Self-organizing natural intelligence: issues of knowing, meaning, and complexity
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Dordrecht
Springer
2006
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Klappentext |
| Beschreibung: | Includes bibliographical references (p. 329-353) and index |
| Beschreibung: | XXXII, 359 S. Ill., graph. Darst. 25 cm |
| ISBN: | 1402052758 9781402052750 1402052995 9781402052996 |
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| 245 | 1 | 0 | |a Self-organizing natural intelligence |b issues of knowing, meaning, and complexity |c by Myrna Estep |
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| 300 | |a XXXII, 359 S. |b Ill., graph. Darst. |c 25 cm | ||
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Datensatz im Suchindex
| _version_ | 1804137292517867520 |
|---|---|
| adam_text | Table
of
Contents
INTRODUCTION
xvii
LIST OF ABBREVIATIONS
xxvii
LIST OF FIGURES AND TABLES
xxix
ACKNOWLEDGEMENTS
xxxi
1.
THE PROBLEM OF INTELLIGENCE
1
1.1
Some of the Basic Issues
4
The Single and Multiple Capacity Views
5
Where Are the Facts of Intelligence Found?
6
1.2
The Faulty Sciences of Intelligence
7
1.2.1
The Anti-Theory Bias
8
A Misleading Heritage of Inductivism
8
Confusing Cause and Correlation
9
1.2.2
Invalid Reductionism
10
The Faulty Genetic Argument
11
A Neo-Darwinist Influence
12
1.2.3
Neglect of Emerging Intelligence
14
Inadequacies of the Classical Linear Approach
15
1.2.4
Neglect of Theory Construction and Concept Formation
16
Mechanism and
Organicism
17
Narrowing the Intelligence Domain to Suit
Tools At Hand
21
1.2.5
Unexamined Assumptions, Concepts, and Fallacies
21
The Scope of Cognition
22
vii
viii Table of
Contents
1.2.6
A Bankrapt
Theory of Knowing in the
Sciences
of Intelligence
25
Kinds of Knowing and the
Intellectualist
Legend
26
1.2.7
A Missing Distinction between Rule-govemed
and Rule-bound Intelligence
27
1.2.8
Neglect of Multiple Signs and Disclosure of Intelligence
29
Signals, Cues, and Clues
29
Exhibiting and Disclosing Intelligence
30
1.2.9
Mechanical Hard-Wired and Natural Intelligence:
Absent the Difference
32
1.3
Requirements for a New Science of Intelligence
33
1.3.1
A Broader Theory of Knowing
34
Knowledge That, Knowing How, Immediate Awareness
34
1.3.2
A Broader Theory of Signs of Intelligence
37
Toward Three-Dimensional Signs and Patterns
3 8
1.3.3
Methods of Nonlinear Science: The Emergence
of Self-Organizing Dynamical Intelligence
40
Self-Organization
40
Theory Models Approach to Intelligence Inquiry
42
Set Theory
42
Information Theory
43
Graph Theory and Dynamical Systems Theory
43
From a Symbol-based View to a Geometric View
of Natural Intelligence
44
1.4
Summary
45
2
THE UNIVERSE OF INTELLIGENCE
49
2.1
Carving the Problem Space
49
2.1.1
Rational Inquiry and Ideology: The Differences
50
2.1.2
Careless Carving
52
Table
of
Contents ix
2.2
Classical Origins and Fabric of Intelligence Theory: Cut on Biases
53
2.2.1
Plato and Aristotle s Conflicting Theoretical Stage
54
Plato s Dichotomy of Mind and Body
55
Aristotelian Dictum: Anatomy and Intelligence are Destiny
5 6
Early Differences Between Theory and Practice
57
2.2.2
Anthropocentrism, Language, Gender, Race, Size,
Wealth, and Place
58
The Intrinsic and Instrumental Intelligence Difference
59
The Intelligence Center of the Universe
59
2.2.3
The Fabric of Concepts Defining Intelligence Since Darwin
60
Reason, Logic and Language
62
Number
63
Knowledge
64
The Continuing Cartesian Split : Body and Mind
65
Making the Natural Artificial
69
The Intelligence of the Large and Small
70
Brainless Intelligence and Intentionality
? 71
2.3
Today s IQ Tests: Circularity, Bias, and American Eugenics
73
2.3.1
The Economic Argument
74
The Issue of Test Validity
75
2.3.2
Reification and the Eugenics Argument
76
2.3.3
A Static Hierarchy:
g
the Controller
78
Missing From g: Experience
80
2.3.4
Biological Determinism Revisited
83
Neo-Darwinism and the Heritability Argument
83
A Short History of Rising IQ Scores
87
Suspect Racial Sorting
89
2.4
Summary
90
χ
Table of Contents
3
THE GENESIS OF INTELLIGENCE: INNATE
AND EMERGENCE ARGUMENTS
93
3.1.
