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 If you do not have a specific article of interest in mind, please refer to "Road Map" at the end of "Listing" for directions. 

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Evolutionary Process of                                                                                 (1)Hominids                                                                                                   

The Beginning

Evolution is a continuous process and progresses in a step-wise manner where physiology and cognition evolve in tandem. It is stimulated by external forces, such as environmental changes or internal force like adaptation to a niche. Human’s niche is the brain, which enlarged during every step of our evolutionary process through stimuli of adaptation to this niche. Bipedal walking, power/precision grips of our hands and telescopic vision provided the initial impetus for us to evolve down the path of our ever-enlarging brain.

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Knowledge of the hominid evolutionary process helps us to better understand the human mind and behavior in the context of man’s biological origin. Hominid evolutionary process also provides us a framework to study the development of human cognition. Development of bipedalism, for example, illustrates its impact upon the physical changes of early man which in turn influences the development of human cognition.

The evolutionary history of hominids covers over seven million years, if one accepts TM266 ( Sahelantropus) as an ancestor of hominids leading to Ardipithecus/ Australopithecus, Homo habilis/Homo erectus to Homo sapiens sapiens. Our brain volume, over this time, range from 300ml, approximate the same size as apes of today, to 1600 ml where more than half of the encephalization occurred during the last million years of the evolutionary history of hominids. Such significant increase in the brain volume of hominids during its evolutionary history is the main thesis of the following articles.

The evolutionary process of hominids is best illustrated by the physiological progression of primates. It began with primates developing eyes forward or so-called "orbital convergence" providing telescopic vision to give depth perception with 3D pictures. Also with lemur, the forelimbs evolved as hands for grasping. This was followed by the rotation of the shoulder. Instead of forelimbs pointing downward for walking on four legs, the shoulder blades rotated about ninety degrees away from a nearly parallel to each other positon to facilitate the hanging motion of the body with the forelimbs. Combination of the grasping hands and rotated shoulder allowed monkeys to swing from tree branch to tree branch in search of food. Next, apes discovered knuckle walk, further improving the mobility of primates. The final step is the special bipedal walk by human in an upright position so that the hands were free to perform tasks while the legs provide desired mobility at the same time. This is a significant evolutionary advance, which equips us to be what we are today.

Human hands are more dexterous than apes. Brachiation helps to develop power grasp but contributes only some to the development of fine grasp. Human hands and fingers are more flexible than apes. We can touch all of our fingers with the thumb and also bend each finger independently. The movements of our hands entail complex mechanism involving bones, muscles, nerves and neurons in our brain. It was found, studying the wrist bones, that some bones in the human wrists, comparing to monkeys', were modified through evolution to facilitatethe fine movements of the thumb and the index finger in order to conduct delicate manipulations with the hands, such as making stone tools. It must be a slow evolutionary process to attain these capabilities, millions of years. The developments of such hands were also in tandem with the development of the specific bipedal movements, during the days of early hominids.

Adam Chou                                                                                                                     (1)            

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Evolutionary Process of Hominids                                                                               (2)

Bipedal Development -I

Bipedal walking of modern man is a very complicated process requiring a delicate balancing act. It is not a shuffling gait like an ape but a flexible striding action of the legs. We do not know when early hominids descended from the trees and evolved gradually to this bipedal walk. Such manner of walking is the most effective and efficient means of traversing various types of terrain for a long distance. The consequence of this event is not only human walked on two legs but also evolved with articulated leg joints, allowing kneeling, squatting and crouching. In addition, the joints of the lower body can bend and rotate in running, jumping, pivoting, and walking backwards or on toes. The effect of this evolutionary process allowed hominids to move in a smooth motion into positions facilitating precise placement of the hands in conducting their tasks. Such flexibility of the legs and body is necessary as a moving base for performing conveniently many tasks humans encounter daily. Furthermore, it freed the hands to carry objects and to do other tasks. Knuckle walk of the apes is an inefficient bipedal movement in term of energy consumption and does not allow carrying objects for any significant range of distance.

To understand the development of bipedal walking, we need to appreciate the complex skills related to walking on two legs. While taking a step forward, one shifts weight to the leg and foot on the ground with the big toe providing the needed balance of the body. While moving the other leg forward, the body weight is temporarily balanced on one foot till the body leans forward for the next step. At this moment, man is essentially unbalanced and falling forward till the forward-moving leg touches the ground to restore balance. This type of bipedal motion is energy conservative because we only spend energy at the push-off and landing during a stride, but the forward-movement of the suspended leg swings like a pendulum, falling by gravity without the expenditure of energy. Further more, the articulate joints at the ankles, knees and hip gives flexibility to cushion the impact of landing the swing foot and the eyes help to select suitable spots for placing the foot while touching the ground. As result we are able to negotiate difficult and uneven terrain with easy and efficiency. We know that such bipedalism could not have developed quickly. There must have been a lot of trial and error till the bipedal technique of walking was perfected during the time of Australopithecus or later.

Adam Chou                                                                                                                             (2)

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Evolutionary Process of Hominids                                                                                      (3)   

Bipedal Development II

More thoughts on walking.

In view of the teetering position during walking on two legs, the nerve systems at the bottom of the feet play an important role in sensing the ground for gentle landing to minimize the impact to our legs and hip joints. Telescopic vision also further provides feed-forward messages to our brain about the contour of the path in front of us. We all experienced this while walking, when we misjudged a bump in front of us and stumbled. All of these imply a large amount of neurons involved in the development of bipedal walking.

Humans have a more flexible torso than apes. We can bend at the waist and rotate our upper body without moving the hips. This could be the effect of differences in shape of the upper bodies between apes and humans. Apes have bell shape rib cages while human rib cages are barrel shape. Such difference in configurations could lead to the narrowing of human waists allowing the flexibility of our upper torsos to perform tasks beyond apes.

The physical requirements for upright bipedal movement are numerous. To support the body weight, the spine formed in a “S” shaped curve in order to distribute the load. Though this is not an ideal solution, as evident by the back problems suffered by today’s humans, it is still the best compromise for supporting body weight in an upright position. In addition, the alignment of legs and hipbones were modified to accommodate the striding movement needed for bipedal beings. These physical changes impacted upon the diameter and limited the opening shape of the pelvic girdle of the birth canal with the resulting restriction upon the size of babies’ head at birth. As result human babies are born prematurely as compared to other primates.

Our bipedal movement is very complex involving many aspects of our physiology and it is not certain what were the stages evolved in the development of our walking strides. A recent discovery of a female child fossil about 3.3 million years old in Ethiopia might shed some light upon this matter. Based on the shape of the thighs and shins, she walked upright, but several apelike lower-body traits implied that her arms were used to climb in trees like apes to make nests or to escape from predators, as did Lucy, a previously discovered 3.2 million years old partial skeleton of an adult female. This topic, though still in hot debate, might be an indication that the free-striding style of walking by human might still be in the early developmental stage over 3 million years ago.

The big toes and the balls of the feet also play an important role during walking, in terms of neural signals for balancing when we shift weight from one leg to another. I found that as I age, I became less balanced resulting from walking more by shuffling my feet than walking on the balls of my feet. This is commonly known as “loss of spring in the legs”. I also notice that some toddlers, while learning to walk, walk on their toes instead of the flat of their feet. It finally dawn to me that our sense of balance while walking requires the neural signal from the big toes to the brain. To avoid stumbling, I have relearned to walk on the balls of my feet instead shuffling along.

Recently discovered 1.5 million years old footprints in eastern Africa might provide new clues to the evolution of upright stand and walking strides of modern human. Mathew Bennett of Bournmouth University in England identifies preserved footprints across terrain near what is now Ileret, Kenya as virtually modern human feet belonging to Homo erectus. In contrast to the 3.5 million years old foot prints showing shallow arch and angled big toe from Laetoli, Tanzania, these footprints revealed short toes and forward-oriented big toe with distinct high arch like modern human foot prints. These prints, with complementing leg and pelvis fossils discovered previously, add further evidence that early Homo erectus had a body adapted to traveling long distant. It also verifies that the development of modern stride took at least one million years from Lucy’s time to the current discovery.

Further evidence upon the length of time for developing human stride is verified by the recent completed study of Aridipthecus ramidus (Ardi for short), which was discovered fifteen years ago as a link to our ancestor's separation from other apes. The recent report dates Ardi as 4.5 million years old. The nearly complete skeleton shows Ardi's feet had yet to develop the arch-like structure that came later with Lucy and on to human. Such data comfir that it took at least 2.5 million years or more to evolve the homonid's strde in order for Homo erectus to migrate ouit of Africa. 

Adam Chou                                                                                                                    (3)

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Evolutionary Process of Hominids                                                                              (4)    

Bipedal Development III

The development of hominid brainpower can be traced to bipedalism. Humans inherited telescopic vision and thumbs opposite to the other fingers from the monkeys/apes. Combining this physical attribute with bipedalism, we have the very powerful assets of bipedal movement and hand handiness. Bipedalism not only means walking on two legs to any desired location, the legs are also flexible for up/down and right/left adjustments to provides a platform for our hands to do precise tasks. This is a unique feature possessed only by man. From this combination, our hands developed precision and power grips to perform tasks unknown to other species. We learned to make tools, hunt, gather, and other activities. These activities challenged us in engaging our brainpower and acted as the driving forces leading to a significant brain enlargement of humans through the evolutionary process of selection. It is also a self-feeding cyclic process where new activities led to encephalization, which in turn led to more new activities. A recently discovered nearly complete female Homo erectus pelvis is, in appearance, very similar to the modern female and larger than Lucy ( A. afarensis). This fact implies that hominids, a million years ago, had hips wide enough to bear babies with head large in circumference like modern newborn babies to pass through the birth canal. In other words, these early human babies had nearly the same brain, except apparently smaller frontal lobe compared to ours. This discovery supports the notion that, physiologically, Homo erectus is very similar to the early Homo sapiens. Furthermore, some studies claim that genetic diversity of Homo sapiens declined between 150,000 and 50,000 years ago based on sequencing the Neanderthal genome. This declining genetic diversity might extent to Homo erectus if their DNA is available.

It is evident that there was an increase in the size of brain resulting from evolving to bipedal walking and use of hands. Maintenance of brain is very expensive in terms of nutrition. Our brain is only a few percent of our body weight, but requires 20% of the energy derived from our food intake. Australopithecus brains are larger than apes. Feeding on leaves and fruits was not sufficient to sustain the additional brain functions for walking and hand movements acquired by the early hominids. More nutritious food such as meat, nuts and root bulbs containing high protein was needed. Intense seeking of food became required daily activity in order to support the nutrition demanding enlarging brain.

One source of high protein food is meat. Early man scavenged meat from the carcass of animals killed by predators. They probably discovered that they could disjoint the bones of the carcass with the sharp edge of stone fragments and consumed the previously hidden meat. They also found that they could crack the animal bone with stones to extract the rich mallows. From scavenging, they later developed hunting skills and making sophisticated stone tools

Supply of meat from scavenging, even hunting, was sparse. It did not guarantee frequent success and was not reliable for daily supply of highly nutritious food. For pregnant women, nursing mothers or small children, a steady supply of food was crucial. Gathering nuts, tuber roots and other plants was more reliable for daily supply of nutrients than hunting alone. Picking and cracking nuts, digging for roots required dexterity of hands. To perform these tasks, one needs to be close to the ground by kneeling, squatting or crouching, frequently done as a group. These activities are the basis for the  Social Development.

Adam Chou                                                                                                                    (4)

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Evolutionary Process of Hominids                                                                               (5)   

Social Development I

The evolution of flexible legs, dexterous hands and enlarging brain is a cyclic process of adaptation. The impetus of this evolutionary process is to acquire food to maintain the ever enlarging brain. To perform all of these tasks, dexterous hands evolved. In parallel, bipedal walking played a large role in the evolutionary process of hominid by providing flexible legs as mobile platform for the hands to perform needed tasks. This tandem development of bipedal movement of the legs and advancement of dexterous hands led to the enlargement of the brain. In order to maintain the large brain, hominids developed skill for hunting and gathering food, which further fed back to the development of hands, legs and brain.

Social Development is a large “Bang” in the evolutionary process of hominids with the appearance of cognitive functions unique to human. Tool making is the keystone for abstract thoughts leading to the earliest technological and cognitive breakthrough beyond apes. Apes use tools such as sticks for fishing termites, rocks for cracking nuts. It is a behavior of perceptual reaction understanding the cause and effect of using the tool during the performance of the tasks but not the principles behind it. Though others repeat such tasks, but each has to rediscover the technique anew. In contrast, tool making by humans is based on conceptual thinking, which involves goals, abstract thoughts, planning, etc. Stone tool technology was passed on from individual to individual and generation to generation. Furthermore there is ratchet effect of improving technique with time by the toolmakers. We know the invention of stone tools was during the time of Homo habilis. Such cognitive activity did not appear previously which required higher intellectual functions with additional neurons to be provided by evolution. 

We learned to make tools, hunt, gather, and other activities. These activities challenged us in engaging our brainpower and acted as the driving forces netting a significant brain enlargement of humans through the evolutionary process of selection. It is also a self-feeding cyclic process where new activities led to encephalization, which in turn led to further activities. Such enlargement of brain is evident from the fossil skulls of Homo erectus where the brain volume more than doubles during the time from Australopithecus to Homo erectus. The consequence of these events is an important factor to human's development of large brain.

Adam Chou                                                                                                                    (5)

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Evolutionary Process of Hominids                                                                             (6)   

Social Development II

The awareness of oneself and ones role in a group is an important advancement in the development of cognition of hominids. Such traits must have involved when early man became migratory nomads. Homo erectus fossils were found all over Africa and spread out into Europe and Asia into what we now know as Georgia, China and Java. It is evident that these hominids, like the later nomads searching of pasture land for their herds, were migrating from place to place as they exhausted the fauna and flora of a location about one to two millions years ago. Their migratory movement, instead circular as present day nomads, seems to follow waterways such as rivers, streams, etc. This implies mutually supporting social structures searching for improved livelihood by moving into unknown territories. (Contrarily, the annual migration of other species over the same route is based on instincts.) Such migration of hominids could be caused by climatic changes at the time and the migration paths of the early hominids being along existing or ancient waterways based on fossils and artifacts found. These migrations were conducted by groups of several dozen individuals traveling over thousands of miles under various weather conditions. At the same time, they were hunting and gathering food along the way for survival. Such undertakings require social interaction and teamwork in addition to the newly acquired tool making abilities and adaptation to changing environment. These achievements are clear indications the possession of self-consciousness, self-awareness and social intelligence of early man more than a million years ago.

