Technology of mental functional representations as a first stage of conceptualization and implementation of complex scientific knowledge in innovation processes

In innovation processes, it is common to deal with highly cross-multidisciplinary topics. For example, an innovation process may integrate psychological, neuroscientific, biological and engineering disciplines, among many others dealing with bio-cybernetic systems. One specific type of those theories is related to cognitive processes, knowledge representation and self-learning systems. Therefore, there is a need to easily and rapidly understand, as well as apply and share knowledge of complex theories by innovation managers, engineers, scholars, training practitioners, computational modelers, managers, and stakeholders, among others. In this regard, the present article provides with a graphical tool to represent complex crossmultidisciplinary theories, concepts and processes in a simple, concise, and logical manner, by using functional principles and graphical representations that have been successfully used in engineering and technology areas such as adaptive control systems, algorithmic flow charts, and computational cognitive neuroscience. Once described the models that have been typically used to represent and model knowledge and cognition, functional cognitive modeling is introduced, and then applied to represent and model complex cognitive theories from psychology and neuroscience such as Jean Piaget’s Theory of Intellectual Growth, Antonio Damasio’s Somatic Marker Hypothesis, and Dante Dorantes’ Soft Skills Model

Ключевые слова: cross-disciplinary, multidisciplinary, mental model, mental representation, functional model.

