An adaptive temporal-causal network model for decision making under acute stress

Jan Treur*, S. Sahand Mohammadi Ziabari

*Corresponding author for this work

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

Abstract

In recent literature from Neuroscience the adaptive role of the effects of stress on decision making is highlighted. The problem addressed in this paper is how that can be modelled computationally. The presented adaptive temporal-causal network model addresses the suppression of the existing network connections in a first phase as a result of the acute stress, and then as a second phase relaxing the suppression after some time and give room to start new learning of the decision making in the context of the stress again.

Original languageEnglish
Title of host publicationComputational Collective Intelligence
Subtitle of host publication10th International Conference, ICCCI 2018, Proceedings
EditorsNgoc Thanh Nguyen, Bogdan Trawinski, Elias Pimenidis, Zaheer Khan
PublisherSpringer/Verlag
Pages13-25
Number of pages13
Volume2
ISBN (Electronic)9783319984469
ISBN (Print)9783319984452
DOIs
Publication statusPublished - 2018
Event10th International Conference on Computational Collective Intelligence, ICCCI 2018 - Bristol, United Kingdom
Duration: 5 Sep 20187 Sep 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11056 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th International Conference on Computational Collective Intelligence, ICCCI 2018
CountryUnited Kingdom
CityBristol
Period5/09/187/09/18

Keywords

  • Adaptive temporal-causal network model
  • Hebbian learning
  • Stress

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