RAS Social ScienceSotsialogicheski issledovania

  • ISSN (Print) 0132-1625
  • ISSN (Online)3034-6010

Natural Computations and Artificial Intelligence

PII
S023620070019511-9-
DOI
10.31857/S023620070019511-9
Publication type
Article
Status
Published
Authors
Volume/ Edition
Volume 33 / Issue 2
Pages
65-83
Abstract

The research program focused on the analysis of computational approaches to natural and artificial intelligence is one of four accepted for implementation at the Center for the Philosophy of Consciousness and Cognitive Sciences of the Institute of Philosophy, Russian Academy of Sciences. Presumably, it should become a direction of interdisciplinary research at the crossroad of philosophy, cognitive psychology, cognitive and social neuroscience, and artificial intelligence. The working hypothesis proposed for discussion attended by the relevant specialists is as follows: if an acceptable computational theory of mind appears, we will be able to restrict our research to a simple scientific ontology describing only parts of a physical implementation of computational algorithms, adding a relevant version of computational mathematics thereto. Another hypothesis proposed is that there is an essential ontological intersection between the mechanisms underlying human cognitive abilities and their social organization, both of which serving as an implementation medium for complex distributed cognitive computations. Particularly those which are associated with social organization are responsible for logical and verbal (“rational”) cognitive abilities. As a result of some previous research, an ontology of nested distributed computational systems was generally formulated, which, as expected, can demonstrate significant heuristic potential if supplemented with an adequate mathematical apparatus. Since only individuals with certain cognitive abilities can be social agents, a philosophical problem arises: are cognitive abilities necessary or sufficient to involve their carriers in stable social interactions? In the first case, we have a weak thesis about the cognitive determination of sociality, in the second — the strong one. The choice between these positions is, too, a subject of future research.

Keywords
cognitive computations, artificial intelligence, cognitive psychology, cognitive social neuroscience, scientific ontology, rationality, multi-agent systems
Date of publication
11.05.2022
Number of purchasers
12
Views
975

