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The evolution of brain architectures for predictive coding and active inference.

Giovanni Pezzulo1, Thomas Parr2, Karl Friston2

  • 1Institute of Cognitive Sciences and Technologies, National Research Council, Via S. Martino della Battaglia, 44, 00185 Rome, Italy.

Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences
|December 27, 2021
PubMed
Summary
This summary is machine-generated.

Brain mechanisms for predictive processing evolved gradually from simple reflexes, not just in advanced species. These predictive loops, modeled as generative models, expanded in complexity to solve adaptive regulation problems throughout evolution.

Keywords:
active inferencebrain architecturebrain evolutionmodel selectionnatural selectionpredictive processing

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Area of Science:

  • Evolutionary neuroscience
  • Systems neuroscience
  • Computational neuroscience

Background:

  • Predictive processing theories explain perception and action.
  • Brain mechanisms for predictive processing are often considered a recent evolutionary development.

Purpose of the Study:

  • To propose that predictive processing mechanisms evolved gradually from simpler ancestral forms.
  • To characterize the evolution of brain architectures for predictive processing.

Main Methods:

  • Formal characterization of simpler-to-more-complex brains using generative models.
  • Analysis of evolutionary elaboration pathways: multimodal expansion, duplication, hierarchical depth, and temporal depth.

Main Results:

  • Predictive loops originated from basic autonomic and motor reflexes in early ancestors.
  • Evolutionary elaboration involved increasing hierarchical and temporal depth in generative models.
  • These elaborations enabled adaptive regulation in increasingly complex animals.

Conclusions:

  • Predictive processing is an ancient, gradually evolved capability fundamental to biological regulation.
  • This framework integrates predictive processing theories with evolutionary and comparative neurobiology.