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Self-Organizing Natural Intelligence brings new scientific methods to intelligence research that is currently under the influence of largely classical 19th century single causal theory and method. This out-dated classical approach has resulted in the single-capacity g-theory, a "central processor," top-down, genetically determined linguistic view of intelligence that is directly contradicted by empirical facts of human and animal studies of intelligence.

This book proposes, utilizes, and demonstrates the research superiority of a highly developed multidisciplinary theory models approach to intelligence. With conceptual tools, concepts and mathematical methods more suited to continuous, dynamic phenomena of living things, the entire scope of natural intelligence based upon empirical studies of actual human and animal experience is addressed. Results show that human and animal intelligence is largely self-organizing and emergent across a spectrum of major categories of kinds of natural intelligence, not limited to a single "top down" capacity as current proponents of the single-capacity g-theory and IQ approach support.

Contrary to the single-capacity verbal theory of intelligence, this work argues and shows evidence for three major categories of natural intelligence. Overwhelming empirical evidence is given to show that our understanding of cognition itself must be broadened to include nonverbal immediate awareness as a category of natural intelligence that is embedded within sensory and somatosensory-motor processes that make possible yet another category of intelligence, knowing how.

While most current theories of intelligence assume that the mind is entirely computational, and also assume that sensation is cognitively "neutral," having no intentionality, here empirical evidence is presented from numerous clinical studies showing that certain primitive sensory processes are not cognitively neutral, nor dothey require a representational language interface in order to be accessible to cognitive (intelligence) processes.

This volume describes a rigorous treatment and exhaustive classification of natural intelligence while also demonstrating a more adequate scientific and mathematical approach than current statistical and psychometric approaches shoring up the out-dated and misused IQ hypothetical construct.

ABOUT THE AUTHOR:

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.

