Computational model for reward-based generation and maintenance of motivation

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

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.

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

Fingerprint

Reward
Computational Model
Maintenance
Hebbian Learning
Student Learning
Dynamic Properties
Prediction Error
Mathematical Analysis
Simulation Experiment
Pathway
Verify
Scenarios
Model
Students
Experiments

Keywords

  • Cognitive modelling
  • Motivation
  • Reward-based learning

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
Taj, Fawad ; Klein, Michel C.A. ; van Halteren, Aart. / Computational model for reward-based generation and maintenance of motivation. Brain Informatics: International Conference, BI 2018, Proceedings. editor / Yang Yang ; Vicky Yamamoto ; Shouyi Wang ; Erick Jones ; Jianzhong Su ; Tom Mitchell ; Leon Iasemidis. Springer - Verlag, 2018. pp. 41-51 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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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.",
keywords = "Cognitive modelling, Motivation, Reward-based learning",
author = "Fawad Taj and Klein, {Michel C.A.} and {van Halteren}, Aart",
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language = "English",
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Taj, F, Klein, MCA & 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. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11309 LNAI, Springer - Verlag, pp. 41-51, International Conference on Brain Informatics, BI 2018, Arlington, United States, 7/12/18. https://doi.org/10.1007/978-3-030-05587-5_5

Computational model for reward-based generation and maintenance of motivation. / Taj, Fawad; Klein, Michel C.A.; van Halteren, Aart.

Brain Informatics: International Conference, BI 2018, Proceedings. ed. / Yang Yang; Vicky Yamamoto; Shouyi Wang; Erick Jones; Jianzhong Su; Tom Mitchell; Leon Iasemidis. Springer - Verlag, 2018. p. 41-51 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11309 LNAI).

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

TY - GEN

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N2 - 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.

AB - 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.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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EP - 51

BT - Brain Informatics

A2 - Yang, Yang

A2 - Yamamoto, Vicky

A2 - Wang, Shouyi

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PB - Springer - Verlag

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