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