Список использованных источников

  1. M. W. Eysenck, M. T. Keane. Cognitive Psychology. A student’s handbook. Hove and New York, East Sussex: Psychology Press, 2010. P. 20.
  2. P. N. Johnson-Laird. Mental models in cognitive science//Cognitive Science, 4 (1), 1980. 71-115.
  3. C. Ramirez, B. Valdes. A general knowledge representation model of concepts/In Advances in Knowledge Representation, T. Hobbes (Ed.). Croatia, Rijeka: InTech, 2012. P. 43-76.
  4. T. Hobbes. Elements of Law, Natural and Political. London: Routledge, 1969.
  5. J. A. Fodor. The Language of Thought. Cambridge, MA: Harvard University Press, 1975.
  6. D. N. Osherson, E. E. Smith. On the adequacy of prototype theory as a theory of concepts/Cognitio, 9, 1981. 3-58.
  7. G. L. Murphy, D. L. Medin. The role of theories in conceptual coherence/Psychological Review, 92 (3), 1985. 289-316.
  8. L. S. Vygotsky. Thought and language. A. Kozulin (Ed.). New York, USA: MIT Press, 1986.
  9. A. K. Crisp-Bright. Knowledge selection in category-based inductive reasoning. Ph.D. Dissertation, Durham University, UK, 2010.
  10. A. K. Crisp-Bright. The effects of domain and type of knowledge on category-based inductive reasoning//Proceedings of the Annual Meeting of the Cognitive Science Society, 32, 2010. 67-72.
  11. N. Chomsky. A review of B.F. Skinner’s verbal behavior/In L. A. Jakovits, M. S. Miron (Eds.), Readings in the psychology of language. Englewood Cliffs, N.J.: Prentice-Hall psychology, 1967. P. 142-143.
  12. A. L. Brown. Similarity and analogical reasoning. Edited by S. Vosniadou and A. Ortony. New York, USA: Cambridge University Press, 1989.
  13. J.-P. Doignon, J.-C. Falmagne. Knowledge Spaces. Berlin: Springer Verlag, 1999.
  14. A. Newell. Unified theories of cognition. Cambridge, MA: Harvard University Press, 1994.
  15. J. R. Anderson. The adaptive character of thought (Studies in Cognition Series). London: Psychology Press, 1990.
  16. Y. Wang. The theoretical framework of cognitive informatics//International Journal of Cognitive Informatics and Natural Intelligence, 1 (1), 2007. 1-27.
  17. Y. Wang. On cognitive informatics//Brain and Mind, 4 (2), 2003. 151-167.
  18. J. H. Flavell. Theory-of-mind development: Retrospect and prospect//Merrill-Palmer Quarterly, 50 (3), 2004. 274-290.
  19. J. W. Santrock. Educational Psychology. 5th ed. New York, NY: McGraw-Hill, 2011.
  20. B. Rehder. Causal-based property generalization//Cognitive science, 33 (3), 2009. 301-44.
  21. S. A. Sloman. The empirical case for two systems of reasoning//Psychological Bulletin, 119 (1), 1996. 3-23.
  22. S. J. Russell, P. Norvig. Artificial intelligence: A modern approach. Englewood Cliffs, New Jersey: Prentice Hall, 1995.
  23. M. R. Quillian. Semantic information processing. M. Minsky (Ed.). Cambridge, Massachusetts: MIT Press, 1968. P. 227-270.
  24. G. Gentzen. Untersuchungen ber das logische Schlie en. In The collected works of Gerhard Gentzen. Amsterdam: North-Holland Publishing Co., 1969. P. 68-131.
  25. Y. Wang. The OAR model of neural informatics for internal knowledge representation in the brain//International Journal of Cognitive Informatics and Natural Intelligence, 1(3), 2007. 66-77.
  26. A. Newell, H. A. Simon. Human problem solving. Englewood Cliffs, N.J: Prentice-Hall, 1972.
  27. M. Minsky. A framework for representing knowledge/In P. H. Winston (Ed.), The Psychology of Computer Vision. Massachusetts: McGraw-Hill, 1975. P. 211-277.
  28. R. C. Schank. Conceptual Information Processing. New York, USA: Elsevier Science Inc., 1975.
  29. R. C. Schank. Dynamic memory: A theory of reminding and learning in computers and people. Cambridge: Cambridge University Press, 1982.
  30. T. R. Gruber. Toward principles for the design of ontologies used for knowledge sharing//International Journal Human-Computer Studies, 43 (5-6), 1993. 907-928.
  1. C. M. Chen. Ontology-based concept map for planning a personalised learning path//British Journal of Educational Technology, 40 (6), 2009. 1028-1058.
  2. S. L. Reed, D. B. Lenat. Mapping ontologies into Cyc. In Proceedings of the American Association for Artificial Intelligence 2002 Conference Workshop on Ontologies for The Semantic Web. Edmonton, Canada, 2002. P. 1-6.
  3. H. Helbig, I. Glцckner, R. Oswald. Layer structures and conceptual hierarchies in semantic representation for NLP/In A. Gelbukh (Ed.) Computational Linguistics and Intelligent Text Processing. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer, 2008. P. 4919.
  4. V. Carchiolo, A. Longheu, M. Malgeri. Adaptive formative paths in a web-based learning environment//Educational Technology & Society, 5 (4), 2002. 64-75. https://www.j-ets.net/ets/journals/5_4/carchiolo.html.
  5. K. Van Marcke. GTE: An epistemological approach to instructional modeling//Instructional Science, 26 (3-4), 1998. 147-191.
  6. H. Nwana. Intelligent tutoring systems: an overview. Artificial Intelligence Review, 4 (4), 1990. 251-277.
  7. P. Brusilovsky. Adaptive navigation support: From adaptive hypermedia to the adaptive web and beyond//Knowledge creation diffusionutilization, 2 (1), 2004. 7-23. http://www.psychnology.org/File/psychnology_journal_2_1_brusilovsky.pdf.
  8. A. Zaknich. Principles of adaptive filters and self-learning systems (Advanced textbooks in control and signal processing). London: Springer-Verlag, 2005. P. 20.
  9. J. Piaget. Psychology of intelligence. London and New York: Routledge/Taylor & Francis, 1960. P. 8.
  10. J. Piaget. The origins of intelligence in children. New York: International University Press, 1952. P. 174.
  11. A. R. Damasio. The somatic marker hypothesis and the possible functions of the prefrontal cortex//Philosophical Transactions of the Royal Society B: Biological Sciences, 351 (1346), 1996, 1413-1420.
  12. A. Damasio, G. B. Carvalho. The nature of feelings: Evolutionary and neurobiological origins//Nature Reviews Neuroscience, 14, 2013. 143-152.
  13. J. Panksepp. Affective neuroscience: The foundations of human and animal emotions. New York: Oxford University Press, 1998. P. 50.
  14. H. M. Wellman. The child's theory of mind. Cambridge, MA: MIT Press, 1990. P. 100-109.
  15. K. Bartsch, H. M. Wellman. Children talk about the mind. New York & Oxford: Oxford University Press, 1995. P. 7.

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