Tabla de contenidos provista por Syndetics

• Introduction(p. xvii)
• List of Abbreviations(p. xxvii)
• List of Figures and Tables(p. xxix)
• Acknowledgements(p. xxxi)
• 1 The Problem of Intelligence(p. 1)
• 1.1 Some of the Basic Issues(p. 4)
• The Single and Multiple Capacity Views(p. 5)
• Where Are the Facts of Intelligence Found?(p. 6)
• 1.2 The Faulty Sciences of Intelligence(p. 7)
• 1.2.1 The Anti-Theory Bias(p. 8)
• A Misleading Heritage of Inductivism(p. 8)
• Confusing Cause and Correlation(p. 9)
• 1.2.2 Invalid Reductionism(p. 10)
• The Faulty Genetic Argument(p. 11)
• A Neo-Darwinist Influence(p. 12)
• 1.2.3 Neglect of Emerging Intelligence(p. 14)
• Inadequacies of the Classical Linear Approach(p. 15)
• 1.2.4 Neglect of Theory Construction and Concept Formation(p. 16)
• Mechanism and Organicism(p. 17)
• Narrowing the Intelligence Domain to Suit Tools At Hand(p. 21)
• 1.2.5 Unexamined Assumptions, Concepts, and Fallacies(p. 21)
• The Scope of Cognition(p. 22)
• 1.2.6 A Bankrupt Theory of Knowing in the Sciences of Intelligence(p. 25)
• Kinds of Knowing and the Intellectualist Legend(p. 26)
• 1.2.7 A Missing Distinction between Rule-governed and Rule-bound Intelligence(p. 27)
• 1.2.8 Neglect of Multiple Signs and Disclosure of Intelligence(p. 29)
• Signals, Cues, and Clues(p. 29)
• Exhibiting and Disclosing Intelligence(p. 30)
• 1.2.9 Mechanical "Hard-Wired" and Natural Intelligence: Absent the Difference(p. 32)
• 1.3 Requirements for a New Science of Intelligence(p. 33)
• 1.3.1 A Broader Theory of Knowing(p. 34)
• Knowledge That, Knowing How, Immediate Awareness(p. 34)
• 1.3.2 A Broader Theory of Signs of Intelligence(p. 37)
• Toward Three-Dimensional Signs and Patterns(p. 38)
• 1.3.3 Methods of Nonlinear Science: The Emergence of Self-Organizing Dynamical Intelligence(p. 40)
• Self-Organization(p. 40)
• Theory Models Approach to Intelligence Inquiry(p. 42)
• Set Theory(p. 42)
• Information Theory(p. 43)
• Graph Theory and Dynamical Systems Theory(p. 43)
• From a Symbol-based View to a Geometric View of Natural Intelligence(p. 44)
• 1.4 Summary(p. 45)
• 2 The Universe of Intelligence(p. 49)
• 2.1 Carving the Problem Space(p. 49)
• 2.1.1 Rational Inquiry and Ideology: The Differences(p. 50)
• 2.1.2 Careless Carving(p. 52)
• 2.2 Classical Origins and Fabric of Intelligence Theory: Cut on Biases(p. 53)
• 2.2.1 Plato and Aristotle's Conflicting Theoretical Stage(p. 54)
• Plato's Dichotomy of Mind and Body(p. 55)
• Aristotelian Dictum: Anatomy and Intelligence are Destiny(p. 56)
• Early Differences Between Theory and Practice(p. 57)
• 2.2.2 Anthropocentrism, Language, Gender, Race, Size, Wealth, and Place(p. 58)
• The Intrinsic and Instrumental Intelligence Difference(p. 59)
• The Intelligence Center of the Universe(p. 59)
• 2.2.3 The Fabric of Concepts Defining Intelligence Since Darwin(p. 60)
• Reason, Logic and Language(p. 62)
• Number(p. 63)
• Knowledge(p. 64)
• The Continuing Cartesian "Split": Body and Mind(p. 65)
• Making the Natural Artificial(p. 69)
• The Intelligence of the Large and Small(p. 70)
• Brainless Intelligence and Intentionality?(p. 71)
• 2.3 Today's IQ Tests: Circularity, Bias, and American Eugenics(p. 73)
• 2.3.1 The Economic Argument(p. 74)
• The Issue of Test Validity(p. 75)
• 2.3.2 Reification and the Eugenics Argument(p. 76)
• 2.3.3 A Static Hierarchy: g the Controller(p. 78)
• Missing From g: Experience(p. 80)
• 2.3.4 Biological Determinism Revisited(p. 83)
• Neo-Darwinism and the Heritability Argument(p. 83)
• A Short History of Rising IQ Scores(p. 87)
• Suspect Racial Sorting(p. 89)
• 2.4 Summary(p. 90)
• 3 The Genesis of Intelligence: Innate and Emergence Arguments(p. 93)
• 3.1 Categorization, Classification, Concepts and Representation(p. 93)
• 3.1.1 Reality and the Influence of Representationalism(p. 95)
• 3.2 The Continuing Problem with Universals (Concepts): Some History(p. 96)
