Mental Models and Their Dynamics, Adaptation, and Control: A Self-Modeling Network Modeling Approach

Preface
Contents
Part I Introduction
1 Dynamics, Adaptation and Control for Mental Models: A Cognitive Architecture
	1.1 Introduction
	1.2 Mental Models and What They Model
		1.2.1 Mental Models as Small-Scale Models Within the Head
		1.2.2 Mental Models for Individual Processes
		1.2.3 Mental Models in Social Processes
		1.2.4 A Mental Models Overview According to Mental Versus World and Static Versus Dynamic
	1.3 Learning and Development of Mental Models
		1.3.1 Learning and Development as Adaptation of Mental Models
		1.3.2 Learning of Mental Models by Observation and by Instruction
		1.3.3 Control for Learning of Mental Models Based on Metacognition
	1.4 A Cognitive Architecture for Mental Models
		1.4.1 Higher-Order Relations
		1.4.2 What Exactly Do Mental Models Do?
		1.4.3 A Cognitive Architecture for Handling Mental Models
	1.5 Discussion
	References
2 Bringing Networks to the Next Level: Self-modeling Networks for Adaptivity and Control of Mental Models
	2.1 Introduction
	2.2 Modeling Adaptivity by Self-modeling Networks
		2.2.1 Network-Oriented Modeling
		2.2.2 Using Self-modeling Networks to Model Adaptive Networks
	2.3 Modeling Adaptation Principles
		2.3.1 First-Order Self-models for First-Order Adaptation Principles
		2.3.2 Second-Order Self-models for Second-Order Adaptation Principles
	2.4 A Second-Order Adaptive Mental Self-modeling Network Model for Emotion Regulation Dysfunction
		2.4.1 Design of the Adaptive Network Model for Emotion Regulation Dysfunction
		2.4.2 Specification of the Adaptive Network Model for Emotion Regulation Disfunction
		2.4.3 Simulations for the Adaptive Network for Emotion Regulation Dysfunction
	2.5 An Example Network Model for Mental Model Handling
		2.5.1 An Example Scenario for Mental Model Handling
		2.5.2 Connectivity and Aggregation for the Adaptive Network Model
		2.5.3 Specification of the Example Network Model for Mental Model Handling
	2.6 Discussion
	References
Part II Self-Modelling Network Models for Mental Models in Individual Processes
3 On Becoming a Good Driver: Modeling the Learning of a Mental Model
	3.1 Introduction
	3.2 Literature Overview
	3.3 An Adaptive Network Model for Mental Model Development
		3.3.1 Observational Learning
		3.3.2 Self-directed Learning
		3.3.3 Learning from Instruction
		3.3.4 Integrating Self-Directed Learning and Learning from Instruction
	3.4 Example Simulations
	3.5 Discussion and Conclusion
	3.6 Explanation of All States of the Model
	References
4 Controlling Your Mental Models: Using Metacognition to Control Use and Adaptation for Multiple Mental Models
	Abstract
	4.1 Introduction
	4.2 Metacognition and Multiple Mental Models
	4.3 Higher-Order Adaptive Network Models
	4.4 A Mental Network Model for Metacognitive Control of Learning from Multiple Internal Mental Models
		4.4.1 Network Characteristics: Connectivity and Timing
		4.4.2 Network Characteristics: Aggregation
	4.5 Example Simulation Scenarios
	4.6 Discussion
	References
5 Disturbed by Flashbacks: A Controlled Adaptive Network Model Addressing Mental Models for Flashbacks from PTSD
	5.1 Introduction
	5.2 Background Knowledge on Adaptation Principles Used
		5.2.1 First-Order Adaptation Principle: Hebbian Learning
		5.2.2 Second-Order Adaptation Principle: Stress Reduces Adaptation Speed
	5.3 The Second-Order Adaptive Network Model
		5.3.1 The General Format
		5.3.2 Translating the Domain Knowledge into a Conceptual Causal Model
		5.3.3 Transcribing the Conceptual Model Into Role Matrices
	5.4 Example Simulations
	5.5 Discussion
	5.6 Appendix: Full Specification of the Adaptive Network Model
	References
6 ‘What if I Would Have Done Otherwise…’: A Controlled Adaptive Network Model for Mental Models in Counterfactual Thinking