Categorization, Classification, Concepts and Representation
93
3.1.1
Reality and the Influence of Representationalism
95
3.2
The Continuing Problem with
Universais
(Concepts): Some History
96
Plato
96
Aristotle
99
3.2.1
Realists, Conceptualists, and Nominalists on
Universais
101
3.2.2
Theories of Knowledge and the Scope of Intelligence
102
Realism, Coherence, and Pragmatism
103
The Language Interface Issue
105
A Postmodern Heritage and Realist Counterargument
108
3.2.3
Today s
Representationalist
Myths: Cognitive Maps
in the Brain
110
3.3
The Innate Versus Emergence Arguments
113
3.3.1
The Genetically Encoded Syntax Argument
113
3.3.2
Nonverbal Communication: Beyond Alphanumeric
Symbols and Vocalizations
115
Gestures
116
From Manual Gestures to Whole Body Performances
118
3.3.3
Evolutionary Argument against Innatists
120
3.3.4
Cognitivism, Mechanism, and Irmateness :
How the Mind Does Not Work
122
Innate Learning Mechanisms
123
The Classical Computational View of Mind and Intelligence
YLA
Missing Practical Intelligence
125
Rationalist Sources of Innate Arguments
126
3.4
Summary
128
Table
of Contents
xi
THE INTELLIGENCE OF DOING: SENSORIMOTOR
DOMAINS AND KNOWING HOW
131
4.1
The Intelligence of Doing
131
4.1.1
A Two-Pronged Approach to Intelligence Inquiry
133
Fallacies to Avoid
134
4.1.2
Cognition, Consciousness, Awareness
136
4.2
The Science of Awareness
138
4.2.1
Cortical Structures and Information: Neural Bases
of Awareness and Intelligent Doing
140
Reticulo-Thalamo-Cortical (RTC) System
143
4.2.2
How Concepts
(Universais)
Get Formed:
A Global Map Theory
143
The Bogus Process of Abstraction
145
A Spurious Sense of Induction: The Appeal to Sampling
146
A Problem with Attention
147
4.2.3
Primitive Awareness
147
Scientific Definitions of Awareness
148
Possible Subject Bias
149
Awareness of and Awareness that
150
4.2.4
Experimental Evidence of Immediate Awareness
150
Evidence of Awareness Under Anesthesia
154
What the Experiments Show
156
4.2.5
Primitives of the Preattentive Phase of Awareness
157
Visual Fields
158
Preattentive and Automatic Processes
160
Primitive Preattentive Features, Processes and Cognition
161
Preattentive Feature Integration
164
Possible Dichotomy of Visual Discrimination
165
Detection and Attention to Faces
166
xii
Table
of
Contents
4.3 Primitive
Intelligence of Moving and Touching
167
4.3.1
Multiple Spaces of the Senses, Images and Probing
168
4.3.2
Smoothness and Timing in Intelligent Doing
173
Limitations of Computational Models of Awareness:
Selection without Classification
174
4.4
Summary
176
Where We Enter the Circle of Cognition:
Immediate Awareness
176
Primitive Selection and Problems with Consciousness
178
5
UNIVERSALS, MATHEMATICAL THOUGHT
AND AWARENESS
181
5.1
On the Origins and Nature of Mathematical Thought
182
The Genetic Fallacy
183
5.1.1
A Postmodern View: The Body Shapes Development
and Content of Mathematics
184
Conceptual Metaphors and Begging the Question
187
5.1.2
The Language Causal Argument: Language Shapes the
Development and Content of Mathematics
188
The Neurological Evidence
190
5.1.3
Thinking in Patterns and Images
192
Mathematical Thought and Space
193
Space and Theorem-Proving
194
5.1.4
The Realism Argument: Reality and Reason Shape
the Development and Content of Mathematics
196
Ontological and
Epistemologicul
Issues
197
Structure of Our Inquiry
198
Platonic and Hilbertian Mathematics: The Issues
199
The Second Theorem
200
Table
of
Contents xiii
The Non-algorithmic Nature of Mathematical Insight
202
Implications of Non-algorithmic Insight to a Science
of Intelligence
203
Other Mathematical Sources of Non-algorithmic Intelligence
203
5.2
Problems with Representation Theories Revisited
204
Naming, Indexes, Classification, Sets, Kinds and Types
205
5.2.1
Classification and the Nature of
Sui
Generis Objects
of Immediate Awareness
208
5.3
Phenomenal Experience and Mathematics
209
Demonstrating the Problem with Indexicals
211
Retroduction, Reality and Non-algorithmic Insight
