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.
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Stocker, K.: Digital causal cognition. Int. J. Cogn. Linguist. 4(1), 9–34 (2013)
Miller, P.: Itinerancy between attractor states in neural systems. Curr. Opin. Neurobiol. 40, 14–22 (2016). https://doi.org/10.1016/j.conb.2016.05.005
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)
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
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
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
Damasio, A.: Descartes’ Error: emotion, reason, and the human brain. Putnam 1994. Revised edition, Penguin (2005)
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)
Kahneman, D.: Thinking, Fast and Slow. Farrar, Straus & Giroux (2011)
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)
Lazarus, R.: On the primacy of cognition. Am. Psychol. 39, 124–129 (1984)
Zajonc, R.B.: On the primacy of affect. Am. Psychol. 39, 117–123 (1984)
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/b978-0-12-801872-9.00008-9
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)
Thomsen, K.: The Ouroboros Model in the light of venerable criteria. Neurocomputing 74, 121–128 (2010)
Thomsen, K.: Concept formation in the Ouroboros Model. In: Proceedings of AGI 2010 Third Conference on Artificial General Intelligence (2010)
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/s11229-015-0815-0
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/10.1080/13546783.2013.801367
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_19
Thomsen, K.: The Cerebellum according to the Ouroboros Model, the ‘Interpolator Hypothesis’. J. Commun. Comput. 11, 239–254 (2014)
Friston, K.: The free-energy principle: a unified brain theory? Nat. Rev. Neurosci. 11, 127–138 (2010)
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
Thomsen, K.: The Ouroboros Model embraces its sensory-motoric foundations. Studies in Logic, Grammar and Rhetoric 41, 105–125 (2015)
Bowers, K.S., Regehr, G., Balthazard, C., Parker, K.: Intuition in the context of discovery. Cogn. Psychol. 22, 72–110 (1990)
Thomsen, K.: Consciousness for the Ouroboros model. J. Mach. Conscious. 3, 163–175 (2011)
Baars, B.J.: A Cognitive Theory of Consciousness. Cambridge University Press, Cambridge (1988)
Dehaene, S., Naccache, L.: Towards a cognitive neuroscience of consciousness: basic evidence and a workspace framework. Cognition 79, 1–37 (2001)
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)
Treisman, A., Gelade, G.: A feature integration theory of attention. Cogn. Psychol. 12, 97–136 (1980)
Krauzlis, R.J., Billimunta, A., Arcizet, F., Wang, L.: Attention as an effect not a cause. Trends Cognit. Sci. 18, 457–464 (2014)
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.v38i4.2744
Goertzel, B.: OpenCogPrime: a cognitive synergy based architecture for artificial general intelligence. In: International Conference on Cognitive Informatics, Hong Kong (2009)
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Thomsen, K. (2018). It Is Time to Dissolve Old Dichotomies in Order to Grasp the Whole Picture of Cognition. In: Fagan, D., Martín-Vide, C., O'Neill, M., Vega-Rodríguez, M.A. (eds) Theory and Practice of Natural Computing. TPNC 2018. Lecture Notes in Computer Science(), vol 11324. Springer, Cham. https://doi.org/10.1007/978-3-030-04070-3_25
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