	6.1 Introduction
	6.2 Literature Review
	6.3 The Modeling Approach for Controlled Adaptive Networks
	6.4 A Controlled Adaptive Network Model for Counterfactual Thinking
	6.5 Simulation Results
	6.6 Verification of the Model by Analysis of Stationary Points
	6.7 Discussion
	6.8 Appendix: Full Specification of the Adaptive Network Model by Role Matrices.
	References
7 Do You Get Me: Controlled Adaptive Mental Models for Analysis and Support Processes
	7.1 Introduction
	7.2 Network Models Using Self-models
	7.3 Modeling the Adaptation Principles Used
		7.3.1 First-Order Self-models for the First-Order Adaptation Principles Used
		7.3.2 Second-Order Self-model for the Second-Order Adaptation Principle
	7.4 Analysis and Support Processes
	7.5 The Second-Order Adaptive Network Model
		7.5.1 The Base Level
		7.5.2 First-Order Self-models
		7.5.3 Second-Order Self-models
	7.6 Simulation Scenarios
		7.6.1 Using Adaptive Excitability Thresholds and Constant Connection Weights
		7.6.2 Using Both Adaptive Excitability Thresholds and Connection Weights
	7.7 Discussion
	7.8 Appendix: Specification of the Network Model by Role Matrices
	References
8 Who Am I Really: An Adaptive Network Model Addressing Mental Models for Self-referencing, Self-awareness and Self-interpretation
	8.1 Introduction
	8.2 Perspectives from a Psychiatric Context
		8.2.1 Self-referentiality
		8.2.2 Self-awareness
		8.2.3 Self-interpretation
		8.2.4 Other Literature
		8.2.5 Point of Departure for the Case Study Used
	8.3 Self-modeling Network Models
		8.3.1 Using Self-models Within a Network Model
		8.3.2 Self-modeling Network Modeling
	8.4 The Overall Cognitive Architecture
		8.4.1 Base Level
		8.4.2 First Self-model Level: Self-referencing
		8.4.3 Second Self-model Level: Self-awareness
		8.4.4 Third Self-model Level: Self-interpretation
	8.5 The Four-Level Self-modeling Network Model for the Case Study
	8.6 Detailed Specification
	8.7 Example Simulation for the Case Study
	8.8 Discussion
	References
Part III Self-Modelling Network Models for Mental Models in Social Processes
9 In Control of Your Instructor: Modeling Learner-Controlled Mental Model Learning
	9.1 Introduction
	9.2 Overview of Background Knowledge on Mental Models
		9.2.1 Learning by Observation
		9.2.2 Learning by Instruction
		9.2.3 Learner-Controlled Learning
	9.3 Network Architecture for Controlled Mental Model Learning
	9.4 Detailed Description of the Second-Order Adaptive Network Model for a Case Study
	9.5 Simulation Results for an Example Scenario
	9.6 Verification of the Network Model by Equilibrium Analysis
		9.6.1 Criterion for Equilibria of Self-modeling Network Models
		9.6.2 Equilibrium Analysis of the LW-States and the CIW-States
		9.6.3 Equilibrium Analysis of the IW-States
		9.6.4 Equilibrium Analysis of the RW-States
	9.7 Discussion
	9.8 Appendix: Full Specification of the Second-Order Adaptive Network Model
	References
10 Work Together or Fight Together: Modeling Adaptive Cooperative and Competitive Metaphors as Mental Models for Joint Decision Making
	10.1 Introduction
	10.2 Background Knowledge
		10.2.1 Mirror Neurons and Internal Simulation
		10.2.2 Ownership and Empathic Understanding
		10.2.3 Cognitive Metaphors as Mental Models
	10.3 The Self-modeling Network Modeling Approach Used
		10.3.1 Network States and Network Characteristics
		10.3.2 Self-models Representing Network Characteristics by Network States
