Abstract
Inspired by the work of Elaine Aron, in this paper a human-like adaptive computational agent model of the internal processes of a highly sensitive person (HSP) is presented. This agent model was used to get a better understanding of what goes wrong in these internal processes once this person gets upset. A scenario is addressed where a highly sensitive person will get upset by an external stimulus and will not be able to calm down by herself. Yet in a social context the interaction with a second person (without high sensitivity) will calm the HSP down, thus contributing to regulation. To obtain an adaptive model a Hebbian learning connection was integrated. During interaction with a second person this Hebbian learning link will become stronger, which makes it possible for a HSP to become independent after some time and be able to regulate upsetting external stimuli all by herself.
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Acknowledgement
We would like to express our gratitude to Fawad Taj for his support for the Matlab Network-Oriented Modeling template in which the model was implemented.
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Tran, L., Treur, J., Tuinhof, D.J. (2018). A Network-Oriented Adaptive Agent Model for Learning Regulation of a Highly Sensitive Person’s Response. In: Demazeau, Y., An, B., Bajo, J., Fernández-Caballero, A. (eds) Advances in Practical Applications of Agents, Multi-Agent Systems, and Complexity: The PAAMS Collection. PAAMS 2018. Lecture Notes in Computer Science(), vol 10978. Springer, Cham. https://doi.org/10.1007/978-3-319-94580-4_20
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DOI: https://doi.org/10.1007/978-3-319-94580-4_20
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