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Cover Red’ko V. Modeling of Cognitive Evolution: Toward the Theory of Evolutionary Origin of Human Thinking
Id: 240942
29.9 EUR

Modeling of Cognitive Evolution:
Toward the Theory of Evolutionary ORIGIN OF HUMAN THINKING. № 73

Modeling of Cognitive Evolution: Toward the Theory of Evolutionary Origin of Human Thinking
Red’ko V. «Modelado de la evolución cognitiva: hacia la teoría del origen evolutivo del pensamiento humano». (In English).
Red’ko V. «Modélisation de l'évolution cognitive: vers la théorie de l'origine évolutive de la pensée humaine». (In English).
Red’ko V. «Modellierung der kognitiven Evolution: Zur Theorie des evolutionären Ursprungs menschlichen Denkens. (In English).
Редько В.Г. «Моделирование когнитивной эволюции: На пути к теории эволюционного происхождения мышления». (In English).
URSS. 304 pp. (English). ISBN 978-5-396-00872-4.
White offset paper
  • Hardcover


The book discusses a new direction of investigation, namely the modeling of cognitive evolution, meaning the evolution of the cognitive abilities of biological organisms.

Human thinking is the result of this evolution. The philosophical foundations for studies of cognitive evolution are characterized.

Backgrounds to the modeling of cognitive evolution are developed in two areas of investigation. (1) Models of autonomous agents, and (2) biological... (More)

