Advertisement

It Is Time to Dissolve Old Dichotomies in Order to Grasp the Whole Picture of Cognition

  • Knud Thomsen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11324)

Abstract

Models of efficient computation aiming for artificial general intelligence routinely draw a wealth of inspiration from the human brain and behavior. This applies to many diverse details and building blocks, and the most notable ones so far are artificial neural networks. As soon as it comes to more general architectural and algorithmic questions difficulties arise as there is a wide variety of models purportedly describing the basis and the working of specific mental processes. Here, it shall be sketched how a novel cognitive architecture under the name of the Ouroboros Model allows the reconciliation of many competing views by offering an overall conception, in which earlier attempts can be traced to specific and limited perspectives focusing on particular features, tasks and contexts. It is claimed that the Ouroboros Model constitutes a novel and promisingly comprehensive approach, which is still waiting exploitation for detailed formalization, modelling and working implementations.

Keywords

Cognition Schemata Iterative cyclic processing Discrepancy monitoring Consistency curation Self-reflective Self-steered Autocatalytic 

References

  1. 1.
    Stocker, K.: Digital causal cognition. Int. J. Cogn. Linguist. 4(1), 9–34 (2013)Google Scholar
  2. 2.
    Miller, P.: Itinerancy between attractor states in neural systems. Curr. Opin. Neurobiol. 40, 14–22 (2016).  https://doi.org/10.1016/j.conb.2016.05.005CrossRefGoogle Scholar
  3. 3.
    Brewer, W.F., Loschky, L.: Top-Down and Bottom-Up influences on observation: evidence from cognitive psychology and the history of science. In: Cognitive Penetrability of Perception, ed.: Athanassios Raftpoulos, pp. 31–47, Nova Science Publishers, Inc. (2005)Google Scholar
  4. 4.
    Rauss, K., Pourtois, G.: What is bottom-up and what is top-down in predictive coding? Frontiers Psychol. 4, 276 (2013).  https://doi.org/10.3389/fpsyg.2013.00276
  5. 5.
    Logan, G.D.: Parallel and serial processes. In: Pashler, H., Wixted, J. (eds.) Stevens’ Handbook of Experimental Psychology, vol. 4, 3rd edn. Methodology in Experimental Psychology, pp. 271–300. John Wiley & Sons (2002).  https://doi.org/10.1002/0471214426.pas0407
  6. 6.
    Fischer, R., Plessow, F.: Efficient multitasking: parallel versus serial processing of multiple tasks. Frontiers Psychol. 6 (2015).  https://doi.org/10.3389/fpsyg.2015.01366
  7. 7.
    Damasio, A.: Descartes’ Error: emotion, reason, and the human brain. Putnam 1994. Revised edition, Penguin (2005)Google Scholar
  8. 8.
    Gosche, T., Bolte, A.: Emotional modulation of control dilemmas: The role of positive affect, reward, and dopamine in cognitive stability and flexibility. Neuropsychologia 62, 403–423 (2014)CrossRefGoogle Scholar
  9. 9.
    Kahneman, D.: Thinking, Fast and Slow. Farrar, Straus & Giroux (2011)Google Scholar
  10. 10.
    Horr, N.K., Braun, C., Zander, T., Volz, K.G.: Timing matters! The neural signature of intuitive judgments differs according to the way information is presented. Conscious. Cogn. 38, 71–87 (2015)CrossRefGoogle Scholar
  11. 11.
    Lazarus, R.: On the primacy of cognition. Am. Psychol. 39, 124–129 (1984)CrossRefGoogle Scholar
  12. 12.
    Zajonc, R.B.: On the primacy of affect. Am. Psychol. 39, 117–123 (1984)CrossRefGoogle Scholar
  13. 13.
    Goya-Martinez, M.: The emulation of emotion in artificial intelligence: another step in anthropomorphism. In: Emotion, Technology, and Design. Elsevier Inc. (2016).  https://doi.org/10.1016/b978-0-12-801872-9.00008-9CrossRefGoogle Scholar
