A Computational Cognitive Model of Self-Monitoring and Decision Making for Desire Regulation

Research output: Scientific - peer-reviewConference contribution

Abstract

Desires can be triggered both by physiological conditions and by environmental factors, or a combination of these. For example, a desire for eating can be triggered by a need for food such as hungriness or it can be triggered by seeing tempting food. Humans often apply various desire regulation strategies to control their desires. Persons with poor desire regulation may suffer regarding their health, e.g., from overweight and obesity. Desire regulation can make use of different regulation strategies; this implies an underlying decision making process, which makes use of some form of self-monitoring. The aim of this work is to develop a neurologically inspired computational cognitive model of desire regulation and these underlying self-monitoring and decision making processes. In this model four desire regulation strategies have been incorporated. A self-monitoring mechanism continuously monitors and assesses the desire level and based on this a decision mechanism performs the selection of one or multiple strategies, depending on personality characteristics. Simulation experiments have been performed based for the domain of food choice.
Original languageEnglish
Title of host publicationProceedings of the 10th International Conference on Brain Informatics, BI'17
PublisherSpringer
StatePublished - 16 Nov 2017

Publication series

NameLecture Notes in Computer Science
PublisherSpringer

Cite this

Abro, A. H., & Treur, J. (2017). A Computational Cognitive Model of Self-Monitoring and Decision Making for Desire Regulation. In Proceedings of the 10th International Conference on Brain Informatics, BI'17 (Lecture Notes in Computer Science). Springer.

Abro, A.H.; Treur, J. / A Computational Cognitive Model of Self-Monitoring and Decision Making for Desire Regulation.

Proceedings of the 10th International Conference on Brain Informatics, BI'17. Springer, 2017. (Lecture Notes in Computer Science).

Research output: Scientific - peer-reviewConference contribution

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Abro, AH & Treur, J 2017, A Computational Cognitive Model of Self-Monitoring and Decision Making for Desire Regulation. in Proceedings of the 10th International Conference on Brain Informatics, BI'17. Lecture Notes in Computer Science, Springer.

A Computational Cognitive Model of Self-Monitoring and Decision Making for Desire Regulation. / Abro, A.H.; Treur, J.

Proceedings of the 10th International Conference on Brain Informatics, BI'17. Springer, 2017. (Lecture Notes in Computer Science).

Research output: Scientific - peer-reviewConference contribution

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AB - Desires can be triggered both by physiological conditions and by environmental factors, or a combination of these. For example, a desire for eating can be triggered by a need for food such as hungriness or it can be triggered by seeing tempting food. Humans often apply various desire regulation strategies to control their desires. Persons with poor desire regulation may suffer regarding their health, e.g., from overweight and obesity. Desire regulation can make use of different regulation strategies; this implies an underlying decision making process, which makes use of some form of self-monitoring. The aim of this work is to develop a neurologically inspired computational cognitive model of desire regulation and these underlying self-monitoring and decision making processes. In this model four desire regulation strategies have been incorporated. A self-monitoring mechanism continuously monitors and assesses the desire level and based on this a decision mechanism performs the selection of one or multiple strategies, depending on personality characteristics. Simulation experiments have been performed based for the domain of food choice.

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Abro AH, Treur J. A Computational Cognitive Model of Self-Monitoring and Decision Making for Desire Regulation. In Proceedings of the 10th International Conference on Brain Informatics, BI'17. Springer. 2017. (Lecture Notes in Computer Science).