Abstract
Using a proper mental model during mental processes is often crucial. Such a mental model has to be learnt and maintained; this involves mental model adaptation. Metacognition is applied to control use and adaptating in a context-sensitive manner. In this chapter, a second-order adaptive network model for handling mental models, covering their use, adaptation and control, is discussed and used to illustrate these processes.
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References
Abraham, W.C., Bear, M.F.: Metaplasticity: the plasticity of synaptic plasticity. Trends Neurosci. 19(4), 126–130 (1996)
Bhalwankar, R., Treur, J.: Modeling learner-controlled mental model learning processes by a second-order adaptive network model. PLoS ONE 16(8): e0255503 (2021)
Canbaloğlu, G., Treur, J.: Modeling context-sensitive metacognitive control of focusing on a mental model during a mental process. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds.) Data Science and Intelligent Systems. Proceedings of CoMeSySo 2021. Lecture Notes in Networks and Systems, vol. 231, pp. 992–1009. Springer Nature (2021). https://www.researchgate.net/publication/353667091
Canbaloğlu, G., Treur, J., Wiewiora, A. (eds.).: Computational Modeling of Multilevel Organizational Learning and its Control Using Self-Modeling Network Models. Springer Nature (2023) (this volume)
Craik, K.J.W.: The nature of explanation. Cambridge, MA: University Press. (1943).
Darling-Hammond, L., Austin, K., Cheung, M., Martin, D.: Thinking about Thinking: Metacognition (2008)
Flavell, J.H.: Metacognition and cognitive monitoring: a new area of cognitive–developmental inquiry. Am. Psychol. 34(10), 906–911 (1979)
Gentner, D., Stevens, A.L.: Mental Models. Erlbaum, Hillsdale NJ (1983)
Greca, I.M., Moreira, M.A.: Mental models, conceptual models, and modelling. Int. J. Sci. Educ. 22(1), 1–11 (2000)
Hebb, D.O.: The Organization of Behavior: A Neuropsychological Theory. Wiley (1949)
Koriat, A.: Metacognition and consciousness. In: Zelavo, P.D., Moscovitch, M., Thompson, E. (eds.) Cambridge Handbook of Consciousness. Cambridge University Press, New York (2007)
Magerl, W., Hansen, N., Treede, R.D., Klein, T.: The human pain system exhibits higher-order plasticity (metaplasticity). Neurobiol. Learn. Mem. 154, 112–120 (2018)
Mahdavi, M.: An overview: metacognition in education. Int. J. Multidisc. Curr. Res. 2, 529–535 (2014)
Pintrich, P.R.: The role of goal orientation in self-regulated learning. In: Boekaerts, M., Pintrich, P., Zeidner, M. (eds.) Handbook of Self-Regulation Research and Applications, pp. 451–502. Academic Press, Orlando, FL (2000)
Robinson, B.L., Harper, N.S., McAlpine, D.: Meta-adaptation in the auditory midbrain under cortical influence. Nat. Commun. 7, 13442 (2016)
Seel, N.M.: Mental models in learning situations. In: Advances in Psychology, vol. 138, pp. 85–107. North-Holland, Amsterdam (2006)
Shannon, S.V.: Using metacognitive strategies and learning styles to create self-directed learners. Inst. Learn. Styles J. 1, 14–28 (2008)
Shih, Y.F. Alessi, S.M.: Mental models and transfer of learning in computer programming. Journal of Research in Computing Education, 26(2), 154–175 (1993)
Sjöström, P.J., Rancz, E.A., Roth, A., Hausser, M.: Dendritic excitability and synaptic plasticity. Physiol. Rev. 88(769–840), 2008 (2008)
Skemp, R.R.: The Psychology of Learning Mathematics. Penguin Books, Harmondsworth (1971)
Treur, J.: Network-Oriented Modeling: Addressing Complexity of Cognitive. Springer Publishers, Affective and Social Interactions (2016)
Treur, J.: Multilevel network reification: representing higher order adaptivity in a network. In: Aiello, L., Cherifi, C., Cherifi, H., Lambiotte, R., Lió, P., Rocha, L. (eds.) Proceedings of the 7th International Conference on Complex Networks and their Applications, Complex Networks'18, vol. 1. Studies in Computational Intelligence, vol. 812, pp. 635–651, Springer Nature (2018)
Treur, J.: Modeling higher-order adaptivity of a network by multilevel network reification. Netw. Sci. 8, S110–S144 (2020a)
Treur, J.: Network-Oriented Modeling for Adaptive Networks: Designing Higher-Order Adaptive Biological, Mental and Social Network Models. Springer Nature Publishing, Cham, Switzerland (2020b)
Treur, J.: An adaptive network model covering metacognition to control adaptation for multiple mental models. Cogn. Syst. Res. 67, 18–27 (2021)
Treur, J., Van Ments, L. (eds.): Mental Models and their Dynamics, Adaptation and Control: A Self-Modeling Network Modeling Approach. Springer Nature, Cham, Switzerland (2022)
Van Gog, T., Paas, F., Marcus, N., Ayres, P., Sweller, J.: The mirror neuron system and observational learning: Implications for the effectiveness of dynamic visualizations. Educational Psychology Review 21(1), 21-30 (2009)
Van Ments, L., Treur, J.: Reflections on dynamics, adaptation and control: a cognitive architecture for mental models. Cogn. Syst. Res. 70, 1–9 (2021)
Van Ments, L., Treur, J., Klein, J., Roelofsma, P.H.M.P.: A second-order adaptive network model for shared mental models in hospital teamwork. In: Nguyen, N.T., et al. (eds.) Proceedings of the 13th International Conference on Computational Collective Intelligence, ICCCI'21. Lecture Notes in AI, vol 12876, pp 126–140. Springer Nature (2021)
Yi, M.Y., Davis, F.D.: Developing and validating an observational learning model of computer software training and skill acquisition. Information Systems Research 14(2), 146–169 (2003)
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Canbaloğlu, G., Treur, J. (2023). Modeling Mental Models: Their Use, Adaptation and Control. In: Canbaloğlu, G., Treur, J., Wiewiora, A. (eds) Computational Modeling of Multilevel Organisational Learning and Its Control Using Self-modeling Network Models. Studies in Systems, Decision and Control, vol 468. Springer, Cham. https://doi.org/10.1007/978-3-031-28735-0_4
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