Chapter 3

The first step of the evolution of the brain is based on concept models. A concept model fits an entire class of entities of the external reality. During the interaction, the brain will use a concept model and then, in M-ZM, new properties will be added, or even new elements, if necessary, to understand better and better the external reality.

The evolution of the brain continues with level 2. This new facility is based on label-models. As we know, faced with a given external reality, the brain makes an M-ZM model that is able to predict the evolution of the present external reality. Such models are called local-M-ZM. On level 2, it is possible to make a new type of models, which are called label-models. A label- model is able to activate a ZM-model, from the available models of the brain, regardless of the local-M-ZM.

Example: an animal senses a specific smell. This can be associated with food or with danger, for instance. In such a situation, the animal can activate a specific ZM-model, which has no direct connection with the local-M-ZM model. This is level 2 of the evolution of the brain.

At this level, a special kind of communication between animals occurs. This kind of communication based on label-models is used by human beings as well. It is not precise enough and is also very limited, but useful in many situations, and very fast too.

The level 2 is the highest level achieved by the animal brain. The evolution of the brain continues with level 3.

We already saw that, at level 2, a label-type model activates a ZM model. The next step is to activate not the whole model, but only some associated truth of the ZM-model. In this way, the brain has to manage a reduced quantity of information and so becomes more efficient.

This is a critical point, because it is the barrier to separate the animal world of human world.

Thus, there is a ZM-model and an associated label-model. The problem is to associate to the label-model only some truths generated by the associated ZM- model. A ZM-model is an image model, and so its truths are also of image-type. The problem is to record such truths in a different way, based on a totally new function.

MDT cannot indicate how exactly this facility works. The theory is not concerned with the technological implementation of the functions. The theory just says that some truths generated by a ZM-model have to be recorded in a different way. In this way, the label-models become words, and the associated truths become symbolic definitions of the words.

On level 3 a label-type model can activate an associated ZM model, but it can activate only a collection of truths as well, which are different from the 'ordinary' image-truths of the ZM.

It is possible that the General Communication Language (GCL) appeared based on this facility. The presence of a GCL in a brain will characterize that brain as a human brain.

Example: when the word "dog" is heard, it is very probable that we activate at least one suitable ZM. But when we use the sentence "I go to the forest with a gun and a dog", it is very probable that we do not activate any ZM-model. The sentence is understood based on symbolic models and based on logic and so we do not need any image-model. In this way, the quantity of information that has to be processed by the brain is reduced very much. The image models will be used only when we have to make a precise model of the action.

The human brain continues to evolve with level 4. On this level we have words and associated symbolic definitions, but no ZM-image-model.

Example: Let's take the following words: "this apple", "apple", "fruit", "food". "This apple" is associated with a pure image model. "Apple" is a concept type image model. "Fruit" and "food" cannot be associated with any image model (we cannot imagine what is fruit or what is food).

So, on level 4, the human brain can make and operate symbolic models without any connection with image-models.

On this level it is possible to develop logical and mathematical languages and, in this way, to make positive sciences associated to the external reality.

Example: Newton's Mechanics is a symbolic model associated with the physical bodies. The basic terms of this symbolic model are mass, space and time. None of these terms can be associated with image models.

The evolution of the brain continues with level 4+, but I prefer to call it level 5 (up to now it is the highest). This level was attained only 100 years ago. On this level the symbolic models break totally with image models.

Example: Newton's Mechanics describes the movements of physical bodies. But we can imagine such movements. Here Newton's symbolic model can be translated also in image models.

The pure symbolic models cannot be translated in any image models. The only symbolic model of this type is Quantum Mechanics.

Example: in association with Quantum Mechanics there is a "classical" problem called "the dual nature of light". There are some experiments, which prove that light is a wave. But there are also some other experiments, which prove that light is made of particles. It seems that we have big logical problems here. The aberration with "the dual nature of light" is supported also by some great physicists (R. Feynman, for instance).

Physicists in Quantum Mechanics already solved the problem of the nature of the light. The "dual nature of light" is not a problem of Physics, but a problem of thinking.

The problem occurs when the physicists try to explain to us what happens. At that moment, they use terms as "wave" or "particle" which are associated with image models. The same terms, in Quantum Mechanics, are associated with mathematical formulae. There is no connection between the world of Quantum Mechanics and the world of image models. If someone forces such a connection, then some big logical problems can occur.

As MDT says, any information is non-sense without declaring the model that generated that information. In the above example, the nature of light is well understood by physicists in the symbolic model called Quantum Mechanics. If we don't know Quantum Mechanics, then it is not possible to understand the answer. So, if we do not know Quantum Mechanics, then it is forbidden to ask any question associated with that field.

Let's evaluate the world based on these levels. There is a fraction of the population who is staying on level 2, and just occasionally goes on level 3. The majority of the population is on level 3, and occasionally goes on level 4. There is a small fraction, which is on level 4, and occasionally on level 5. This fraction produces scientific and technological advance.

