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Cognitive Graphs: Representational Substrates for Planning.

Jungsun Yoo1, Elizabeth R Chrastil1,2,3, Aaron M Bornstein1,2

  • 1Department of Cognitive Sciences, University of California, Irvine.

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|October 16, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Internal models of environment-states and actions enable efficient planning. Cognitive graphs unify these representations, showing how memory structures impact planning and knowledge generalization.

Keywords:
cognitive graphplanningreinforcement learning

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Area of Science:

  • Cognitive Science
  • Neuroscience
  • Artificial Intelligence

Background:

  • Planning is computationally intensive.
  • Internal models reduce planning costs by representing environment-states and action sequences.
  • These models evolve from associative networks to compressed representations.

Purpose of the Study:

  • To review diverse representations supporting planning.
  • To introduce cognitive graphs as a unifying framework.
  • To explore how memory structures influence planning and knowledge generalization.

Main Methods:

  • Literature review of research on planning and internal representations.
  • Conceptual analysis using the cognitive graph framework.
  • Discussion of learning, transfer, and generalization mechanisms.

Main Results:

  • Internal models, initially associative, become compressed for efficient planning.
  • Cognitive graphs provide a spectrum of representations for planning.
  • Learning shapes these graphs for knowledge transfer across environments.

Conclusions:

  • Associative memory structures significantly impact planning capabilities.
  • Cognitive graphs offer a unified perspective on internal models for planning.
  • Compressed representations facilitate rapid planning for novel goals.