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Updated: Oct 6, 2025

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Planning in the brain.

Marcelo G Mattar1, Máté Lengyel2

  • 1Department of Cognitive Science, University of California, San Diego, San Diego, CA, USA; Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK.

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Summary
This summary is machine-generated.

The brain plans using focused, depth-limited, and serial algorithms, similar to artificial intelligence (AI). Further research is needed to fully understand these neural planning mechanisms.

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Cognitive Science

Background:

  • Artificial intelligence (AI) has advanced significantly, enabling machines to perform complex planning tasks.
  • The neural mechanisms underlying human and animal planning remain largely unelucidated.
  • Sequential decision-making tasks offer a window into the brain's planning processes.

Purpose of the Study:

  • To review neural and behavioral data on planning in sequential decision-making tasks.
  • To create a taxonomy of AI planning algorithms to systematically compare with biological data.
  • To determine how the brain plans and identify limitations in current understanding.

Main Methods:

  • Systematic review of neural and behavioral data across species and task paradigms.
  • Development of a taxonomy of AI planning algorithms based on design choices.
  • Comparison of biological data with the AI planning algorithm taxonomy.

Main Results:

  • Converging evidence suggests the brain represents future states using focused, depth-limited, and serial planning algorithms.
  • This aligns with a specific class of algorithms within the developed AI taxonomy.
  • Current data are insufficient to address more granular algorithmic questions.

Conclusions:

  • The brain employs a specific type of planning strategy that shares characteristics with certain AI algorithms.
  • Further research is required to fully resolve detailed algorithmic questions about neural planning.
  • Leveraging AI advancements can drive new experimental approaches to investigate brain planning.