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Scale-free architectures support representational diversity

Published online by Cambridge University Press:  19 June 2020

Chris Fields Open the ORCID record for Chris Fields [Opens in a new window]  and
James F. Glazebrook
Chris Fields
Affiliation:
Independent, 11106Caunes-Minervois, France. [email protected] https://chrisfieldsresearch.com
James F. Glazebrook
Affiliation:
Department of Mathematics and Computer Science, Eastern Illinois University, Charleston, [email protected] https://faculty.math.illinois.edu/~glazebro/
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Abstract

Gilead et al. propose an ontology of abstract representations based on folk-psychological conceptions of cognitive architecture. There is, however, no evidence that the experience of cognition reveals the architecture of cognition. Scale-free architectural models propose that cognition has the same computational architecture from sub-cellular to whole-organism scales. This scale-free architecture supports representations with diverse functions and levels of abstraction.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 43 , 2020 , e133
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Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

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References

Chater, N. (2018) The mind is flat. Allen Lane.Google Scholar
Craig, A. D. (2010) The sentient self. Brain Structure and Function 214:563–577.CrossRefGoogle ScholarPubMed
Fields, C. & Glazebrook, J. F. (2019) A mosaic of Chu spaces and Channel Theory II: Applications to object identification and mereological complexity. Journal of Experimental and Theoretical Artificial Intelligence 31:237–65.CrossRefGoogle Scholar
Fields, C. & Marcianò, A. (2019) Markov blankets are general physical interaction surfaces. Physics of Life Reviews, in press. https://doi.org/10.1016/j.plrev.2019.08.004.Google ScholarPubMed
Friston, K. (2013) Life as we know it. Journal of the Royal Society, Interface 10(86):20130475.Google Scholar
Friston, K., Levin, M., Sengupta, B. & Pezzulo, G. (2015) Knowing one's place: A free-energy approach to pattern regulation. Journal of the Royal Society Interface 12:20141383.CrossRefGoogle ScholarPubMed
Goguen, J. A. (1991) A categorical manifesto. Mathematical Structures in Computer Science 1:49–67.CrossRefGoogle Scholar
Hoffman, D. D. (2018) The interface theory of perception. In: The Stevens’ handbook of experimental psychology and cognitive neuroscience, vol. II, ed. Serences, J.. Wiley (Ch. 16).Google Scholar
Hoffman, D. D., Singh, M. & Prakash, C. (2015) The interface theory of perception. Psychonomic Bulletin & Review 22:1480–1506.CrossRefGoogle ScholarPubMed
Kuchling, F., Friston, K., Georgiev, G. and Levin, M. (2019) Morphogenesis as Bayesian inference: A variational approach to pattern formation and control in complex biological systems. Physics of Life Reviews, in press. https://doi.org/10.1016/j.plrev.2019.06.001.Google ScholarPubMed
Seth, A. K. & Tsakiris, M. (2018) Being a beast machine: The somatic basis of selfhood. Trends in Cognitive Sciences 22:969–81.CrossRefGoogle ScholarPubMed
Simons, J. S., Garrison, J. R. & Johnson, M. K. (2017) Brain mechanisms of reality monitoring. Trends in Cognitive Sciences 21:462–73.CrossRefGoogle ScholarPubMed
Thomas, K., Malcolm, B. & Lastra, D. (2017) Psilocybin-assisted therapy: A review of a novel treatment for psychiatric disorders. Journal of Psychoactive Drugs 49:446–55.CrossRefGoogle ScholarPubMed
Uddin, L. Q. (2015) Salience processing and insular cortical function and dysfunction. Nature Reviews Neuroscience 16:55–61.CrossRefGoogle ScholarPubMed