A Computational Agent Model for Hebbian Learning of Social Interaction

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

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.
Original languageEnglish
Pages (from-to)9-19
JournalLecture Notes in Computer Science
Volume7062
DOIs
Publication statusPublished - 2011
Event18th International Conference on Neural Information Processing, ICONIP'11 - Berlin-Heidelberg
Duration: 1 Jan 20111 Jan 2011

Fingerprint

Hebbian Learning
Social Interaction
Person
Neuroscience
Neurons
Mirrors
Social Learning
Virtual Agents
Gaming
Model
Computational Model
Neuron
Mirror
Simulation

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.

Cite this

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abstract = "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. {\circledC} 2011 Springer-Verlag.",
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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 JournalArticleAcademicpeer-review

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