Predictive Maps in Rats and Humans for Spatial Navigation

Overview

Journal Curr Biol

Publisher Cell Press

Specialty Biology

Date 2022 Jul 21

PMID 35863351

Authors

William de Cothi

Nils Nyberg

Eva-Maria Griesbauer

Carole Ghaname

Fiona Zisch

Julie M Lefort

Lydia Fletcher

Coco Newton

Sophie Renaudineau

Daniel Bendor

Roddy Grieves

Eleonore Duvelle

Caswell Barry

Hugo J Spiers

Affiliations

Soon will be listed here.

Abstract

Much of our understanding of navigation comes from the study of individual species, often with specific tasks tailored to those species. Here, we provide a novel experimental and analytic framework integrating across humans, rats, and simulated reinforcement learning (RL) agents to interrogate the dynamics of behavior during spatial navigation. We developed a novel open-field navigation task ("Tartarus maze") requiring dynamic adaptation (shortcuts and detours) to frequently changing obstructions on the path to a hidden goal. Humans and rats were remarkably similar in their trajectories. Both species showed the greatest similarity to RL agents utilizing a "successor representation," which creates a predictive map. Humans also displayed trajectory features similar to model-based RL agents, which implemented an optimal tree-search planning procedure. Our results help refine models seeking to explain mammalian navigation in dynamic environments and highlight the utility of modeling the behavior of different species to uncover the shared mechanisms that support behavior.

Citing Articles

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