An Introduction to Artificial Psychology. Application Fuzzy Set Theory and Deep Machine Learning in Psychological Research using R

Online Resources
Preface
	Don´t You Know the Statistics? Don´t be Afraid and Enter
Acknowledgments
Contents
About the Authors
Chapter 1: In Search of a Method
	1.1 What Is Artificial Psychology?
	1.2 In Search of a Method
	1.3 From p-value to p-war
		1.3.1 p-value as Evidence to Confirm or Unconfirm a Null Hypothesis
		1.3.2 Reverse Interpretation of the p-value
Chapter 2: Artificial Psychology
	2.1 Artificial Psychology
	2.2 Why Artificial Psychology?
	2.3 Artificial Psychology in Practice
	2.4 Interpretability and Explainability in a Knowledge-Based Approach
	2.5 Achilles´ Heel in Psychology
Chapter 3: Fuzzy Set Theory and Psychology
	3.1 Fuzzy Set Theory and Psychology: Theoretical View
	3.2 The Gray World of Mind
	3.3 The Fuzzy Logic Under Psychological View
	3.4 Why Fuzzy Logic Theory?
	3.5 What Is the Fuzzy Map?
	3.6 Fuzzy Modelling of Psychological Systems
	3.7 Properties of Fuzzy Sets
	3.8 Types of Fuzzy Sets (Membership Functions)
	3.9 Practical Example Using R
	3.10 Fuzzy Set Composition
		3.10.1 Practical Example Using R
	3.11 Mamdani Fuzzy Inference System
		3.11.1 Mamdani Fuzzy System Steps
		3.11.2 Practical Example Using R
	3.12 Toward Fuzzy Rule Mining
		3.12.1 Adaptive Network-Based Fuzzy Inference System (ANFIS)
			Practical Example Using R
		3.12.2 Genetic Cooperative-Competitive Learning (GCCL)
			Practical Example Using R
		3.12.3 Structural Learning Algorithm on Indefinite Environment (SLAVE)
			Practical Example Using R
Chapter 4: Fuzzy Cognitive Maps
	4.1 Fuzzy Modeling of Human Knowledge: Toward Fuzzy Cognitive Maps in Psychology
	4.2 Modeling Based on Psychological Knowledge
	4.3 Optimization in FCMs
	4.4 Scenario Analysis in FCMs
		4.4.1 Practical Example Using R
Chapter 5: Network Analysis in AP
	5.1 Network Analysis in AP
	5.2 Structural Analysis of Psychological Network
	5.3 Steps in Network Analysis
	5.4 Descriptive Statistics of Networks
	5.5 Network Accuracy
	5.6 Accuracy of Centrality Indices
	5.7 Network Science in Psychology
	5.8 Network Science in Cognitive Psychology and Neuroscience
		5.8.1 Complex System
		5.8.2 The Brain as a Complex System
		5.8.3 Brain Connectome
		5.8.4 Various Scales for Network Analysis of the Brain
		5.8.5 Networks in the Brain
			Structural Connectivity
			Functional Connectivity
			Effective Connectivity
		5.8.6 Definition of a Brain Graph
			Graph Analysis Metrics in the Brain
		5.8.7 Brain Network Identification and Analysis in Graph
		5.8.8 The Brain´s Important Networks
			The Sensorimotor Network
			The Visual System
			Limbic System Network
			The Central Executive Network (CEN)
			Default Mode Network
			Salience Network
			The Dorsal Attention Network
		5.8.9 Applications of Graph in Cognitive and Behavioral Science
		5.8.10 Machine Learning in Analysis of Resting-State fMRI (Rs-fMRI) Data
	5.9 Designing Conceptual Networks
	5.10 Sample Size in Network Analysis
	5.11 Moderated Psychological Network Analysis
		5.11.1 Practical Example Using R
Chapter 6: Deep Neural Network
	6.1 Deep Neural Network (DNN)
	6.2 Neural Network
	6.3 Neuron
	6.4 Artificial Neural Network (ANN)
	6.5 Types of Training
	6.6 Usage of Neural Network
	6.7 The Artificial Neural Network Structure
	6.8 Modeling an Artificial Neural Network
		6.8.1 Classical Optimization Methods
		6.8.2 Intelligent Optimization Methods
	6.9 Types of Data in Machine Learning Algorithms
	6.10 Basic Concepts
	6.11 Types of Artificial Neural Networks
	6.12 Comparing Multilayer Neural Network with Regression
		6.12.1 Practical Example Using R
	6.13 Hyper-Parameter Tuning
	6.14 Evaluation of DNNs
	6.15 Interpretability and Explainability in DNNs
	6.16 Difference between LIME and SHAP
		6.16.1 Practical Example Using R
Chapter 7: Feature Selection in AP
	7.1 Feature Selection Problem
	7.2 Feature Categorization
	7.3 General Procedure of Feature Selection
	7.4 Feature Selection Methods
		7.4.1 Practical Example Using R
	7.5 Metaheuristic Algorithms
	7.6 An Introduction to the Genetic Algorithm
	7.7 Basics of the Genetic Algorithm
	7.8 The Initial Design of the Genetic Algorithm
	7.9 Feature Selection Using the Genetic Algorithm
	7.10 The Genetic Algorithm´s Application in Artificial Psychology
		7.10.1 Practical Example Using R
	7.11 The Genetic Algorithm´s Application in Neural Network Sciences
Chapter 8: Bayesian Inference and Models in AP
	8.1 Bayesian Inference and Models in Artificial Psychology
	8.2 Bayesian Statistics in a Nutshell
	8.3 A Critique on the Use of p-value
		8.3.1 Significance or Existence of a Network
		8.3.2 Practical Example Using R
	8.4 Naïve Bayes Classifier
	8.5 Cross-validation
		8.5.1 A Practical Example Using R
			Leave-One-Out Cross-validation
	8.6 Bayesian Binary Logistic Regression
		8.6.1 A Practical Example Using R
	8.7 Bayesian Network Analysis
		8.7.1 A Practical Example Using R
	8.8 Bayesian Model Averaging
		8.8.1 Practical Example Using R
References
Index