Self-Organizing Natural Intelligence (eBook)

Myrna Lynne Estep holds the B.A., M.S., and Ph.D. degrees from Indiana University, Bloomington, where she also taught and where much of the research for this book was originally done. She has also held faculty positions at the University of Zimbabwe; a branch of the University of Texas; and held various professional positions with the U.S. Government. She is the author of numerous scientific and technological papers, two books, co-editor of another, and has conducted studies in social and political theory affecting women, minorities, and the poor. Her biography appears in numerous reference books including The Directory of American Scholars; various editions of the Marquis Who's Who and Who’s Who in Science and Engineering. She was elected a member of the Honor Society of Phi Kappa Phi in 1992, and is an active researcher and writer in the behavioral sciences and artificial intelligence. Along with her husband, philosopher-scientist Richard Schoenig, she spends much of her time in West Texas and in the high San Juan Mountains of southwest Colorado.

Dedication 5
Table of Contents 7
Introduction 17
The Continuing Influence of Behaviorism 18
Myths of the Representational, Top-Down, Linguistic Mind 19
and Sensorimotor Intelligence 22
Toward Signs and Self-Organization 23
List of Abbreviations 27
List of Figures and Tables 29
Acknowledgements 31
1 The Problem of Intelligence 33
1.1 Some of the Basic Issues 36
1.2 The Faulty Sciences of Intelligence 39
1.2.1 The Anti-Theory Bias 40
1.2.2 Invalid Reductionism 42
1.2.3 Neglect of Emerging Intelligence 46
1.2.4 Neglect of Theory Construction and Concept Formation 48
1.2.5 Unexamined Assumptions, Concepts, and Fallacies 53
1.2.6 A Bankrupt Theory of Knowing in the Sciences of Intelligence 57
1.2.7 A Missing Distinction between Rule-governed and Rule- bound Intelligence 59
1.2.8 Neglect of Multiple Signs and Disclosure of Intelligence 61
1.2.9 Mechanical “Hard-Wired” and Natural Intelligence: Absent the Difference 64
1.3 Requirements for a New Science of Intelligence 65
1.3.1 A Broader Theory of Knowing 66
1.3.2 A Broader Theory of Signs of Intelligence 69
1.3.3 Methods of Nonlinear Science: The Emergence of Self- Organizing Dynamical Intelligence 72
1.4 Summary 77
2 The Universe of Intelligence 81
2.1 Carving the Problem Space 81
2.1.1 Rational Inquiry and Ideology: The Differences 82
2.1.2 Careless Carving 84
2.2 Classical Origins and Fabric of Intelligence Theory: Cut on Biases 85
2.2.1 Plato and Aristotle’s Conflicting Theoretical Stage 86
2.2.2 Anthropocentrism, Language, Gender, Race, Size, Wealth, and Place 90
2.2.3 The Fabric of Concepts Defining Intelligence Since Darwin 92
2.3 Today’s IQ Tests: Circularity, Bias, and American Eugenics 105
2.3.1 The Economic Argument 106
2.3.2 Reification and the Eugenics Argument 108
2.3.3 A Static Hierarchy: 110
the Controller 110
2.3.4 Biological Determinism Revisited 115
2.4 Summary 122
3 The Genesis of Intelligence: Innate and Emergence Arguments 125
3.1. Categorization, Classification, Concepts and Representation 125
3.1.1 Reality and the Influence of Representationalism 127
3.2 The Continuing Problem with Universals (Concepts): Some History 128
3.2.1 Realists, Conceptualists, and Nominalists on Universals 133
3.2.2 Theories of Knowledge and the Scope of Intelligence 134
3.2.3 Today’s Representationalist Myths: Cognitive Maps in the Brain 142
3.3 The Innate Versus Emergence Arguments 145
3.3.1 The Genetically Encoded Syntax Argument 145