To understand the MDT theory, at least level 4 is necessary.

HUMAN BRAIN: EVOLUTION OR EXTERNAL INTERVENTION

Some activities of the human and animal beings are similar. So, there is an idea that evolution from animal brain to human brain could be possible.

As we already emphasized, MDT is just a tool, which is used here to see if there is any possibility to evolve from an animal brain to a human brain. The theory does neither support, nor reject such a possibility.

Based on MDT, the main difference between a human brain and an animal brain is the facility of the human brain only, to make and operate symbolic models. The common part of the two types of brains is the facility to make and operate image models.

The evolution problem is to see if there is any possibility to change some parameters in the structure of image-model devices to reach the capability of making and operating symbolic models. On the other hand, a new hardware that should be added to the animal brain is considered as not compatible with an evolutive process.

As we saw in the previous section, the highest level reached by the animal brain is level 2. With a peak on level 5, the superiority of the human brain is huge.

Let's see some arguments that support the evolutive process. For instance, let's analyze whether by increasing the level of conceptualization of the models, it will be possible to get closer to the ability to make and operate symbolic models. Thus, if a class of models is more and more conceptualized, such models should be so simplified that they could be very close to a symbolic definition. Therefore, a change from level 2 to level 3 could be reached by evolution.

But, let's analyze an example. So, we have "this apple", "an apple", "a fruit", "food". This is an example of increasing level of conceptualization with the last two items as symbolic elements. The animals have a shortcut by making a model to tell them if what they meet is or not food. In this way, the animals have a fast solution for problems based on image models. There is no advantage to increase the level of conceptualization. Thus the evolution could be blocked by a fast solution, based on image-models.

The advanced conceptualization should be supported in a group of vulnerable animals. To survive, the communication could be decisive. By increasing the level of conceptualization, the communication could be more and more precise. This seems to be the only serious argument for increasing the level of conceptualization. On the other hand, there is already a system of communication on level 2. Thus, a sound or a combination of sounds is associated with a label-type model. It can activate any ZM-model. This type of communication is faster than that based on symbolic models and usually precise enough for the normal necessities of a group of animals. Unfortunately, here we did not see again any advantage from increasing the level of conceptualization.

But, if, for a group of animals, there is a lot of information which comes in fast succession, then the animals will be forced to make more and more simplified models and this should force them to increase the level of conceptualization.

Let's see another example. A person goes somewhere in the desert. Without special equipment, his chance to survive should be very low. But, around him, could be some animals which survive without special efforts. For animals, it is more important "to invest" in "equipment" then to increase the level of conceptualization of the models.

Anyways, at least in theory, it is possible to evolve from an animal brain to a human brain based on an increase in the level of conceptualization. If the animals have or not the tendency to do this, is another issue.

Let's analyze again the evolution of the brain. A concept model is a model which fits a large number of entities. It has to be recorded, maybe, by the same hardware as the hardware that records a normal image-model. Also, there must be a connection between a concept model and every particular model covered by it.

By increasing the level of conceptualization (e.g. from "apple" to "fruit") the structure becomes very complex. The structure becomes even more complex when it evolves from "fruit" to "food". In theory, an evolutive process could produce this process but the increase of the complexity is so huge that it is difficult to believe that this could be produced without specialized hardware.

Level 2 is very close to level 3, but, as we see, no animal was able to reach level 3. Even the most advanced animals, like dolphins, have no tendency towards level 3.

The first drawings on cave walls were dated back to about 150000 years ago. Such drawings must be produced by some long-range image-models. But, such drawings are of no use without some explanations (symbolic messages). The reason is that the same drawing can be associated with a lot of situations. It is fair to consider that, at that moment, the primitive human beings were able to use a symbolic model for communication (a primitive language).

One idea is that the increasing capacity of the brain to make long range image-models was a support to make also symbolic models. This idea cannot be supported, based on MDT.

Indeed, the drawings made by 5 to 12 year old children are rather primitive drawings. At such age, children have very few long-range models. But they are able to make and operate symbolic models, including languages to communicate with computers.

Thus, it seems that the long-range image models are not necessary to make symbolic models. Also, this supports the idea that the symbolic models are made by a special hardware.

The existence of a specialized hardware is based on the following:

There is an image model and the associated label-model (a word). The word has a definition (based on other words). It is clear that there must be a hardware to record the image-model and another (associated) hardware to record the definition. On level 4, the image model does not exist anymore.

If this new hardware should be build based on evolution, it is difficult to understand why we have no intermediate stages. The dolphins, which are considered as the most advanced animals, have no tendency to build symbolic models.

There are some experiments with monkeys, which can be understood as support that some monkeys are able to make symbolic models. Such cases can be generated by a software implementation of the function to build and operate symbolic models.

