今天收到美国学术出版社寄来的印刷书籍。现代物流技术的发展，从美国邮寄过来也就几天时间，这倒是让我很吃惊。300多页的书籍印刷和装订质量都非常棒，是我出版的几本著作中最好的一本。可以看出出版社的编辑们是非常用心的。
《Cognitive Development Theory》这本书经过准备，写作到正式出版，已经经过了一年多的时间。然而回顾这本书的整个内容的形成，实际上我已经研究思考了近十年的时间。
整个研究探索过程最早可以追述到在科学网上我发布的第一篇博文：“教师专业成长的阶段性特征”。这个时候我开始注意到作为一个教师，由于个人认知能力的不断变化，必然会出现与生理年龄相对应的专业成长的阶段性特征。而这种专业成长，又与教师个人的认知成长密切结合在一起。为了能够反映出这种认知的更一般性的规律，建立一个更具一般性的认知模型是非常有必要的。经过一段时间尝试之后，2010年6月我将结果发表在中国科技论文在线上，论文名称为：“一个个体认知发展模型及其在教师专业成长中的应用。”，开始的时候还是比较定性的分析，后来根据评审老师的意见，我将标题更改为：“基于神经网络理论的认知发展模型的构建及其在教师专业发展中的应用”。这样一个利用人工神经网络为基础来构建的人的大脑认知模型就开始逐渐形成，同时还可以做一些数值分析。
刚刚开始研究这一模型时间比较仓促，平时我的教学任务也比较繁重，因此初始的理论结构不太完善。为了能够始终保持自己的思考过程处于开放状态，我主要还是利用科学网博客这个平台来发表自己的看法。同在传统的期刊上发表研究成果不同，利用博客来发表自己的观点效率高多了。将自己的想法写出来，一方面能够更好地理清自己的思路，另一方面则可以有机会获得一个与其他专业人士交流的机会，这对于理论的完善是非常有帮助的。其后几年时间中，我经常将自己的一些想法在博客中写出来，虽然都是随笔，但确实是一种很好的与外界进行交流的方法。其中 “人生的长河”（http://blog.sciencenet.cn/blog-361477-674530.html，2013）这一篇博文有幸获得科学网博文精选，让我对这个模型的信心大增。因此在这里我要特别感谢科学网能够给我这样一个平台，促进我对各种问题的深入思考，同时也从其他科学网博客博主那里获益良多。
成书之后，就面临了一个选择出版社的问题。由于是学术书籍，因此我首先定位在各种学术出版社。我的这本书属于跨学科内容，里面还包含了很丰富的儒家学说观点在其中，这对于一些国际出版社可能是不容易接受的。经过搜索，我发现美国学术出版社（AAP）出版过《匈奴通史》等著作，对中国文化不会有什么偏见。另外这是一家在2018年国家社科基金国外出版机构指导目录中又一次被推选为中华学术海外传播平台的出版社。因此我最终确定选择AAP来出版我的这本著作。投稿之后证明我的选择是正确的。AAP的编辑们有非常丰富的给中国作者出版的经验，整个出版过程都是通过电子邮件进行的。包括合同的签署和邮寄，稿件的校对等，全部电子文档。从我现在手头上的印刷版本来看，这一过程效果非常好，应该是今后书籍出版的主流方式。
当然在投稿之后，我又搜索到一条消息，就是我们的科学出版社收购了法国著名的Edition Diffusion Press（EDP）学术出版社（http://blog.sciencenet.cn/home.php?mod=space&uid=980214&do=blog&id=1187367）。我觉得今后的书稿直接向EDP投稿也应该是一个非常好的选择，这样可以将中英文版本的出版高效率地衔接在一起。
最后附上本书的前言和目录，敬请参考。谢谢！
程智
Preface
Cognitive development is a very important topic. Since the history of mankind, people have a strong interest in cognitive development. The philosophers of China and ancient Greece more than 2,000 years ago have begun to explore human cognition, although these are some of the results of individual speculation. However, the thoughts of these ancient philosophers have always influenced every stage of our personal cognitive development.
