Abstract
| Language | English |
|---|---|
| Pages | 9-19 |
| Journal | Lecture Notes in Computer Science |
| Volume | 7062 |
| DOIs | |
| Publication status | Published - 2011 |
| Event | 18th International Conference on Neural Information Processing, ICONIP'11 - Berlin-Heidelberg Duration: 1 Jan 2011 → 1 Jan 2011 |
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Bibliographical note
Proceedings title: Proceedings of the 18th International Conference on Neural Information Processing, ICONIP'11, Part I.Publisher: Springer Verlag
Place of publication: Berlin-Heidelberg
Editors: B.L. Lu et al.
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A Computational Agent Model for Hebbian Learning of Social Interaction. / Treur, J.
In: Lecture Notes in Computer Science, Vol. 7062, 2011, p. 9-19.Research output: Contribution to Journal › Article › Academic › peer-review
TY - JOUR
T1 - A Computational Agent Model for Hebbian Learning of Social Interaction
AU - Treur, J.
N1 - Proceedings title: Proceedings of the 18th International Conference on Neural Information Processing, ICONIP'11, Part I. Publisher: Springer Verlag Place of publication: Berlin-Heidelberg Editors: B.L. Lu et al.
PY - 2011
Y1 - 2011
N2 - In social interaction between two persons usually a person displays understanding of the other person. This may involve both nonverbal and verbal elements, such as bodily expressing a similar emotion and verbally expressing beliefs about the other person. Such social interaction relates to an underlying neural mechanism based on a mirror neuron system, as known within Social Neuroscience. This mechanism may show different variations over time. This paper addresses this adaptation over time. It presents a computational model capable of learning social responses, based on insights from Social Neuroscience. The presented model may provide a basis for virtual agents in the context of simulation-based training of psychotherapists, gaming, or virtual stories. © 2011 Springer-Verlag.
AB - In social interaction between two persons usually a person displays understanding of the other person. This may involve both nonverbal and verbal elements, such as bodily expressing a similar emotion and verbally expressing beliefs about the other person. Such social interaction relates to an underlying neural mechanism based on a mirror neuron system, as known within Social Neuroscience. This mechanism may show different variations over time. This paper addresses this adaptation over time. It presents a computational model capable of learning social responses, based on insights from Social Neuroscience. The presented model may provide a basis for virtual agents in the context of simulation-based training of psychotherapists, gaming, or virtual stories. © 2011 Springer-Verlag.
U2 - 10.1007/978-3-642-24955-6_2
DO - 10.1007/978-3-642-24955-6_2
M3 - Article
VL - 7062
SP - 9
EP - 19
JO - Lecture Notes in Computer Science
T2 - Lecture Notes in Computer Science
JF - Lecture Notes in Computer Science
SN - 0302-9743
ER -