The physical development of bipedalism also influenced the psychological development of human. There is a clear interrelation between the physical and psychological development of hominid. The change in female sexuality from conspicuous to hidden leading to the awaking of self-consciousness about estrous as a part of oneself is clearly influenced by bipedal development. Though some primates are capable of self-recognition, however, self-consciousness requires deliberate thought process and is uniquely human. The use of skins and furs as covers for the bodies to shield from the cold climate further reduced person-to-person contacts. Adapting oneself to these new situations resulted psychological changes of hominids. Such developmental activities led to the awareness of ones-self. All of these factors induced self-consciousness and self-awareness.

Social intelligence is a natural product of interactions between people. The enlargement of human brain through evolution resulted in the birth of immature infants in order for their passage through the mother’s birth canal. The first few months after birth were crucial for the infant to be nursed with plenty of mother’s milk in order to gain weight and survive. In turn, the mother needed to be well fed to produce the needed milk. Modified K mode of childbearing (Pregnancy could occur immediately after child birth while still nursing the newborn.) caused the necessity for caring for several offspring at one time. Before the understanding and practice of birth control, a single female could give birth to a dozen or so babies in her lifetime, though not all of them would survive. Since a single mother alone would not be able to care for all of these remaining ones, couples and group cooperation sharing these duties appeared. Hunting and gathering for food required teamwork. These changes effected an increase in the people-to-people interactions and greatly expanded social intelligence of the early man

Adam Chou                                                                                                                       (6)

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Evolutionary Process of Hominids                                                                                  (7)   

Communication Development I

Communication development could have an even larger impact upon the encephalization of humans than Social Development. During the Social Developmental stage, social interactions were required from diverse activities such as hunting, gathering, and child care leading to cooperative efforts as a group. Gesturing with limited guttural sounds was insufficient for communication and efforts were made to generate more different sounds, which led to the reshaping of the vocal tract. The vocal tract of early man was modified for emitting wider range of sounds through evolution, such as lowering of the vocal box, doming of the mouth roof, additional nerve system for tongue, lips, diaphragm, etc. At the same time, fine tuning of hearing to receive different range of sounds was also evolved. To support these developments, enamors numbers of neurons were required. In addition to support the mechanics of vocal tract and fine-tuned hearing, addition neurons were needed for communicating complex thoughts by speech, hearing, interpret thought and storage of knowledge. By this mean, knowledge was accumulated, complex thoughts were passed on to others, and the succeeding generations were trained to learn complicated tasks.

Now we have arrived at a milestone in our effort to pinpoint the evolutionary process of human. The ability to communicate in the form of speaking and listening of complicated thoughts may be the crucial driving force to push the development of our brain and elevate us from Homo erectus to Homo sapiens. The ability to communicate to others and passing on knowledge to others is a very unique ability of humans separating us from the other animals. We all have observed that young children are taught by their parents in clear instructions  about their behavior for their daily life. This is a very important learning process. Only humans are able to pass on the wealth of knowledge in details from generation to generation by means of vocal communication. Speaking and listening played a much larger role in influencing our evolutionary process to become modern man than our ability to manipulating our fingers. These abilities were absent or very primitive with Homo erectus.

Adam Chou                                                                                                                           (7)

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Evolutionary Process of Hominids                                                                                      (8)

Communication Development II

It is well known that modern man has the unique skill to think symbolically in addition to thinking in pictures as when one tries to visualize a scene or maps of roads. We also know that animal thinks in term of pictures, therefore we can assume that early man also thinks in pictures. When did the early man adding to the picture type of thought process with the symbolic ones would be interesting to know.

Autism could provide an insight to our thought process and could even help us to hypothesize the development of our cognitive ability in verbal communication. Temple Grandin, an autistic person and author, gives us the understanding of the autistic world where people thinks in pictures. She sees connections between animal behavior with certain autistic instincts, which help her to work on systems to improve treatment of livestock such as designing slaughterhouse for meat plants. One important element in her belief is that animals think in pictures.

In Oliver Sacks’ book, “The Man Mistook His Wife for a Hat”, he describes his encounter with Jose, who is autistic. He asked him to draw a picture representing various objects. He found Jose, before drawing, observed the objects with absolute stillness and complete concentration. He, then, closed his eyes for a moment and then drew. When he drew, he was bold, without hesitation, drew swiftly with clear lines without erasures. The products, though distorted in some aspects, contained extreme details with subtle self-expression. Such results one often seen with people who associate their thinking in pictures.

Human verbal communication, as we practice today, developed late in our evolutionary process. If we assume that humans first thought in picture, later development communication skill with gestures and grunts, which is an inefficient way of communication but it works. I remember that when I first arrived in this country from China, my thought process was in Chinese, and then I translated my thoughts from Chinese into English followed by vocalizing them. Such steps must have been taken by the Australopithecus when they communicated with thoughts in picture and then translated into gestures and grunts. The development of verbal communication in abstract thought followed the thinking in pictures. The cave paintings in France have always struck me as being drawn by master artists as pictures in simple lines of charcoal or ochre representing animals in motions. Now I am not sure; could they be painted by ones thinking in pictures? Are they different in their thought process from Australian aborigines and American Indians, whose paintings are stylized in abstract symbols?

The impact of communication in abstract symbols upon human cognition can best be illustrated by the use of a word “sheen” related to taste. In the western world tastes are classified as sweet, sour, salty and hot where hot means either hot in temperature or spicy. The fifth sense of taste, sheen is a term is not defined, at best, people say “ it tastes like chicken”. Now it is realized in the West that the taste introduced in food by sodium glutamate, which is sheen. The absence of this word in English to describe a taste, which exists but is not identified, presents great difficulty in discussing the taste of food and the goal or the results of culinary efforts. It is easy to visualize what impact this abstract word has upon our thinking and advancement in our culinary knowledge

The three stages of evolutionary process of hominids provide us with some understanding the continuous and stepwise progress of evolutionary mechanism. It also shows that physiology and cognition evolves in tandem. Can this knowledge be used as a guideline to search the role neurons played in cognition?

Adam Chou                                                                                                                                (8)

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Evolutionary Process of Hominids                                                                                         (9)

Appendix

Peking Man I

Much might be learned from the habitats of early man; unfortunately such sites are limited, either obliterated by weather, human or others and Peking Man’s cave was no exception. This cave was a limestone quarry, from hearsay evidence as old as Song Dynasty (960 –1279). It was also a source for “dragon bone”, marketed to be used for medicinal purpose. In 1921, Dr. J. G. Andersson, Director of the Geological Survey of Sweden as a mining advisor to the Chinese Government, also an archaeologist, discovered the cave based on a clue provided by a local kiln worker. By 1927, a team led by Professor C. C. Young, a paleontologist, began the excavation of the site. After it had been discovered as a cave where hominids used it as shelter for several hundred thousand years, some publications still called into question whether the habitation was the cause of early man. Here are some examples.

Following the publication of an article in Science (1998), several newspapers in the world declared that Peking man did not used fire. Though the article did not support the use of fire by Peking man, it raised other questions for further investigation. The investigators found some siliceous aggregates but without any inorganic matter, such as Si, Fe, K or Al. This lack of inorganic elements with the aggregates is not surprising since these chemicals could exist originally in the organic matrix associated with the siliceous aggregates produced by fire. After soaking in water for thousands of years, these materials would decompose, disperse or dissolve leaving no trace of the associated inorganic matter.

There are some other interesting results published in the article. “Only 2.5% of the microfaunal bones were burnt as compared to 12% of the macrofaunal bones. These values are roughly similar to those obtained in much younger caves where fire was undoubtedly used by humans.” Also, “ The few burned bones we did observe above the base of Layer 4 and in the lower part of Layer 10 were turquoise colored, and we assume that they are fossil bones that were somehow burned by natural processes.” The first statement, regarding the percentages of burnt bone in the cave comparing agreeably to what was found in a younger cave, supports the use of fire by men. With the second statement, the author found a different cause of burning matters. However, their experiment showed that turquoise colored bone could be derived from black fossil bone (from layer 10) when heated for two hours at 400 to 800 degree C with optimal temperature of 600 degrees. This fact strongly supports that fire was built above these fossil bones. When heated to the required temperature these fossil bones turned color from gray to turquoise. Other evidence indicates that the fire was not caused by natural process such as burning of guano. From these results, the study, rather than disputing the use of fire by Peking man, may actually support it.

Adam Chou                                                                                                                                 (9)

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Evolutionary Process of Hominids                                                                                         (10) 

Appendix

Peking Man II

Peking man cave (or known as Zoukoudian or in other spellings representing Chinese pronunciations) is located thirty miles southwest from Beijing or Peking in a hill named Lungkushan ( translated as Dragon Bone Hill). In this vicinity there are twenty-six locations including several caves containing signs of early man activities: stone tools, ashes or fossil bone of hominids. Peking man cave is the largest and best known with the greatest amount of artifacts and human fossils.

Peking man cave consists mainly of limestone deposit and was first excavated in 1930. The excavated part is about 40 m high, 175 m long and 50 m wide; the volume of this cavity is larger than several three-story houses with three-bedrooms and baths. Parts of the cave are un-excavated to be saved for the future explorations, which have happened since the early excavation in 1930s. Eighteen layers of sediments in this partially excavated cave were resulted from flooding and collapsing of the roof numbers of times in the past, during a large part of Middle Pleistocene. Stalagmites were found in Layer 5. Active hyena presence was observed for this layer too. Many writers treated all excavated layers in the cave as one identity, which, to me, is unscientific. Some consider the total history of the layers, 1st to 10th, as 300,000 to 500,000 years, then, a minimum average time span of each layer must be over 30,000 years with some being longer and others shorter in the time span for its accumulation of debris. Much could happen during theses thousands of years and one would be in great error assuming that each layer was formed under a single circumstance. Let Layer 4 to be an example.

Layer 4 is general considered an ash layer due to its dark color. Hominid fossils were found in this layer and also hundreds bundles of fine bones appeared to be pellets ejected by raptors, which might not coexist in the cave with early man. It is reasonable to assume hominids lived in the cave during part of the time when the cave was dry; at the other times, birds and hyena might take refuge there. Other scenarios would be that hominids stayed in the cave during darkness since they had fire, or in the winter only. Therefore the intermingling of these remains do not preclude the existence of others. Layer 4 is the thickest of all layers at four meters, which might indicate an even longer time of accumulation than 30,000 years given as average value for each layer. If Peking man resided in the cave for tens or hundreds of years at a time, it is only a fraction of the time comparring to the age of Layer 4. Still, it is more than transient occupancy of the cave by the early man, as some believe.

Adam Chou                                                                                                                           (10)

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Evolutionary Process of Hominids                                                                                     (11)

Appendix

Peking man III

Binford’s article* was published over the protest of numerous Chinese scientists, the contents of the article “suggested’ or “implied” by stating that the cave was not inhabited by hominids. Incurring at this conclusion, Binford committed a mathematical error by overstating his case with too many examples to show alternative explanations for the existing evidences being from natural causes instead of man-made, where these evidences were previously presented by others as from hominid activities in the cave. He stated that human fossils were washed down from the hillside into the cave, so, also, happened to the thousands of stone artifacts, the charcoal fragments were the result of nature fire, the ash layer was discolored from manganese oxide, the location was hyena den, etc. Statistically he could be right some time with one example pointing to natural causes, the odds reduced largely with two examples to be both agreeing with him, and each additional example would reduce further his chance for all of his examples to be correct. In this case with numerous examples, as stated by him to be coincidentlaly caused by nature, would be most unlikely that all of his examples would favor his position. One single exception would be sufficient to refute his negative conclusions and to reinstate the commonly accepted belief that Zoukoudian cave was used by hominids. Furthermore, more than two layers in the cave show signs of human activities, and also Peking Man Cave is not the only site with hominid activities. On this hill, there are up to twenty-six locations, including other caves, contain artifacts, human remains or signs of human activities, some of these locations are older than Peking Man Cave.

Binford’s discussion about the tools found in the cave also raises interesting interpretations. Among the artifacts, there was a pastern bone of a horse, which seems to be used as an anvil. Also, he listed in a table the lithic materials from the 1966 excavation at Location I (also used to designate Peking man cave) which represented 60% as “untouched “ including cores, flakes and debris. The remaining included 27% as hammers and 14% as scrapers, points, burins and points. Such composition of lithic artifacts for this location is not surprising because the cave was used by early man, intermittently, over tens of thousands of years. When they left for some yet unidentified reasons, they would carry the finished tools and leave the detritus. Furthermore, the predominance of hammer stones can be easily explained. This cave is high above the river, the hammers stone were river pebbles which were carried from the riverbed into the cave and they were heavy, having no value to them when early man left this residence since they were easily replenished from the riverbed. According to a count made in 1955, stone tools, flakes, and unused stones removed from the cave are no less than 100,000 pieces.

We should all keep in mind the story of six blind men and the elephant while quoting publications.

* Binford, L R. and C. K. Ho (1985) Taphonomy at a Distance: Zhoukooudian, “The Cave Home of Beijing Man”? Current Anthropology vol. 26, no. 4, p. 413 - 442

Postscript

My papers entitled “Two Genetic Traits in East Asia” (presented in Japan, 1999) finds support in what was stated by Russell L. Clochon as: “ ….and other through the interior of Eurasia to Zhoukoudian and the surrounding areas.” My hypothesis is slightly different from this statement as explained below:

I proposed two routes of migration into East Asia by the Homo erectus about one or two millions years ago from Africa. The northern route followed what we called “Silk Route” from Eurasia into northern part of nowadays China. This hypothesis gives a reasonable explanation for the existence of Dmanisi fossils in Georgia. One realizes that the topography of this region (along the Chinese boarder), one to two million years ago, would be quite different from today. The high plateaus might not exist since the uprising of the Himalayan mountain range is a recent event within the past two million years. Some literature indicates that Himalayan mountains rise 5mm/yr resulted from the collision of the Indo-Australian Plate and the Eurasian Plate. In this case, there may not have been any mountain in this region two million years ago, since the tallest peak of this mountain range is only 8,848 meters high. Matter of fact, it might have been a huge savanna with drastically varying climates.