The true scientific study of cognitive processes began in the late 19th and early 20th centuries, and the conditional reflection theory proposed by the Russian physiologist Pavlov opened the way for people to conduct scientific cognitive processes. Although limited to the lack of understanding of cognitive laws at the time, the contradiction between cognitive theory and behaviorism theory was created. But it is also the mutual competition of these two theories that led to the emergence of modern cognitive theory, which in turn opened up a whole new perspective for us to study the series of rules of human cognitive processes.
Along with the experimental psychology trajectory pioneered by Ivan Petrovich Pavlov, the classical behaviorism theory represented by John Broadus Watson and the operational conditional reflection theory represented by Burrhus Frederic Skinner appeared in the later periods. The characteristics of these two theories are supported by very accurate experimental data. According to the variable control method in natural science research, various factors in the experimental process are controlled, so as to obtain various laws that can be verified by repeated experiments.
The classical cognitive theory emerged with a research method that is opposite to the behaviorism theory. It includes Wolfgang Kohler's "Insight learning" theory, but this is only a theoretical difference. Kohler also studies human cognition through scientific experiments, obtains important experimental data, and analyzes experimental data. Get meaningful scientific conclusions based on it.
Another important sign of the development of cognitive theory is the cognitive development theory proposed by Jean Piaget in the 1930s. By using a semi-empirical method to conduct experiments on children's cognitive processes, a more accurate and natural cognitive development law can be obtained with minimal interference to children. Piaget's four stages of child cognitive development are also profoundly influencing all aspects of our modern compulsory education system.
With the deeper study of cognitive processes, people began to realize the complexity of cognitive processes. Behaviorism research methods appear pale and weak in the face of these complex cognitive processes. The constructivism theory constructed by Piaget's cognitive development theory contains too much experience. How to make the research of cognitive science obtain objective and repeatable conclusions like the research of natural science, the development of computer science and technology provides important material conditions for this.
The cognitive information processing theory that emerged in the 1970s is a useful attempt to use computer science and technology to conduct cognitive law research, and has obtained many meaningful conclusions. For example, the three stages of brain information processing and other judgments. The cognitive information processing theory tries to use the computational program design to verify that the human cognitive process is actually a programmatic information processing process, which also promotes the development of artificial intelligence theory to a certain extent.
In the 1980s, people were not satisfied with the use of computer programs to simulate human cognitive decision-making processes, but instead wanted to directly construct a system similar to human brain neural networks for cognition. During this period, Hopfield's fully interconnected neural network successfully solved the Traveling Salesman Problem, which caused people to pay attention to the neural network system. The later development of BP neural network and convolutional neural network has promoted the development of neural network technology to a new height, and thus constructed the deep learning theory of neural network. According to the analysis of this book, the cognitive ability of convolutional neural networks has reached the third stage of Piaget's cognitive development, namely the concrete operational. It seems that it is also possible to use artificial neural network theory and technology to study human brain neural network cognition.
So how do we use artificial neural networks to study human cognitive processes? This book uses a very simple and fully interconnected neural network model as a simplified model of the brain neural network, and then dynamic analysis of the simplified brain neural network model to obtain some important dynamic parameters of the brain neural network system. According to the different characteristics of these dynamic parameters in different time periods, different stages of human cognitive development are divided. Different from Piaget's four stages of child cognitive development, this book can obtain all stages of human cognitive development by analyzing the simplified brain model. These stages include both the four stages of Piaget's cognitive development in childhood and all other stages of cognitive development in youth and adults. Therefore, in the book's view, people's lifelong cognitive development can be divided into ten stages, which are the four stages of children's cognitive development, including the sensorimotor stage, the pre-operational stage, the concrete operational stage, and the formal operational stage. The two stages of youth include the early youth and the late youth. The four stages of adult cognitive development include the years of the standing, the years of not confused, the years of ear obedience and the years of heart-following.
Since the cognitive ability of artificial neural networks has now reached the concrete operational stage of children's cognitive development, the BP neural network and convolutional neural networks are directly simulated in the relevant chapters of this book to help understand children’s cognitive development at the first three stages. In the formal operational stage, we construct a new fully interconnected neural network, and propose the concept of "conceptual triangle", using the formation of conceptual triangles and the judgment of the meaning of sentences that have not been learned to analyze the children’s cognitive characteristics in the formal operational stage. In Chapter 9, the Hopfield neural network is used to simulate the formation mechanism of the depression state of the brain neural network and suggest how to respond.
