The active inference approach to ecological perception

general information dynamics for natural and artificial embodied cognition

Adam Linson*, Andy Clark, Subramanian Ramamoorthy, Karl Friston

*Corresponding author for this work

Research output: Contribution to journalArticle

17 Citations (Scopus)
7 Downloads (Pure)

Abstract

The emerging neurocomputational vision of humans as embodied, ecologically embedded, social agents-who shape and are shaped by their environment-offers a golden opportunity to revisit and revise ideas about the physical and information-theoretic underpinnings of life, mind, and consciousness itself. In particular, the active inference framework (AIF) makes it possible to bridge connections from computational neuroscience and robotics/AI to ecological psychology and phenomenology, revealing common underpinnings and overcoming key limitations. AIF opposes the mechanistic to the reductive, while staying fully grounded in a naturalistic and information-theoretic foundation, using the principle of free energy minimization. The latter provides a theoretical basis for a unified treatment of particles, organisms, and interactive machines, spanning from the inorganic to organic, non-life to life, and natural to artificial agents. We provide a brief introduction to AIF, then explore its implications for evolutionary theory, ecological psychology, embodied phenomenology, and robotics/AI research. We conclude the paper by considering implications for machine consciousness.

Original languageEnglish
Article number21
Pages (from-to)1-22
Number of pages22
JournalFrontiers in Robotics and AI
Volume5
DOIs
Publication statusPublished - 8 Mar 2018

Bibliographical note

Copyright the Author(s) 2018. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • Affordances
  • Embodiment
  • Evolution
  • Frame problem
  • Free energy
  • Self-organization
  • Skilled expertise
  • Uncertainty

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