212
5.3.1
Perception and Mathematical Objects
213
5.3.2
The Reality of Sets and Concepts
215
5.3.3
Intersubjective
Requirements of Mathematical Thought
218
5.4
Summary
219
INTELLIGENCE AS SELF-ORGANIZING EMERGING
COMPLEXITY
223
6.1
Categories of Natural Intelligence
223
6.2
Self-Organization and Pattern Formation
224
Emergence
226
6.2.1
Interactive Systems and Self-Organization
227
Complexity
228
6.3
Mechanism and
Organicism
Revisited
230
6.3.1
Organized Simplicity and Unorganized Complexity
230
6.3.2
Organized Complexity
232
Causality
234
6.4
Nonlinear Theory Models Approach to Natural Intelligence
235
6.4.1
The SIGGS Theory Model
238
xiv
Table
of
Contents
6.4.2
Information
Theory
240
Information-Theoretic
Extensions
of
Simple Feedback
Model
242
6.5
SIGGS
Applied
to Natural Intelligence Systems
244
Elements and Signs of Natural Intelligence
244
6.5.1
The Use of Digraph Theory to Characterize
Intelligence Relations
246
Social Network Theory and Patterns of Intelligence
249
Fundamental Properties of Networks: Density
and Connectedness
251
Partial Order on the Intelligence Set
253
6.5.2
Information-Theoretic Measures on Natural Intelligence
Systems
255
Information-Theoretic (Uncertainty) Measures of Intelligence
256
Measures of Uncertainty and Intelligence Categories
of Occurrences
257
Information-Theoretic Measures of the Universal
Intelligence Set
259
6.6
From a Symbol-based View to a Geometric View of Natural
Intelligence
260
6.6.1
Boolean Networks
260
Random Boolean NetworL·
262
Discrete Digital and Continuous Analogue Domains
262
6.7
Summary
264
7
MAPPING NATURAL INTELLIGENCE TO MACHINE SPACE
269
7.1
Classical Architectures for Natural Intelligence
270
7.1.1
Learning, Knowledge, Knowing and Intelligence
271
Vectors, States, and Trajectories
273
Functions and Operators
275
Table
of Contents
xv
7.1.2
Goal-seeking Intentional Behavior
277
Hierarchical Control
279
7.1.3
Control System Information Limitations
280
7.2
Biologically-Inspired Architectures: VLSI
282
7.2.1
Neuromorphic Architectures
284
Learning Algorithms
286
Self-Organizing Feature Map (SOFM)
287
7.2.2
The Problem of Brittleness
288
The Party
289
Noise and Uncertainty
293
The Role oflndexicals in Natural Intelligence
294
7.2.3
Problems with Pattern Recognition and Limits
of Classification
295
7.2.4
Kinds of Space: Revisiting the Problem with
Universais
299
Costs of Ignoring Phenomenological First-Person Experience
301
7.3
Problems with Complexity
303
7.3.1
Decidability
303
Computability of Rule-Governed and Rule-Bound Natural
Intelligence
305
Recursively Enumerable Natural Intelligence
307
7.4
Summary
310
SUMMARY AND CONCLUSIONS OF SELF-ORGANIZING
NATURAL INTELLIGENCE
315
8.1
A History of Biased Intelligence Space
316
8.2
Natural Intelligence as Self-Organizing and Emerging
318
8.2.1
Multidimensional and Multilayered Intelligence
320
8.2.2
Three Major Kinds of Natural Intelligence
320
8.3
Nonlinear Methods for a Science of Intelligence
321
xvi
Table
of
Contents
8.4
Some
Issues
Left Unresolved
324
The Problem of
Universais
324
The Problem of Indexicals
325
The Problem of Awareness
325
The Problem of Autonomy
327
REFERENCES
329
INDEX
355
Estep
Self-Organizing Natural Intelligence
Issues of Knowing, Meaning, and Complexity
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 deve¬
loped 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 pro¬
ponents 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 computatio¬
nal, and also assume that sensation is cognitively neutral, having no intentionality, here
empirical evidence is presented from numerous clinical studies showing that certain pri¬
mitive sensory processes are not cognitively neutral, nor do they 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 intel¬
ligence while also demonstrating a more adequate scientific and mathematical approach
than current statistical and psychometric approaches shoring up the out-dated and misu¬
sed 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.
