative analysis across species and computational models refines understanding of mammalian navigation.
The successor representation is a key mechanism for navigation in dynamic environments.
Integrating diverse agents highlights conserved neural and behavioral mechanisms for spatial navigation.