The second migration route, commonly accepted, would be somewhere through the region of Viet Nam into the southern part of China. Since this group, mainly Han, developed “bamboo culture”, which was superior to the stone tools fashioned by the northern group, they pushed the northern group into Tibet, Mongolia and Siberia. This hypothesis yields a reasonable explanation for the diverse genetic data throughout these areas. This might provide an explanation for the stone artifacts found in Mongolia. If one assumes that two million years ago, Homo erectus could travel from Dmanisi eastward into East Asia with some degree of ease before the uplifting of the terrain and they might left a trail of stone tools on their migrating route. As the land gradually rising from the upheaval resulting from the collision of the tectonic plates, these stone tools would be washed down into the valley. Comparing the napping technique of these stone tools and their composition with the ones found in Dmanisi might be a proof for the “two genetic traits” hypothesis.

Adam Chou                                                                                                                      (11)

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Evolutionary Perspective of Cognition                                                                          (12) 

Background I

Evolution is a continuous process in a step-wise manner and at the same time it is conservative in its changes, as we see the developmental progression of single cell to multi-cell, further to marine animals, amphibians, birds and land animals. During each stage of the evolution, some features of the previous stage are retained. For us, the most noticeable ones are vertebrates, we notice the same vertebral configuration for mammals with backbone and four limbs, birds with leg and wing and even fishes have backbone and fins. The discovery of Tiktaalik, a new found fossil, could be the link to fill the gap between fishes and vertebrates where the fins of this scale-covered fossil fish evolved into protolimbs included bones analogous to human wrists and fingers, enablig it to wade in marshland. Is there such conservation in the evolution of cognition?

Let us review the relationships between the developments of physical body and nervous system. The single cell animal like ameba appears to be sensitive to its surroundings, it moves away from light. Other animals with multiple-arms, such as hydra, jellyfish or anemone, possess a “nerve net” with neurons interconnected like a cylindrical fish net. Starfish has a central nerve ring with radial nerves into each arm. Chordate, an early form of vertebrates, has a small brain connected to a hollow nerve cord without the protection of the hard vertebral bones. Such primitive configurations of the nervous system evolved to the nervous system of humans with a large brain continuously increasing in size. From early form of a brain stem, it grew into cerebrum with four lobes, cerebellum, midbrain, thalamus, hypothalamus, limbic system, and basal ganglia, each with its own function and some overlapping. Recent discoveries by the Howard Hughes Medical Institute and the University of California, Santa Crus, show some genetic evidence for the evolutionary progression of the brain. Areas of some human DNA, HARI, changed partly after separation from chimpanzees, while the other part of the DNA had remained almost unchanged for millions of years, i.e. 18 out of 118 nucleotides had changed since the separation of chimpanzees and humans but only two had changed during 310 millions years, by extrapolation, between the separation of chimpanzees and chickens. (HARI, a regulatory gene produces RNA, is evolved with the migrations of neurons to the cortex of the brain during the formative period of human brain from 7 to 19 weeks of gestation.)  Unfortunately, such conservation of the vertebral system led to the back problems occurring commonly with upright walking man where a single column of bones is not able to provide proper leverage without straining this column of bones and nerves while exerting our body in performing daily tasks. Nevertheless, it is obvious that physiological evolution carries on with the evolution of the nervous system.

Another good example of evolutionary conservatism is the size of human skull which is limited by its proportion to the body carrying it and the tolerance of the birth canal for passing through the head of an infant without endangering the mother. To accommodate the ever expanding needs of extra neurons on the surface of the brain as cortex, the human brain, instead of growing larger and larger to provide the needed surface for the neurons, began to wrinkle with hills and valleys (gyrus and sulcus ) ending with a surface area about four typing sheets of paper which is many time of what would be if it has been smooth as the surface of a sphere. In this way, human has a very large cortex area in order squeeze in billions of neurons.

Adam Chou                                                                                                                              (12)          

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Evolutionary Perspective of Cognition                                                                     (13) 

Background II                   

It is obvious that when we speak about physiological evolution, we must include the evolution of the nervous system, which further extends to the control center of all - the brain. Does this trend apply to the evolution of cognition? The dictionary defines “cognition” as “act, facility, process and capacity of knowing” Cognition is the result derived from the activities of neurons in the brain. How do neurons perform such duties, which appear as Thoughts? In addition, neurons are linked to form certain configurations for carrying messages to different parts of the body. Do these neural configurations evolved from simple to complex and are they always conservative as evolution progresses from one species to another?

We frequently think in terms of body and mind, as distinct parts of the whole, which makes us human. We know, see and feel our body, but not the mind, it represents literal and abstract thoughts, logical thinking, emotion, and feeling, where we feel its metaphorical power. We know the mind is derived from activities produced by the neurons in the brain. We do not know exactly the mechanical functions of the neurons, which produce the phenomena of thinking we encounter every day. What are the configurations of the neurons (which will be referred to in the future as neural system)? Furthermore, what is the role of mirror neuron associated with action and perception? Is infant growth guided by genetics? Autism has its roots in wayward development of amygdala? How does damage to neurons in our brain alters our behavior? What is the relationship between malfunction of neurons and mental disease? What is the effect of hormones on neuron activities?

These are topics worth the joint efforts of scientists from different fields. I hope this website can provide the means for such cross-fertilization. Psychologists work in the metaphoric world of mind, neuroscientists explore the mechanical functions of neurons, studies about animal behavior, infant growth pattern, physiology of body provide us with field data. Aggregation of these data from different sources would greatly enhance our efforts to decode the mechanics of the neural system.

Adam Chou                                                                                                              (13)

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Genetic Data Bias I                                                                                                    (14)

Sampling

DNA Codes give us a most powerful tool to trace the ancestry of hominids. As any tools, there are deficiencies where one should be careful not to accept misleading conclusions. Sampling and molecular clock are two stumbling blocks for accurately tracing human history. We shall first deal with “sampling”.

Genome projects aimed at decipher of human evolutionary history with genetic data are widely funded nowadays, but their value greatly depends upon statistical sampling of the population to acquire a true representative distribution of the traits to be studied. Blood or saliva is taken as the genetic sample of modern men to back trace its history with mathematical technique. Incomplete sampling will lead to erroneous conclusions. Here are two examples.

Chinese Population – The population in present day China is over one billion, the majority being Hans with well-known minorities of Mongolians and Tibetans. In reality there are fifty-six identified ethnic groups derived from Homo erectus migrating into this part of the world over one million years ago. Most of the genome projects have been sampling Hans, with some of the two larger minority groups, without input from the rest of the fifty-three minority groups. In this manner, one can not attain a complete evolutionary history of these populations.

In the past, I published a paper about two genetic traits in East Asia, which hypothesized two migration routes into East Asia by Homo erectus over a million years ago. One was the commonly accepted southern route via Southeast Asia. The other was up through the Silk Route, an ancient trade route from Middle East into the northern part of East Asia. The northern group was overcame by the southern group, probably equipped with bamboo weapons, and scattered the northern group into west and north regions of East Asia as Tibet, Mongolia and parts of Siberia. There might also be possible link of the northern group to the Polynesia, Korean and Japanese as Ainu in term of genetics.

Neanderthal – There are several genome projects studying the genetic codes of Neanderthal and their relationship to modern man. Unfortunately all of these projects share one shortcoming. By filtering the sample to eliminate the DNA which might represent contamination of the samples from being handled by the laboratory technicians. In other word, any DNA representing modern man is removed from the data. From such bias sampling, one should be careful in drawing conclusions since any DNA shared by or with modern man are no longer there or any similarity in genes between Neanderthal and modern man no longer present. The remaining data would naturally point to the conclusion that Neanderthal did not share any DNA with modern man. Also, the sampling of DNA from one or two Neanderthals to study either the possible reproduction of Neanderthal with Homo sapiens sapiens or speciation of the two species would be unproductive since the variations of DNA within Neanderthal group were not evaluated, which could account for a large part of the dozens DNA difference found between Neanderthal and Homo sapiens sapiens.

Adam Chou                                                                                                              (14)

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Genetic Data Bias II                                                                                                   (15)

Molecular Clock

Molecular clock is a method to date the genetic history of hominids or age of species based on the accumulation of genetic mutations. However, the genetic codes change haphazardly and unpredictably resulting random intervals of DNA mutation. Several assumptions were made to apply this method for this purpose. In the case of either mitochondrial-DNA (mt-DNA) or Y-chromosome studies, we need DNA from representative groups of individuals out of the world population. For each group, there should be numbers of individuals. Let us assume that we have obtained such DNA sampling.

The first step to do is to eliminate the gene variations occurring within the groups from the overall gene pool. This is the reason we need several individuals from each group. The remaining gene variations would be shared commonly in different degrees by the different groups of the population selected. For example, some genes would appear in all groups of the population. This fact suggests these genes are the oldest in terms of evolution and would be placed at the main trunk of the tree. Others are possessed by a single population, naturally, they represent the youngest racial group and should be placed as the outer twigs, or as a single twig representing isolated group like Sami or Ainu. The computer program is designed to analyze this information in generating a tree representing the lineage of hominids.

In principle, this computer program algorithm is similar to the least-square–fit method for selecting mathematical function representation of a set of data. For this application, it is called principles of maximum parsimony. To initialize the computer program, some main branches of the tree are hypothesized as inputs to the computer program. Naturally, African gene would be used as the trunk or the first few closest branches. This is where some people dispute the results because a bias is introduced here. However, this issue is not in serious dispute since it is generally accepted that man’s origin was in Africa and numerous migrations out of Africa into the rest of the world occurred in the past. The computer program also provides statistical values to show “goodness of fit”. Changing the branching of the tree can be made to improve the fit.

Molecular clock is used to estimate the diverging time at which the populations evolved, as limbs, twigs, branches from the trunk, in the form of mutation of the genes. The constant used for the molecular clock method is a value calculated from average rates of mutation based on millions of years of evolutionary process, hence, it  introduces errors, especially serious ones for short spans of divergence time. Mutation as a part of evolution is a random process and the rate of change in terms of genes is erratic, not constant. However over a long period of time, as tens of million years, an average value of these rates can be calculated for estimating divergence time of species with tolerable error. Mathematically, molecular clock value is derived from linear approximation of a group of scattering data based on our known knowledge of evolutionary transfromation for species over tens of million years spans. The slope value of this straight line can be used for large time span as millions of years to approximate divergence time between the species or events with some uncertainty. On the other hand, a hundred thousand years time span is near the origin of the time coordinate where the error is the greatest, as in the case of African Eve application. For this application, the obtained result would be in significant error and could render it seriously flawed.

Adam Chou                                                                                                                    (15)

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                                 Gap Between Cognition and Neurons                                       (16)

Brain is the site as governor for numerous body functions such as cognition, emotion, memory, motor, and homeostatic functions. However, most of us usually make a distinction between “mind” and “brain”; the former is recognized as site for mental attributes such as cognition, emotion, beliefs while latter represents the site for electrochemical neural processor governing body functions. Neuroscientists, pursuing “brain”, study neurons and neural systems, and psychologists, pursuing “mind”, study cognitive functions such as emotion or feelings, mental diseases, and other psychological events. In between these two fields, there is somewhat a no-mans land where the knowledge linking the functions of neurons and cognition is limited.

We know that neurons are connected, like mirror neurons, through axons and dendrites across the gaps of synapses, but we have no or little knowledge about the configurations of the neural nets, or how they convert the chemical or electric codes via neural transmitters from neuron to neuron to perform different functions we do daily. We know one of the early body functions of animals is advance or retreat, which even simple ameba knows to avoid light. The understanding of eat/ be-eaten or fight/flight or distinguish between friends and foes requires more complex neural linkage than what ameba have. But it is easy to visualize that the neural network of ameba gradually evolved in succession into other species with additional neurons to form a network to perform the more demanding functions like distinguish friends from foes. If evolution is conservative, would this principle also apply to neural network, so it evolves from a simple configuration to a large network with thousands or million of neuron to perform some specific task?

We also know that infants grow in pre-specified progression, holding up head, rolling over, sitting up, standing on two legs and finally walking. Psychologists have found all infants progressing in this sequence within certain time frame as a rule. What directs these developments? Is it genetically coded to progress with this formidable schedule?

Adam Chou                                                                                                                (16)

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                                                 Thought Process I                                                    (17)    

                                                      Introduction

Evolution is a continuous process and progresses in a step-wise manner where some features from the previous stage of evolution would be conservatively retained for the next stage. Further more, physiology and cognition evolve in tandem, where they are stimulated by external forces, such as environmental changes or internal forces like adaptation to a niche. Human’s niche is our brain; as discussed in The Evolutionary Process of Hominids, our brain enlarged during every step of the evolutionary process through stimuli of adaptation to this niche. Early man inherited from the primates telescopic vision, forelimbs evolved as hands with opposite thumb for grasping, and also the rotation of the shoulder, instead of forelimbs pointing downwards for walking on four legs, the shoulder blades rotated to permit the forelimbs as arms to be used for swinging from tree branch to tree branch in search of food. Changes of climate, according to some scholars, in the region where the early man lived caused the trees to disappear resulting in savanna or grassland. As hominids came down from the trees, rather than adopting the knuckle walk of the apes, they began to develop walking upright with swing arms. Evolved into such bipedal locomotion represented a big break with the past and it must take a long time to reach this walking style. However, it greatly improved the ability of hominids to cross various terrain efficiently. From the achievement of upright walking and freeing of the hands, early men were able to extend their skills in acquisition of food to include anything eatable. The discovery of using stone tools greatly improved their ability to acquire high protein food like meat from scavenging carcass, cracking bones for mallow, even hunting small animals to provide additional nutrients for the maintenance of the brain. These started the cycle of more nutrient food fueling the brain, and larger brain leading to more mental agility in improving their life style through greater dependence upon the brainpower. Early men evolved from Bipedal Development, through Social Development ending with Communication Development. Would such an evolutionary process apply to the evolution of cognition?

