A Cognitive Architecture for Mental Processes Involving Mental Models Analysed from a Self-modeling Network Viewpoint

Laila van Ments; Raj Bhalwankar; Jan Treur

doi:10.1007/978-3-030-90321-3_52

A Cognitive Architecture for Mental Processes Involving Mental Models Analysed from a Self-modeling Network Viewpoint

Laila van Ments, Raj Bhalwankar, Jan Treur^*

^*Corresponding author for this work

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

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Abstract

This paper contributes an analysis of how in mental and social processes, humans often apply specific mental models and learn and adapt them in a controlled manner. It is discussed how controlled adaptation relates to the Plasticity Versus Stability Conundrum in neuroscience. From the analysis an informal three-level cognitive architecture for controlled adaptation was obtained. It is discussed here from a self-modeling network viewpoint how this cognitive architecture can be modeled as a self-modeling network. Making use of the specific network characteristics offered by the self-modeling network structure format, a large number of options for different types of adaptation of mental models and different types of control over adaptation of mental models were obtained. Many of these options were illustrated by a several realistic examples that were formalized by self-modeling networks. Other options that were distinguished from the analysis here, are offered as interesting options for future research.

Original language	English
Title of host publication	Data Science and Intelligent Systems
Subtitle of host publication	Proceedings of 5th Computational Methods in Systems and Software 2021, Vol. 2
Editors	Radek Silhavy, Petr Silhavy, Zdenka Prokopova
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	628-647
Number of pages	20
Volume	2
ISBN (Electronic)	9783030903213
ISBN (Print)	9783030903206
DOIs	https://doi.org/10.1007/978-3-030-90321-3_52
Publication status	Published - 2021
Event	5th Computational Methods in Systems and Software, CoMeSySo 2021 - Virtual, Online Duration: 1 Oct 2021 → 1 Oct 2021

Publication series

Name	Lecture Notes in Networks and Systems
Publisher	Springer
Volume	231 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	5th Computational Methods in Systems and Software, CoMeSySo 2021
City	Virtual, Online
Period	1/10/21 → 1/10/21

Bibliographical note

Publisher Copyright:
© 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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10.1007/978-3-030-90321-3_52

Ments2021_Chapter_ACognitiveArchitectureForMentalProcessesInvolvingMentalModelsAnalysedFromFinal published version, 1.23 MBLicence: Article 25fa Dutch Copyright Act

https://www.researchgate.net/publication/353026335

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van Ments, L., Bhalwankar, R., & Treur, J. (2021). A Cognitive Architecture for Mental Processes Involving Mental Models Analysed from a Self-modeling Network Viewpoint. In R. Silhavy, P. Silhavy, & Z. Prokopova (Eds.), Data Science and Intelligent Systems: Proceedings of 5th Computational Methods in Systems and Software 2021, Vol. 2 (Vol. 2, pp. 628-647). (Lecture Notes in Networks and Systems; Vol. 231 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-90321-3_52

van Ments, Laila ; Bhalwankar, Raj ; Treur, Jan. / A Cognitive Architecture for Mental Processes Involving Mental Models Analysed from a Self-modeling Network Viewpoint. Data Science and Intelligent Systems: Proceedings of 5th Computational Methods in Systems and Software 2021, Vol. 2. editor / Radek Silhavy ; Petr Silhavy ; Zdenka Prokopova. Vol. 2 Springer Science and Business Media Deutschland GmbH, 2021. pp. 628-647 (Lecture Notes in Networks and Systems).

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abstract = "This paper contributes an analysis of how in mental and social processes, humans often apply specific mental models and learn and adapt them in a controlled manner. It is discussed how controlled adaptation relates to the Plasticity Versus Stability Conundrum in neuroscience. From the analysis an informal three-level cognitive architecture for controlled adaptation was obtained. It is discussed here from a self-modeling network viewpoint how this cognitive architecture can be modeled as a self-modeling network. Making use of the specific network characteristics offered by the self-modeling network structure format, a large number of options for different types of adaptation of mental models and different types of control over adaptation of mental models were obtained. Many of these options were illustrated by a several realistic examples that were formalized by self-modeling networks. Other options that were distinguished from the analysis here, are offered as interesting options for future research.",

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van Ments, L, Bhalwankar, R & Treur, J 2021, A Cognitive Architecture for Mental Processes Involving Mental Models Analysed from a Self-modeling Network Viewpoint. in R Silhavy, P Silhavy & Z Prokopova (eds), Data Science and Intelligent Systems: Proceedings of 5th Computational Methods in Systems and Software 2021, Vol. 2. vol. 2, Lecture Notes in Networks and Systems, vol. 231 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 628-647, 5th Computational Methods in Systems and Software, CoMeSySo 2021, Virtual, Online, 1/10/21. https://doi.org/10.1007/978-3-030-90321-3_52

A Cognitive Architecture for Mental Processes Involving Mental Models Analysed from a Self-modeling Network Viewpoint. / van Ments, Laila; Bhalwankar, Raj; Treur, Jan.
Data Science and Intelligent Systems: Proceedings of 5th Computational Methods in Systems and Software 2021, Vol. 2. ed. / Radek Silhavy; Petr Silhavy; Zdenka Prokopova. Vol. 2 Springer Science and Business Media Deutschland GmbH, 2021. p. 628-647 (Lecture Notes in Networks and Systems; Vol. 231 LNNS).

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

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van Ments L, Bhalwankar R, Treur J. A Cognitive Architecture for Mental Processes Involving Mental Models Analysed from a Self-modeling Network Viewpoint. In Silhavy R, Silhavy P, Prokopova Z, editors, Data Science and Intelligent Systems: Proceedings of 5th Computational Methods in Systems and Software 2021, Vol. 2. Vol. 2. Springer Science and Business Media Deutschland GmbH. 2021. p. 628-647. (Lecture Notes in Networks and Systems). Epub 2021 Nov 17. doi: 10.1007/978-3-030-90321-3_52

A Cognitive Architecture for Mental Processes Involving Mental Models Analysed from a Self-modeling Network Viewpoint

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