|
| adam_txt |
Table
of
Contents
INTRODUCTION
xvii
LIST OF ABBREVIATIONS
xxvii
LIST OF FIGURES AND TABLES
xxix
ACKNOWLEDGEMENTS
xxxi
1.
THE PROBLEM OF INTELLIGENCE
1
1.1
Some of the Basic Issues
4
The Single and Multiple Capacity Views
5
Where Are the Facts of Intelligence Found?
6
1.2
The Faulty Sciences of Intelligence
7
1.2.1
The Anti-Theory Bias
8
A Misleading Heritage of Inductivism
8
Confusing Cause and Correlation
9
1.2.2
Invalid Reductionism
10
The Faulty Genetic Argument
11
A Neo-Darwinist Influence
12
1.2.3
Neglect of Emerging Intelligence
14
Inadequacies of the Classical Linear Approach
15
1.2.4
Neglect of Theory Construction and Concept Formation
16
Mechanism and
Organicism
17
Narrowing the Intelligence Domain to Suit
Tools At Hand
21
1.2.5
Unexamined Assumptions, Concepts, and Fallacies
21
The Scope of Cognition
22
vii
viii Table of
Contents
1.2.6
A Bankrapt
Theory of Knowing in the
Sciences
of Intelligence
25
Kinds of Knowing and the
Intellectualist
Legend
26
1.2.7
A Missing Distinction between Rule-govemed
and Rule-bound Intelligence
27
1.2.8
Neglect of Multiple Signs and Disclosure of Intelligence
29
Signals, Cues, and Clues
29
Exhibiting and Disclosing Intelligence
30
1.2.9
Mechanical "Hard-Wired" and Natural Intelligence:
Absent the Difference
32
1.3
Requirements for a New Science of Intelligence
33
1.3.1
A Broader Theory of Knowing
34
Knowledge That, Knowing How, Immediate Awareness
34
1.3.2
A Broader Theory of Signs of Intelligence
37
Toward Three-Dimensional Signs and Patterns
3 8
1.3.3
Methods of Nonlinear Science: The Emergence
of Self-Organizing Dynamical Intelligence
40
Self-Organization
40
Theory Models Approach to Intelligence Inquiry
42
Set Theory
42
Information Theory
43
Graph Theory and Dynamical Systems Theory
43
From a Symbol-based View to a Geometric View
of Natural Intelligence
44
1.4
Summary
45
2
THE UNIVERSE OF INTELLIGENCE
49
2.1
Carving the Problem Space
49
2.1.1
Rational Inquiry and Ideology: The Differences
50
2.1.2
Careless Carving
52
Table
of
Contents ix
2.2
Classical Origins and Fabric of Intelligence Theory: Cut on Biases
53
2.2.1
Plato and Aristotle's Conflicting Theoretical Stage
54
Plato's Dichotomy of Mind and Body
55
Aristotelian Dictum: Anatomy and Intelligence are Destiny
5 6
Early Differences Between Theory and Practice
57
2.2.2
Anthropocentrism, Language, Gender, Race, Size,
Wealth, and Place
58
The Intrinsic and Instrumental Intelligence Difference
59
The Intelligence Center of the Universe
59
2.2.3
The Fabric of Concepts Defining Intelligence Since Darwin
60
Reason, Logic and Language
62
Number
63
Knowledge
64
The Continuing Cartesian "Split": Body and Mind
65
Making the Natural Artificial
69
The Intelligence of the Large and Small
70
Brainless Intelligence and Intentionality
? 71
2.3
Today's IQ Tests: Circularity, Bias, and American Eugenics
73
2.3.1
The Economic Argument
74
The Issue of Test Validity
75
2.3.2
Reification and the Eugenics Argument
76
2.3.3
A Static Hierarchy:
g
the Controller
78
Missing From g: Experience
80
2.3.4
Biological Determinism Revisited
83
Neo-Darwinism and the Heritability Argument
83
A Short History of Rising IQ Scores
87
Suspect Racial Sorting
89
2.4
Summary
90
χ
Table of Contents
3
THE GENESIS OF INTELLIGENCE: INNATE
AND EMERGENCE ARGUMENTS
93
3.1.