To explore further the elements of thought process, we examine other scenarios, as given in the following articles, which seem to contain basic traits shared by many animals. Could they be based on the same basic configuration of neurons evolved into greater complexity?

Adam Chou                                                                                           (17)          

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                                   Thought Process II                                             (18)       

                                      Mirror Neurons I

Mirror neurons are the most interesting and important recent discovery showing the relationship of cognition with specific neurons as linkage between perception and motor action. These neurons react while one observes a scenario where a subject either performs an action or when the subject observes the same action performed by others. Mirror neurons were first found while observing activities in the left middle temporal gyrus and the inferior frontal gyrus of monkey brains, and the existence of these neurons in human brain was confirmed indirectly with neurophysiological and brain-imaging experiments. Besides humans and primates, mirror neurons have also  been observed in birds.

Mirror neurons were discovered by G. Rizzolatti, L Fogassi and V. Gallese during an experiment in which electrodes were placed in the inferior frontal cortex of a macaque monkey to study the control of hand movements by specific neurons. They found that these neurons not only reacted when the monkey reached for food such as fruits in a bowl, but also when the experimenter stood near the bowl containing the fruits. Further experiments confirmed that these neurons responded both while the subject performed the action or observed the same action done by others. However, when the food item was held by a tool, such as a pair of pliers, no response was observed. This is very significant since the monkey, not familiar with this tool, did not associate the food being held by the pliers as meaningful to his experience of being held by hand, hence no response. This is further evidence of mirror neurons’ association with conception related to previously learned experience.  

The test involved 532 neurons, while only 92 reacted to the stimulation of the experimenter. They were identified as in F5 and appear to be related to linkage between hand and mouth. Further tests showed that some neurons responded to the sight of a hand approaching and grasping object, where different manners of grasping were conducted such as precision grip, finger prehension or whole hand prehension. For these tests, less than dozen neurons appeared to be involved at a time. It is very exciting to think such a small group of well-defined neurons will give us new insights into neural configuration and the mechanism of cognition.

Adam Chou                                                                                                                       (18)

Gallese, V., L. Fadiga, L. Fogassi and G. Rizzolatti, (1996) Action Recognition in the Premotor Cortex. Brain, 119, 593-609 (Also available from Goggle Scholar)

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                                              Thought Process II                                                              (19)

                                                 Mirror Neurons II

Recent studies by the University of College of London with ballet dancers showed that the brain acted differently for an individual skilled in a certain dance routine than one who was not. Individuals from groups of expert dancers and non-dancers were asked to lie perfectly still in a MRI scanner watching a video of brief dance movements. It was found that the brain area known as “mirror neurons” lighted up more strongly when the subject, an expert dancer, observed movements he had been trained to do than the dance movements in which he had not been trained. At the same time, the non-dancers’ brain responses were appreciably less compared to the expert dancer. These results demonstrate another aspect of the mirror neuron functions in relationship to conceptual realms where hand and mouth are not involved and also the importance of the existence of prior knowledge.

So far, we have learned that mirror neurons describe a linkage between perception and action, each group involving only a limited numbers of neuron, which are located at frontal lobe, for thinking and parietal lobe, for action. We also know primates and at least some birds have them. Do other animals have them? Is ameba’s flight from light triggered by an early form of mirror neurons? I know that when I learned to play tennis, it was a constant drill to learn the skill and I soon lost what I had learned if I did not practice. How about great athletes, do they learn their sports easier; Tiger Woods, for example, do his mirror neurons perform more effectively than lesser players?  Do we have a set of mirror neurons for every action or every combination of sensor and motivator as in this case of hand and mouth? How many are there? For extreme sports, do  athletes develop new sets of mirror neurons while learning their skills? These are the questions facing us along the road of further research into cognition.

Mirror neurons, as originally discovered with monkeys, reside in F5 area of the premotor cortex. They show congruence between the visual actions they respond to and the motor responses they are coded. According to the degree of congruence, they are grouped as “strictly” and “broadly” congruent. Strictly congruence, representing one third of the F5 neurons, is defined as that the observed and executed actions both strictly correspond to the goal  (grasping) and means for reaching the goal (precision grip). The remaining two third of the F5 neurons, defined as broadly congruent, are less strictly congruent between the goal and the means for reaching the goal. This classification of the mirror neurons might provide insight to the above sports related questions, where strictly congruent neurons are trained such as my learning to play tennis. With Tiger Woods and other outstanding athletes, their broadly congruent neurons are easily trained to become strictly congruent neurons? 

Adam Chou                                                                                                                      (19)

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                                                         Thought Process X                                   (35)
                                                              Tool Using - 1

Both humans and other animals use tools, but there are distinct differences in the manner of acquiring and using the tools.

The fashioning and use of tools is a revealing topic in analyzing the behavior of species in the animal kingdom, including humans. Many species use tools to gain food of some kind to eat, but the ingenuity in tool use is greatly diverse. Is it the result of intuition, imitation or logical deduction? We know chimpanzees have shaped sticks to fish termites, birds crack clams by dropping them from heights, some monkeys even wash potatoes to get rid the sand and dirt. Many of these behaviors are widely spread within its own kind, but some are limited to a tribe as if the technique of washing was discovered by one and later learned by the others within the tribe.

The use of tools by apes is a behavior of perceptual reaction, understanding the cause and effect of using the tool but not the principles behind it during the performance of the task. Though others also perform similar tasks, each has to rediscover the technique anew. Apes have limited range of tools used and little or no improvement was observed in their use of tools through the years. Humans started with stone tools and progressed to the complex equipment we see today. This illustrates that humans and apes arrived at different plateaus of cognition with the resulting different levels in the tool use.

Furthermore, tool making by humans is based on conceptual thinking, which involves goals, abstract thoughts, planning. Stone tool technology was passed from individual to individual and generation to generation. There is a “ratchet” effect of improving the technique with time by the toolmakers as the technique being passed on from generation to generation. One can envision here that perception could be the basis for the development of conception. However, there is a difference between perception, as thinking in pictures, versus perception, as thinking in abstract. It is believed that early hominids think in pictures and later developed abstract thinking. Tool making is the beginning of abstract thoughts and linear thinking instead of pictorial or motion-picture like parallel thinking by the earlier hominids. 

Adam Chou

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                                                             Thought Process X                                            (36)
                                                                       Tool Using - 2

The use of tools by non-humans is well recognized; however its development or origin is a topic of considerable discussion. Tool use, for humans, involves understanding actions performed by oneself and by others, and learning to copy an action from seeing it done by others, known as imitation. There is also the “ratchet” effect of improving the technique as it passes from generation to generation. These might be the factors distinguishing the way humans use tools..

To verify the differences of thinking symbolically versus pictorially would be enlightening to conduct an experiment with chimpanzee to test this hypothesis. The experiment would be to hang a banana by a string beyond the reach of a chimp. A box would be nearby. With the ability to think in pictures, chimp would eventually realize the usefulness of the box, move the box to the banana, and climb up to the top the box taking the banana. After a few runs of this phase of the experiment, the chimp would be shown the box, which would then be hidden in a closet. Would he retrieve the box from the closet to get the banana without further prompting? In the case of a human, the location of something handy for use would be deducted and the box would be moved to retrieve the banana . This could be the shortcoming of thinking in pictures versus logical reasoning in the abstract. I remember that a similar experiment was conducted by some researchers; at the time I was puzzled by the results which I now have a new way of interpreting


Adam Chou

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                                                         Thought Process XI                              (37)
                                                                   Language

All mammals and birds communicate by vocalizing, in addition to other physical forms of communication; deer flash their tails for danger, crows caw signaling the presence of raptors, even insects, such as bee, let others to know the location of nectar by acrobatic flying to instruct fellow workers the direction and distance of the food source. However, only humans can communicate complex and abstract thoughts of vocal language.

The development of human vocal communication, as we practice it, most likely occurred after the modification of the vocal tract through evolution. This change enables humans to emit a wide range of sounds, especially consonants. With this ability, humans were able to communicate in complex notes, which led to words for expressing thoughts. Practice and desire transformed thinking in pictures to abstract thoughts. This might be the great distinction between humans and our next closest relatives.

The Broca and Wernicke areas are the centers for human language and vocal communication. Based on recent laboratory experiments, these areas lit, when the test monkeys heard calls from other monkeys. These responses to sound suggest that human language ability might be rooted in the same areas as our near relatives’ in responding to calls. It is also interesting that human and baboon infants put everything in their mouths to learn the shapes of objects instead with their eyes or hands. This act implies that infants’ neural network in the mouth for interacting with the outer world is developed earlier than other parts of the anatomy and also both humans and baboons share the same developmental schedule of the basic neural networks. This fact leads to an interesting hypothesis, that since gesturing, a physical expression for communication, is generally considered as a precursor to the vocal language, the same brain areas might be involved in the gesture-based language and then switched over to vocal communication in a sequential evolutionary steps.

We know that two years old children learn to speak with “baby talk” which sounds nothing like the words they mean. This could be a result of two different causes: first, they enunciate what they hear; secondly, they hear correctly but their vocal has not achieved sufficient dexterity. For example “fire-truck” become “firrr-chu”. Mostly likely they can pronounce the vowels but have difficulty with some of the consonants. One can notice this kind speech difficulty with speech impaired persons. This fact might support the long evolutionary time for early man to develop their vocal tract to achieve the speech pattern we now have.

The development of language, based on recent research, might be related to FOXP2, which belongs to a family of genes producing proteins, which regulate other genes. Some believe that deviations of this gene effect the oro-facial movement related to speech leading to poor language expression such as grammar. It is more likely that this gene is a part of the cognitive link facilitating speech making.

Adam Chou

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Thought Process XII
Competition                                                   (38)

Competition must originate from the primitive instinct of “fight or flight”, which even the single cell, ameba is capable of. Beginning with “fight”, competition grew in complexity and sophistication as we observed among the animal kingdom competing for food, mate, territory etc. is a common event. Instead of physical competition for dominance as with non-humans, we compete with non-physical probes during social interaction to achieve ranking among our peers. In the patriarch society, the males compete and in the matriarch one, the females compete, both physically and intellectually. However with human, it becomes more disguised. Some call such actions civilized.

If the evolutionary process is continuous and progresses in a stepwise fashion, one can hypothesize the evolution of competition. In the beginning, the “fight” part of the primitive instinct of “fight or flight” for vertebrates must first develop in the brain stem area since it is the earliest form of the brain, known as reptile brain, responsible for instinctive behavior. Then as the strategies of competition become more sophisticated, they expand to the limbic area, known as the mammalian brain, which controls emotional behavior, particularly aggression and sex. Finally, further advances in our strategies for competition, rational thoughts are needed, then evolved the cortex, the outer layer of the brain, which wrinkled and folded to increase the surface area of a given brain volume to contain a maximum numbers of neurons for conducting such rational thoughts.

Adam Chou

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Thought Process XIII
Decision Making -1                                                 (39)

“Decision Making” is a topic worth the attention of anyone interested in the evolution of cognition, since it has its roots in the single cell animals and evolved to all species in the animal kingdom and further extended to humans. Ameba makes decisions for the primal instinct of “flight or fight”, “ prey or predator”, “danger or food” when spotted by light. Mammals constantly make the choice between fight and flight in competing for food or to be eaten. Humans confront dilemma of making decision as “yes or no” in their daily life. It is a process of neural functions evolving from simple to complex. Knowing how the instinct of “flight or fight” evolved in association with decision making by different living beings might shed some light on the works of neural network. We do not know how the logic of ameba neural bio-chemical reaction functions but we can assume that the more advanced decision making must grow out this basic operation of an ameba. To help us in exploring and decode the tangled network of neurons in the process of decision making, different aspects related to decision making are discussed here.

Making decisions in the most advanced form involves a sequence of rational thoughts in the abstract domain, which is the most recent stage of our brain development. It is a trained ability which humans acquired in a later stage of the evolutionary process, the frontal cortex, to perform this task. There must be complex groups of neural networks dedicated to performing these tasks of rational thoughts and it is also well known that these neural networks did not mature till after teen ages. Training of rational thinking, according to neuroscientists, is the activation of some synapses. When signals transmitted from a neuron to another neuron through axon via synapse to dendrite, several events happen. When a single signal is received by a neuron, it is gradually damped out and nothing is transmitted to the next neuron. However, if a burst of signal is received, it is then transmitted to the next neuron. Long term learning also involves the deposit of calcium at the receptors of the neurons. For these reasons, learning requires intense attention in order for the neurons to perform their tasks of storing the information.

I have attended a number of seminars for decision making in my previous life of being an engineer. To conduct logical decisions making process, there are several steps. The first one is to define the objectives of your work or project; then collect all pertinent data or materials related to the topic, regardless how trivial they are. The next step is to organize these items in the order of their importance to the project and evaluate them in term of their statistical chance of occurrence. Then reduce the items to a reasonable list of actions. The last step is to create a critical path for conducting the tasks. Though not all these steps are involved in every decision making but many elements are usually included even if one is not conscious of them

Adam Chou

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Thought Process - XII
Decision Making -2                                                     (40)

Decision making depends not simply on logic but also emotion. Emotion and cognition coexist in the brain; emotion is the older parts of the brain, amygdala and accumbens and cognition resides in the younger parts of the brain, the prefrontal cortex. Emotions such as fear or pleasure may interfere with logical choice or reasonable decision derived by cognition. Brain imaging studies by Brain Knutson of Stanford University show that when a person was shown his favor product, like chocolate, positive emotional engagement was indicated by the activation of the nuclens accumbens. However, when he was told about the price, insula would light up if the price had been too high, indicating negative response. If the person really liked the product but experienced emotional conflict between desire and cost, the prefrontal cortex would light up. This is a graphic illustration of decision making between emotional need and logical action.

