DOI: 10.14704/nq.2019.17.1.1904

Quantum Metalanguage and The New Cognitive Synthesis

Alexey V. Melkikh, Andei Khrennikov, Roman Yampolskiy

Abstract


Problems with mechanisms of thinking and cognition in many ways remain unresolved. Why are a priori inferences possible? Why can a human understand but a computer cannot? It has been shown that when creating new concepts, generalization is contradictory in the sense that to be created concepts must exist a priori, and therefore, they are not new. The process of knowledge acquisition is also contradictory, as it inevitably involves recognition, which can be realized only when there is an a priori standard. Known approaches of the framework of artificial intelligence (in particular, Bayesian) do not determine the origins of knowledge, as these approaches are effective only when “good” hypotheses are made. The formation of “good” hypotheses must occur a priori. To address these issues and paradoxes, a fundamentally new approach to problems of cognition that is based on completely innate behavioral programs is proposed. The process of cognition within the framework of the concept of a quantum metalanguage involves the selection of adequate a priori existing (innate) programs (logical variables and rules for working with them) that are most adequate to a given situation. The quantum properties of this metalanguage are necessary to implement such programs.

Keywords


Knowledge acquisition, Chinese Room, Understanding, Metalanguage, Quantum decision making, Generalization

Full Text:

PDF

References


Abu-Mostafa YS, Magdon-Ismail M, Lin H-T. Learning from data. Pasadena: AMLbook.com, 2012.

Aerts D, Sozzo S, Veloz T. Quantum structure of negation. Frontiers in Psychology 2013; 6: 1447.

Aerts D, Gabora L, Sozzo S, Veloz T. Quantum structure in cognition: fundamentals and applications. arXiv:1104.3344v1, 2011.

Agrawal AA. Phenotypic Plasticity in the Interactions and Evolution of Species. Science 2001; 294: 321-326.

Audi R. Epistemology. Third edition. Routledge. Taylor and Francis Group. New York, London, 2011.

Bagarello F. Quantum dynamics for classical systems: with applications of the number operator. New York: J. Wiley, 2012.

Bagarello F, Basieva I, Khrennikov A. Quantum field inspired model of decision making: Asymptotic stabilization of belief state via interaction with surrounding mental environment. Journal of Mathematical Psychology 2017; 82: 159-168.

de Barros AJ. Quantum-like model of behavioral response computation using neural oscillators. Biosystems 2012; 110: 171-182.

Baronett S. Logic. Upper Saddle River, NJ: Pearson Prentice Hall, 2008.

Barsalou LW. Perceptions of perceptual symbols. Behavioral and Brain Sciences 1999; 22(4): 637-660.

Basieva I, Pothos E, Trueblood J, Khrennikov A, Busemeyer J. Quantum probability updating from zero prior (by-passing Cromwell’s rule). Journal of Mathematical Psychology 2017; 77: 58-69.

Basti G, Capolupo A, Vitiello G. Quantum field theory and coalgebraic logic in theoretical computer science. Progress in Biophysics and Molecular Biology 2017; 130(A): 39-52.

Bishop JM, Nasuto SJ, Coecke B. ‘Quantum Linguistics’ and Searle’s Chinese Room Argument. Müller V. (eds) In “Philosophy and Theory of Artificial Intelligence”. Studies in Applied Philosophy, Epistemology and Rational Ethics, vol 5. Springer, Berlin, Heidelberg, 2013: 17-28.

Bonawitz E, Denison S, Griffiths TL, Gopnik A. Probabilistic models, learning algorithms, and response variability: sampling in cognitive development. Trends in Cognitive Sciences 2014; 18(10): 497-500.

Butcher LM, Davis OSP, Craig IW, Plomin R. Genome‐wide quantitative trait locus association scan of general cognitive ability using pooled DNA and 500K single nucleotide polymorphism microarrays. Genes, Brain and Behavior 2008; 7(4): 435-446.

