Artificial Psychology (AP) is a highly multidisciplinary field of study in psychology. AP tries to solve problems which occur when psychologists do research and need a robust analysis method. Conventional statistical approaches have deep rooted limitations. These approaches are excellent on paper but often fail to model the real world. Mind researchers have been trying to overcome this by simplifying the models being studied. This stance has not received much practical attention recently. Promoting and improving artificial intelligence helps mind researchers to find a holistic model of mental models. This development achieves this goal by using multiple perspectives and multiple data sets together with interactive, and realistic models. In this book, the methodology of approximate inference in psychological research from a theoretical and practical perspective has been considered. Quantitative variable-oriented methodology and qualitative case-oriented methods are both used to explain the set-oriented methodology and this book combines the precision of quantitative methods with information from qualitative methods. This is a book that many researchers can use to expand and deepen their psychological research and is a book which can be useful to postgraduate students. The reader does not need an in-depth knowledge of mathematics or statistics because statistical and mathematical intuitions are key here and they will be learned through practice. What is important is to understand and use the new application of the methods for finding new, dynamic and realistic interpretations. This book incorporates theoretical fuzzy inference and deep machine learning algorithms in practice. This is the kind of book that we wished we had had when we were students. This book covers at least some of the most important issues in mind research including uncertainty, fuzziness, continuity, complexity and high dimensionality which are inherent to mind data. These are elements of artificial psychology. This book implements models using R software.
Author(s): Hojjatollah Farahani; Marija Blagojević; Parviz Azadfallah; Peter Watson; Forough Esrafilian; Sara Saljoughi
Publisher: Springer International Publishing
Year: 2023
Language: English
Pages: 272
Contents
1 In Search of a Method 1
1.1 What Is Artificial Psychology? 1
1.2 In Search of a Method 4
1.3 From p -value to p -war 5
1.3.1 p -value as Evidence to Confirm or Unconfirm a Null Hypothesis 6
1.3.2 Reverse Interpretation of the p -value 7
2 Artificial Psychology 9
2.1 Artificial Psychology 9
2.2 Why Artificial Psychology? 11
2.3 Artificial Psychology in Practice 22
2.4 Interpretability and Explainability in a Knowledge-Based Approach 25
2.5 Achilles’ Heel in Psychology 26
3 Fuzzy Set Theory and Psychology 31
3.1 Fuzzy Set Theory and Psychology: Theoretical View 31
3.2 The Gray World of Mind 32
3.3 The Fuzzy Logic Under Psychological View 34
3.4 Why Fuzzy Logic Theory? 37
3.5 What Is the Fuzzy Map? 41
3.6 Fuzzy Modelling of Psychological Systems 43
3.7 Properties of Fuzzy Sets 44
3.8 Types of Fuzzy Sets (Membership Functions) 46
3.9 Practical Example Using R 47
3.10 Fuzzy Set Composition 51
3.10.1 Practical Example Using R 52
3.11 Mamdani Fuzzy Inference System 54
3.11.1 Mamdani Fuzzy System Steps 57
3.11.2 Practical Example Using R 59
3.12 Toward Fuzzy Rule Mining 66
3.12.1 Adaptive Network-Based Fuzzy Inference System (ANFIS) 68
3.12.2 Genetic Cooperative-Competitive Learning (GCCL) 75
3.12.3 Structural Learning Algorithm on Indefinite Environment (SLAVE) 77
4 Fuzzy Cognitive Maps 81
4.1 Fuzzy Modeling of Human Knowledge: Toward Fuzzy Cognitive Maps in Psychology 81
4.2 Modeling Based on Psychological Knowledge 83
4.3 Optimization in FCMs 90
4.4 Scenario Analysis in FCMs 92
4.4.1 Practical Example Using R 92
5 Network Analysis in AP 99
5.1 Network Analysis in AP 99
5.2 Structural Analysis of Psychological Network 101
5.3 Steps in Network Analysis 103
5.4 Descriptive Statistics of Networks 104
5.5 Network Accuracy 106
5.6 Accuracy of Centrality Indices 107
5.7 Network Science in Psychology 107
5.8 Network Science in Cognitive Psychology and Neuroscience 108
5.8.1 Complex System 108
5.8.2 The Brain as a Complex System 108
5.8.3 Brain Connectome 110
5.8.4 Various Scales for Network Analysis of the Brain 111
5.8.5 Networks in the Brain 112
5.8.6 Definition of a Brain Graph 115
5.8.7 Brain Network Identification and Analysis in Graph 118
5.8.8 The Brain’s Important Networks 119
5.8.9 Applications of Graph in Cognitive and Behavioral Science 121
5.8.10 Machine Learning in Analysis of Resting-State fMRI (Rs-fMRI) Data 123
5.9 Designing Conceptual Networks 125
5.10 Sample Size in Network Analysis 126
5.11 Moderated Psychological Network Analysis 127
5.11.1 Practical Example Using R 127
6 Deep Neural Network 145
6.1 Deep Neural Network (DNN) 145
6.2 Neural Network 146
6.3 Neuron 146
6.4 Artificial Neural Network (ANN) 149
6.5 Types of Training 154
6.6 Usage of Neural Network 155
6.7 The Artificial Neural Network Structure 156
6.8 Modeling an Artificial Neural Network 158
6.8.1 Classical Optimization Methods 159
6.8.2 Intelligent Optimization Methods 160
6.9 Types of Data in Machine Learning Algorithms 160
6.10 Basic Concepts 161
6.11 Types of Artificial Neural Networks 164
6.12 Comparing Multilayer Neural Network with Regression 165
6.12.1 Practical Example Using R 167
6.13 Hyper-Parameter Tuning 172
6.14 Evaluation of DNNs 173
6.15 Interpretability and Explainability in DNNs 175
6.16 Difference between LIME and SHAP 178
6.16.1 Practical Example Using R 178
7 Feature Selection in AP 187
7.1 Feature Selection Problem 187
7.2 Feature Categorization 188
7.3 General Procedure of Feature Selection 188
7.4 Feature Selection Methods 190
7.4.1 Practical Example Using R 191
7.5 Metaheuristic Algorithms 194
7.6 An Introduction to the Genetic Algorithm 196
7.7 Basics of the Genetic Algorithm 197
7.8 The Initial Design of the Genetic Algorithm 198
7.9 Feature Selection Using the Genetic Algorithm 199
7.10 The Genetic Algorithm’s Application in Artificial Psychology 201
7.10.1 Practical Example Using R 202
7.11 The Genetic Algorithm’s Application in Neural Network Sciences 203
8 Bayesian Inference and Models in AP 207
8.1 Bayesian Inference and Models in Artificial Psychology 207
8.2 Bayesian Statistics in a Nutshell 208
8.3 A Critique on the Use of p-value 210
8.3.1 Significance or Existence of a Network 213
8.3.2 Practical Example Using R 213
8.4 Naïve Bayes Classifier 215
8.5 Cross-validation 217
8.5.1 A Practical Example Using R 218
8.6 Bayesian Binary Logistic Regression 220
8.6.1 A Practical Example Using R 221
8.7 Bayesian Network Analysis 225
8.7.1 A Practical Example Using R 229
8.8 Bayesian Model Averaging 231
8.8.1 Practical Example Using R 233
References 237
Index 251
