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

A.H. Abro, J. Treur

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

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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 publicationBrain Informatics
Subtitle of host publicationInternational Conference, BI 2017, Beijing, China, November 16-18, 2017, Proceedings
EditorsYi Zeng, Yong He, Jeanette Hellgren Kotaleski, Maryann Martone, Bo Xu, Hanchuan Peng, Qingming Luo
PublisherSpringer
Pages26-38
Number of pages13
ISBN (Electronic)9783319707723
ISBN (Print)9783319707716
DOIs
Publication statusPublished - 2017

Publication series

NameLecture Notes in Computer Science
PublisherSpringer

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