Canteras NS. The medial hypothalamic defensive system: Hodological organization and functional implications. Pharmacology, Biochemistry and Behavior 2002; 71: 481–491.

Chalmers D. The conscious mind. New York. Oxford University Press, 1996.

Chomsky N. Syntactic Structures. The Hague/Paris: Mouton, 1957.

Chomsky N. Aspects of the Theory of Syntax. MIT Press, 1965.

Chomsky N. The minimalist program. The MIT Press. Cambridge, Massachusetts, 2015.

Cocchi M, Minuto C, Tonello L, Gabrielli F, Bernroider G, Tuszynski JA, Cappello F, Rasenick M. Linoleic acid: Is this the key that unlocks the quantum brain? Insights linking broken symmetries in molecular biology, mood disorders and personalistic emergentism. BMC Neuroscience 2017; 18: 38-48.

Coleman JRI, Bryois J, Gaspar HA, Jansen PR. et al. Biological annotation of genetic loci associated with intelligence in a meta-analysis of 87,740 individuals. Molecular psychiatry 2018; 8: 1.

d'Avila Garcez AS, Lamb LC, Gabbay DM. Neural-Symbolic Cognitive Reasoning, Cognitive Technologies. Springer-Verlag, Berlin Heidelberg, 2009.

Dennett D. Sweet dreams. Philosophical obstracles to a science of consciousness. A Bradford Book, The MIT Press, Cambridge, Massachusetts, and London, England, 2005.

Fodor JA. The Modularity of Mind: an Essay of Faculty Psychology. MIT Press, 1983.

Fodor JA and Pylyshyn ZW. Minds without meanings. An essay on the content of concepts. The MIT Press, 2016.

Garey M and Johnson D. Computers and Intractability: A Guide to the Theory of NP-Completeness. Freeman, San Francisco, 1979.

Gellerman LW. Form Discrimination in Chimpanzees and Two-Year-Old Children: I. Form (Triangularity) Per Se, The Pedagogical Seminary and Journal of Genetic Psychology 1933; 42(1): 3-27.

Glanzman DL. Common Mechanisms of Synaptic Plasticity in Vertebrates and Invertebrates. Minireview. Curr Biol 2010; 20: 31-36.

Goertzel B. The structure of intelligence. A new mathematical model of mind. Springer-Verlag. New York, 1993.

Gopnik A. (2003) The theory theory as an alternative to the innateness hypothesis. Book chapter in: In L. Antony and N. Hornstein (Eds.), Chomsky and his critics. Oxford: Blackwells. Retrieved 2013-04-26.

Gopnik A. Reconstructing constructivism: Causal models, Bayesian learning mechanisms, and the theory theory. American Psychological Association 2012; 138: 1085–1108.

Gorlich D, Artmann S, Dittrich P. Cells as semantic systems. Bioch Bioph Acta 2011; 1810: 914–923.

Gottfredson LS. Mainstream Science on Intelligence (editorial). Intelligence 1997; 24: 13–23.

Griffiths TL. Manifesto for a new (computational) cognitive revolution. Cognition 2015; 135: 21-23.

Hameroff S and Penrose R. Consciousness in the universe: A review of the ‘Orch OR’ theory. Phys Life Rev 2014; 11: 39-78.

Haven E and Khrennikov A. The Palgrave handbook of quantum models in social science. Applications and grand challenges. Palgrave Macmillan, London, 2017.

Hernandez-Orallo J. The measure of all minds. Evaluation of natural and artificial intelligence. Cambridge University Press, 2016.

Hill WD, Marioni RE, Maghzian O, Ritchie SJ, Hagenaars SP, McIntosh AM, Gale CR, Davies G, Deary IJ. A combined analysis of genetically correlated traits identifies 187 loci and a role for neurogenesis and myelination in intelligence. Molecular psychiatry 2018.

Kamsu-Foguem B, Tchuenté-Foguem G, Allart L, Zennir Y, Vilhelm Y, Mehdaoui H, Zitouni D, Hubert H, Lemdani M, Ravaux P. User-centered visual analysis using a hybrid reasoning architecture for intensive care units. Decision Support Systems 2012; 54(1): 496–509.

