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Mental models and their dynamics, adaptation, and controla self-modeling network modeling approach /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Mental models and their dynamics, adaptation, and controledited by Jan Treur, Laila Van Ments.
Reminder of title:	a self-modeling network modeling approach /
other author:	Treur, Jan.
Published:	Cham :Springer International Publishing :2022.
Description:	xi, 616 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Cognitive maps (Psychology)
Online resource:	https://doi.org/10.1007/978-3-030-85821-6
ISBN:	9783030858216$q(electronic bk.)

Mental models and their dynamics, adaptation, and controla self-modeling network modeling approach /
Mental models and their dynamics, adaptation, and controla self-modeling network modeling approach /[electronic resource] :edited by Jan Treur, Laila Van Ments. - Cham :Springer International Publishing :2022. - xi, 616 p. :ill., digital ;24 cm. - Studies in systems, decision and control,v. 3942198-4190 ;. - Studies in systems, decision and control ;v.3..

Dynamics, Adaptation and Control for Mental Models: A Cognitive Architecture -- Bringing Networks to the Next Level: Self-Modeling Networks for Adaptivity and Control of Mental Models -- On Becoming a Good Driver: Modeling the Learning of a Mental Model -- Controlling Your Mental Models: Using Metacognition to Control Use and Adaptation for Multiple Mental Models -- Disturbed by Flashbacks: a Controlled Adaptive Network Model Addressing Mental Models for Flashbacks from PTSD.

This book introduces a generic approach to model the use and adaptation of mental models, including the control over this. In their mental processes, humans often make use of internal mental models as a kind of blueprints for processes that can take place in the world or in other persons. By internal mental simulation of such a mental model in their brain, they can predict and be prepared for what can happen in the future. Usually, mental models are adaptive: they can be learned, refined, revised, or forgotten, for example. Although there is a huge literature on mental models in various disciplines, a systematic account of how to model them computationally in a transparent manner is lacking. This approach allows for computational modeling of humans using mental models without a need for any algorithmic or programming skills, allowing for focus on the process of conceptualizing, modeling, and simulating complex, real-world mental processes and behaviors. The book is suitable for and is used as course material for multidisciplinary Master and Ph.D. students.

ISBN: 9783030858216$q(electronic bk.)

Standard No.: 10.1007/978-3-030-85821-6doiSubjects--Topical Terms:

235165
Cognitive maps (Psychology)

LC Class. No.: BF314 / .M45 2022

Dewey Class. No.: 153

Mental models and their dynamics, adaptation, and controla self-modeling network modeling approach /
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505 0 $a Dynamics, Adaptation and Control for Mental Models: A Cognitive Architecture -- Bringing Networks to the Next Level: Self-Modeling Networks for Adaptivity and Control of Mental Models -- On Becoming a Good Driver: Modeling the Learning of a Mental Model -- Controlling Your Mental Models: Using Metacognition to Control Use and Adaptation for Multiple Mental Models -- Disturbed by Flashbacks: a Controlled Adaptive Network Model Addressing Mental Models for Flashbacks from PTSD.
520 $a This book introduces a generic approach to model the use and adaptation of mental models, including the control over this. In their mental processes, humans often make use of internal mental models as a kind of blueprints for processes that can take place in the world or in other persons. By internal mental simulation of such a mental model in their brain, they can predict and be prepared for what can happen in the future. Usually, mental models are adaptive: they can be learned, refined, revised, or forgotten, for example. Although there is a huge literature on mental models in various disciplines, a systematic account of how to model them computationally in a transparent manner is lacking. This approach allows for computational modeling of humans using mental models without a need for any algorithmic or programming skills, allowing for focus on the process of conceptualizing, modeling, and simulating complex, real-world mental processes and behaviors. The book is suitable for and is used as course material for multidisciplinary Master and Ph.D. students.
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