• Plato(p. 96)
• Aristotle(p. 99)
• 3.2.1 Realists, Conceptualists, and Nominalists on Universals(p. 101)
• 3.2.2 Theories of Knowledge and the Scope of Intelligence(p. 102)
• Realism, Coherence, and Pragmatism(p. 103)
• The Language Interface Issue(p. 105)
• A Postmodern Heritage and Realist Counterargument(p. 108)
• 3.2.3 Today's Representationalist Myths: Cognitive Maps in the Brain(p. 110)
• 3.3 The Innate Versus Emergence Arguments(p. 113)
• 3.3.1 The Genetically Encoded Syntax Argument(p. 113)
• 3.3.2 Nonverbal Communication: Beyond Alphanumeric Symbols and Vocalizations(p. 115)
• Gestures(p. 116)
• From Manual Gestures to Whole Body Performances(p. 118)
• 3.3.3 Evolutionary Argument against Innatists(p. 120)
• 3.3.4 Cognitivism, Mechanism, and "Innateness": How the Mind Does Not Work(p. 122)
• Innate Learning Mechanisms(p. 123)
• The Classical Computational View of Mind and Intelligence(p. 124)
• Missing Practical Intelligence(p. 125)
• Rationalist Sources of Innate Arguments(p. 126)
• 3.4 Summary(p. 128)
• 4 The Intelligence of Doing: Sensorimotor Domains and Knowing How(p. 131)
• 4.1 The Intelligence of Doing(p. 131)
• 4.1.1 A Two-Pronged Approach to Intelligence Inquiry(p. 133)
• Fallacies to Avoid(p. 134)
• 4.1.2 Cognition, Consciousness, Awareness(p. 136)
• 4.2 The Science of Awareness(p. 138)
• 4.2.1 Cortical Structures and Information: Neural Bases of Awareness and Intelligent Doing(p. 140)
• Reticulo-Thalamo-Cortical (RTC) System(p. 143)
• 4.2.2 How Concepts (Universals) Get Formed: A Global Map Theory(p. 143)
• The Bogus Process of Abstraction(p. 145)
• A Spurious Sense of Induction: The Appeal to "Sampling"(p. 146)
• A Problem with Attention(p. 147)
• 4.2.3 Primitive Awareness(p. 147)
• Scientific Definitions of "Awareness"(p. 148)
• Possible Subject Bias(p. 149)
• Awareness of and Awareness that(p. 150)
• 4.2.4 Experimental Evidence of Immediate Awareness(p. 150)
• Evidence of Awareness Under Anesthesia(p. 154)
• What the Experiments Show(p. 156)
• 4.2.5 Primitives of the Preattentive Phase of Awareness(p. 157)
• Visual Fields(p. 158)
• Preattentive and Automatic Processes(p. 160)
• Primitive Preattentive Features, Processes and Cognition(p. 161)
• Preattentive Feature Integration(p. 164)
• Possible Dichotomy of Visual Discrimination(p. 165)
• Detection and Attention to Faces(p. 166)
• 4.3 Primitive Intelligence of Moving and Touching(p. 167)
• 4.3.1 Multiple Spaces of the Senses, Images and Probing(p. 168)
• 4.3.2 Smoothness and Timing in Intelligent Doing(p. 173)
• Limitations of Computational Models of Awareness: Selection without Classification(p. 174)
• 4.4 Summary(p. 176)
• Where We Enter the Circle of Cognition: Immediate Awareness(p. 176)
• Primitive Selection and Problems with Consciousness(p. 178)
• 5 Universals, Mathematical Thought and Awareness(p. 181)
• 5.1 On the Origins and Nature of Mathematical Thought(p. 182)
• The Genetic Fallacy(p. 183)
• 5.1.1 A Postmodern View: The Body Shapes Development and Content of Mathematics(p. 184)
• Conceptual Metaphors and Begging the Question(p. 187)
• 5.1.2 The Language Causal Argument: Language Shapes the Development and Content of Mathematics(p. 188)
• The Neurological Evidence(p. 190)
• 5.1.3 Thinking in Patterns and Images(p. 192)
• Mathematical Thought and Space(p. 193)
• Space and Theorem-Proving(p. 194)
• 5.1.4 The Realism Argument: Reality and Reason Shape the Development and Content of Mathematics(p. 196)
• Ontological and Epistemological Issues(p. 197)
• Structure of Our Inquiry(p. 198)
• Platonic and Hilbertian Mathematics: The Issues(p. 199)
• The Second Theorem(p. 200)
• The Non-algorithmic Nature of Mathematical Insight(p. 202)
• Implications of Non-algorithmic Insight to a Science of Intelligence(p. 203)
• Other Mathematical Sources of Non-algorithmic Intelligence(p. 203)
• 5.2 Problems with Representation Theories Revisited(p. 204)
