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.
| 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 | |
| State | Published - 2018 |
| Event | 10th International Conference on Computational Collective Intelligence, ICCCI 2018 - Bristol, United Kingdom Duration: 5 Sep 2018 → 7 Sep 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 | United Kingdom |
| City | Bristol |
| Period | 5/09/18 → 7/09/18 |
Fingerprint
Keywords
- Adaptive temporal-causal network model
- Hebbian learning
- Stress
Cite this
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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
TY - GEN
T1 - An adaptive temporal-causal network model for decision making under acute stress
AU - Treur,Jan
AU - Mohammadi Ziabari,S. Sahand
PY - 2018
Y1 - 2018
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
KW - Hebbian learning
KW - Stress
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UR - http://www.scopus.com/inward/citedby.url?scp=85053143892&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-98446-9_2
DO - 10.1007/978-3-319-98446-9_2
M3 - Conference contribution
SN - 9783319984452
VL - 2
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 13
EP - 25
BT - Computational Collective Intelligence
PB - Springer/Verlag
ER -