  14. 14.
    Raffone, A., Srinivasan, N., van Leeuwen, C.: The interplay of attention and consciousness in visual search, attentional blink and working memory consolidation. Phil. Trans. R. Soc. B 369, 1641–1656 (2014)CrossRefGoogle Scholar
  15. 15.
    Thomsen, K.: The Ouroboros Model in the light of venerable criteria. Neurocomputing 74, 121–128 (2010)CrossRefGoogle Scholar
  16. 16.
    Thomsen, K.: Concept formation in the Ouroboros Model. In: Proceedings of AGI 2010 Third Conference on Artificial General Intelligence (2010)Google Scholar
  17. 17.
    Hahn, U., Hornikx, J.: A normative framework for argument quality: argumentation schemes with a Bayesian foundation. Synthese 193, 1833–1873 (2016).  https://doi.org/10.1007/s11229-015-0815-0MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Harris, A.J.L., Corner, A., Hahn, U.: James is polite and punctual (and useless): a Bayesian formalization of faint praise. Thinking Reasoning 19, 414–429 (2014).  https://doi.org/10.1080/13546783.2013.801367CrossRefGoogle Scholar
  19. 19.
    Thomsen, K.: The Ouroboros model, selected facets. In: Hernández, C., et al. (eds.) From Brains to Systems, pp. 239–250. Springer, New York, Dordrecht, Heidelberg, London (2011)  https://doi.org/10.1007/978-1-4614-0164-3_19Google Scholar
  20. 20.
    Thomsen, K.: The Cerebellum according to the Ouroboros Model, the ‘Interpolator Hypothesis’. J. Commun. Comput. 11, 239–254 (2014)Google Scholar
  21. 21.
    Friston, K.: The free-energy principle: a unified brain theory? Nat. Rev. Neurosci. 11, 127–138 (2010)CrossRefGoogle Scholar
  22. 22.
    Thomsen, K.: ONE function for the anterior cingulate cortex and general AI: consistency curation. Med. Res. Archives 6 (2018).  https://doi.org/10.18103/mra.v6i1.1669
  23. 23.
    Thomsen, K.: The Ouroboros Model embraces its sensory-motoric foundations. Studies in Logic, Grammar and Rhetoric 41, 105–125 (2015)CrossRefGoogle Scholar
  24. 24.
    Bowers, K.S., Regehr, G., Balthazard, C., Parker, K.: Intuition in the context of discovery. Cogn. Psychol. 22, 72–110 (1990)CrossRefGoogle Scholar
  25. 25.
    Thomsen, K.: Consciousness for the Ouroboros model. J. Mach. Conscious. 3, 163–175 (2011)CrossRefGoogle Scholar
  26. 26.
    Baars, B.J.: A Cognitive Theory of Consciousness. Cambridge University Press, Cambridge (1988)Google Scholar
  27. 27.
    Dehaene, S., Naccache, L.: Towards a cognitive neuroscience of consciousness: basic evidence and a workspace framework. Cognition 79, 1–37 (2001)CrossRefGoogle Scholar
  28. 28.
    Van Gulick, R.: Higher-order global states - an alternative higher-order view. In: Gennaro, R. (ed.) Higher-Order Theories of Consciousness. John Benjamins, Amsterdam (2004)Google Scholar
  29. 29.
    Treisman, A., Gelade, G.: A feature integration theory of attention. Cogn. Psychol. 12, 97–136 (1980)CrossRefGoogle Scholar
  30. 30.
    Krauzlis, R.J., Billimunta, A., Arcizet, F., Wang, L.: Attention as an effect not a cause. Trends Cognit. Sci. 18, 457–464 (2014)CrossRefGoogle Scholar
  31. 31.
    Laird, J.E., Lebiere, C., Rosenbloom, P.S.: A standard model of the mind, toward a common computational framework across artificial intelligence, cognitive science and robotics. Ai Mag. 38 (2017).  https://doi.org/10.1609/aimag.v38i4.2744CrossRefGoogle Scholar
  32. 32.
    Goertzel, B.: OpenCogPrime: a cognitive synergy based architecture for artificial general intelligence. In: International Conference on Cognitive Informatics, Hong Kong (2009)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Paul Scherrer Institut, NUMVilligen PsiSwitzerland

Personalised recommendations