Categorization, Classification, Concepts and Representation
93
3.1.1
Reality and the Influence of Representationalism
95
3.2
The Continuing Problem with
Universais
(Concepts): Some History
96
Plato
96
Aristotle
99
3.2.1
Realists, Conceptualists, and Nominalists on
Universais
101
3.2.2
Theories of Knowledge and the Scope of Intelligence
102
Realism, Coherence, and Pragmatism
103
The Language Interface Issue
105
A Postmodern Heritage and Realist Counterargument
108
3.2.3
Today's
Representationalist
Myths: Cognitive Maps
in the Brain
110
3.3
The Innate Versus Emergence Arguments
113
3.3.1
The Genetically Encoded Syntax Argument
113
3.3.2
Nonverbal Communication: Beyond Alphanumeric
Symbols and Vocalizations
115
Gestures
116
From Manual Gestures to Whole Body Performances
118
3.3.3
Evolutionary Argument against Innatists
120
3.3.4
Cognitivism, Mechanism, and "Irmateness":
How the Mind Does Not Work
122
Innate Learning Mechanisms
123
The Classical Computational View of Mind and Intelligence
YLA
Missing Practical Intelligence
125
Rationalist Sources of Innate Arguments
126
3.4
Summary
128
Table
of Contents
xi
THE INTELLIGENCE OF DOING: SENSORIMOTOR
DOMAINS AND KNOWING HOW
131
4.1
The Intelligence of Doing
131
4.1.1
A Two-Pronged Approach to Intelligence Inquiry
133
Fallacies to Avoid
134
4.1.2
Cognition, Consciousness, Awareness
136
4.2
The Science of Awareness
138
4.2.1
Cortical Structures and Information: Neural Bases
of Awareness and Intelligent Doing
140
Reticulo-Thalamo-Cortical (RTC) System
143
4.2.2
How Concepts
(Universais)
Get Formed:
A Global Map Theory
143
The Bogus Process of Abstraction
145
A Spurious Sense of Induction: The Appeal to "Sampling"
146
A Problem with Attention
147
4.2.3
Primitive Awareness
147
Scientific Definitions of "Awareness
" 148
Possible Subject Bias
149
Awareness of and Awareness that
150
4.2.4
Experimental Evidence of Immediate Awareness
150
Evidence of Awareness Under Anesthesia
154
What the Experiments Show
156
4.2.5
Primitives of the Preattentive Phase of Awareness
157
Visual Fields
158
Preattentive and Automatic Processes
160
Primitive Preattentive Features, Processes and Cognition
161
Preattentive Feature Integration
164
Possible Dichotomy of Visual Discrimination
165
Detection and Attention to Faces
166
xii
Table
of
Contents
4.3 Primitive
Intelligence of Moving and Touching
167
4.3.1
Multiple Spaces of the Senses, Images and Probing
168
4.3.2
Smoothness and Timing in Intelligent Doing
173
Limitations of Computational Models of Awareness:
Selection without Classification
174
4.4
Summary
176
Where We Enter the Circle of Cognition:
Immediate Awareness
176
Primitive Selection and Problems with Consciousness
178
5
UNIVERSALS, MATHEMATICAL THOUGHT
AND AWARENESS
181
5.1
On the Origins and Nature of Mathematical Thought
182
The Genetic Fallacy
183
5.1.1
A Postmodern View: The Body Shapes Development
and Content of Mathematics
184
Conceptual Metaphors and Begging the Question
187
5.1.2
The Language Causal Argument: Language Shapes the
Development and Content of Mathematics
188
The Neurological Evidence
190
5.1.3
Thinking in Patterns and Images
192
Mathematical Thought and Space
193
Space and Theorem-Proving
194
5.1.4
The Realism Argument: Reality and Reason Shape
the Development and Content of Mathematics
196
Ontological and
Epistemologicul
Issues
197
Structure of Our Inquiry
198
Platonic and Hilbertian Mathematics: The Issues
199
The Second Theorem
200
Table
of
Contents xiii
The Non-algorithmic Nature of Mathematical Insight
202
Implications of Non-algorithmic Insight to a Science
of Intelligence
203
