Hostname: page-component-586b7cd67f-vdxz6 Total loading time: 0 Render date: 2024-11-29T18:55:45.226Z Has data issue: false hasContentIssue false
  • English
  • Français

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/
Get access Rights & Permissions [Opens in a new window]

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
Check for updates
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

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