How to build a cognitive map: insights from models of the hippocampal formation
Not on the map. Why?
Abstract (arXiv)
Learning and interpreting the structure of the environment is an innate feature of biological systems, and is integral to guiding flexible behaviours for evolutionary viability. The concept of a cognitive map has emerged as one of the leading metaphors for these capacities, and unravelling the learning and neural representation of such a map has become a central focus of neuroscience. While experimentalists are providing a detailed picture of the neural substrate of cognitive maps in hippocampus and beyond, theorists have been busy building models to bridge the divide between neurons, computation, and behaviour. These models can account for a variety of known representations and neural phenomena, but often provide a differing understanding of not only the underlying principles of cognitive maps, but also the respective roles of hippocampus and cortex. In this Perspective, we bring many of these models into a common language, distil their underlying principles of constructing cognitive maps, provide novel (re)interpretations for neural phenomena, suggest how the principles can be extended to account for prefrontal cortex representations and, finally, speculate on the role of cognitive maps in higher cognitive capacities.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 14 of its 175 references that are in the map. First posted on arXiv 3 Feb 2022.
Builds on
14 of 175 references.
References · 14
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2 more
Built on by
Citing papers per year: 2022: 2; 2023: 0; 2024: 1; 2025: 0; 2026: 2.
5 papers.
- 2024ALPINE: Unveiling the Planning Capability of Autoregressive Learning in Language Modelscited by 18
- 2022On the link between conscious function and general intelligence in humans and machinescited by 15
- 2022Successor Representation Active Inferencecited by 3
- 2026SpaceMind++: Toward Allocentric Cognitive Maps for Spatially Grounded Video MLLMscited by 1
- 2026Differentiable Clone-Structured Causal Graphs for End-to-End Cognitive Map Learning from Image Sequencescited by 0
- 2026Differentiable Clone-Structured Causal Graphs for End-to-End Cognitive Map Learning from Image Sequences14 Jul 2026
- 2026SpaceMind++: Toward Allocentric Cognitive Maps for Spatially Grounded Video MLLMs10 May 2026
- 2024ALPINE: Unveiling the Planning Capability of Autoregressive Learning in Language Models15 May 2024
- 2022Successor Representation Active Inference20 Jul 2022
- 2022On the link between conscious function and general intelligence in humans and machines24 Mar 2022