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Pattern activation/recognition theory of mind.

Bertrand du Castel1

  • 1Schlumberger Research Houston, TX, USA.

Frontiers in Computational Neuroscience
|August 4, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a unified "Pattern Activation/Recognition Theory of Mind," expanding on Kurzweil's ideas. It proposes that the brain unifies sensory and motor functions through pattern activation and recognition, using stochastic grammars.

Keywords:
autapsegrammarhylomorphismmetaphorneuralrecurrentself-descriptionstochastic

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Area of Science:

  • Cognitive Science
  • Neuroscience
  • Artificial Intelligence

Background:

  • Ray Kurzweil's 2012 "Pattern Recognition Theory of Mind" posits the brain uses millions of pattern recognizers.
  • Existing theories often separate sensory and motor functions, and cognitive processes like organization and checking.

Purpose of the Study:

  • To propose a unified theory of mind that integrates pattern activation and recognition.
  • To extend existing pattern recognition models to encompass both sensory and motor functions.
  • To reframe cognitive processes as "patterns of patterns" for a unified theoretical framework.

Main Methods:

  • Developed the "Pattern Activation/Recognition Theory of Mind" based on stochastic grammars.
  • Utilized a probabilistic programming language for implementing grammatical and neural operations.
  • Generated diagrams illustrating stochastic grammars and corresponding neural circuits.

Main Results:

  • Demonstrated that self-describing stochastic grammars can unify pattern activation, recognition, organization, consistency checking, metaphor, and learning.
  • Presented a computational model implementing the unified theory.
  • Visualized the relationship between grammatical structures and neural circuits.

Conclusions:

  • The proposed theory offers a unified framework for understanding diverse cognitive functions.
  • The model provides a basis for exploring artificial network developments, common coding, and neural reuse.
  • Potential validation paths for the theory are discussed, linking computational models to empirical neuroscience.