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Computational Model for Reward-Based Generation and Maintenance of Motivation

  • Fawad Taj
  • Michel C. A. Klein
  • Aart van Halteren
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11309)

Abstract

In this paper, a computational model for the motivation process is presented that takes into account the reward pathway for motivation generation and associative learning for maintaining motivation through Hebbian learning approach. The reward prediction error is used to keep motivation maintained. These aspects are backed by recent neuroscientific models and literature. Simulation experiments have been performed by creating scenarios for student learning through rewards and controlling their motivation through regulation. Mathematical analysis is provided to verify the dynamic properties of the model.

Keywords

Motivation Cognitive modelling Reward-based learning 

References

  1. 1.
    Kim, S.I.: Neuroscientific model of motivational process. Front. Psychol. 4, 98 (2013)Google Scholar
  2. 2.
    Mowrer, O.: Learning theory and the Symbolic Processes. Wiley, New York (1960)CrossRefGoogle Scholar
  3. 3.
    Berridge, K.C.: Motivation concepts in behavioral neuroscience. Physiol. Behav. 81(2), 179–209 (2004)CrossRefGoogle Scholar
  4. 4.
    Ashby, F.G., Turner, B.O., Horvitz, J.C.: Cortical and basal ganglia contributions to habit learning and automaticity. Trends Cogn. Sci. 14(5), 208–215 (2010)CrossRefGoogle Scholar
  5. 5.
    Rushworth, M.F., et al.: Frontal cortex and reward-guided learning and decision-making. Neuron 70(6), 1054–1069 (2011)CrossRefGoogle Scholar
  6. 6.
    Elliott, R., Friston, K.J., Dolan, R.J.: Dissociable neural responses in human reward systems. J. Neurosci. 20(16), 6159–6165 (2000)CrossRefGoogle Scholar
  7. 7.
    Damasio, A.R.: The feeling of what happens: body and emotion in the making of consciousness. N. Y. Times Book Rev. 104, 8 (1999)Google Scholar
  8. 8.
    Fabrega Jr., H.: The feeling of what happens: body and emotion in the making of consciousness. Psychiatr. Serv. 51(12), 1579 (2000)CrossRefGoogle Scholar
  9. 9.
    Damasio, A.R.: The somatic marker hypothesis and the possible functions of the prefrontal cortex. Phil. Trans. R. Soc. Lond. B 351(1346), 1413–1420 (1996)CrossRefGoogle Scholar
  10. 10.
    Treur, J.: Dynamic modeling based on a temporal–causal network modeling approach. Biol. Inspired Cogn. Arch. 16, 131–168 (2016)Google Scholar
  11. 11.
    Treur, J.: The ins and outs of network-oriented modeling: from biological networks and mental networks to social networks and beyond. In: Proceedings of the 10th International Conference on Computational Collective Intelligence, ICCCI, vol. 18 (2018)Google Scholar
  12. 12.
    Gerstner, W., Kistler, W.M.: Mathematical formulations of Hebbian learning. Biol. Cybern. 87(5-6), 404–415 (2002)CrossRefGoogle Scholar
  13. 13.
    Bi, G.-q., Poo, M.-m.: Synaptic modification by correlated activity: Hebb’s postulate revisited. Annu. Rev. Neurosci. 24(1), 139–166 (2001)CrossRefGoogle Scholar
  14. 14.
    Treur, J.: Network-Oriented Modeling. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-45213-5CrossRefzbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Fawad Taj
    • 1
  • Michel C. A. Klein
    • 1
  • Aart van Halteren
    • 1
  1. 1.Behavioural Informatics Group, Department of Computer ScienceVrije Universiteit AmsterdamAmsterdamThe Netherlands

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