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Following the Dynamics of Structural Variants in Experimentally Evolved Populations
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Evolution in Mind: Evolutionary Dynamics, Cognitive Processes, and Bayesian Inference.

Jordan W Suchow1, David D Bourgin1, Thomas L Griffiths1

  • 1Department of Psychology, University of California, Berkeley, CA, USA.

Trends in Cognitive Sciences
|May 29, 2017
PubMed
Summary
This summary is machine-generated.

Evolutionary theory offers a novel mathematical framework for understanding the mind, viewing thoughts and memories as evolving populations. This perspective reveals deep connections between evolution and learning, particularly Bayesian inference, enhancing our grasp of cognition.

Keywords:
Bayesian inferencecognitive processescreativityevolutionlearningmemory

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Area of Science:

  • Cognitive Science
  • Evolutionary Psychology
  • Computational Neuroscience

Background:

  • Evolutionary theory traditionally explains human cognitive capacities as adaptations to ancestral environments.
  • A less common application of evolutionary theory is as a mathematical framework for internal mental processes.
  • Existing frameworks often overlook the dynamic, evolving nature of thoughts and memories within an individual.

Purpose of the Study:

  • To explore evolutionary theory as a mathematical framework for understanding the dynamics of thoughts, ideas, and memories within a single mind.
  • To investigate the mathematical correspondences between evolutionary dynamics and learning processes.
  • To highlight the relevance of these correspondences for cognitive capacities like memory and creativity.

Main Methods:

  • Applying mathematical models from evolutionary theory to populations of mental content (thoughts, memories).
  • Analyzing the deep correspondences between evolutionary dynamics (e.g., selection, drift) and learning algorithms.
  • Examining the equivalence between specific evolutionary dynamics and Bayesian inference principles.

Main Results:

  • Evolutionary theory provides a powerful mathematical framework for modeling the internal evolution of mental content.
  • Deep mathematical correspondences exist between evolutionary processes and learning, notably Bayesian inference.
  • This equivalence allows evolutionary dynamics to be reinterpreted as algorithms for Bayesian inference.

Conclusions:

  • Evolutionary theory offers a novel perspective on cognitive processes, viewing them as evolving populations of mental representations.
  • The mathematical equivalence between evolution and Bayesian inference provides a unified framework for understanding learning and memory.
  • This approach has significant implications for understanding diverse cognitive capacities, including memory, creativity, and potentially others.