This book adopts a new research method, in addition to the artificial neural network intelligent program for simulation, also includes the analysis of dynamic parameters of a simplified brain neural network model. We can mathematical proof of some speculative questions through rigorous mathematical derivation. I believe this will help us to have a more comprehensive understanding of the law of human cognitive development.
In the content arrangement, Chapters 1 through 5 introduce the general knowledge of cognitive theory and artificial neural networks. On this basis, a simplified brain neural network model is constructed. Based on the analysis of the brain model, the conclusions of the ten stages of human cognitive development are obtained.
Chapters 6 through 8 analyze the characteristics of the four stages of cognitive development in children. These four stages are completely consistent with the four stages of Piaget's cognitive development. In these three chapters, the characteristics of each stage are analyzed by analyzing four dynamic parameters such as network capacity, network depth, cognitive patterns and cognitive depth. The results of artificial neural network simulation are also given, which can help us understand how the cognitive processes of these four stages proceed. In the last section of each chapter, we also specifically analyze the cognitive barriers that may occur at these stages, including mental retardation, cognitive errors, and attention problems.
Chapter 9 analyzes the characteristics of cognitive development in youth and focuses on the relationship between cognitive and personality improvement processes. Through the simulation of Hopfield neural network, some rules of depression status are discussed.
Chapters 10 through 12 explore the rules of cognitive development in adults, including the prime, middle and old age. A total of four divisions of cognitive development are involved. Each chapter analyzes the characteristics of cognitive patterns and cognitive depth in the corresponding stages, and discusses the cognitive problems that may arise at these stages in the last section, including cerebral hemorrhage and cerebral embolism, Alzheimer's disease, aging problems.
Because the book covers a wide range of subject knowledge, including mathematics, computer, physics, psychology, physiology, etc., readers do not necessarily need to read through this entire book. They can choose to read according to their actual needs. Readers who are more interested in education and psychology can skip the mathematical derivation and computer programming, and only care about the conclusions. Those who are interested in artificial intelligence can read the neural network algorithm and perform the actual program debugging operation. Most of the program commands have been given comments to help understand. These programs can be run in the VBA editor in Microsoft Excel. Those who are interested in dynamic analysis, if they want to understand the formula derivation in the book, only need the knowledge of college mathematics.
Finally, the writing process of this book is slightly rushed. Although it has been repeatedly proofread, there must still be many mistakes, and you are welcome to give us valuable advices.
Zhi Cheng
Jul. 2019, Guangzhou, China
Content
PREFACE ........................................................................................................................... I
1 OVERVIEW .................................................................................................................... 1
1.1 COGNITION AND DEVELOPMENT ............................................................................................. 1
1.1.1 What is cognition .................................................................................................... 1
1.1.2 Human cognition and knowledge accumulation .................................................... 5
1.1.3 Human growth and cognitive development ........................................................... 6
1.1.4 Methodology for studying cognitive development ................................................ 7
1.2 PIAGET'S THEORY OF COGNITIVE DEVELOPMENT ........................................................................ 9
1.2.1 Piaget's cognitive development stages of children ................................................. 9
1.2.2 Experimental evidence ......................................................................................... 12
1.3 COGNITIVE AND BRAIN NEURAL NETWORKS ............................................................................ 14
1.3.1 Biological neural network ..................................................................................... 14
1.3.2 Impulsiveness of biological neural networks ........................................................ 16
1.3.3 Neural network and cognition .............................................................................. 17
1.4 DYNAMIC SYSTEM ............................................................................................................. 18
1.4.1 Equilibrium and non-equilibrium systems ............................................................ 18
1.4.2 Statistical mechanics and phase transition ........................................................... 21
2 ARTIFICIAL NEURAL NETWORKS .................................................................................. 24
2.1 INTRODUCTIONS ............................................................................................................... 24
2.1.1 Basic structure of artificial neural network .......................................................... 24
2.1.2 Classification ......................................................................................................... 28
2.1.3 The applications of artificial neural network ........................................................ 30
2.2 TWO ALGORITHMS OF ARTIFICIAL NEURAL NETWORK ................................................................ 33
2.2.1 BP algorithm ......................................................................................................... 33
2.2.2 Hopfield algorithm ................................................................................................ 35
