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Related Experiment Video

Updated: Nov 19, 2025

Author Spotlight: A Novel Setup to Conduct Naturalistic Laboratory Experiments with Real Human Actors in Scenarios
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The Active Inference Approach to Ecological Perception: General Information Dynamics for Natural and Artificial

Adam Linson1,2,3, Andy Clark4,5, Subramanian Ramamoorthy6,7

  • 1Department of Computing Science and Mathematics, University of Stirling, Stirling, United Kingdom.

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|January 27, 2021
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Summary

The active inference framework (AIF) unifies computational neuroscience, AI, and ecological psychology by viewing humans as embodied agents. This approach uses free energy minimization to explain life, mind, and consciousness from particles to artificial intelligence.

Keywords:
affordancesembodimentevolutionframe problemfree energyself-organizationskilled expertiseuncertainty

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Ecological Psychology
  • Phenomenology
  • Information Theory

Background:

  • Emerging neurocomputational vision of humans as embodied, ecologically embedded, social agents.
  • Need to revisit physical and information-theoretic underpinnings of life, mind, and consciousness.
  • Limitations in bridging computational neuroscience/robotics with ecological psychology/phenomenology.

Purpose of the Study:

  • Introduce the active inference framework (AIF) as a unifying theory.
  • Explore AIF's implications for diverse scientific fields.
  • Provide a naturalistic, information-theoretic foundation for understanding agents.

Main Methods:

  • Introduction to the active inference framework (AIF).
  • Application of the principle of free energy minimization.
  • Exploration of AIF's theoretical implications across disciplines.

Main Results:

  • AIF bridges computational neuroscience, robotics/AI, ecological psychology, and phenomenology.
  • Free energy minimization offers a unified treatment of particles, organisms, and machines.
  • Demonstrates common underpinnings across inorganic, organic, natural, and artificial agents.

Conclusions:

  • AIF provides a robust framework for understanding embodied, social agents.
  • Implications for evolutionary theory, embodied phenomenology, and AI research are significant.
  • Potential for advancing research on machine consciousness.