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

Jan Treur; S. Sahand Mohammadi Ziabari

doi:10.1007/978-3-319-98446-9_2

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 proceeding › Conference contribution › Academic › peer-review

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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 language	English
Title of host publication	Computational Collective Intelligence
Subtitle of host publication	10th International Conference, ICCCI 2018, Proceedings
Editors	Ngoc Thanh Nguyen, Bogdan Trawinski, Elias Pimenidis, Zaheer Khan
Publisher	Springer/Verlag
Pages	13-25
Number of pages	13
Volume	2
ISBN (Electronic)	9783319984469
ISBN (Print)	9783319984452
DOIs	https://doi.org/10.1007/978-3-319-98446-9_2
Publication status	Published - 2018
Event	10th International Conference on Computational Collective Intelligence, ICCCI 2018 - Bristol, United Kingdom Duration: 5 Sept 2018 → 7 Sept 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11056 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	10th International Conference on Computational Collective Intelligence, ICCCI 2018
Country/Territory	United Kingdom
City	Bristol
Period	5/09/18 → 7/09/18

Keywords

Adaptive temporal-causal network model
Hebbian learning
Stress

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1007/978-3-319-98446-9_2

Treur-MohammadiZiabari2018_Chapter_AnAdaptiveTemporalCausalNetworkFinal published version, 417 KBLicence: Article 25fa Dutch Copyright Act

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Treur, J., & Mohammadi Ziabari, S. S. (2018). An adaptive temporal-causal network model for decision making under acute stress. In N. T. Nguyen, B. Trawinski, E. Pimenidis, & Z. Khan (Eds.), Computational Collective Intelligence: 10th International Conference, ICCCI 2018, Proceedings (Vol. 2, pp. 13-25). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11056 LNAI). Springer/Verlag. https://doi.org/10.1007/978-3-319-98446-9_2

Treur, Jan ; Mohammadi Ziabari, S. Sahand. / An adaptive temporal-causal network model for decision making under acute stress. Computational Collective Intelligence: 10th International Conference, ICCCI 2018, Proceedings. editor / Ngoc Thanh Nguyen ; Bogdan Trawinski ; Elias Pimenidis ; Zaheer Khan. Vol. 2 Springer/Verlag, 2018. pp. 13-25 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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.",

keywords = "Adaptive temporal-causal network model, Hebbian learning, Stress",

author = "Jan Treur and {Mohammadi Ziabari}, {S. Sahand}",

year = "2018",

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isbn = "9783319984452",

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editor = "Nguyen, {Ngoc Thanh} and Bogdan Trawinski and Elias Pimenidis and Zaheer Khan",

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Treur, J & Mohammadi Ziabari, SS 2018, An adaptive temporal-causal network model for decision making under acute stress. in NT Nguyen, B Trawinski, E Pimenidis & Z Khan (eds), Computational Collective Intelligence: 10th International Conference, ICCCI 2018, Proceedings. vol. 2, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11056 LNAI, Springer/Verlag, pp. 13-25, 10th International Conference on Computational Collective Intelligence, ICCCI 2018, Bristol, United Kingdom, 5/09/18. https://doi.org/10.1007/978-3-319-98446-9_2

An adaptive temporal-causal network model for decision making under acute stress. / Treur, Jan; Mohammadi Ziabari, S. Sahand.
Computational Collective Intelligence: 10th International Conference, ICCCI 2018, Proceedings. ed. / Ngoc Thanh Nguyen; Bogdan Trawinski; Elias Pimenidis; Zaheer Khan. Vol. 2 Springer/Verlag, 2018. p. 13-25 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11056 LNAI).

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

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AU - Mohammadi Ziabari, S. Sahand

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N2 - 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.

AB - 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.

KW - Adaptive temporal-causal network model

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A2 - Nguyen, Ngoc Thanh

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Treur J, Mohammadi Ziabari SS. An adaptive temporal-causal network model for decision making under acute stress. In Nguyen NT, Trawinski B, Pimenidis E, Khan Z, editors, Computational Collective Intelligence: 10th International Conference, ICCCI 2018, Proceedings. Vol. 2. Springer/Verlag. 2018. p. 13-25. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Epub 2018 Aug 8. doi: 10.1007/978-3-319-98446-9_2

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

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Keywords

UN SDGs

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