An Adaptive Temporal-Causal Network Model for Enabling Learning of Social Interaction

Charlotte Commu; Mathilde Theelen; J. Treur

doi:10.1007/978-3-319-60285-1_22

An Adaptive Temporal-Causal Network Model for Enabling Learning of Social Interaction

Charlotte Commu, Mathilde Theelen, J. Treur

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

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Abstract

In this study, an adaptive temporal-causal network model is present-ed for learning of basic skills for social interaction. It focuses on greeting a known person and how that relates to learning how to recognize a person from seeing his or her face. The model involves a Hebbian learning process. The model also addresses avoidance behavior related to enhanced sensory pro-cessing sensitivity. In scenarios persons without and with enhanced sensory processing sensitivity are compared. Mathematical analysis was performed to verify correctness of the model.

Original language	English
Title of host publication	Highlights of Practical Applications of Cyber-Physical Multi-Agent Systems
Subtitle of host publication	International Workshops of PAAMS 2017, Porto, Portugal, June 21-23, 2017, Proceedings
Editors	Javier Bajo, Zita Vale, Kasper Hallenborg, Ana Paula Rocha, Philippe Mathieu, Pawel Pawlewski, Elena Del Val, Paulo Novais, Fernando Lopes, Nestor D. Duque Méndez, Vicente Julián, Johan Holmgren
Publisher	Springer Verlag
Pages	257-270
Number of pages	14
ISBN (Electronic)	9783319602851
ISBN (Print)	9783319602844
DOIs	https://doi.org/10.1007/978-3-319-60285-1_22
Publication status	Published - 2017

Publication series

Name	Communications in Computer and Information Science
Publisher	Springer
Volume	722

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1007/978-3-319-60285-1_22

An Adaptive Temporal-Causal Network Model for Enabling Learning of Social InteractionFinal published version, 1.71 MBLicence: Article 25fa Dutch Copyright Act

https://www.researchgate.net/publication/315107908_An_Adaptive_Temporal-Causal_Network_Model_for_Enabling_Learning_of_Social_Interaction

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Commu, C., Theelen, M., & Treur, J. (2017). An Adaptive Temporal-Causal Network Model for Enabling Learning of Social Interaction. In J. Bajo, Z. Vale, K. Hallenborg, A. P. Rocha, P. Mathieu, P. Pawlewski, E. Del Val, P. Novais, F. Lopes, N. D. Duque Méndez, V. Julián, & J. Holmgren (Eds.), Highlights of Practical Applications of Cyber-Physical Multi-Agent Systems: International Workshops of PAAMS 2017, Porto, Portugal, June 21-23, 2017, Proceedings (pp. 257-270). (Communications in Computer and Information Science; Vol. 722). Springer Verlag. https://doi.org/10.1007/978-3-319-60285-1_22

Commu, Charlotte ; Theelen, Mathilde ; Treur, J. / An Adaptive Temporal-Causal Network Model for Enabling Learning of Social Interaction. Highlights of Practical Applications of Cyber-Physical Multi-Agent Systems: International Workshops of PAAMS 2017, Porto, Portugal, June 21-23, 2017, Proceedings. editor / Javier Bajo ; Zita Vale ; Kasper Hallenborg ; Ana Paula Rocha ; Philippe Mathieu ; Pawel Pawlewski ; Elena Del Val ; Paulo Novais ; Fernando Lopes ; Nestor D. Duque Méndez ; Vicente Julián ; Johan Holmgren. Springer Verlag, 2017. pp. 257-270 (Communications in Computer and Information Science).

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title = "An Adaptive Temporal-Causal Network Model for Enabling Learning of Social Interaction",

abstract = "In this study, an adaptive temporal-causal network model is present-ed for learning of basic skills for social interaction. It focuses on greeting a known person and how that relates to learning how to recognize a person from seeing his or her face. The model involves a Hebbian learning process. The model also addresses avoidance behavior related to enhanced sensory pro-cessing sensitivity. In scenarios persons without and with enhanced sensory processing sensitivity are compared. Mathematical analysis was performed to verify correctness of the model.",

author = "Charlotte Commu and Mathilde Theelen and J. Treur",

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editor = "Javier Bajo and Zita Vale and Kasper Hallenborg and Rocha, {Ana Paula} and Philippe Mathieu and Pawel Pawlewski and {Del Val}, Elena and Paulo Novais and Fernando Lopes and {Duque M{\'e}ndez}, {Nestor D.} and Vicente Juli{\'a}n and Johan Holmgren",

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Commu, C, Theelen, M & Treur, J 2017, An Adaptive Temporal-Causal Network Model for Enabling Learning of Social Interaction. in J Bajo, Z Vale, K Hallenborg, AP Rocha, P Mathieu, P Pawlewski, E Del Val, P Novais, F Lopes, ND Duque Méndez, V Julián & J Holmgren (eds), Highlights of Practical Applications of Cyber-Physical Multi-Agent Systems: International Workshops of PAAMS 2017, Porto, Portugal, June 21-23, 2017, Proceedings. Communications in Computer and Information Science, vol. 722, Springer Verlag, pp. 257-270. https://doi.org/10.1007/978-3-319-60285-1_22

An Adaptive Temporal-Causal Network Model for Enabling Learning of Social Interaction. / Commu, Charlotte; Theelen, Mathilde; Treur, J.
Highlights of Practical Applications of Cyber-Physical Multi-Agent Systems: International Workshops of PAAMS 2017, Porto, Portugal, June 21-23, 2017, Proceedings. ed. / Javier Bajo; Zita Vale; Kasper Hallenborg; Ana Paula Rocha; Philippe Mathieu; Pawel Pawlewski; Elena Del Val; Paulo Novais; Fernando Lopes; Nestor D. Duque Méndez; Vicente Julián; Johan Holmgren. Springer Verlag, 2017. p. 257-270 (Communications in Computer and Information Science; Vol. 722).

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

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N2 - In this study, an adaptive temporal-causal network model is present-ed for learning of basic skills for social interaction. It focuses on greeting a known person and how that relates to learning how to recognize a person from seeing his or her face. The model involves a Hebbian learning process. The model also addresses avoidance behavior related to enhanced sensory pro-cessing sensitivity. In scenarios persons without and with enhanced sensory processing sensitivity are compared. Mathematical analysis was performed to verify correctness of the model.

AB - In this study, an adaptive temporal-causal network model is present-ed for learning of basic skills for social interaction. It focuses on greeting a known person and how that relates to learning how to recognize a person from seeing his or her face. The model involves a Hebbian learning process. The model also addresses avoidance behavior related to enhanced sensory pro-cessing sensitivity. In scenarios persons without and with enhanced sensory processing sensitivity are compared. Mathematical analysis was performed to verify correctness of the model.

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Commu C, Theelen M, Treur J. An Adaptive Temporal-Causal Network Model for Enabling Learning of Social Interaction. In Bajo J, Vale Z, Hallenborg K, Rocha AP, Mathieu P, Pawlewski P, Del Val E, Novais P, Lopes F, Duque Méndez ND, Julián V, Holmgren J, editors, Highlights of Practical Applications of Cyber-Physical Multi-Agent Systems: International Workshops of PAAMS 2017, Porto, Portugal, June 21-23, 2017, Proceedings. Springer Verlag. 2017. p. 257-270. (Communications in Computer and Information Science). Epub 2017 May 28. doi: 10.1007/978-3-319-60285-1_22

An Adaptive Temporal-Causal Network Model for Enabling Learning of Social Interaction

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