2.3 ARTIFICIAL NEURAL NETWORK DYNAMICS SYSTEM .................................................................... 36
2.3.1 The fully interconnected neural network for dynamic analysis ............................ 36
2.3.2 Dynamic equations ............................................................................................... 38
2.3.3 Phase transition .................................................................................................... 44
3 BRAIN MODEL BASED ON ARTIFICIAL NEURAL NETWORK ............................................ 45
3.1 ARTIFICIAL NEURAL NETWORK AND BRAIN NEURAL NETWORK ..................................................... 45
3.1.1 The relationship between the two ....................................................................... 45
3.1.2 Limitations of artificial neural networks ............................................................... 47
3.2 ARTIFICIAL NEURAL NETWORK MODEL OF THE BRAIN ................................................................ 48
3.2.1 Hierarchical structure of brain neural network model ......................................... 48
3.2.2 Partition of the brain ............................................................................................ 51
3.2.3 Brain neural network model and cognitive ability ................................................ 53
3.3 COGNITIVE PATTERN .......................................................................................................... 58
3.3.1 The meaning of cognitive pattern ........................................................................ 58
3.3.2 Network capacity and cognitive pattern .............................................................. 59
3.4 COGNITIVE DEPTH ............................................................................................................. 62
3.4.1 The meaning of cognitive depth ........................................................................... 62
3.4.2 Network computational complexity theory of neural networks .......................... 63
3.4.3 Network depth and cognitive depth ..................................................................... 65
4 THE GROWTH AND DECLINE PROCESS OF COGNITIVE DEVELOPMENT .......................... 69
4.1 DYNAMIC NEURAL NETWORK ............................................................................................... 69
4.1.1 Dynamic structure of neural networks ................................................................. 69
4.1.2 Neuronal growth and decline ............................................................................... 71
4.1.3 Neural network growth and decline ..................................................................... 73
4.2 CHANGE IN COGNITIVE PATTERNS ......................................................................................... 74
4.2.1 Influence of neural network growth and decline on network capacity ................ 74
4.2.2 Cognitive pattern curve ........................................................................................ 76
4.3 COGNITIVE DEPTH CHANGE ................................................................................................. 78
4.3.1 Neural network growth decline and computational complexity .......................... 78
4.3.2 Cognitive depth curve ........................................................................................... 80
5 STAGES OF COGNITIVE DEVELOPMENT ........................................................................ 82
5.1 NONLINEARITY OF COGNITIVE DEVELOPMENT .......................................................................... 82
5.1.1 A simple summary ................................................................................................ 82
5.1.2 Parameter confirmation ....................................................................................... 84
5.2 STAGE DIVISION OF COGNITIVE DEVELOPMENT ........................................................................ 86
5.2.1 Nonlinearity of neural networks ........................................................................... 86
5.2.2 Nonlinearity of cognitive development in childhood ........................................... 89
5.2.3 Nonlinear characteristics of cognitive development in youth .............................. 91
5.2.4 Nonlinear characteristics of cognitive development in adulthood ....................... 92
5.2.5 Summary of human life cognitive development stages ........................................ 94
6 SENSORIMOTOR AND PREOPERATIONAL STAGES ......................................................... 97
6.1 SENSORIMOTOR STAGE AND BP NEURAL NETWORK .................................................................. 97
6.1.1 Classic conditioning and operational conditioning ............................................... 97
6.1.2 The characteristic of sensorimotor stage ............................................................. 98
6.1.3 BP neural network ................................................................................................ 99
6.1.4 A simple XOR logic BP network learning example .............................................. 100
6.1.5 BP neural network algorithm and sensorimotor ................................................ 111
6.2 PATTERN RECOGNITION AND PRE-OPERATIONAL STAGE ............................................................ 113
6.2.1 Main task of the pre-operational stage .............................................................. 113
6.2.2 Pattern recognition ............................................................................................. 114
6.2.3 Identify face in a picture ..................................................................................... 115
6.2.4 Pattern recognition ability at pre-operational stage ........................................... 123
6.3 COGNITIVE PATTERNS AND COGNITIVE DEPTH ........................................................................ 124
6.3.1 Characteristics of cognitive patterns and cognitive depth in the sensorimotor stage ............................................................................................................................ 124
