What Is a Cognitive Map? Organizing Knowledge for Flexible Behavior
Open accessAbstract
It is proposed that a cognitive map encoding the relationships between entities in the world supports flexible behavior, but the majority of the neural evidence for such a system comes from studies of spatial navigation. Recent work describing neuronal parallels between spatial and non-spatial behaviors has rekindled the notion of a systematic organization of knowledge across multiple domains. We review experimental evidence and theoretical frameworks that point to principles unifying these apparently disparate functions. These principles describe how to learn and use abstract, generalizable knowledge and suggest that map-like representations observed in a spatial context may be an instance of general coding mechanisms capable of organizing knowledge of all kinds. We highlight how artificial agents endowed with such principles exhibit flexible behavior and learn map-like representations observed in the brain. Finally, we speculate on how these principles may offer insight into the extreme generalizations, abstractions, and inferences that characterize human cognition.
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The pieces of information which are bound together in a conscious experience are not only scattered in different areas, they are also expressed in different frame of references.
“More precisely, the pieces of information which are bound together in a conscious experience are not only scattered in different areas, they are also expressed in different frame of references, for instance eye-centered in early visual regions up to more complex cognitive spaces 201 .”
From Interoceptive rhythms in the brain (2023) · cites this paper as [201]
These default mode network activation patterns are evident whenever one is engaged in simulation, whether spontaneous or intentional, focused on one's own past and future or someone else's, or on entirely fictional worlds.
“These activation patterns are evident whenever we are engaged in simulation, whether that simulation is spontaneous (e.g., mind-wandering; Christoff, Irving, Fox, Spreng, & Andrews-Hanna, 2016) or intentional, is focused on our own past and future, or someone else's (i.e., theory of mind; Buckner & Carroll, 2007), or entirely fictional worlds (Behrens et al., 2018;Hasson, Chen, & Honey, 2015;Mar,”
From Mental Time Travel? A Neurocognitive Model of Event Simulation. (2020) · cites this paper as Behrens et al., 2018
The ERC achieves this outcome by learning the transition structure of an environment and the regularities that occur within it, offering a more generalized code that is optimal for spatial schema instantiation.
“The ERC achieves this outcome by learning the transition structure of an environment and the regularities that occur within it, offering a more generalized code that is optimal for spatial schema instantiation 157 .”
From From cognitive maps to spatial schemas (2023) · cites this paper as [157]
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Human entorhinal cortex represents visual space using a boundary-anchored grid
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Retroactive modulation of spike timing-dependent plasticity by dopamine
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Hippocampal Remapping after Partial Inactivation of the Medial Entorhinal Cortex
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Reward-Guided Learning with and without Causal Attribution
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Self-citationA dynamic code for economic object valuation in prefrontal cortex neurons
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Neuronal remapping and circuit persistence in economic decisions
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Grid Cell Responses in 1D Environments Assessed as Slices through a 2D Lattice
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The Human Ventromedial Prefrontal Cortex Is Critical for Transitive Inference
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Grid Cells, Place Cells, and Geodesic Generalization for Spatial Reinforcement Learning
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Frontal-Temporal Disconnection Abolishes Object Discrimination Learning Set in Macaque Monkeys
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Transitive inference in pigeons: Measuring the associative values of Stimuli B and D
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Effects of selective lesions of Fimbria-Fornix on learning set in the rat
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Implicit Value Updating Explains Transitive Inference Performance: The Betasort Model
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Higher-Order Conditioning Is Impaired by Hippocampal Lesions
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Grid cells map the visual world
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Value Representations in the Rodent Orbitofrontal Cortex Drive Learning, not ChoicePreprint
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Intuitive planning: global navigation through cognitive maps based on grid-like codesPreprint
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Self-citationOffline Replay Supports Planning: fMRI Evidence from Reward RevaluationPreprint
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Object-vector coding in the medial entorhinal cortexPreprint
Høydal, Ø., Skytøen, E. R., Moser, M., & Moser, E. (2018) · bioRxiv · doi:10.1101/286286 · Cited by 6 · see citing works
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The default mode network in cognition: a topographical perspective
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Reinforcement learning, fast and slow
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Self-citationHuman replay spontaneously reorganizes experience
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Self-citationIf deep learning is the answer, what is the question?
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Visceral Signals Shape Brain Dynamics and Cognition
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Structuring knowledge with cognitive maps and cognitive graphs
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Deep reinforcement learning and its neuroscientific implications
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Self-citationMedial and orbital frontal cortex in decision-making and flexible behavior
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Mental Time Travel? A Neurocognitive Model of Event Simulation.
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Interoceptive rhythms in the brain
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Learning structures: Predictive representations, replay, and generalization
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Meta-learning in natural and artificial intelligence
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Spatial goal coding in the hippocampal formation
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Hippocampal neurons represent events as transferable units of experience
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Memory consolidation as an adaptive process
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Predictive maps in rats and humans for spatial navigation
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Structure learning and the posterior parietal cortex
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From cognitive maps to spatial schemas
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Identifying Transdiagnostic Mechanisms in Mental Health Using Computational Factor Modeling
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“They found that con fi dence in or models of the world to guide behavior in a prospective fashion (133 – 135) (Figure 2).”
Do hippocampal pyramidal cells respond to nonspatial stimuli?
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From remembering to reconstruction: The transformative neural representation of episodic memory.
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Mental navigation and the neural mechanisms of insight
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Private–public mappings in human prefrontal cortex
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The human brain uses spatial schemas to represent segmented environments
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Evaluating Cognitive Maps and Planning in Large Language Models with CogEval
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Cognitive Maps for a Non-Euclidean Environment: Path Integration and Spatial Memory on a Sphere
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Rethinking Predictive Processing
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Place and behavioral modulation of hippocampal neurons during immobility
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A neuro‐behavioural model of neophobia
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- Authors
- Behrens, T. E. J., Muller, T. H., Whittington, J. C. R., Mark, S., Baram, A. B., Stachenfeld, K. L., & Kurth-Nelson, Z.
- Affiliations
- Wellcome Centre for Human Neuroimaging (GB)
- John Radcliffe Hospital (GB)
- Google DeepMind (United Kingdom) (GB)
- Corresponding author
- Behrens, Timothy E.J.
- ORCID
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2018-10
- Volume
- 100
- Issue
- 2
- Pages
- 490-509
- ISSN
- 0896-6273 (ISSN-L)
- OpenAlex
- W2854445389
- Cited by
- 1,055
- References
- 132
- Citations by year
- 2026: 144 · 2025: 179 · 2024: 170 · 2023: 144 · 2022: 142 · 2021: 136 · 2020: 99 · 2019: 38 · 2018: 2
- FWCI
- 9.06
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 388 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Neuron (publishedVersion)
- PubMed (publishedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Funders
- Wellcome Trust (104765/Z/14/Z, WT104765MA)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Physical Sciences › Engineering › Automotive Engineering › Spatial Cognition and Navigation
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- cognitive maps, spatial navigation, knowledge organization, behavioral flexibility, abstract generalization, artificial agents, human cognition
- MeSH terms
- Brain, Humans, Mental Processes, Models, Neurological
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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