The organization of a hundred billion neurons and five hundred to one thousand trillion synaptic connections must be in a hierarchical manner in order to conduct its business. For example, memory is classified as short and long term where inputs reside temporarily at the short term memory area and are distributed to the long term memory at the different parts of the brain. Information related to facts and events are sent to the hippocampus and the nearby medial temporal lobes; data related to habits and skills are routed to basal ganglia and cerebellum, and emotion related inputs are funneled to amygdala. This may be a model for all of the brain functions, such as decision making, where inputs or stimuli are passed from top to bottom bureaucratically to the lowest neural network at various parts of the brain.

Caution should be used in interpreting the functions of neural networks in terms of our nomenclature. “Behavior syndromes” implies a trait favorable in one situation may be a hindrance in another. A macho spider might catch a lot of prey but fail to prevail in courtship rituals. Ann Hedrick of the University of California, Davis, tested funnel spiders for their personalities by observing their behavior in front of their escape tunnels. She found the ones, aggressively ready to pounce on lunch, were not necessarily the ones readily to return to the post at the entrance of the tunnel after escaping to hide in the tunnel from harm. In other word, their behavior is not dictated by what we call “bravery” but some other factor. Here is an example where erroneous conclusions could be derived from our interpretation of a given behavior based our nomenclature of this behavior.

Adam Chou

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Development of Computers I
Overview                                                                                                      41

There are similarities and differences between the organization of the neural network in the brain and the structure of the computer system. The hardware portion of the computer is driven by the digital software system which is programmed by humans and introduced into the computer with the input devices while the operation of neural networks in our brain is stimulated by biochemical reactions of the body through million of years of evolution. Nevertheless, the similarities can be instructive for us to decipher the operational principles of neural network. Modern computers may not be as capable and fast as human brain, but its overall organization, in my opinion, is very similar to our brain.

There are three functional major divisions of a computer: operation, interface, and memory. All exist both in hardware and software. Furthermore, they are being linked together through both hardware and software. The hardware consists of electronic components, which are not changeable without destroying the integrity of the hardware. Software is composed of programs, which are changeable through some hardware interfaces. CPU (central processing unit) is the brain of the computer, which contains electronic circuits and silicon chips mounted on a board called microprocessor. Keyboard and monitor screen or CRT (cathode ray tube) are the interfaces for communication with the outside world. Keyboard is used to input instructions into the computers and CRT or printer displays messages generated by the computer.

There are two types of memory: temporary or permanent ones. The temporary memory is used for current tasks, such as calculations to be performed. If electric power had been interrupted, data stored in this memory would be lost without a power backup. Permanent memory is coded onto the surface of silicon chips mounted on microprocessor boards. Information generated at the temporary memory area can be transferred to the permanent area for storage to be used in the future.

Adam Chou

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Development of Computers II
ASCII                                                                   42

To understand the operational principle of a computer, we need to go back into the history of computation. Counting, in prehistoric days, was done with beads or knots in strings. Mechanization of computation has challenged humans for thousands of years. The abacus is the earliest form of accounting device. The invention of a mechanical calculator with wheels to perform the tasks of computation was a great leap forward. With the discovery of electronics, scientists searched for a way to utilize the on/off switching capability of electronic gadgets, such as vacuum tubes, to represent the binary system for computation. This primitive form of vacuum tube computer gradually evolved by incorporating silicon chips, starting with the transistor, to our present day compact computers. To understand the works of a computer, we need to know the ASCII (American Standard Code for Information Interchange) in order to appreciate how the computer functions.

A typewriter enables us to transcribe our thoughts into a written text. It has also inspired us to transform inanimate electronics into a thinking machine, the computer. The basic units for processing by computer are called bits, which are on/off switches and are represented in computer codes as one/zero; further combinations of bits become bytes. ASCII is an eight bit binary unit representing symbolic and alphanumeric characters. With combination of these eight bits of zeros and ones, one hundred twenty eight values in a binary representation can be achieved, used to represent all of the key strokes in a typewriter, including upper and lower case letters of the alphabet, numerals of the decimal system, assorted punctuation marks and control symbols. In this manner, the information input into the computer becomes as familiar to us as the keyboard of a typewriter through ASCII codes.

Adam Chou


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Development of Computers III
Computer Language                                                                     43

Binary system, ASCII, provides a method to introduce intelligence to the CPU, where the silicon chip can be programmed to perform tasks. In addition, it provides a tool to introduce instructions to the computer. This is known as computer language or computer software. Computer software is a hierarchical system with many layers.

Machine language is the most basic language for the computer, which consists of a series of zeros/ones based on ASCII formulation to represent information, which can be loaded into the computer to communicate directly with internal structure of the computer. Machine language is defined by the hardware where IBM and Macintosh would not understand each other. Higher-level language programs can be converted into machine language by programs called interpreters or compilers.

Assembly language is the lowest level language above machine language, where the instructions to the computer is written in English-like mnemonics of command words to represent machine-operations, LD for load data, AD for addition. In this case, each step of the instruction is given and is interpreted directly by ASCII to the binary system and loaded into the computer.

The next level of programming language (3GLs), such as Fortran, Cobol, Basic, Pascal, Java, uses English-like phrases for the ease in the development of programs and debugging of errors in programming. Fortran (FORmula TRANslator) was designed for mathematical and engineering tasks, while Cobol (Common Business Oriented Language) was developed by government to help solve incompatibility among computer companies in 1960.

The next generation of language is easier to use for specific applications than the above languages. 3GLs for specific environment, or VB (Visual Basic) for 4GLs, which offers many tools in terms of packages of programs for design and build visual displays. 5GLs, for artificial intelligence, perform deductive thinking simulating human thought process.

Additional groups of programs orientated to applications include MUSIC for process control or SAP for inventory control. These programs are used by persons engaged in their specialized fields in order to ease the programming for their applications.

Adam Chou

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Development of Computers IV
Software Systems                                                              44

Modern computers consist of microprocessors, which are boards mounted with silicon chips and connected by printed circuits each serving a specific function; as arithmetic operation and logical comparison performed on data is carried out by the ALU (arithmetic/logic unit). There are also sets of memory boards, called registers, which serve as scratch pads during operations; RAM (random access memory) is working memory where a user can store active data and programs, ROM (read only memory) contains low level programming instructions, loaded during the fabrication of the chip, to provide instructions for the system operation. PROM (programmable read only memory) can be programmed one time only, according to customer specifications for their own application.

Computer system programs are composed of modules with collections of files serving a specific purpose; Executable (EXE), Dynamic Link Library (DLL), Initialization (INI), Help (HLP), or Batch (BAT) files. EXE is the operational file, which sends commands to the processor in charge the operation of the CPU. DLL is a subset of EXE, which provides an effective way of breaking a large executive program into replaceable parts to ease future upgrade. INI contain information needed when computer is turn on after shutdown. HLP provides users with online help information. BAT contains program, which is commonly shared or used time after time.

Bootstrap is a simple executable program, which lets the computer to do something entirely on its own to activate the operating system after power has been turned on. It first runs POST (power-on self-test), which clears the working memory of any leftover data from the previous operation to be ready for the new day. It then establishes necessary instructions for the CPU to pick up files such as INI until the entire operating system is loaded in order to begin operation.

A computer program is a hierarchical system of files, generally, with a master program, as kernel, associated with subprograms or routines. The kernel calls the routines if needed to perform some task such as display or open another file. The kernel with the subprograms together provides a way or rules to receive (input) information from and send (output) data to the keyboard, memory, ports or files and any other I/O devices.

Adam Chou

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Computer versus Human Neural System -I
General                                                                                       45

Human brain is far superior to the computer “brain”. Furthermore, the human brain is self-motivated while computer functions are designed and installed by humans. Nevertheless, design principles of a computer system can reveal the intrinsic nature of the human neural system. For example, both computer and human brain are made of input/output and control systems. For the computer, these consist of keyboard for input, CRT for display, controllers as computational, logic and memory boards. Our eyes, ears, tongue, other sensors are inputs, our hands, legs and head with its language and facial expressions are the output systems. Our brain performs the processing of information and also memory for storage. The basic elements of the computer are silicon bits, as neurons are for the human brain.

The operation and memory areas are parts of computers, which are made up of microprocessor boards. Silicon chips are the working component with detailed circuitry etched on the surface and these silicon chips are mounted on a board with contacts for installation to make up as a computer. Electronic impulses as bits and bytes representing information in terms of ASCII codes are manipulated to translate input information into useful forms recognizable by the computer. After processing by the computer, this information is stored in the permanent memory or displayed on the CRT for human use. Our brain also functions in this hierarchical structure manner of the computer. Our eyes, ears, nose and other sensing organs are the input interfaces and our mouth, tongue, hands, feet and other human parts are the output mechanism. Our brain is divided into rational function (equivalent to the processor of the computer), temporary and permanent memory. Naturally our brain is many times faster than the computer in processing data and many times larger in memory for storing data. However, we lack the information about how the neurons code the electric impulses into knowledge, as in the case of computer with its ASCII codes to convert on/off switches into messages. From understanding computer technology, we may gain insight into the operation of the neural network of our brain.

Adam Chou

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Computer versus Human Neural Network II
Sub-systems                                                                                (46)

Mirror neurons are the MARCO (a subroutine in computer programming) for the input/output system of our neural network. We know that monkeys link what they observed, such as fruits, with hand movements to reach for them. Furthermore there are feedback systems, either within the neurons, i.e. its dendrite to its own axon or dendrite from another axon which receives messages from this neuron. In this manner the signal can be re-enforced. As discussed in Decision Making (Article 39) it requires a strong signal for the neuron to be activated. Feedback signals are important for such strengthening of the signal, known as potentiate. According to literature, there is other mirror neuron linking to the mouth. In my opinion, there must exist many different neurons link all inputs to all outputs of the human body.

If one accepts that mirror neurons are the linkage for the I/O system of the brain, the brain scan data obtained during the mirror neuron experiments might provide insights into the organization of the neural network in the brain. For example, while a tested monkey watches a hand reaching for a fruit, not only the mirror neuron lit up but also other parts of the brain, which are associated in this scenario; such as the parts of the brain performing cognitive task, the occipital lobe input from the eyes, frontal cortex analyze the situation, the parietal lobe out put to the hands and other area also participate for additional tasks. A mapping of these scans from different tests might describe associations of different parts of the brain for different tasks to give us a better understanding of the hierarchy of neural network organization.

Counting in numbers is an abstract concept which humans are capable of doing. There are occasions in nature where non-humans seem to be aware of the significance of numbers. For example, female mallards nest high in the tree holes to hatch their young and encourage them to leave the nest after they are hatched. For one nest, about twenty-four feet above the ground, the mother called to her seven broods to leave the nest after being hatched. One after another jumped flipping the featherless wings and landed in a pile of dried leaves. The mother did not leave until she noticed all of her ducklings were on the ground. How did she know there were seven ducklings? Can she count in a binary system or she simply see a picture of seven ducklings?

Similar to a computer, the memory system in our brain is grouped as short term and long term memories (Articles 30-34). We use the short term memory as a working memory and, when desired, the event would be shifted to the long term memory. In the meantime the short term memory is cluttered with numerous data as the day goes on. During sleep, delta waves repeat dreams where these accumulated information are discharged. For this reason, we have dreams of replaying some of the events which occurred during the day. In other cases, I classified them as “stress” dreams where some scenario reoccurred time after time in varying versions. Why do we remember some dreams and not others? Are they different in their replay? How these data of an event or episode are coded and stored in memory to be recalled for displaying as dreams?

Adam Chou

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Computer Versus Human Neural System III
Blind Person                                                                                       (47)

We are first handicapped by not knowing the principle of the neural network organization. We interpret the functions of the neural network by our experience. We do not know what part of the neural network was innate and what part was programmed after birth. From “Crashing Through” by Robert Kurson based on true story, Mike May was blinded at age three from an accident. Despite his blindness he skied, rode bicycle and even broke the Paralympics downhill speed skiing record. This book explains his experience of gaining eyesight in one eye through an operation after fort-six years without sight, where he was only able to recognize items, which he had been aware of while being blind. Also, when he gained eyesight, he sensed colored, though did not know what names he gave them. This color recognition must be an innate instinct we are born with. Most interesting point was his difficulty in recognizing faces. Though he had known his wife for forty years having two grown children, he still could not develop the knowledge to distinguish his wife’s face from others. He also found that he even was a avid skier while he was blind, but it was difficult to ski with his eyesight. He now resorts to closing his eyes and following instructions from his wife skiing down the hill. These facts reflect that our brain has certain innate ability, which cannot be defined but also receives knowledge from programming later through experience.

Another documented case of a blind person who regained eyesight is given in that book with similar consequences. Sidney Bradford of Britain, a married cobbler, lost his eyesight to infection at ten months of age. He lived an active life, building things in his workshop using a circular saw, riding a bicycle through the countryside by holding the shoulder of an adjacent cyclist.. At fifty-two, he had two corneal grafts which restored his vision with the ability to see obstacles in the path, color of items and even reading time from watches. However, similar to Mike May, he could not recognize faces. Further test showed that he had no depth perception, optical illusion tests did not fool him as person with born eye sight. While in the hospital, he only recognized items by touch and previously known to be there, otherwise he did not seem to see items without attention called to them. When he first saw a quarter moon, he was surprised that it was not like quarter of a piece pie. Unfortunately, he had imagined, while blind, the world as a kind of heaven; with eyesight, he noticed the imperfect world with chipped paint, dirt and ugliness. Looking in the mirror, he found his own face to be repulsive and his wife being ugly. Nineteen months later after surgery, he died, possibly from depression. His version of the world was confusing, frustrating , fast moving and tiring and he struggled with the scale, perspective, shape, mosaics of colors.

