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Related Experiment Videos

Probabilistic pathway representation of cognitive information.

Andrei Khrennikov1

  • 1International Center for Mathematical Modeling in Physics and Cognitive Sciences, University of Växjö, S-35195, Sweden. Andrei.khrennikov@msi.vxu.se

Journal of Theoretical Biology
|October 19, 2004
PubMed
Summary

This study maps mental processes onto mathematical spaces using a p-adic model, viewing neuronal hierarchies as fundamental to mental space. It proposes a probabilistic model where mental states are probability distributions.

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Area of Science:

  • Cognitive Science
  • Mathematical Psychology
  • Neuroscience

Background:

  • Previous work successfully mapped physical processes mathematically.
  • The brain's complexity necessitates novel approaches beyond simple physical simulation.
  • Understanding mental processes requires abstract mathematical frameworks.

Purpose of the Study:

  • To present a program for mathematical mapping of purely mental processes.
  • To introduce and justify the p-adic model for representing mental space.
  • To develop a probabilistic model for mental states.

Main Methods:

  • Philosophical, mathematical, information, and neurophysiological arguments.
  • Utilizing the hierarchical tree structure of p-adic spaces to model neuronal hierarchy.

Related Experiment Videos

  • Developing a probabilistic neural pathway model.

    • Main Results:

    • The p-adic model provides a framework for mapping mental processes.
    • Hierarchical neural pathways are identified as fundamental information processing units.
    • Mental space is conceptualized as being generated by the entire neural system.

    Conclusions:

    • The p-adic model offers a viable mathematical framework for mental processes.
    • Mental states can be represented as probability distributions within this framework.
    • This approach provides a novel perspective on the mathematical underpinnings of cognition.