Kant I. Critique of pure reason. Cambridge University Press, 1998.

Khrennikov A. Classical and quantum mechanics on information spaces with applications to cognitive, psychological, social and anomalous phenomena. Foundations of Physics 1999; 29(7): 1065-1098.

Khrennikov A. Ubiquitous quantum structure: from psychology to finances, Springer, Berlin-Heidelberg-New York, 2010a.

Khrennikov A. Modelling of psychological behavior on the basis of ultrametric mental space: Encoding of categories by balls. P-Adic Numbers, Ultrametric Analysis, and Applications 2010b; 2, N. 1: 1-20.

Khrennikov A. Quantum-like model of processing of information in the brain based on classical electromagnetic field. Biosystems 2011; 105(3): 250-262.

Kheirbeck MA and Hen R. Dorsal vs ventral hippocampal neurogenesis: implications for cognition and mood. Neuropsychopharmacology 2011; 36(1): 373–374.

Koch C and Tononi G. Consciousness as integrated information. Biological Bulletin 2008; 215(3): 216-242.

Korf J. Quantum and multidimensional explanations in a neurobiological context of mind. The Neurocsientist 2015; 21(4): 345-355.

Lake BM, Salakhutdinov R, Tenenbaum JB. Human-level concept learning through probabilistic program induction. Science 2015; 350: 1332-1338.

LeCun Y, BengioY, Hinton G. Deep learning. Nature 2015; 521: 436–444.

Legg S, Hutter M. (2007) A collection of definitions of intelligence. Advances of Artificial General Intelligence: Concepts, Architectures and Algorithms. Eds. Goertzel, B., Wang, P., IOP Press: 17-24.

Lorenz KZ. (1950) The comparative method in studying innate behavior patterns. in “Physiological Mechanisms of Animal Behavior”. 221-268. Cambridge University Press.

Luger GF. Cognitive Science: The Science of Intelligent Systems. Academic Press, San Diego and New York, 1994.

Luger GF. Artificial intelligence. Structures and strategies for complex problem solving. Fourth edition. Addison Wesley, 2003.

Maruyama Y. AI, Quantum Information, and External Semantic Realism: Searle’s Observer-Relativity and Chinese Room, Revisited. InFundamental Issues of Artificial Intelligence 2016; pp: 115-127. Springer, Cham.

Mathôt S, Melmi J-B, Van Der Linden L, Van Der Stigchel S. The mind-writing pupil: a human-computer interface based on decoding of covert attention through pupillometry. PLoS ONE 2016; 11 (2): e0148805.

Melkikh AV. The No Free Lunch Theorem and hypothesis of instinctive animal behavior. Artificial Intelligence Research 2014a; 3(4): 43-63.

Melkikh AV. Congenital programs of the behavior and nontrivial quantum effects in the neurons work. Biosystems 2014b; 119: 10-19.

Melkikh AV. Quantum information and the problem of mechanisms of biological evolution. BioSystems 2014c; 115: 33-45.

Melkikh AV and Meijer DKF. On a generalized Levinthal’s paradox: the role of long- and short range interactions in complex bio-molecular reactions, including protein and DNA folding. Progress in Biophysics and Molecular Biology 2018; 132: 57-79.

Melkikh AV and Khrennikov A. Nontrivial quantum and quantum-like effects in biosystems: unsolved questions and paradoxes. Progress in Biophysics and Molecular Biology 2015; 119(2): 137-161.

Melkikh AV and Mahecha DS. On the Broader Sense of Life and Evolution: Its Mechanisms, Origin and Probability across the Universe: Journal of Astrobiology & Outreach 2017; 5(3).

Miller GA. The cognitive revolution: a historical perspective. Trends in Cognitive Sciences 2003; 7(3): 141-144.

Minsky ML. The Society of Mind. William Heinemann Ltd, London, 1987.