	10.4 The Second-Order Adaptive Network Model
		10.4.1 The Base Model for Metaphors in Joint Decision Making
		10.4.2 Modeling First- and Second-Order Self-models for Adaptation and Control
	10.5 Simulation of an Example Scenario
	10.6 Discussion
	10.7 Appendix: Specification of the Network Model by Role Matrices
	References
11 How Empathic is Your God: An Adaptive Network Model for Formation and Use of a Mental God-Model and Its Effect on Human Empathy
	11.1 Introduction
	11.2 Literature Overview
	11.3 The Adaptive Network Model
		11.3.1 Mirror Neurons and Internal Simulation
		11.3.2 Action Ownership States for God and Self
		11.3.3 The Input Used for the Mental God-Model
		11.3.4 Conceptual Description of the Mental God-Model
		11.3.5 Conceptual Representation of the Overall Network Model
		11.3.6 Numerical Representation of the Network Model
	11.4 Simulation Scenarios
		11.4.1 A Person with a Neutral Mental God-Model
		11.4.2 A Person with an Empathic Mental God-Model
		11.4.3 A Person with a Disempathic Mental God-Model
		11.4.4 A Person with Autism
		11.4.5 A Person that is Atheist
		11.4.6 A Person with Fundamentalist Tendencies
	11.5 Discussion and Conclusion
	11.6 Appendix: Specification of the Adaptive Network Model by Role Matrices
	References
12 You Feel so Familiar, You Feel so Different: A Controlled Adaptive Network Model for Attachment Patterns as Adaptive Mental Models
	12.1 Introduction
	12.2 Attachment Theory
	12.3 The Modeling Approach Used
	12.4 Designing the Adaptive Network Model for Attachment Theory
	12.5 Simulation Scenarios
	12.6 Discussion
	12.7 Appendix: Specification of the Network Model by Role Matrices
	References
13 Taking Control of Your Bonding: Controlled Social Network Adaptation Using Mental Models
	13.1 Introduction
	13.2 Higher-Order Adaptive Network Models
	13.3 A Network Model for Controlled Social Network Adaptation
	13.4 Simulation for a Tetradic Relationship Example
	13.5 A Social Network Model for Bonding Based on Faking
	13.6 Simulation: Faking Homophily for Bonding
	13.7 Discussion
	13.8 Specification of the Main Adaptive Network Model
	References
14 Are We on the Same Page: A Controlled Adaptive Network Model for Shared Mental Models in Hospital Teamwork
	14.1 Introduction
	14.2 Background
		14.2.1 Mental Models
		14.2.2 Shared Mental Models
		14.2.3 Case Description
		14.2.4 Network-Oriented Modeling
		14.2.5 Self-modeling Networks to Model Adaptivity and Control
	14.3 The Adaptive Network Model Using a Shared Mental Model
		14.3.1 Base Level: Overview
		14.3.2 Base Level: Memory States in the Mental Models
		14.3.3 Base Level: Action Ownership States
		14.3.4 Middle Level: Adaptation of the Mental Models (Plasticity)
		14.3.5 Upper Level: Control of the Adaptation of Mental Models (Metaplasticity)
	14.4 Simulation for the Example Scenario
		14.4.1 The World States
		14.4.2 The Doctor’s Mental Processes Based on Her Mental Model
		14.4.3 The Nurse’s Mental Processes Based on Her Mental Model
		14.4.4 The Learning and Forgetting States
	14.5 Discussion
	14.6 Appendix: Specification of the Network Model
	References
Part IV Relating Mental Models to Brain, Body and World
15 How Do Mental Models Actually Exist in the Brain: On Context-Dependent Neural Correlates of Mental Models
	15.1 Introduction
	15.2 Literature on Neural Correlates for Mental Models
		15.2.1 Some Literature from Neuroscience
		15.2.2 Internal Simulation
