Learning Emotion Regulation Strategies: A Cognitive Agent Model

T. Bosse; C. Gerritsen; J. de Man; J. Treur

doi:10.1109/WI-IAT.2013.116

Learning Emotion Regulation Strategies: A Cognitive Agent Model

T. Bosse, C. Gerritsen, J. de Man, J. Treur

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

Abstract

Learning to cope with negative emotions is an important challenge, which has received considerable attention in domains like the military and law enforcement. Driven by the aim to develop better training in coping skills, this paper presents an adaptive computational model of emotion regulation strategies, which is inspired by recent neurological literature. The model can be used both to gain more insight in emotion regulation training itself and to develop intelligent virtual reality-based training environments. The behaviour of the model is illustrated by a number of simulation experiments and by a mathematical analysis. In addition, a preliminary validation points out that it is able to approximate empirical data obtained from an experiment with human participants. © 2013 IEEE.

Original language	English
Title of host publication	Proceedings of the 13th International Conference on Intelligent Agent Technology, IAT'13
Publisher	IEEE Computer Society Press
Pages	245-252
DOIs	https://doi.org/10.1109/WI-IAT.2013.116
Publication status	Published - 2013

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10.1109/WI-IAT.2013.116

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title = "Learning Emotion Regulation Strategies: A Cognitive Agent Model",

abstract = "Learning to cope with negative emotions is an important challenge, which has received considerable attention in domains like the military and law enforcement. Driven by the aim to develop better training in coping skills, this paper presents an adaptive computational model of emotion regulation strategies, which is inspired by recent neurological literature. The model can be used both to gain more insight in emotion regulation training itself and to develop intelligent virtual reality-based training environments. The behaviour of the model is illustrated by a number of simulation experiments and by a mathematical analysis. In addition, a preliminary validation points out that it is able to approximate empirical data obtained from an experiment with human participants. {\textcopyright} 2013 IEEE.",

author = "T. Bosse and C. Gerritsen and \{de Man\}, J. and J. Treur",

year = "2013",

doi = "10.1109/WI-IAT.2013.116",

language = "English",

pages = "245--252",

booktitle = "Proceedings of the 13th International Conference on Intelligent Agent Technology, IAT'13",

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AU - Gerritsen, C.

AU - de Man, J.

AU - Treur, J.

PY - 2013

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N2 - Learning to cope with negative emotions is an important challenge, which has received considerable attention in domains like the military and law enforcement. Driven by the aim to develop better training in coping skills, this paper presents an adaptive computational model of emotion regulation strategies, which is inspired by recent neurological literature. The model can be used both to gain more insight in emotion regulation training itself and to develop intelligent virtual reality-based training environments. The behaviour of the model is illustrated by a number of simulation experiments and by a mathematical analysis. In addition, a preliminary validation points out that it is able to approximate empirical data obtained from an experiment with human participants. © 2013 IEEE.

AB - Learning to cope with negative emotions is an important challenge, which has received considerable attention in domains like the military and law enforcement. Driven by the aim to develop better training in coping skills, this paper presents an adaptive computational model of emotion regulation strategies, which is inspired by recent neurological literature. The model can be used both to gain more insight in emotion regulation training itself and to develop intelligent virtual reality-based training environments. The behaviour of the model is illustrated by a number of simulation experiments and by a mathematical analysis. In addition, a preliminary validation points out that it is able to approximate empirical data obtained from an experiment with human participants. © 2013 IEEE.

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M3 - Conference contribution

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EP - 252

BT - Proceedings of the 13th International Conference on Intelligent Agent Technology, IAT'13

PB - IEEE Computer Society Press

ER -

Learning Emotion Regulation Strategies: A Cognitive Agent Model

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