6.3.2 Characteristics of cognitive patterns and cognitive depth in the pre-operational stage ............................................................................................................................ 125
6.4 MENTAL RETARDATION ..................................................................................................... 126
6.4.1 The influence of neural network development retardation on cognition .......... 126
6.4.2 Impact of growth and development disorders on network capacity and network depth ........................................................................................................................... 127
7 CONCRETE OPERATIONAL STAGE ............................................................................... 131
7.1 DEEP LEARNING AND CONCRETE OPERATIONAL ...................................................................... 131
7.1.1 Overview of deep learning ................................................................................. 131
7.1.2 Deep learning of cognitive processes ................................................................. 132
7.2 CONVOLUTIONAL NEURAL NETWORK ................................................................................... 134
7.2.1 Structure and Algorithm of Convolutional Neural Networks .............................. 134
7.2.2 Learning mechanism of convolutional neural networks ..................................... 140
7.2.3 Program design ................................................................................................... 144
7.2.4 Solve pattern recognition problems with TensorFlow ........................................ 156
7.3 COGNITIVE PATTERNS AND COGNITIVE DEPTH ........................................................................ 157
7.3.1 The influence of convolution algorithm on cognitive pattern ............................ 157
7.3.2 Pattern depth recognition and neural network computational complexity ....... 160
7.3.3 Concrete operational and cognitive depth ......................................................... 162
7.4 COGNITIVE ERROR ........................................................................................................... 163
7.4.1 The effect of learning rate on neural network learning outcomes ..................... 163
7.4.2 Autonomous oscillation of neural network system ............................................ 165
7.4.3 Dynamic Analysis of Neural Networks ................................................................ 166
8 FORMAL OPERATIONAL STAGE .................................................................................. 172
8.1 CONCEPT AND CLASS ....................................................................................................... 172
8.1.1 The feature map of conventional neural network .............................................. 172
8.1.2 The formation of concept ................................................................................... 173
8.1.3 Classes and objects in programming theory ....................................................... 174
8.1.4 Create and use a class ......................................................................................... 174
8.2 CONCEPTUAL TRIANGLES AND ASSOCIATIONS ........................................................................ 176
8.2.1 Correlation between concepts ........................................................................... 176
8.2.2 Conceptual correlation example - Chinese phrase ............................................. 177
8.2.3 Correlation of high-level concepts ...................................................................... 181
8.2.4 Conceptual triangle ............................................................................................ 183
8.2.5 Conceptual triangle and cognitive ability ........................................................... 187
8.3 MINI CHINESE SYSTEM ..................................................................................................... 188
8.3.1 Mini Chinese system and its characteristics ....................................................... 188
8.3.2 Mini Chinese neural network ............................................................................. 191
8.3.3 Mini Chinese system learning and association case ........................................... 192
8.4 IMPACT ON COGNITIVE PATTERNS AND COGNITIVE DEPTH......................................................... 206
8.4.1 The requirement of cognitive patterns for concepts .......................................... 206
8.4.2 The effect of conceptual triangles on cognitive depth ....................................... 209
8.5 ATTENTION .................................................................................................................... 210
8.5.1 Cognition and energy consumption ................................................................... 210
8.5.2 Control of the number of conceptual triangles .................................................. 211
8.5.3 Attention control ................................................................................................ 212
9 PERSONALITY PERFECTION ........................................................................................ 214
9.1 PERSONALITY AND COGNITION ........................................................................................... 214
9.1.1 Personality theory .............................................................................................. 214
9.1.2 The impact of cognitive development on personality ........................................ 219
9.2 PEAK OF PHYSIOLOGICAL SYSTEM ........................................................................................ 222
9.2.1 Physiological growth and decline ....................................................................... 222
9.2.2 The effect of physiological growth process on personality stability ................... 223
9.2.3 Peak number of neurons .................................................................................... 224
9.3 PERSONALIZED COGNITIVE SYSTEM ..................................................................................... 225
9.3.1 Complex system diversity ................................................................................... 225