Adam Chou

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Computer Versus Human Neural Network IV
Maze                                                                                      (48)

What we generally considered as “instinct” is really not instinct but learned experience. Both of these blind persons showed dysfunction, which we do not faced with from learning at very early age without realizing it. For example their problem of not recognizing faces, most of us have no problem in knowing our parents or sibling even at few months old. It is obvious that face recognition is a learned skill, at the same time, it is obvious that the mechanics or the neural network, whatever it is, exists at birth. To recognize a face, is a simple matter of inputting data to this mechanism. However at the present day, we do not know what this mechanism is or even to define it.

Our brain is like a gigantic maze with countless passages. There are billions of neurons, each with hundreds of thousands of synapses, in our brain. Signals from one neuron are passed through theses synapses to the dendrites of another neuron. A single signal would be dispersed within the neuron without passing to the next one, unless there are bursts of signals. Further, only constantly bombarded by strong signals, the signaling pathway would be marked with a calcium atom. This sequence of events leads to energizing a pathway for the thought process.

One way to view this concept of energizing the neural network is to illustrate it by understanding how one explores a maze. Assume one is in a cornfield maze where we know there is an entrance and an exit. We start from the entrance and explore different passages and record in our memory the paths we tried and failed to find the exit. As time goes on, we have tried all of the passages, if lucky only a part of the field, and eventually reach the exit. Now we know how to go from the entrance to the exit without trying. This is what I mean by energizing the neural network, where repeatedly review of the problem so that a pathway is gradually formed within the maze of our synapses between the neurons. Eventually proper sets of axons and dendrites are energized to facilitate the solution to the problem.

Adam Chou

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Computer Versus Human Neural System V
Music                                                                                        (49)

Our knowledge of neurons, neurotransmitters, and hormones increases greatly year by year, but how the pieces fit together to present the message we encounter daily is still hard to see. It is like predating the invention of computers where we knew the hardware switches of on/off could be used to represent messages but not until the invention of the ASCII code (Article 42) that humans were able to use these switches to convey messages. ASCII code uses the binary system to convert the on/off switches to represent all of the keys of a typewriter which we have been familiar with for decades in typing messages. So it is now our problem with the neural networks where we have no knowledge how the neural network composes messages, though we are quite familiar with the functions of neurons. We now need a Rosette stone to decode the true functions of neural networks.

The commonly known “Mozart” effect might provide hints about the cognitive development. Literature shows that young musicians, especially classical music players are good with mathematics, geometry especially. Some professionals advocate playing Mozart to infants to improve their cognitive ability. Though it is a controversial exercise, it might have scientific basis. If we assume some neural network is devoted to mathematic functions, such as fractions, music tones are in geometric ratios in terms of frequencies. The brain needs to distinguish the tonal relationship of the notes to understand music. These logic circuitries can also be applied to some forms of mathematical manipulations. Therefore training in the musical field might re-enforce the same neural network for mathematics.

We do not know how music improves exactly the performance of neural networks, nevertheless we can postulate the relationships. When we listen to music, it energizes numerous parts of our sensors and our brain; i.e. we perceive the tones of the music in terms of pitch, rhythm, timber and also how hard the musicians exert upon their instruments, such as bowing in playing different types of string instruments, or blowing at the wind or brass instruments. The ability of discerning the sound of a solo instrument or a section of instruments from the background of the orchestra is difficult to explain. Musical notes are in geometric progression and recognizing the precise pitch of a note requires precise knowledge of metmemectic impact of fractions. From knowing the scores of the music, we anticipate and expect the future musical movements as “what next”, expressed by M. R. Jones, as what and when the coming of next notes or contaso. This type of mental exercise also applies in our other activities. This may be the benefit that we derive from listening to music by building these gateways and paths of the trillions of synapses to connect the numerous neurons for other future tasks.

Adam Chou 

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Computer versus Human Neural System VI
Neural Network                                                                           (50)

Another way to view the inner capacity of neural network (not yet defined) is to analyze infants’ ability in learning languages. It is well known that infants from all over the world are able to learn whatever language or several of them by exposing them to hearing the languages spoken. This ability diminishes at a few years of age. We do not know how the neural network is configured for learning language, but we are beginning to gain some insight to the hierarchy of neural network, i.e. there might be centers of neural network, named here as Neural Net Block (NNB), which can perform certain tasks and contain certain algorithms adaptable to learn various skills upon exposure to the environment. After a time limit, the neural net block served for learning language must become less tolerant to new language, or learning new language is routed through different sets of neural net blocks.

Now I realize that “sharpen your wit” might have scientific basis. We all have experiences when we are faced with difficulty in solving problems; we would continuously go over it step by step. Then the solution came suddenly, either in the middle of a night or while driving. Years ago I read Oliver Saks’ book. “My wife mistaken me for a hat”. At the time, I thought, it was merely about mental illness; but now, after I read about the two blind man (Article 47), my realization of a scientific explanation is that there are certain neural circuits at birth, which can be reinforced by exposure or usage to become activated. Bhattacharya described this process in four steps: impasse where one is confronted with a problem for a duration of time without a solution; restructuring where one mentally transits from intense focus on limited information to a broader field; understanding where one gradually begins to grasp the problem, with solution still not in sight; then eureka, the solution suddenly appears as being a miracle. Such process implies that patches of neural networks (NNB), each for special function, are gradually energized together forming a unit to finally arriving at a correct solution.

Another interesting scenario of the innate nature of our neural network is how the brain processes inputs, such as sights, sounds, smells, tastes and touches, from our five sensors. For each sensor, the brain allocates an area to process the inputs, which is further transmitted to other areas, such as frontal cortex, for further analysis and then output into action, for example mirror neurons. In the case of persons, who were blinded at a tender age and gained eye sights decades later, they still rely upon other sensors than eye sight. It has been found that, after first gaining their eyesight after operation, they would not see items, which they were not aware of while they were blind. Synesthesia is a neurological mistake where the inputs from one sensor are routed to an area for processing in the brain for inputs from another sensor; for example while hearing different music notes, one would automatically experience different tastes, or some else would see different colors. It has been found that by mounting a sensor attaching to the tongue, the tongue could become the sensor for blind person, in place of eyes, to direct sight signals to the pertinent part of the brain for sight. These examples support a hypothesis that parts of neural networks are programmed at birth. These groups of neural network are named here as Neural Net Blocks (NNB)

Adam Chou

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Computer Versus Human Neural System VII
Village                                                                                              (51)

Another way to visualize the organization of the neural networks in the brain is to regard neurons as populations in the world, which form countries, states or provinces, all the way down to communities or villages, which are composed of families with individuals. These different organizations interact in thousands of different ways to reveal the dynamics of the world. The basic unit for the brain is Neural Network Block (NNB).

First consider our unconscious balancing of our body, which is like a space ship mounted on a three stages rockets. To launch this long body into space requires precise balance in the firing of each stage of these rockets so that the propelling force directs it through the center of gravity of this long tubular object so that it will be traveling in a prescribed path instead of erratically, eventually falling back to earth. The balancing of our body starts with the semicircular canal system of the inner ear. The three orthogonal planes of the semicircular canal system detect the position of our head in terms of gravity to maintain the position of the body through a sequence of messages to the different muscles in the body, including the big toe, which is a crucial element in the achieving upright position of our body. With aging, the semicircular canal system might become calcified and sluggish and many of the muscles in the body atrophy from less usage. As a result, elders more often lack the sense of balance from malfunctions associated with the various parts of the body that can result in falling. For the whole body to function properly, all the parts of the body should function properly, like requiring all of the individuals in the village to cooperate in performing a given task.

Another way to visualize NNB is to regard them as pieces of a Lego set where many blocks of the same shape are used as building blocks for different finished objects such as car, boat, airplane, or building. At the same time, there are some specially designed blocks to serve specific purposes in representing the assembled products. Similarly, some NNB are universally used. Others are specially linked neurons to serve well defined purposes. Evolution must play a crucial role in the development of these blocks.

Adam Chou

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Computer versus Human Neural System VIII
Brain Waves                                                                                                (52)

Brain waves appear to be sinusoidal continuous analog waves, but appearance may be deceiving. Let us take a closer look by comparing them to the electromagnetic signals used in communication. Basically they can be classified as digital and analog. Digital signal is applied with computers where on/off switches are coded as 0/1 to represent a binary system with numbers from zero to nine in ASCII codes to represent all of the keystrokes in a typewriter. (Article 42). Electromagnetic waves for transmitting signals to our radios are truly analog where the sound waves of our voice are converted into electromagnetic waves and transmitted to radio receivers where they are converted back to sound waves. However the signals for transmitting picture to TV are analog but not continuous like the radio signals. They consist of bursts of electromagnetic energy from scanning images point by point and transmitting to a TV where they are faithfully recreated into a representative image dot by dot on the TV screen. Brain waves appear on paper as continuous waves; but upon close examination they are composed of rhythmic burst of energy, wax and wane in a sinusoidal progression, being generated by neurons in the brain reflecting our conscious or unconscious thoughts as they glide over peaks and valleys. Though computer and brain adopt two different means to express knowledge, one in digital form and other in bursts of energy, there are similarities in the principles applied for the structure of the two systems as discussed in the previous articles ( Articles 45). With this understanding of the brain waves, we now have a tool to decipher the brain waves and relate them to the functions of the neural network in the brain as the example below:

With training we are able to control brain functions such as during meditation where unneeded brain activities are lessen. Brain scans of expert martial art person splitting a stake of planks or small arms expert shooting a pistol at a target shows strong peak or alpha brain wave at the instant of their performing the act of splitting or shooting. This peaking of energy represents their concentration by removing all other thoughts prior to the act to be performed and the burst of energy represents the short time period while conducting their feats. Such phenomena are experienced by all of us. When I, at times, played well at tennis, the balls seemed to travel slowly and allowed me to adjust my stroke to hit the ball with perfect control. Also, some of us have experienced during car accidence, the time slowdown where one became fully aware every details of the event even though the incident was over in seconds or less. Our ability to relate these scenarios to the recording of alpha waves helps us to visualize the functions of the neural network in our brain under stress or high concentration where extraneous brain activities are diminished and a high concentration of activities from a limited group of neurons are activated to perform the required task yielding the observed alpha wave peaks. Some professional tennis players meditate between ”games” or “sets” with intention to recoup such “slow motion” time lapse? Parkinson patients getting temporarily relief from their ailment, while dancing, is another example of one signaling pathway overshadowing another one. Could meditation yield similar results? A simple explanation might be that our thought process, during these times, is switched to the right hemispere only where information inputs are processed in pictures. In this case, events would progress like in a slow motion movie.

Adam Chou

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Computer versus Human Neural Network IX
Jennifer Aniston Neuron  - 1                                                                          (53)

The recent discovery of mirror and Jennifer Aniston neurons provided a peek into the organization of the neural network in the brain. As discussed before (Article 51,Village) it is hypothesized that neurons group into NNB which further link to form neural signaling pathway to serve specific purpose. There must be hundreds of such neural pathways in our brain. For example, mirror neurons (Articles 18-20) are the input/out system, which receive, in one case, messages from the eyes, which are passed to the other parts of the brain; based on the analysis of the data as whether the arms should or should not take action. How some neural signaling pathways become energized is the topic of discussion here.

Some neurons in the brain, discovered by R. Quian Quiroga of University of Leicester, fire in response to a particular individual or object; this case involved the actress named Jennifer Aniston, hence the name Jennifer Aniston neurons. It is hypothesized that the object of interest, Jennifer Aniston, was first stored in the hippocampus for a short term and later transferred to the cortex. It is interesting to note this neuron would fire on not only Jennifer’s image but any abstract article associated with her, such as her name or personal items. Based on studies by Fei Fei Li, the response time of a brain for any images is less than 100 ms. Therefore these neurons can quickly and promptly respond to outside stimuli such as danger. For example, image of an enemy would lead instant reaction for self preservation in terms of flight or fight, as with the computer where interruption occurs. “Grandmother” neurons are of similar nature as Aniston neurons, though have not been proven to exist with tested monkeys. The failure could caused by applying wrong stimuli, which the tested monkey did not recognize. Pheromones, chemicals secreted in urine or sweat by a host being, seem to trigger behavior or mannerism which have been imprinted into a given animal’s neural networks through millions of years of evolutionary process. Such triggers could be Aniston or Grandmother neurons, which received notice from the sensor about detecting Pheromones and relayed to a proper processing neural network where orders for expression of behavior or displays were issued without the delay of seeking through the amaze of numerous neural pathways. 


Adam Chou

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Computer Versus Neural Network X
Jennifer Aniston Neuron – 2                                        (54)


Another aspect of brain function worth mentioning is our interpretation of the function of NNB where we mistakenly defined as what we are familiar with. A recent study by Li Hai Tan at Hong Kong Research University shows that different parts of the brain are better used for learning Chinese versus English, though both are languages. The difference is that one language uses alphabetic symbols while the other uses pictorial ones. It is evident that the brain processes them differently. This is a crucial point in the research of brain functions reminding us not to define a research topic based on what is familiar to us but keeping open to what is yet to be discovered. Such misrepresentation can lead to erroneous conclusion. A recent study on brain activities related to lovers shows that people having 20 years intense love of their partners versus ones recently married displayed similar activation in the ventral tegmental area of the brain. However, people with long term relationship showed higher levels of activity in a part of the brain associated with calmness and pain suppression, whereas people in love for shorter periods had higher activity in a region associated with obsession and anxiety.

It has been found by brain scan that the image for “hate and love” reside in the same area of the brain which is believed being related to “prediction of other’s behaviors”. It is easy to see how they are related because hate and love are two extremes of the same emotion and also they are derived from interactions with others by observing their behavior toward oneself. This type of development implies how neural networks might expand from one level to another through evolution with linkage of additional NNBs. One can further extrapolate its evolutionary roots to the basal neural block of “fight or flight”, which is also related to the prediction of other’s behavior.