3.3.2 Nonverbal Communication: Beyond Alphanumeric Symbols and Vocalizations 147
3.3.3 Evolutionary Argument against Innatists 152
3.3.4 Cognitivism, Mechanism, and “Innateness”: How the Mind Does Not Work 154
3.4 Summary 160
4 The Intelligence of Doing: Sensorimotor Domains and Knowing How 163
4.1 The Intelligence of Doing 163
4.1.1 A Two-Pronged Approach to Intelligence Inquiry 165
4.1.2 Cognition, Consciousness, Awareness 168
4.2 The Science of Awareness 170
4.2.1 Cortical Structures and Information: Neural Bases of Awareness and Intelligent Doing 172
4.2.2 How Concepts (Universals) Get Formed: A Global Map Theory 175
4.2.3 Primitive Awareness 179
4.2.4 Experimental Evidence of Immediate Awareness 182
4.2.5 Primitives of the Preattentive Phase of Awareness 189
4.3 Primitive Intelligence of Moving and Touching 199
4.3.1 Multiple Spaces of the Senses, Images and Probing 200
4.3.2 Smoothness and Timing in Intelligent Doing 205
4.4 Summary 208
5 Universals, Mathematical Thought and Awareness 213
5.1 On the Origins and Nature of Mathematical Thought 214
5.1.1 A Postmodern View: The Body Shapes Development and Content of Mathematics 216
5.1.2 The Language Causal Argument: Language Shapes the Development and Content of Mathematics 220
5.1.3 Thinking in Patterns and Images 224
5.1.4 The Realism Argument: Reality and Reason Shape the Development and Content of Mathematics 228
5.2 Problems with Representation Theories Revisited 236
5.2.1 Classification and the Nature of Sui Generis Objects of Immediate Awareness 240
5.3 Phenomenal Experience and Mathematics 241
5.3.1 Perception and Mathematical Objects 245
5.3.2 The Reality of Sets and Concepts 247
5.3.3 Intersubjective Requirements of Mathematical Thought 250
5.4 Summary 251
6 Intelligence as Self-Organizing Emerging Complexity 255
6.1 Categories of Natural Intelligence 255
6.2 Self-Organization and Pattern Formation 256
6.2.1 Interactive Systems and Self-Organization 259
6.3 Mechanism and Organicism Revisited 262
6.3.1 Organized Simplicity and Unorganized Complexity 262
6.3.2 Organized Complexity 264
6.4 Nonlinear Theory Models Approach to Natural Intelligence 267
6.4.1 The SIGGS Theory Model 270
6.4.2 Information Theory 272
6.5 SIGGS Applied to Natural Intelligence Systems 276
6.5.1 The Use of Digraph Theory to Characterize Intelligence Relations 278
6.5.2 Information-Theoretic Measures on Natural Intelligence Systems 287
6.6 From a Symbol-based View to a Geometric View of Natural Intelligence 292
6.6.1 Boolean Networks 292
6.7 Summary 296
7 Mapping Natural Intelligence to Machine Space 301
7.1 Classical Architectures for Natural Intelligence 302
7.1.1 Learning, Knowledge, Knowing and Intelligence 303
7.1.2 Goal-seeking Intentional Behavior 309
7.1.3 Control System Information Limitations 312
7.2 Biologically-Inspired Architectures: VLSI 314
7.2.1 Neuromorphic Architectures 316
7.2.2 The Problem of “Brittleness” 320
7.2.3 Problems with Pattern Recognition and Limits of Classification 327
7.2.4 Kinds of Space: Revisiting the Problem with Universals 331
7.3 Problems with Complexity 335
7.3.1 Decidability 335
7.4 Summary 342
8 Summary and Conclusions of Self-Organizing Natural Intelligence 347
8.1 A History of Biased Intelligence Space 348
8.2 Natural Intelligence as Self-Organizing and Emerging 350
8.2.1 Multidimensional and Multilayered Intelligence 352
8.2.2 Three Major Kinds of Natural Intelligence 352
8.3 Nonlinear Methods for a Science of Intelligence 353
8.4 Some Issues Left Unresolved 356
References 361
Index 387

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