References

  1. 1. Dzhokhadze I.D. Analiticheskiĭ pragmatizm Roberta Brehndoma. IFRAN; 2015.
  2. 2. Mikhajlov I.F. Virtual'nye proektsii chelovecheskogo mira: mul'tiagentnye sistemy // Filosofskie problemy informatsionnykh tekhnologij i kiberprostranstva. 2017, №13(1). S. 18–28. doi:10.17726/philIT.2017.1.1
  3. 3. Mikhajlov I.F. Vospriyatie, logika i mnozhestvennost' ratsional'nykh reprezentatsiĭ mira. Filosofskie nauki. 2019, № 62(7) S. 37–53. doi:10.30727/0235-1188-2019-62-7-37-53
  4. 4. Mikhajlov I.F. Vychislitel'nyj podkhod v sotsial'nom poznanii. Filosofiya nauki i tekhniki. 2021, №26(1), S. 23–37. doi:10.21146/2413-9084-2021-26-1-23-37
  5. 5. Mikhajlov I.F. K gipersetevoj teorii soznaniya [Toward a Hyper-Network Theory of Consciousness]. Voprosy filosofii. 2015, №11. S. 87–98.
  6. 6. Mikhajlov I.F. Kognitivnye vychisleniya i sotsial'naya organizatsiya. Voprosy filosofii. 2020, №11, S. 125–128. doi:10.21146/0042‒8744‒2020‒11-125-128
  7. 7. Mikhajlov I.F. Sotsial'naya ontologiya: vremya vychislenij. Vestnik Tomskogo gosudarstvennogo universiteta. Filosofiya Sotsiologiya Politologiya. 2020, № 55, S. 36–46. doi:10.17223/1998863X/55/5
  8. 8. Mikhajlov I.F. Filosofskie problemy modelirovaniya mul'tiagentnykh sistem. Filosofskie nauki. 2018, №12, S. 56–74. doi:10.30727/0235-1188-2018-12-56-74
  9. 9. Mikhajlov I.F. Chelovek, soznanie, seti. M.: IFRAN, 2015.
  10. 10. Anderson J. R. The Architecture of Cognition. Harvard: Harvard University Press, 1983.
  11. 11. Attneave F. In defense of homunculi. Sensory Communication. Contributions to the Symposium on Principles of Sensory Communication, July 19-Aug. 1, 1959, Endicott House. Cambridge: MIT Press, 1961. P. 777–782.
  12. 12. Brandom R.B. Making It Explicit: Reasoning, Representing, and Discursive Commitment. Harvard: Harvard University Press, 2001.
  13. 13. Buckley C.L., Kim C.S., McGregor S., Seth A. K. The free energy principle for action and perception: A mathematical review. Journal of Mathematical Psychology. 2017 N 81. P. 55–79. doi:10.1016/j.jmp.2017.09.004
  14. 14. Calzavarini F. Chapter 3. Inferential processing with concrete vs. abstract words and visual cortex. Bolognesi M., Steen G. J. (Eds.). Perspectives on Abstract Concepts. Cognition, Language and Communication. Amsterdam: John Benjamins Publishing Company, 2019. P. 59–74. doi:10.1075/hcp.65.04cal
  15. 15. Calzavarini F. Inferential and referential lexical semantic competence: A critical review of the supporting evidence. Journal of Neurolinguistics, 2017, N 44. P. 163-189. doi:10.1016/j.jneuroling.2017.04.002
  16. 16. Carvalho E.M. Socially Extending the Mind Through Social Affordances. Curado M., Gouveia S. S. (Eds.) Automata’s Inner Movie: Science and Philosophy of Mind. Wilmington: Vernon Press, 2019. P. 193–212.
  17. 17. Clark A. Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences. 2013, N 36(3), P. 181–204. doi:10.1017/S0140525X12000477
  18. 18. Colombo M., Wright C. First principles in the life sciences: the free-energy principle, organicism, and mechanism. Synthese. Published online: Sept. 10 2018. doi:10.1007/s11229-018-01932-w
  19. 19. Craver C., Bechtel W. Mechanism. In: Pfeifer J., Sarkar S. (Eds.) The Philosophy of Science: An Encyclopedia. Psychology Press; 2006:469-478.
  20. 20. Debruille J.B., Brodeur M.B., Franco Porras C. N300 and Social Affordances: A Study with a Real Person and a Dummy as Stimuli. In: di Pellegrino G (Ed.) PLoS ONE. 2012, N 7(10): e47922. doi:10.1371/journal.pone.0047922
  21. 21. DeSilva J.M., Traniello J.F. A., Claxton A.G., Fannin L.D. When and Why Did Human Brains Decrease in Size? A New Change-Point Analysis and Insights from Brain Evolution in Ants. Frontiers in Ecology and Evolution. 2021, N 9. doi:10.3389/fevo.2021.742639
  22. 22. Fagin R., Halpern J., Moses Y., Vardi M. Reasoning About Knowledge. Cambridge: The MIT Press, 2003. doi:10.7551/mitpress/5803.001.0001
  23. 23. Fetzer J. H. Thinking and Computing: Computers as Special Kinds of Signs. Minds and Machines. 1997, N 7(3). P. 345–364. doi:10.1023/A:1008230900201
  24. 24. Friston K., Frith C. A. Duet for one. Consciousness and Cognition. 2015, N 36, P. 390–405. doi:10.1016/j.concog.2014.12.003
  25. 25. Friston K., Kiebel S. Predictive coding under the free-energy principle. Philosophical Transactions of the Royal Society: Biological Sciences. 2009, N 364(1521), P. 1211–1221. doi:10.1098/rstb.2008.0300
  26. 26. Friston K.J. Waves of Prediction. PLOS Biology. 2019, N 17(10), P. e3000426. doi:10.1371/journal.pbio.3000426
  27. 27. Friston K.J., Frith C.D. Active Inference, Communication and Hermeneutics. Cortex. 2015, N 68, P. 129–143. doi:10.1016/j.cortex.2015.03.025
  28. 28. von Helmholtz H. Treatise on Physiological Optics. NY: Dover Publications, 2013.
  29. 29. Hohwy J. The Predictive Mind. Oxford: Oxford University Press, 2014. doi:10.1093/acprof:oso/9780199682737.001.0001
  30. 30. Keller G.B., Mrsic-Flogel T. D. Predictive Processing: A Canonical Cortical Computation. Neuron. 2018. N 100(2), P. 424–435. doi:10.1016/j.neuron.2018.10.003
  31. 31. Kelly M.P., Kriznik N.M., Kinmonth A.L., Fletcher P.C. The Brain, Self and Society: a social-neuroscience model of predictive processing. Social Neuroscience. 2019, N 14(3), P. 266-276. doi:10.1080/17470919.2018.1471003
  32. 32. Kilner J.M., Friston K.J., Frith C.D. Predictive Coding: An Account of the Mirror Neuron System. Cognitive Processing. 2007, N 8(3), P. 159–166. doi:10.1007/s10339-007-0170-2
  33. 33. Kremer M. Representation or Inference: Must We Choose? Should We? Weiss B., Wanderer J. (Eds.). Reading Brandom: On Making It Explicit. London; New York: Routledge, 2010.
  34. 34. Marr D. Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. Cambridge: The MIT Press, 2010. doi:10.7551/mitpress/9780262514620.001.0001
  35. 35. McDermott D.V. Mind and Mechanism. Cambridge: MIT Press, 2001. [Electronic resource] URL: https://books.google.cm/books?id=aSm4BhlmHYEC
  36. 36. Pylyshyn Z.W. Computation and Cognition: Toward a Foundation for Cognitive Science. Cambridge: The MIT Press, 1986.
  37. 37. Ryder D., Favorov O.V. The New Associationism: A Neural Explanation for the Predictive Powers of Cerebral Cortex. Brain and Mind. 2001, N 2(2), P. 161–194. doi:10.1023/A:1012296506279
  38. 38. Seth A.K, Hohwy J. Predictive Processing as An Empirical Theory for Consciousness Science. Cognitive Neuroscience. 2021, N 12(2), N 89–90. doi:10.1080/17588928.2020.1838467
  39. 39. Weiss B., Wanderer J. Introduction. Weiss B., Wanderer J. (Eds.). Reading Brandom: On Making It Explicit. London; New York: Routledge, 2010.
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