Various parts of the brain perform different functions. The hippocampus is for memory storage. Lateral habenula, beneath the corpus calbsum near the thalamus and in front of the pineal gland, is the “decider” where information passes through this region, decision is made by the neural network in this area and messages for action are forwarded to different parts of the brain. It is easy to visualize the neurons in this area of the brain forming elements of logic and group into a NNB for decision making. As experiences in decision making accumulate from exposure or training, this network expands and grows for more complex needs or activity. At the sometime, the amygdala performs the roll in processing and storing memory of emotional reactions, such as happiness, anger, etc. It might receive messages from sensors as eyes, ears or other parts of the brain and decide to order activation of neurotransmitters, such as dopamine, norepinephrine and einephrine to be sent to other NNBs for the expression of emotions. But it is more difficult to assess how to express such emotion in term of the functions of the NNBs.

One clue might lead to understanding the function of the various nuclei in the brain resulting from the recent introduction and use of antidepressants, which help to reduce depression but occasionally generate suicidal tendency. Though these two behavior patterns seem to be miles apart, on close inspection, they are related in terms of disappointments with life. People suffering from depression feel surrounded by despair and hopelessness and at the same time people with suicidal tendency feel life is worthless. If one assumes that the same brain areas are in control for these two emotional conditions, it appears that the same drug, treating depression, should minimize suicidal tendency. For some not fully understudied reason, certain “antidepressants” might lead to suicide. This fact reveals that there is a misunderstanding about the true functions of the particular nuclei or a group of them in the brain. We label the functions for the various parts of the brain in terms of our own psychological interpretation of our emotion, which might not be how the neural networks work. We need to bring a less biased prognostic diagnosis toward the matter of neural functions. Instead of assigning our interpretation in naming the brain functions, it would be better to search the evolutionary roots of these emotional conditions to understand the true mechanism of the drug effect upon the neural functions. For example, knowing what part of the human brain is effected by the drug, one can trace to primate or other vertebrate brains to learn its original function. This knowledge might lead to the discovery of the evolutionary path of this function to the present day dysfunctions.

Adam Chou

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Roundworm, Caenorhabditi elegans (55)


To explore fractal elements and NNB Neural net block) in relationship to neural network, the pioneering study of the roundworm, C. elegans, by Sydney Brenner of Cambridge University in 1970s provides some insights onto this topic. C. elegans, a primitive multicellular animal found in soil, is only one mm long, is transparent, shapes like an earthworm with mouth at one end and anus at the other end, and feeds on bacteria and fungi. C. elegans is composed of 1031 cells for adult male ( 959 cells for adult hermaphrodite) including 302 neuron cells which form a ring near the neck or the front end of worm. This ring servs as the neural center or a primitive brain. About three quarter of the neurons send at least one nerve fiber to this nerve ring. There are about 5000 connections between the 302 neurons and nearly all of the connections have been identified.


The 302 neurons of C. elegans can be divided into three groups: sensory, motor and interneurons. Sensory neurons perceive environment or internal cues, motor neurons activate muscle cells, stimulate internal organs and trigger release of hormones, and interneurons handle communications between neurons. C. elegans shows complexities in behavioral responses to environmental cues that are typical of animals with more sophisticated nervous systems. The worm takes evading action while first being touched, but it learns to ignore further contacts after being touched a few times. It also can distinguish 200 different chemicals. An associative learning experiment showed that C. elegans could be trained to respond to chemicals associated with bacteria as food.


C. elegans with its 302 fully identified neural network could be a test ground for the hypothesis of fractal element (FA) and neural net block (NNB). First identify the neurons in the neural ring and then map the neurons receiving inputs from the sensors and motor neurons outputting to others. Based on the related input and output pairs, including the interneurons, and their performing functions, the neurons in the ring can be grouped into NNBs. Based on the configurations of neurons in each NNB, a common relationship between the neurons within each NNB might reveal the structure of FA.


Adam Chou


Clark, William R. & Michael Grunstein, “ We Are Hardwired? ” Chapter IV - Are the Worm Learning the Memory in the Roundworm C. elegans, (2000) Oxford University Press 


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Fractal                                                               (56)

From a distance, trees appear to be a mass of leaves, branches and trunk, but its growth pattern is well organized following a mathematical phenomenon known as “fractal”. Fractal is a mathematical term describing a seemingly complex geometric pattern can be decomposed into smallest uniform parts, both in shape and dimension; if these parts are reunited, by some rules, the original geometric picture would be restored. In other words, complex geometric patterns can be generated by mathematical equations undergoing iteration based on recursion with feedback of a basic set of equations representing the basic elements of this composite picture. For example, a tree begins with a trunk, along which spout branches; each branch forks into two more branches etc to become a tree as we see around us. Leaves then grow along the youngest branches. Such patterns of growth are also repeated in the leaves where the stem of a leaf serves as the “trunk” of the leaf with “branches” as the veins in the leaf. This pattern of growth is also observed for leaves of non-uniform shapes, such as oak or maple. For leaf with seriated edges or notches such as oak or maple, the stem serves as the trunk of a tree, the primary branches extend into the elongated parts of the leaf and then vein-like branches cover all parts of the leaf. One can also observe soapsuds, which appear to be a glob of foam, but in reality, are composed of small individual soap bubbles congregated into blobs. Similarly, beautiful geometric snowflakes are configured by uniform ice crystals in a predetermined order of nature law as fractal.

The structure of neural network, as discussed previously, has an evolutionary basis, i.e. it began in a simple form gradually evolving into the complexity we know. In other words, some form of fractal principles applies here. The earliest form of cognition could be generated by neural networks in some type of factual elements as ‘and’, “or”, “nand” and ‘nor” used in Boolean algebra, and these elements would link together to form neural net block performing early primitive function such as fight/flight. These primitive blocks are named here as neural net blocks, NNBs. Through ages of evolution, the neural network expanded by linking more blocks together to perform complex tasks, such as predicting behavior of others leading to love/hate emotion. Based on this hypothesis, the key to understand cognition is to discover what are the “fractal” elements in the construction of neural network to form NNB, which further expands to perform the cognitive functions of daily life.

The architectural structure of neural network can be compared to Leggo blocks, which my grandchildren use to build various models such as car, boat, airplane, etc. These blocks can be categorized into two groups, special purpose blocks and general blocks. The former are designed in suitable shape to fit specific part of a model, such as blunt end of a boat, flag pole, people, etc. The general blocks are in various rectangular shapes and sizes, also some squares. An expert, with these blocks, can build human size buildings like Statue of Liberty, Titanic, Boeing 747,etc. In my view, the organization of neurons can also be separated into two groups as special purpose and general ones. The special purpose groups include the long axon neurons linking the brain to the extremities of our body such as hands and feet. The general purpose ones, as the majority of neurons, are mostly innerneurons, organized as in fractal elements, dispersed throughout our brain such as the ones in hippocampus for memory, one in frontal lobe for rational thinking, etc.

The architectural structure of neural network can be compared to Leggo blocks, which my grandchildren use to build various models such as car, boat, airplane, etc. These blocks can be categorized into two groups, special purpose blocks and general blocks. The former are designed in suitable shape to fit specific part of a model, such as blunt end of a boat, flag pole, people, etc. The general blocks are in various rectangular shapes and sizes, also some squares. An expert, with these blocks, can build human size buildings like Statue of Liberty, Titanic, Boeing 747,etc. In my view, the organization of neurons can also be separated into two groups as special purpose and general ones. The special purpose groups include the long axon neurons linking the brain to the extremities of our body such as hands and feet. The general purpose ones, as the majority of neurons, are mostly innerneurons, organized as in fractal elements, dispersed throughout our brain such as the ones in hippocampus for memory, one in frontal lobe for rational thinking, etc.

Adam Chou

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                               Aging                               (57)

Human ages where our skin wrinkles, body sags, even our cells in the body multiplies with accumulative genetic errors. What happens to our brain while we are aging? Brain cells do not reproduce the same way as the cells in the rest of our body? My experience in life might yield some clues:

Movements of our body parts are delicately orchestrated and manipulated by the various parts of the brain. For example, picking up a grain of rice consists of gross movement of the hand toward the rice. While close the rice, the movement of the hand is constantly adjust in small movements guided by observing with the eyes so that the hand would be precisely without error on top the rice preparing to pick up the rice. Finally, the fingers would get in position with the aid of the eyes picking up the rice without randomly searching for it. To achieve it is a coordinated effort between the cerebellum, the command center of motion, limbic system, the control center for relaying messages to the muscle, and sensor, in this case are the eyes.

In my youth, I could clap my hands and smash mosquito, which I saw and hated. Nowadays, when I clap my hands to kill a mosquito, I frequently miss it because my hands do not meet squarely together, sometime my hands even miss each other in their motion toward each other.

When I leave my house for errant or other social events, I need to decide to make left or right turn at the driveway depending where I suppose to go. If I turn right, the next decision is right to Highway 31 to my chiropractic, or our favor restaurant and turn right would be County Road 523 to the airport. If I leave our driveway by left turn, which would lead to Princeton, Lambertville, or Rutgers. Frequently, I made a wrong turn. Such mistakes did not occur when I was younger. In addition, like all other elder ones, I do not recall names as easily.

Plasticity of brain is well known where one part of the brain injured and other parts of the brain would take over its function after long training. On the flip side, it is known that “one does not use it, we lose it”. This might apply for our loss of momory in recalling names as we grow old. My explanation would be that when we were young, new faces and new events constantly bombaned us and the neurons involved in storing and retrieving information were constantly acytive. Asone grows older, life become more sedate with association with te same people and dailt chore becomes routine,and there is less needs for restoring and retrieving of this type of information. Naturslly this path of neural acticvity becomes rusty. It is nor we loss our momery capacity but the retrieving system became less efficient.

All of these activities have one similarity; the coordination between different parts of the brain; between the hemispheres or nucleus. Linking these different parts of the brain are the axons of neurons. It is well known the myelin sheath of the axon degenerate and loss efficiency in its electric transmission of signals resulting poor communications between the parts

Adam Chou

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Journey of a Fetal Brain (58)

Scientists have discovered that the brain changes throughout one’s life in repairing, enhancing or creation of new neurons. For example, the linkages of the synapses in the brain, at birth, contain number of random connections and during the first few years of the infant’s life, much pruning and reconnecting occurs based on inputs from the environment.

The journey of a fetal brain begins between 12 and 20 weeks of gestation, the greatest amount of mitotic activity occurs where 250,000 neurons are produced every minute. After migration from this mitotic zone, the neurons differentiate into different types depending upon the function it serves. First, the long axon neurons form the associational, commissural and projection pathways; these are the basis for the motor, sensorial and basic operations of the fetus. The short axon neurons are known as interneurons and make local synaptic connections within a nucleus. From the third trimester until 2 years of age is the most interesting period for the formation of synaptic connections between neurons. About 70% of the neurons disappear during this time with only 100 billion neurons left for the remaining of the life..

Let us review the pruning process. We know that natural events do not occur randomly, There must be a programmatic process for the pruning to occur. If one accepts the fractal elements as the basic units for cognition, it provides a guideline for the interneurons, in frantic effort, to seek connections for survival. Assume that a fractal element is a self sustaining unit with feedback and feedforward connections between the neurons needed for the activation of the neurons. During pruning of the neurons, where ones are not properly connected and could not function as unit, these would be eliminated. This could be the scenario at birth; the neurons surviving are the ones forming the pathway or associated in linking the nucleus providing basic activities. At the same time the neurons, connected as fractals, would be ready to continue future activities. Others would be eliminated by pruning

Adam Chou

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Changing Brain (59)

The human’s brain continuously changes through the ages of one’s life. For example, an infant can easily learn several languages, but could not easily do the same even ten years later. By year 5-6, a child’s brain is fully developed for the basic needs; only lacking extensive experience and knowledge. By adolescence, the brain goes through an overhaul toward rational thinking. In the young adult, the front of the brain is in sync with back of the brain. As brain ages, the two regions becomes less coordinate with each other, showing a degree of slowness in mental power.

I am always struck by the unconditional love of a child to others and also that “amnesia” associated with adults not remembering the first three years of their life. Reading a book recently by Jill Bolte Taylor, “My Stroke of Insight” might point to some explanation of these observations. After a serious stroke impinging upon the orientation association cortex and the language areas, Broca’s and Wernicke’s areas, in the left hemisphere of the brain, she lost speech and also self-awareness. She was in the euphoria of unconcern about fate and she was thinking largely in pictures instead of the symbolic abstract thought process. If one accept what she felt at that time is similar to what an infant experiences, one can conclude that the left brain of a child has not been totally programmed from experience and he or she uses the right brain mainly for daily encounters; in other words, infants are thinking in pictures. By three years old, the child gradually shifts his or her thought process to the left brain for abstract data processing; the experience previously stored as pictures can no longer be retrieved easily or not at all by the newly established data processing system.

According to Tracy J. Shors of Rutgers University, studies show that 5,000 to 10,000 new neurons arise in hippocampus every day and most of these neurons disappear within a few weeks if not used. These cells are not generated like clockwork, but influenced by a number of different environmental factors such as exercise. It would be interesting to learn the mechanism instilling the growth of neurons and by what manner neurons die. As we know, muscle builds up from exercise and also atrophies from disuse. Are these cells sufficiently similar to suffer the rise and wane through the same mechanism of genetic activities?

Adam Chou

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Mature Brain (60)

The autonomic nervous system is composed of two parts; sympathetic and parasympathetic. The former releases epinephrine and norepinephrine, which are used for emergencies while the latter releases acetylcholine, which triggers a calm peaceful state. These chemicals balance each other for a stable altitude at the same time being able to respond to emergencies. It is easy to see that excess amounts of either chemical, due to poor metering, would lead to erratic behavior. Such a balancing act must come later as we mature.

A team led by psychologist Avshalom Caspi of King’s College London finds that breast-feeding children boost their IQs by 6 to 7 points if they inherited a gene variant associated with enhanced chemical process of mothers’ milk. Two groups of children participated in this study: 1037 boys and girls born 34 to 35 years ago and still living in New Zealand. Also 2232 boys and girls born 12 to 13 years ago born and grown up in England. A gene, Fatty Acid Desaturase 2 or FADS2, was found to assist in breaking down fatty acids in the human milk. The product derived from these fatty acids seems to support the need to develop the myelin sheath around the axon for effective transmitting of the electric signals to the tip of the axon engaging in releasing neurotransmitters. At later years, the weakening of this myelin sheath could lead to the uncontrolled movements of Parkinson severer.