9.3.2 Cognitive growth in the face of slowing down in physiological growth ............. 226
9.3.3 The formation of a personalized cognitive system ............................................. 228
9.3.4 Accelerated development of cognition .............................................................. 231
9.4 CHARACTERISTICS OF COGNITIVE PATTERNS AND COGNITIVE DEPTH IN YOUTH .............................. 232
9.4.1 Youth cognitive patterns ..................................................................................... 232
9.4.2 Youth cognitive depth ......................................................................................... 233
9.5 DEPRESSION .................................................................................................................. 235
9.5.1 Energy attractor of complex networks ............................................................... 235
9.5.2 Depression and formation .................................................................................. 240
10 COGNITIVE MATURITY ............................................................................................. 243
10.1 LAG OF COGNITIVE DEVELOPMENT .................................................................................... 243
10.1.1 Characteristics of physiological decline ............................................................ 243
10.1.2 Continuous growth in cognitive patterns and cognitive depth ........................ 245
10.2 THE FORMATION OF PERSONAL KNOWLEDGE SYSTEM AND WORLDVIEW .................................... 247
10.2.1 Human knowledge system ................................................................................ 247
10.2.2 Personal knowledge system.............................................................................. 248
10.2.3 Assimilation and accommodation .................................................................... 249
10.2.4 The formation of a complete world view ......................................................... 250
10.3 COGNITIVE PATTERNS AND THE LIMITS OF COGNITIVE DEPTH .................................................. 251
10.3.1 Network capacity and network depth decline.................................................. 251
10.3.2 The limit value of the cognitive pattern curve .................................................. 252
10.3.3 The influence of cognitive patterns declines on cognitive depth ..................... 254
10.3.4 Limit value of cognitive depth curve ................................................................ 255
10.4 CARDIOVASCULAR DISEASE .............................................................................................. 256
10.4.1 The contradiction between cerebrovascular function decline and cognitive development ............................................................................................................... 256
10.4.2 Cerebral hemorrhage and cerebral thrombosis ............................................... 259
11 COGNITIVE RECESSION ............................................................................................ 261
11.1 PHYSIOLOGICAL AND COGNITIVE SYNCHRONOUS DECLINE ...................................................... 261
11.1.1 Rapid decline in network parameters ............................................................... 261
11.1.2 Comprehensive decline in cognitive ability ...................................................... 262
11.2 ASSIMILATION AND ACCOMMODATION OF THE COGNITIVE PATTERNS ....................................... 264
11.2.1 New requirements for the information society ................................................ 264
11.2.2 Cognitive patterns update ................................................................................ 265
11.3 COGNITIVE DEPTH CONTROL AND DEEPENING ..................................................................... 266
11.3.1 Cognitive depth under physiological constraints .............................................. 266
11.3.2 Cognitive depth control .................................................................................... 267
11.3.3 How to gain greater cognitive depth ................................................................ 269
11.4 ALZHEIMER'S DISEASE .................................................................................................... 272
11.4.1 The effect of reduced number of effective neurons on cognition .................... 272
11.4.2 Discussion ......................................................................................................... 277
12 COGNITIVE AGING ................................................................................................... 280
12.1 EXPONENTIAL DECAY CHARACTERISTICS .............................................................................. 280
12.1.1 Attenuation of exponential function ................................................................ 280
12.1.2 Exponential function attenuation and physiological dysfunction .................... 282
12.2 CRITICAL END OF COGNITIVE DECLINE ................................................................................ 283
12.2.1 Changes in network capacity ............................................................................ 283
12.2.2 Attenuation of network depth .......................................................................... 284
12.3 COGNITIVE PATTERNS AND COGNITIVE DEPTH ...................................................................... 285
12.3.1 The effect of network function decreasing on the cognitive patterns ............. 285
12.3.2 Decreased cognitive depth ............................................................................... 287
12.4 SENESCENCE ................................................................................................................ 290
12.4.1 Senescence process .......................................................................................... 290
12.4.2 The impact of cognition on senescence ........................................................... 291
REFERENCES ................................................................................................................ 295
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