Predictive maps in rats and humans for spatial navigation

William de Cothi; Nils Nyberg; Eva-Maria Griesbauer; Carole Ghanamé; Fiona Zisch; Julie M Lefort; Lydia Fletcher; Coco Newton; Sophie Renaudineau; Daniel Bendor; Roddy Grieves; Éléonore Duvelle; Caswell Barry; Hugo J Spiers

doi:10.1016/j.cub.2022.06.090

Predictive maps in rats and humans for spatial navigation

Curr Biol. 2022 Sep 12;32(17):3676-3689.e5. doi: 10.1016/j.cub.2022.06.090. Epub 2022 Jul 20.

Authors

Affiliations

¹ Department 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. Electronic address: w.decothi@ucl.ac.uk.
² Institute of Behavioral Neuroscience, Department of Experimental Psychology, Division of Psychology and Language Sciences, University College London, London, UK.
³ Institute of Behavioral Neuroscience, Department of Experimental Psychology, Division of Psychology and Language Sciences, University College London, London, UK; The Bartlett School of Architecture, University College London, London, UK.
⁴ Department of Cell and Developmental Biology, University College London, London, UK.
⁵ Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
⁶ Institute of Behavioral Neuroscience, Department of Experimental Psychology, Division of Psychology and Language Sciences, University College London, London, UK; Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA.
⁷ Institute of Behavioral Neuroscience, Department of Experimental Psychology, Division of Psychology and Language Sciences, University College London, London, UK. Electronic address: h.spiers@ucl.ac.uk.

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.

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

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Animals
Hippocampus
Humans
Learning
Mammals
Rats
Reinforcement, Psychology
Spatial Navigation*

Abstract

Publication types

MeSH terms

Grants and funding