		15.2.3 Neural Correlates for Adaptation and Control for Mental Models
	15.3 Context-Dependent Realisation of Mental States
		15.3.1 Context-Dependent Multiple Realisation of Mental States
		15.3.2 An Illustration from Biology: Multiple Realisation of Behavioural Choice
		15.3.3 An Illustration from Physics: Multiple Realisation of Force
	15.4 Context-Dependent Realisation of Mental Models
	15.5 Context-Dependent Realisation from Different Perspectives
		15.5.1 Context-Dependent Bridge Principle Realisation
		15.5.2 Context-Dependent Interpretation Mapping Realisation
		15.5.3 Relating Bridge Principle Realisation and Interpretation Mapping Realisation
	15.6 Discussion
	References
16 How the Brain Creates Emergent Information by the Development of Mental Models: An Analysis from the Perspective of Temporal Factorisation and Criterial Causation
	16.1 Introduction
	16.2 Temporal Factorisation Versus Criterial Causation
		16.2.1 Temporal Factorisation
		16.2.2 Criterial Causation
		16.2.3 How Criterial Causation Relates to Temporal Factorisation
	16.3 Network-Oriented Modeling for Adaptive Networks
		16.3.1 Network Models
		16.3.2 Modeling Adaptive Networks as Self-Modeling Networks
	16.4 Temporal Factorisation Modeled by Networks
		16.4.1 An Example Network Model Illustrating Temporal Factorisation
		16.4.2 Simulation for the Network Model Illustrating Temporal Factorisation
		16.4.3 Application of the Network Model to Delayed Response Behaviour
	16.5 Modeling Criteria for Criterial Causation for Network Models
		16.5.1 Criteria Using Logistic Combination Functions
		16.5.2 Criteria Using Other Combination Functions
	16.6 How a Developing Mental Model Creates Emergent Information in the Brain
		16.6.1 An Example Scenario for Learning and Use of a Mental Model
		16.6.2 Connectivity and Aggregation for the Adaptive Network Model
		16.6.3 Specification of the Adaptive Network Model by Role Matrices
	16.7 Simulation of the Development and Use of the Mental Model
		16.7.1 Past Pattern a (Time Point 0 to 100)
		16.7.2 Criterion Formed at Time Point 100
		16.7.3 Future Pattern b (Time Point 100–200)
	16.8 Defining Informational Content by Temporal Relational Specification
		16.8.1 Relational Specification of Mental Content
		16.8.2 Applying Temporal Relational Specification to Informational Content
	16.9 Formalisation of Temporal Factorisation and Criterial Causation in Temporal Trace Predicate Logic
		16.9.1 Formalisation of Temporal Factorisation in Temporal Trace Predicate Logic
		16.9.2 Formalisation of Temporal Factorisation in Reified Temporal Trace Predicate Logic
	16.10 Discussion
	References
Part V Design and Analysis of Self-Modelling Network Models
17 With a Little Help: A Modeling Environment for Self-modeling Network Models
	17.1 Introduction
	17.2 Role Matrices as Specification Format for Self-Modeling Networks
		17.2.1 The Role Matrix Format
		17.2.2 Splitting the Role Matrices and Copying Them into the Software Environment
	17.3 The Combination Function Library
		17.3.1 The Standard Format of Combination Functions
		17.3.2 Different Types of Combination Functions
		17.3.3 Composing New Combination Functions from Available Combination Functions
	17.4 The Computational Self-modeling Network Engine
		17.4.1 Retrieving Information from the Role Matrices
		17.4.2 The Iteration Step from t to t + Δt
	17.5 Discussion
	References