Other Mathematical Sources of Non-algorithmic Intelligence
203
5.2
Problems with Representation Theories Revisited
204
Naming, Indexes, Classification, Sets, Kinds and Types
205
5.2.1
Classification and the Nature of
Sui
Generis Objects
of Immediate Awareness
208
5.3
Phenomenal Experience and Mathematics
209
Demonstrating the Problem with Indexicals
211
Retroduction, Reality and Non-algorithmic Insight
212
5.3.1
Perception and Mathematical Objects
213
5.3.2
The Reality of Sets and Concepts
215
5.3.3
Intersubjective
Requirements of Mathematical Thought
218
5.4
Summary
219
INTELLIGENCE AS SELF-ORGANIZING EMERGING
COMPLEXITY
223
6.1
Categories of Natural Intelligence
223
6.2
Self-Organization and Pattern Formation
224
Emergence
226
6.2.1
Interactive Systems and Self-Organization
227
Complexity
228
6.3
Mechanism and
Organicism
Revisited
230
6.3.1
Organized Simplicity and Unorganized Complexity
230
6.3.2
Organized Complexity
232
Causality
234
6.4
Nonlinear Theory Models Approach to Natural Intelligence
235
6.4.1
The SIGGS Theory Model
238
xiv
Table
of
Contents
6.4.2
Information
Theory
240
Information-Theoretic
Extensions
of
Simple Feedback
Model
242
6.5
SIGGS
Applied
to Natural Intelligence Systems
244
Elements and Signs of Natural Intelligence
244
6.5.1
The Use of Digraph Theory to Characterize
Intelligence Relations
246
Social Network Theory and Patterns of Intelligence
249
Fundamental Properties of Networks: Density
and Connectedness
251
Partial Order on the Intelligence Set
253
6.5.2
Information-Theoretic Measures on Natural Intelligence
Systems
255
Information-Theoretic (Uncertainty) Measures of Intelligence
256
Measures of Uncertainty and Intelligence Categories
of Occurrences
257
Information-Theoretic Measures of the Universal
Intelligence Set
259
6.6
From a Symbol-based View to a Geometric View of Natural
Intelligence
260
6.6.1
Boolean Networks
260
Random Boolean NetworL·
262
Discrete Digital and Continuous Analogue Domains
262
6.7
Summary
264
7
MAPPING NATURAL INTELLIGENCE TO MACHINE SPACE
269
7.1
Classical Architectures for Natural Intelligence
270
7.1.1
Learning, Knowledge, Knowing and Intelligence
271
Vectors, States, and Trajectories
273
Functions and Operators
275
Table
of Contents
xv
7.1.2
Goal-seeking Intentional Behavior
277
Hierarchical Control
279
7.1.3
Control System Information Limitations
280
7.2
Biologically-Inspired Architectures: VLSI
282
7.2.1
Neuromorphic Architectures
284
Learning Algorithms
286
Self-Organizing Feature Map (SOFM)
287
7.2.2
The Problem of "Brittleness"
288
The Party
289
Noise and Uncertainty
293
The Role oflndexicals in Natural Intelligence
294
7.2.3
Problems with Pattern Recognition and Limits
of Classification
295
7.2.4
Kinds of Space: Revisiting the Problem with
Universais
299
Costs of Ignoring Phenomenological First-Person Experience
301
7.3
Problems with Complexity
303
7.3.1
Decidability
303
Computability of Rule-Governed and Rule-Bound Natural
Intelligence
305
Recursively Enumerable Natural Intelligence
307
7.4
Summary
310
SUMMARY AND CONCLUSIONS OF SELF-ORGANIZING
NATURAL INTELLIGENCE
315
8.1
A History of Biased Intelligence Space
316
8.2
Natural Intelligence as Self-Organizing and Emerging
318
8.2.1
Multidimensional and Multilayered Intelligence
320
8.2.2
Three Major Kinds of Natural Intelligence
320
8.3
Nonlinear Methods for a Science of Intelligence
321
xvi
Table
of
Contents
8.4
Some
Issues
Left Unresolved
324
The Problem of
Universais
324
The Problem of Indexicals
325
The Problem of Awareness
325
The Problem of Autonomy
327
REFERENCES
329
INDEX
355
Estep
Self-Organizing Natural Intelligence