• Naming, Indexes, Classification, Sets, Kinds and Types(p. 205)
• 5.2.1 Classification and the Nature of Sui Generis Objects of Immediate Awareness(p. 208)
• 5.3 Phenomenal Experience and Mathematics(p. 209)
• Demonstrating the Problem with Indexicals(p. 211)
• Retroduction, Reality and Non-algorithmic Insight(p. 212)
• 5.3.1 Perception and Mathematical Objects(p. 213)
• 5.3.2 The Reality of Sets and Concepts(p. 215)
• 5.3.3 Intersubjective Requirements of Mathematical Thought(p. 218)
• 5.4 Summary(p. 219)
• 6 Intelligence as Self-Organizing Emerging Complexity(p. 223)
• 6.1 Categories of Natural Intelligence(p. 223)
• 6.2 Self-Organization and Pattern Formation(p. 224)
• Emergence(p. 226)
• 6.2.1 Interactive Systems and Self-Organization(p. 227)
• Complexity(p. 228)
• 6.3 Mechanism and Organicism Revisited(p. 230)
• 6.3.1 Organized Simplicity and Unorganized Complexity(p. 230)
• 6.3.2 Organized Complexity(p. 232)
• Causality(p. 234)
• 6.4 Nonlinear Theory Models Approach to Natural Intelligence(p. 235)
• 6.4.1 The SIGGS Theory Model(p. 238)
• 6.4.2 Information Theory(p. 240)
• Information-Theoretic Extensions of Simple Feedback Model(p. 242)
• 6.5 SIGGS Applied to Natural Intelligence Systems(p. 244)
• Elements and Signs of Natural Intelligence(p. 244)
• 6.5.1 The Use of Digraph Theory to Characterize Intelligence Relations(p. 246)
• Social Network Theory and Patterns of Intelligence(p. 249)
• Fundamental Properties of Networks: Density and Connectedness(p. 251)
• Partial Order on the Intelligence Set(p. 253)
• 6.5.2 Information-Theoretic Measures on Natural Intelligence Systems(p. 255)
• Information-Theoretic (Uncertainty) Measures of Intelligence(p. 256)
• Measures of Uncertainty and Intelligence Categories of Occurrences(p. 257)
• Information-Theoretic Measures of the Universal Intelligence Set(p. 259)
• 6.6 From a Symbol-based View to a Geometric View of Natural Intelligence(p. 260)
• 6.6.1 Boolean Networks(p. 260)
• Random Boolean Networks(p. 262)
• Discrete Digital and Continuous Analogue Domains(p. 262)
• 6.7 Summary(p. 264)
• 7 Mapping Natural Intelligence to Machine Space(p. 269)
• 7.1 Classical Architectures for Natural Intelligence(p. 270)
• 7.1.1 Learning, Knowledge, Knowing and Intelligence(p. 271)
• Vectors, States, and Trajectories(p. 273)
• Functions and Operators(p. 275)
• 7.1.2 Goal-seeking Intentional Behavior(p. 277)
• Hierarchical Control(p. 279)
• 7.1.3 Control System Information Limitations(p. 280)
• 7.2 Biologically-Inspired Architectures: VLSI(p. 282)
• 7.2.1 Neuromorphic Architectures(p. 284)
• Learning Algorithms(p. 286)
• Self-Organizing Feature Map (SOFM)(p. 287)
• 7.2.2 The Problem of "Brittleness"(p. 288)
• The Party(p. 289)
• Noise and Uncertainty(p. 293)
• The Role of Indexicals in Natural Intelligence(p. 294)
• 7.2.3 Problems with Pattern Recognition and Limits of Classification(p. 295)
• 7.2.4 Kinds of Space: Revisiting the Problem with Universals(p. 299)
• Costs of Ignoring Phenomenological First-Person Experience(p. 301)
• 7.3 Problems with Complexity(p. 303)
• 7.3.1 Decidability(p. 303)
• Computability of Rule-Governed and Rule-Bound Natural Intelligence(p. 305)
• Recursively Enumerable Natural Intelligence(p. 307)
• 7.4 Summary(p. 310)
• 8 Summary and Conclusions of Self-Organizing Natural Intelligence(p. 315)
• 8.1 A History of Biased Intelligence Space(p. 316)
• 8.2 Natural Intelligence as Self-Organizing and Emerging(p. 318)
• 8.2.1 Multidimensional and Multilayered Intelligence(p. 320)
• 8.2.2 Three Major Kinds of Natural Intelligence(p. 320)
• 8.3 Nonlinear Methods for a Science of Intelligence(p. 321)
• 8.4 Some Issues Left Unresolved(p. 324)
• The Problem of Universals(p. 324)
• The Problem of Indexicals(p. 325)
• The Problem of Awareness(p. 325)
• The Problem of Autonomy(p. 327)
• References(p. 329)
• Index(p. 355)

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