18 Where is This Leading Me: Stationary Point and Equilibrium Analysis for Self-Modeling Network Models
	18.1 Introduction
	18.2 Modeling and Analysis of Dynamics within Network Models
	18.3 Verification of a Network Model via Checking the Stationary Point Equations
	18.4 Verification of a Network Model via Solving Equilibrium Equations
	18.5 Using a Linear Solver to Symbolically Solve Linear Equilibrium Equations
	18.6 Solving Nonlinear Equilibrium Equations for Euclidean Functions
	18.7 Solving Nonlinear Equilibrium Equations for Geometric Functions
	18.8 Solving Nonlinear Equilibrium Equations for Examples of Self-Model States
		18.8.1 Solving Nonlinear Equations for Self-Model States for Hebbian Learning
		18.8.2 Solving the Nonlinear Equations for Self-Model States for Bonding by Homophily
	18.9 General Equilibrium Analysis for a Class of Nonlinear Functions
	18.10 Additive, Multiplicative, Log-like and Exp-like Functions
	18.11 Weakly Scalar-Free and Scalar-Free Functions
	18.12 Scalar-Free Functions based on Function Conjugates
	18.13 Appendix: Proofs
		18.13.1 Additive, Multiplicative, Log-Like and Exp-Like Functions
		18.13.2 Weakly Scalar-Free And Scalar-Free Functions
		18.13.3 Creating Scalar-Free Functions Based on Conjugates
	18.14 Discussion
	References
19 Does This Suit Me? Validation of Self-modeling Network Models by Parameter Tuning
	19.1 Introduction
	19.2 Determining Characteristics and the Use of Requirements
		19.2.1 The Choice of Network Characteristics in a Network Model
		19.2.2 Direct Measuring of Network Characteristics in a Real-World Situation
		19.2.3 Using Requirements to Find Characteristics of a Situation
		19.2.4 Using Error Measures for Requirements Based on Data Points
	19.3 Description of an Example Model
	19.4 Parameter Tuning by Exhaustive Search
	19.5 Parameter Tuning by Simulated Annealing
	19.6 Pros and Cons of Different Parameter Tuning Methods
	19.7 Applying Parameter Tuning by the Modeling Environment
		19.7.1 Basic Elements Needed for Parameter Tuning
		19.7.2 Preparation for the Tuning Process
		19.7.3 Running the Tuning Process
		19.7.4 How It Works
	19.8 Discussion
	References
20 How Far Do Self-Modeling Networks Reach: Relating Them to Adaptive Dynamical Systems
	20.1 Introduction
	20.2 The State-Determined System Assumption
	20.3 Dynamical Systems and First-Order Differential Equations
	20.4 Self-Modeling Network Modeling
	20.5 Relating Dynamical Systems to Network Models
		20.5.1 Transforming a Dynamical System Model into a Network Model
		20.5.2 Illustration of the Transformation for an Example Dynamical System
	20.6 Relating Adaptive Dynamical Systems to Self-Modeling Network Models
		20.6.1 Transforming an Adaptive Dynamical System Model into a Self-Modeling Network Model
		20.6.2 Illustration of the Transformation for an Example Adaptive Dynamical System
	20.7 Discussion
	References
Part VI Design and Analysis of Self-Modelling Network Models
21 Dynamics, Adaptation, and Control for Mental Models Analysed from a Self-modeling Network Viewpoint
	21.1 Introduction
	21.2 Self-modeling Network Models
		21.2.1 Network Models
		21.2.2 Modeling Adaptive Networks as Self-Modeling Networks
	21.3 Modeling the Cognitive Architecture for Mental Models as a Self-Modeling Network
	21.4 How Mental Models Can Be Used
	21.5 How Mental Models Can Be Adapted
	21.6 How Mental Model Adaptation Can Be Controlled
	21.7 Discussion
	References
Index