Noble D. The Music of Life. Biology Beyond Genes, Oxford University Press, Oxford, 2006.

Osherson D, Stob M, Weinstein S. Systems that learn. MIT Press. Cambridge MA, 1986.

Pask G. The cybernetics of human learning and performance. Hutchinson, 1975.

Penrose R. Shadows of mind. A search of the missing science of consciousness. Oxford University Press. New York, Oxford, 1994.

Pothos EM and Busemeyer, J.M. Can quantum probability provide a new direction for cognitive modeling? Behavioral and brain sciences 2013; 36: 255–327.

Pylkkanen P. Can Bohmian quantum information help us to understand consciousness? In: Atmanspacher H., Filk T., Pothos E. (eds) Quantum Interaction. Lecture Notes in Computer Science, Springer, Cham 2016; l 9535: 76-87.

Russell B. History of western philosophy. Routledge. Tailor and Francis Group. London and New York, 2009.

Samuels R. Innateness in cognitive science. Trends in Cognitive Sciences 2004; 8(3): 136-141.

Searle JR. Minds, Brains, and Programs. Behavioral and Brain Sciences 1980; 3(3): 417–457.

Silver D, Hubert T, Schrittwieser J, Antonoglou I, Lai M, Guez A, Lanctot M, Sifre L, Kumaran D, Graepel T, Lillicrap T, Simonyan K, Hassabis D. Mastering chess and shogi by self-play with a general reinforcement learning algorithm. arXiv:1712.01815v1. 2017.

Smith CU. The ‘hard problem’ and the quantum physicists. Part 1: the first generation. Brain Cogn 2006; 61: 181–188.

Smith CU. The ‘hard problem’ and the quantum physicists. Part 2: modern times. Brain Cogn 2009; 71: 54–63.

Sverdlik A. How our emotions and bodies are vital for abstract thought: perfect mathematics for imperfect minds. Taylor and Francis. New York, 2018.

Swanson LW. Cerebral Hemisphere Regulation of Motivated Behavior. Brain Research 2000; 886: 113–164.

Sze V, Chen Y-H, Yang T-J, Emer J. Efficient processing of deep neural networks: a tutorial and survey. arXiv:1703.09039v2. 2017.

Tomasello M. Constructing a language. A usage-based theory of language acquisition. Harvard University Press. Cambridge, Massachusetts, 2009.

Todorov A and Engell AD. The role of the amygdala in implicit evaluation of emotionally neutral faces. Social Cognitive and Affective Neuroscience 2008; 3(4): 303–312.

Vilhelm C, Ravaux P, Calvelo D, Jaborska A, Chambrin M-C, Boniface M. Think!: a unified numerical-symbolic knowledge representation scheme and reasoning system. Artificial Intelligence 2000; 116(1–2): 67–85.

Wigner E. The unreasonable effectiveness of mathematics in natural sciences. In Symmetries and Reflections. Indiana University Press, 1967; pp: 222-237.

Whitman DW and Agrawal A. What is phenotypic plasticity and why is it important? Phenotypic Plasticity of Insects: Mechanisms and Consequences (ed. by D. W. Whitman and T. N. Ananthakrishnan), Science Publishers, Enfield, New Hampshire 2009; pp: 1–63.

Yampolsky RV. On the Limits of Recursively Self-Improving AGI. In: Bieger J., Goertzel B., Potapov A. (eds) Artificial General Intelligence. AGI 2015. Lecture Notes in Computer Science, Springer, Cham, 2015; 9205.

Yampolskiy RV. Form seed AI to technological singularity via recursively self-improving software. arXiv: 1502.06512, 2015.

Yampolskiy RV. Detecting qualia in natural and artificial agents. arXiv:1712.04020, 2017.

Zizzi P. Non-algorithmic side of the mind. arXiv:1205.1820, 2012.


Supporting Agencies





| NeuroScience + QuantumPhysics> NeuroQuantology :: Copyright 2001-2019