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Metabolic considerations for cognitive modeling.

Philipp Haueis1, David J Colaço2

  • 1Department of Philosophy, Institute for Studies of Science, Universität Bielefeld, Germany Institute of Philosophy, Leibniz Universität Hannover, Germany Email: philipp.haueis@uni-bielefeld.de URL: https://philipp-haueis.de/.

The Behavioral and Brain Sciences
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PubMed
Summary
This summary is machine-generated.

Cognitive models should incorporate metabolic factors, as the brain

Keywords:
Artificial IntelligenceCognitionConstraintsEnergy UseInformation ProcessingMetabolismModelsNeural Computation

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Computational Neuroscience

Background:

  • The human brain uses 20% of the body's energy despite being 2% of body mass.
  • Biological brains are more energy-efficient than most computers.
  • Current cognitive models often overlook metabolic constraints.

Purpose of the Study:

  • To argue for the integration of metabolic considerations into cognitive models.
  • To distinguish between evaluative and generative uses of metabolic factors in modeling.
  • To highlight the relevance of metabolic constraints for understanding brain function.

Main Methods:

  • Distinguishing evaluative and generative roles of metabolic considerations.
  • Proposing metabolic limits as constraints for model evaluation.
  • Suggesting metabolic knowledge for generating new cognitive models.

Main Results:

  • Metabolism acts as an explanatory constraint, limiting possible computations in brains.
  • Metabolism structures neural information flow.
  • Metabolic considerations can generate novel models of cognitive capacities.

Conclusions:

  • Cognitive models must be consistent with the brain's metabolic limits.
  • Metabolic factors offer insights into mental effort, multiple realization, and AI comparisons.
  • Integrating metabolism enhances the explanatory power of cognitive models.