Issues of Knowing, Meaning, and Complexity
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 deve¬
loped 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 pro¬
ponents 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 computatio¬
nal, and also assume that sensation is cognitively "neutral," having no intentionality, here
empirical evidence is presented from numerous clinical studies showing that certain pri¬
mitive sensory processes are not cognitively neutral, nor do they 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 intel¬
ligence while also demonstrating a more adequate scientific and mathematical approach
than current statistical and psychometric approaches shoring up the out-dated and misu¬
sed 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. |
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| dewey-hundreds | 100 - Philosophy & psychology |
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| discipline | Informatik Psychologie Philosophie |
| discipline_str_mv | Informatik Psychologie Philosophie |
| format | Book |
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| id | DE-604.BV023057066 |
| illustrated | Illustrated |
| index_date | 2024-07-02T19:27:11Z |
| indexdate | 2024-07-09T21:09:59Z |
| institution | BVB |
| isbn | 1402052758 9781402052750 1402052995 9781402052996 |
| language | English |
| lccn | 2007295639 |
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| physical | XXXII, 359 S. Ill., graph. Darst. 25 cm |
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| spelling | Estep, Myrna Verfasser aut Self-organizing natural intelligence issues of knowing, meaning, and complexity by Myrna Estep Dordrecht Springer 2006 XXXII, 359 S. Ill., graph. Darst. 25 cm txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references (p. 329-353) and index Intellect Self-organizing systems Knowledge, Theory of Erkenntnistheorie (DE-588)4070914-0 gnd rswk-swf Erkenntnistheorie (DE-588)4070914-0 s DE-604 Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016260336&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016260336&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Estep, Myrna Self-organizing natural intelligence issues of knowing, meaning, and complexity Intellect Self-organizing systems Knowledge, Theory of Erkenntnistheorie (DE-588)4070914-0 gnd |
| subject_GND | (DE-588)4070914-0 |
| title | Self-organizing natural intelligence issues of knowing, meaning, and complexity |
| title_auth | Self-organizing natural intelligence issues of knowing, meaning, and complexity |
| title_exact_search | Self-organizing natural intelligence issues of knowing, meaning, and complexity |
| title_exact_search_txtP | Self-organizing natural intelligence issues of knowing, meaning, and complexity |
| title_full | Self-organizing natural intelligence issues of knowing, meaning, and complexity by Myrna Estep |
| title_fullStr | Self-organizing natural intelligence issues of knowing, meaning, and complexity by Myrna Estep |
| title_full_unstemmed | Self-organizing natural intelligence issues of knowing, meaning, and complexity by Myrna Estep |
| title_short | Self-organizing natural intelligence |
| title_sort | self organizing natural intelligence issues of knowing meaning and complexity |
| title_sub | issues of knowing, meaning, and complexity |
| topic | Intellect Self-organizing systems Knowledge, Theory of Erkenntnistheorie (DE-588)4070914-0 gnd |
| topic_facet | Intellect Self-organizing systems Knowledge, Theory of Erkenntnistheorie |
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