Standard IQ tests are commonly accepted for gauging intelligence. However, experience shows that high IQ is only one necessary condition but not sufficient for future success in life. The reason is that intelligence is an abstract quality which cannot be defined without a criterion. If one accepts intelligence as related to achievement, it then can be quantified in the following ways as given in some literature.

(1) The ability to acquire and retrieve knowledge – This, I consider, is the first level of intelligence where one appears to be able to make good grades and conduct intelligent social interaction. This ability is probably a strong base for the IQ test.

(2) The ability to apply learned knowledge - Most people, accomplishing competently in the working world have this ability, but quantifying it is difficult.

(3) The ability to think abstract thoughts – This ability is most difficult to quantify. It is also mostly needed for creative activities. All great thinkers, inventors, scientists, etc, possess this quality where they extend known knowledge to a new frontier.

Items (1) and (2) could be used as a basis for testing to select from the general public to become successful in life in the average manner, but Item (3) is selective to the specific goal of testing. Besides initiative and leadership as needed for any top positions, what other criterions would be desirable qualities to test for potential gratleaders.

Adam Chou

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Parallel Universe (61)

Parallel universe is the foundation of Chinese philosophy where Ying/Yang represent natural dualities, such as light/dark, male/female, hot/cold etc. This philosophy extends to the human body where sickness is combated with herb medicines and natural plants to neutralize these apparently opposing forces which, when unbalanced, lead to sickness. This approach to life has recently infused into western culture, especially among physicists who working in quantum mechanics.

Parallel universe is a western version of multiple worlds coexisting in parallel to one another with various events occurring at the same time. It all started with superstring theory where physicists realize that three dimensions might not be sufficient to describe the world we live in; matter of fact, there might be eleven dimensions. Dr. Hugh Everett, a quantum mechanic physicist, wrote in his doctoral dissertation at Princeton University about “Probability of Wave Mechanics” that when two quantum systems interact or exchange energy, the larger of the two automatically correlates with each element of the related supposed system. Multiple personality is another example of parallel universe which is a cognitive malfunction where one person posses several personalities which appear separately or sometime simultaneously during daily routine.

Parallel universe can also apply to our brain function. For example our right hemisphere processes information in terms of pictures while the left brain thinks in abstract. How the two different approaches merge into our unique thought process is an interesting topic for research.

In view of a parallel universe, we might propose an explanation for the effect of antidepressants, which relieve depression in most cases, while at the same time can lead to suicidal tendencies; for example, one assumes these two emotions involve different parts in the right and left hemispheres of our brain. Self-recognition is generated in the right hemisphere of our brain helping to identify our body dimensions in order to avoid collision with the surrounding obstacles. Let us assume that this primeval instinct, through evolution, becomes associated with self-awareness and at the same time, the instinct of fight/flight, residing in the left brain, which through evolution, expresses stress. If antidepressants relieve stress in the left brain but decrease the feeling of self worth in the right brain, the end result could be the observed contradictory effects of the medicine. Since the body chemistry is a balance of numerous “uppers” and “downer”, it is inevitable in treating one psychological symptom would upset another part of the body chemistry with ill gotten effect.

Adam Chou

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Companionship (62)

Companionship among humans has many meanings, which include camaraderie, loyalty. trust, etc. What does it mean among lower animals? The following stories illustrate some of their behaviors with others:

Elephants normally pair with other elephants, But in a Tennessee elephant sanctuary, during 2002, a former tire store mascot weighing 8,500 lb,, was found asleep with a newly arrived mutt of 35 lb. Five years later , the dog was injured , the elephant stood for weeks outside the onsite clinic, till one day she was granted a visit with the dog. She extended her trunk and touched the dog so gently that dog immediately relaxed.

Similarly, I had a horse, which befriended with our ram. They ate grass together. After a winter of isolation in the barn, they romped in the field, when released into the open field during the early spring. However, while the ewes were in “heat”, the ram would chase his friend, the horse, away from his harem, if the horse moved to close to them. One day, the ram butted the horse in the chest and the horse picked the ram up off the ground by the skin of its neck without breaking the skin in the ram’s neck.. When the horse died, his friend, the ram, was dispirited for weeks as if mourning.

Such friendships between different animals are well known, especially with racehorses. Can we attach some meaning to these friendships in human terms? What is the origin of these feelings? Was it evolved from motherly love?

A recent study of animal group behavior shows a new twist on companionship:

Rock ants, as studied by Nigel Franks and Elva Robinson of the University of Bristol in England, conducted a complex procedure in finding a suitable site for a new nest after the old had been disturbed. After a scout found a site exceeding her “good-enough” threshold, she returned to the old site and dashed around, tapping her antennae on other ants releasing a pheromone from her sting gland. Usually she found a volunteer within a minute to follow her in tandem to the new site. If the recruit explored the site and reached a favorable assessment, she would return to the old site to recruit yet another scout. When enough of number of scouts gathered at the new site and encountered each other at a sufficiently high rate, they had arrived at a decision. Each scout would then rush back to the old nest and piggyback someone to the new site till rest of the colony moved to the new home.

Similarly the three-spined stickle back fish prefers shadowy nooks where other fishes rest, as studied in the lab by Ashley Ward of University of Sydney in Australia He found that towing one fake fish to a particular corner influenced a loner fish to choose that direction. However, tricking a group of live fish with the fake fish prove to be more difficult.

Such tendencies toward grouping is an interesting theme for understanding companionship.


Adam Chou

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My Space (63)

“Car rage” is a modern phenomenon where even the most mild person could become a maniac behind the wheel of an automobile. This psychological anomaly might have an evolutionary basis.

It is well known that all animals have a sense of protecting an area as his or her own to live, hunt and raise young. They mark the boundary, patrol and defend it against intruders. Some birds sing their own song declaring ownership of a specific territory. In the case of humans, we have stake legal rights to our properties. This need to identify and hold territory is a basic instinct for survival based on needs for food, mate, and raising young.

With humans, this sense of territory has evolved into something less tangible, “my space”. Depending upon the culture, we sense the proper distance between others during social interaction, such as conversation, where talking right into one’s face may be a challenging act. We also include definition for privacy, not well defined where others should not intrude.

In the modern age, the sense of space is extended while we are driving. With the isolation of a car, holding the power of this mechanical beast, we suddenly acquire a sense of territory way beyond what we have previously defined. My car and my road are my territory moving along with me as I travel. This phenomenon leads to road rage because our sense of territory is violated when other intrude upon, this modern day definition of pace, while driving. Is this the evolutionary process of cognition in progress?

Adam Chou

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Pattern Recognition (64)

I am always puzzled by how the brain “thinks”; for example, how does it recognize faces, how does it derive solution to a problem, etc. Recent experience seems to shed some light on this matter:

(1) Our friend sent us an article including a strangely written message; the first sentence is given below:

“ I cdnout biveiee that I cluod aulaclity uesdnatnrd waht I was rdanieg.”

Though the words are garbled but the sequence of them are in order as a normally written sentence. Most well read people can easily recognize this sentence as:

“ I couldn’t believe that I could actually understand what I was reading”

This test shows that when we read, we do not notice the spelling of each word but its shapes and context. In other words our brain interprets what we see without dealing with the details of each word in spelling unless it is out of context.

(2) I was out hunting deer with a friend who was experienced in this activity. While searching for a deer, he pointed to a patch of woods to alert me about a deer behind trees. However, I did not see anything like a deer. He told me that a hunter observed the woods for what did not belong to the pattern in the trees, not the shape of a deer.

(3) I am ethnically Chinese and my wife is Caucasian. Naturally, she has difficulty in distinguishing one Chinese from another while I have the similar problem with Caucasians. She was puzzled by my difficulty since Caucasians vary in hair and eye color. Then it occurred to me, that when I look at a person I do not notice their hair or eyes but the details of their features such as the shape of the eyes, nose, or lips. Similarly, I told her that many people in British isles, especially in the countryside, a number of people had slanted eyes that she did not notice.

The results of these experiences reveal to me that our brain function is preprogrammed for given task. For example, in facial recognition, I must have in my brain an average Chinese man or woman facial details and my recognition of them is based on deviations from the normal. Applying the technique used in AI (artificial intelligence) as given in two steps: first “feature extraction” where specific items such as shape of eyes, nose, and outline of the face of a stranger are noted but not the color of the eyes or hair since nearly all Chinese having black hair and dark brown eyes and then, “pattern detection” where deviation from the normal of these items are then noted. At the end of this exercise, one’s facial features are categorized as friend or stranger. So, this hypothesis explains all of the above examples. Furthermore, learning or experience, based on pattern recognition, is nothing more than establishing a base for future activities. We might even assume the drastic wiring changes for teenagers is to program the brain in establishing a basis for a next set of activities to prepare them to be adults

It is well-known that toddlers, under three years old, can easily learn language, even more than one at a time. Anne Christophe of the Laboratory of Cognitive Science and Psycholinguistics in Paris has discovered that brain networks responsible for language processing get organized extremely early for toddlers and two year old know more about grammar than they can speak. Youngsters fitted with electrode caps show that electrical activity, mainly relegated to the left-frontal brain, spiked when hearing nouns in a verb position. Similarly, electrical responses in the left temporal lobe jumped when hearing verbs in a noun position. Such behavior indicates some type of pattern recognition mechanism for grammar is in place long before toddlers learn words.

In the book, “Making Up The Mind”, by Chris Frith, he shows a drawing of a Necker cube to a native person in a remote island to test his theory of “brain modeling”. People, who recognize the solid shape of a cube, when shown the drawing of a Necker cube, have the tendency to find it changing its orientation. In the case of a native person, who does not know what a cube is, would only see the drawing as twelve lines, not a cube which changes its orientation. This is an excellent example, where the brain stores models of images for future reference. In other words, networks of neurons are constructed when we are in the process of learning and these networks of the models are used later as reference points for further thoughts.

Adam Chou 

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Subroutines (65)

How does the brain incorporate new knowledge from learning into the existing neural networks? “Subroutines” used for computer programming might provide an explanation to what training does to the mind in the process known as learning. Many of us studied while in school, or as adults attended training courses for management or technical matters. It is obvious that these trainings are intended to program our brain for a specific task. Here, I use “subroutine” to illustrate and demonstrate the mechanics of learning most of us have experienced.

Frequently I have deliberated about a solution to a difficult problem for days without success. Then one day while driving or sometime waking up in the middle of the night, the solution appears! Based on my newly discovered hypothesis of neural networks, an explanation is plausible as discussed below:

“Patching” is a term used in computer programming to link a newly prepared program, as a subroutine, into the existing system for a specific designed function. This task is achieved in computer code as a “branch” to leave the main program, and “return” to reenter into the main program so to insert the subroutine into the working computer system. Such logic of patching must occur within our brain when we learn new tasks. For example, a child can learn several languages without effort if the child is under three years old. But in the later years, learning a new language becomes a difficult task and requires much longer study. Why there are such great differences? The explanation is in patching. When the young brain is in the formulating age of three years or less, it is easy to introduce new linkages to the existing neural network since the relationships of various neurons have not yet been firmly fixed. As adults, our neurons have been specifically linked to perform tasks we encounter daily; in order to introduce new connections to a new group of neurons much more effort is required in fitting the new linkages into the existing system, with many trial and error connections.

Let us apply the analogy of subroutine and patching to our brain function based on the hypothesis of fractals and BNN as discussed previously. During the period of learning a skill, the brain incorporates gradually numerous fractals, which either previously existed or are formed from recently developed neurons to form BNN’s, which further link to form a network for conducting the intelligence of the learned skill. This newly formed neural network is then patched into the functioning neural networks already extant in the brain to perform the newly learned skill. In this manner, we gain knowledge or skills through learning or experience. While I deliberate about a problem, the neurons in the brain are busily linked in a prescribed step-by-step manner into an additional architecture of a subroutine specifically for solving this thorny problem. On its own time, my brain arrives at the point where the newly organized neural net completes its connections, providing a solution to the nagging problem. This only appears as a sudden epiphany.

Adam Chou

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Bird Brain (66)

I recently had an occasion to peek into a bird’s brain.

Our grandchildren built a birdhouse, which we hang by a cedar tree twenty feet away from where we frequently sit to enjoy our outdoor garden. The birdhouse is about 6x6 inches with a one-inch hole on one side and a stick in front of the hole for perching. A pair of house wrens built a nest in it and raised a family. We watched them flying in and out to feed the babies. One day, the hatchlings flew and left the nest. My wife, as any good housekeeper, decided to clean the birdhouse by throwing away the nest materials in the birdhouse. Unfortunately, the bottom of the birdhouse unhinged. I took the damaged birdhouse down and left it by the tree for future repair. The male wren, who was more familiar with us than his mate since he was the one who discovered the birdhouse, could not see the birdhouse where it should have been and become greatly disturbed. I found my mistake and proceeded to hang the birdhouse back to its original place with a temporary fix of the bottom. He was pleased with this recovery and landed on the roof of the birdhouse, which caused the bottom to become unhinged again. When he looked inside the birdhouse and found the floor slanting with daylight showing, he looked as though he could not believe what had happened. He flew to the roof and looked around to be sure he was in the right place. He went inside for further inspection. Naturally he slid to the edge of the floorboard, looking outside at the bottom into the world. He appeared astonished. If he could talk, I am sure, I would have heard curse words. He repeatedly jumped around the house to confirm the disaster. Finally, he flew away and I fixed the bottom of the birdhouse. When he came back, all was set and that night he rebuilt the nest,

This incident with the house wren demonstrates that a birdbrain must behave very much like human with our right brain, which processes data in pictures. I can visualize myself parking a car along the curb. If someone removed the junk in my car, I would behave unbelievingly for some moments and proceed to assure my self that the door of the car I opened really belonged to my car. This might well be an evolutionary landmark.

Some literature shows that a basic understanding of our brain function about our ability to speak and to understand spoken sounds can be aided by studying songbirds, such as zebra finches, because of the similarities in some parts of the brain functions between these two species. Such study might be useful in understanding the evolutionary process of our brain.

Adam Chou

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