Computational model for reward-based generation and maintenance of motivation

Fawad Taj*, Michel C.A. Klein, Aart van Halteren

*Corresponding author for this work

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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.

Original languageEnglish
Title of host publicationBrain Informatics
Subtitle of host publicationInternational Conference, BI 2018, Proceedings
EditorsYang Yang, Vicky Yamamoto, Shouyi Wang, Erick Jones, Jianzhong Su, Tom Mitchell, Leon Iasemidis
PublisherSpringer - Verlag
Pages41-51
Number of pages11
ISBN (Electronic)9783030055875
ISBN (Print)9783030055868
DOIs
Publication statusPublished - 2018
EventInternational Conference on Brain Informatics, BI 2018 - Arlington, United States
Duration: 7 Dec 20189 Dec 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11309 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on Brain Informatics, BI 2018
CountryUnited States
CityArlington
Period7/12/189/12/18

Keywords

  • Cognitive modelling
  • Motivation
  • Reward-based learning

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  • Cite this

    Taj, F., Klein, M. C. A., & van Halteren, A. (2018). Computational model for reward-based generation and maintenance of motivation. In Y. Yang, V. Yamamoto, S. Wang, E. Jones, J. Su, T. Mitchell, & L. Iasemidis (Eds.), Brain Informatics: International Conference, BI 2018, Proceedings (pp. 41-51). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11309 LNAI). Springer - Verlag. https://doi.org/10.1007/978-3-030-05587-5_5