The Ising model as a discovery tool in cognitive sciences

Kozhin, Foma (2022) The Ising model as a discovery tool in cognitive sciences. PsyArXiv . (Submitted to Publisher)

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Abstract

In this contribution we will explore how the Ising model can connect different physical phenomena with the cognitive sciences.

For decades, physicists have pursued the use ideas from statistical mechanics to capture the collective phenomena of life. Biological systems have a subtle structure that is not described neither by ordered crystals nor disordered gases. Moreover, these states are far-from-equilibrium, being maintained by a constant flow of energy and matter through the system. There are special states for a functional living system and, at the same time, their activity cannot rely on fine-tuning the parameters of the system. Of the many ideas originated in statistical physics that have been suggested to characterize these states, perhaps the most suggestive and speculative is the idea of self-organized criticality. The theory of self-organized criticality originated in models of inanimate objects (sand mountains, earthquakes, etc.) [1], [2], but then the theory was to include biological systems through the analysis of simple toy models [3]. A simple model the evolution of interacting species can self-organize into a critical state where the quiescent period is interrupted by avalanches of all sizes [4], describing a behavior similar to the idea of punctuated equilibrium in evolution [5]. Similarly, the brain has been suggested to be in a self-organized critical state at the boundary between being nearly dead and becoming completely epileptic [6].

References
[1] P. Bak, C. Tang, and K. Wiesenfeld, ‘Self-organized criticality’, Physical review A, vol. 38, no. 1, p. 364, 1988.
[2] P. Bak, C. Tang, and K. Wiesenfeld, ‘Self-organized criticality: An explanation of the 1/f noise’, Phys. Rev. Lett., vol. 59, no. 4, pp. 381–384, Jul. 1987, doi: 10.1103/PhysRevLett.59.381.
[3] P. Bak, How nature works: the science of self-organized criticality. New York: Copernicus, 1996.
[4] P. Bak and K. Sneppen, ‘Punctuated equilibrium and criticality in a simple model of evolution’, Phys. Rev. Lett., vol. 71, no. 24, pp. 4083–4086, Dec. 1993, doi: 10.1103/PhysRevLett.71.4083.
[5] S. J. Gould and N. Eldredge, ‘Punctuated Equilibria: The Tempo and Mode of Evolution Reconsidered’, Paleobiology, vol. 3, no. 2, pp. 115–151, 1977.
[6] M. Usher, M. Stemmler, and Z. Olami, ‘Dynamic Pattern Formation Leads to 1/f Noise in Neural Populations’, Phys. Rev. Lett., vol. 74, no. 2, pp. 326–329, Jan. 1995, doi: 10.1103/PhysRevLett.74.326.

Item Type:	Article
Journal / Publication Title:	PsyArXiv
Publisher:	Society for the Improvement in Psychology Science
Departments:	Institute of Science and Environment > STEM
Additional Information:	Foma Kozhin, Institute of Science and Environment, University of Cumbria, UK. This article is a preprint. It may not have been peer reviewed.
Depositing User:	Anna Lupton
Date Deposited:	01 Feb 2023 17:28
Last Modified:	13 Jan 2024 13:33
URI:	https://insight.cumbria.ac.uk/id/eprint/6850

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