Chapter 1. Philosophical foundation of investigations of cognitive evolution18
1.1. Toward the theory of evolutionary origin of the human thinking18
1.1.1. Einstein’s lesson18
1.1.2. The supertask20
1.1.3. Hume → Kant → Lorenz24
1.1.4. “The unreasonable effectiveness of mathematics in natural sciences” and the problem of principal applicability of the human thinking to cognition of nature28
1.1.5. Internal model and prediction30
1.2. Conceptual theories30
1.2.1. Petr Anokhin’s functional system: the general scheme of animal control system31
1.2.2. Turchin’s theory of metasystem transitions35
1.3. Concluding remarks38
Chapter 2. Backgrounds for modeling of cognitive evolution43
2.1. Backgrounds in computer science43
2.2. Biological experiments on “elementary thinking of animals”48
Chapter 3. Method of reinforcement learning55
3.1. Reinforcement learning55
3.2. Adaptive critic designs58
Chapter 4. Analysis of evolutionary optimization methods65
4.1. Estimation of the rate and effectiveness of evolutionary algorithms65
4.1.1. Quasispecies model65
4.1.2. Qualitative picture of evolution in the quasispecies model70
4.1.3. Stochastic nature of the evolutionary process. The role of neutral selection72
4.1.4. Estimation of rate and efficiency of evolutionary process74
4.1.5. Results of computer simulation76
4.1.6. Comparison of evolutionary search with other methods79
4.1.7. The case of several symbols of optimized chains80
4.1.8. Model of a narrow channel and majority model81
4.1.9. Spin-glass model of evolution85
4.1.10. Conclusions to the estimations of the rate and efficiency of evolution90
4.2. Model of interaction between learning and evolution92
4.2.1. Background studies92
4.2.2. Description of the model95
4.2.3. Results of computer simulation99 Scheme and parameters of simulation99 Comparison of regimes of pure evolution and evolution combined with learning101 Hiding effect105 Influence of the learning load on the modeled processes110 Probabilistic and deterministic selection113 Modeling of Lamarckian evolution114
4.2.4. Comparison with the approach by Hinton and Nowlan115
4.2.5. Conclusion to the model of interaction between learning and evolution120
4.3. Model of imprinting formation by means of learning and evolution121
4.3.1. Model description122
4.3.2. Simulation results127 Scheme and parameters of simulation127 Comparison of regimes of pure evolution and evolution combined with learning129 Hiding effect133 Influence of the learning load on the modeled processes136
4.3.3. Discussion of the model of formation of imprinting138
Chapter 5. Models of autonomous adaptive agents142
5.1. Adaptive syser: model of a proto-organism that adapts to variable external environment142
5.1.1. Model of sysers143 General scheme of sysers143 Mathematical description of sysers144 Sysers and self-reproducing automata by John von Neumann149
5.1.2. Model of adaptive syser150 Biological prototype of adaptive syser151 General scheme of functioning of the adaptive syser153 Mathematical description of mini- and adaptive sysers155
5.1.3. Conclusion to the model of adaptive syser162
Appendix to Section 5.1. Analysis of the dynamic system163
5.2. “Grasshopper”: model of evolutionary origin of goal-directed adaptive behavior166
5.2.1. Model description166 Main assumptions of the model166 Overview of environment and agents167 Agent physiology169 Neural network of an agent171 Scheme of evolution173
5.2.2. Computer simulation173 Parameters of computer simulation173 Simulation results176
5.2.3. Analysis of results179
5.2.4. Conclusion to themodel “Grasshopper”182
5.2.5. Development of the model “Grasshopper”: the emergence of the naturally branched hierarchy of goals182
5.3. Biologically inspired model of adaptive searching behavior184
5.3.1. Searching behavior of living and modeled organisms184
5.3.2. The searching behavior of caddisflies larvae. Results of biological experiment187
5.3.3. Model of searching behavior of caddisflies larvae189 Description of the main variant of the model189 Results of computer simulation191 Additional model197 Conclusion to the model of searching behavior of caddisflies larvae199
5.3.4. Biologically inspired method of functions optimization200
5.3.5. Conclusion to the biologically inspired method of searching206
5.4. Modeling of searching agent behavior by means of neural gas207
5.4.1. One-dimensional case209
5.4.2. Two-dimensional case214
5.4.3. Conclusion to modeling of searching agent behavior by means of neural gas216
5.5. Chapter summary217
Chapter 6. The sketch program for future investigations of cognitive evolution221
Chapter 7. The initial steps of modeling of cognitive evolution225
7.1. Model of autonomous agents with several natural needs225
7.1.1. Model of agents with natural needs and motivations225 Modeled world226 Agent control system226 Hierarchy of motivations227 Scheme of learning230
7.1.2. Results of computer simulation230
7.1.3. Conclusion to the model of agents with natural needs232
7.2. Model of formation of generalized notions by autonomous agents232
7.3. Models of fish exploratory behavior in mazes236
7.3.1. Short description of biological experiments. Behavior of fish in mazes237 Behavior of zebrafish in the cross-shaped maze238 Behavior of fish in the maze with 11 arms239
7.3.2. Models of fish movements, accumulation of knowledge, formation of predictions240 Model of knowledge acquisition240 Model of predictions of future situations244
7.3.3. Hypothetical model of planning of movement in the maze with 11 arms247
7.3.4. Conclusion to models of fish exploratory behavior in mazes254
7.4. Modeling of mechanism of plan formation by New Caledonian crows255
7.4.1. Biological experiment on NC crows256
7.4.2. Mechanism of plan formation257 Description of the model257 Results of computer simulation262
7.4.3. Discussion of the model of plan formation by NC crows264
7.5. Chapter summary266
Chapter 8. Possible applications related to modeling of cognitive evolution268
8.1. Development of scientificpoint of view. Approaches to harmonious development of mankind269
8.2. Elimination of genes of aggressiveness in the evolving population of conflicting agents and idea of a project for the Nobel peace award271
8.3. Agent-based model of transparent market economy277
8.3.1. Description of the model278 General scheme of the model278 Description of the iterative process280
8.3.2. Results of computer simulation282
8.3.3. Conclusion to the model of transparent market economy287
8.4. Chapter summary288
Chapter 9. Interdisciplinary relations of modeling of cognitive evolution290

About the author
photoРедько Владимир Георгиевич
PhD and Doctor of Science (Physics and Mathematics). He is Chief Scientific Researcher in Scientific Research Institute for System Analysis, Russian Academy of Sciences, and in National Research Nuclear University MEPhI (Moscow Engineering Physics Institute). He is the author of more than 200 scientific publications, including 3 books; member of scientific councils of the Russian Association for Artificial Intelligence and the Russian Neural Networks Society. His research interests are the following: origin of human intelligence, evolution of animal cognition abilities, modeling of cognitive evolution.