As we already know, a model in PSM is very efficient but it blocks the evolution (the model is transmitted unchanged or with small changes, from a generation to another). If an animal builds, e.g. by accident, an advanced model of interaction with the external reality, such a model cannot be transmitted to the next generation. Only if a hardware implementation exists, a new model will be transmitted to the next generation. This seems to be a big problem for the evolution of the beings.

Without a hardware implementation, the solution is to transmit such models based on education. If there were groups of monkeys which lived together for a very long time, then a good model could be transmitted from a generation to another by education. In this way, a hardware implementation is built up also if the time available is long enough.

After many generations of monkeys who are forced to build symbolic models, it is possible, theoretically, that some hardware occurs to support the symbolic model building. This could be the process that generated the human brain by an evolution process.

The main argument against evolution from animals to humans is the fact that the 2 years old children are able to build and operate symbolic models. At that age they haven't either enough long-range models to understand the external reality and they are not capable to build such models. The maturity of a human being is reached around the age of 18, and thus the facility to build symbolic models is clearly a hardware facility.

Conclusions: 1. Long-range image-models are not an explanation for the occurrence of symbolic models. 2. The symbolic-models could occur from image-models by a huge increase in the level of conceptualization in very special conditions (e.g. large groups of monkeys which live together for a very long time). 3. The symbolic-models are built and operated by a specialized hardware.

There are two possibilities: either evolution if statement 2 is valid or external intervention if not.

BASIC DESIGN DEFFICIENCIES OF THE HUMAN BRAIN

The theory treats the brain as a technological product. So, the theory considers that a designer existed. He had to fulfil some design requirements. Any technological design has some deficiencies. We shall guess them in this section.

This theoretical and abstract designer is outside of the theory and we are not interested by it. It could be "Mother Nature" or God or an extraterrestrial civilization or anything else.

These deficiencies are described here mainly for the human brain, but some can be met also in the animal brain. The design deficiencies as MDT can detect them, are:

XD1: The tendency to associate an image-model to any situation met by a person. This deficiency is explained due to the "image nature" of the brain. This deficiency explains why so many persons "stay" on level 3, when level 5 is accessible since 100 years ago. This deficiency can be corrected by education.

XD2: There is no hardware protection to prevent the uncontrolled jump from a model to another, in interaction with a complex external reality. The stability in a model is a quality parameter of a brain.

Long-range models can stabilize a person. The XD2 deficiency is not related to them. XD2 is related to the capacity to stay in a model, when faced with a complex external reality. This deficiency can be corrected by software (education, for instance).

The lack of stability in a model can induce the illness called schizophrenia because this lack of stability has the tendency to favor short-range models. Indeed, when there is no stability in a model, the brain will make a specialized model for any particular situation met in the external reality. Such models are not able to see that some different facts can be correlated. Only a long-range model can detect such correlation. So, the stability in a model is a parameter of quality for a brain and the lack of stability indicates a low quality brain.

This deficiency can be met in the animal world too. For example, a dog has to watch a perimeter. That dog can jump from watch-model to food-model, if it gets food from strangers. Such a dog is a low quality dog, due to the lack of stability in the model.

The dolphins have a good stability in a model, and so, we consider them as advanced animals.

For human beings, the lack of stability in a model is a major drawback. Such persons are not good for any complex activity.

XD3: This is a basic deficiency. Let's start with its description, based on examples.

So, the brain interacts with an external reality and makes a harmonic model with 3 elements. If, that external reality has, in fact, 4 elements, the missing element cannot be discovered based on the 3-element model. As a 3- element model has a number of wrong predictions, it is not easy to see what is the problem from the analysis of the mistakes. The reason is that, once the 3- element model is activated, the reality is just that one which is generated by this model. There is no other reality! We cannot be outside of our active model. In such a case, the brain tries to correct the model. Usually, it will try to correct the model by changing the importance of some elements or relations. Sometimes this procedure works, and the brain will continue to use the 3-element model.

Such a situation occurs when we have not enough long-range models. In the above example, the situation can be corrected if there is a long-range model, which contains a 3-element model as an element of it. But even so, by analyzing the mistakes, it is not easy to understand what is the problem.

A brain affected by XD3A is not able to predict that a model might be missing some elements. A person, who can fight XD3A, can predict such a situation and will treat any model as preliminary.

The brain makes models based on the available data. Such models are made in a harmonic/logic way, but the stability of a model is not a guarantee that the model is good in interaction with a complex external reality.

We define XD3A as a design deficiency, which means that a brain is not able to predict the possibility of a missing element or relation in a stable (harmonic or logic) model.

Another case: a brain has a stabilized model with 100 elements. This model already generated a big number of correct predictions. At one moment, the external reality is changed, and now there are 101 elements. As we know, to correct a model means to reconstruct everything from scratch, using or not components from the old model. This task could be so difficult that it exceeds the technical capacity of the brain. In such a situation the old model is fragmented, and the brain uses it in this way. Of course, this can produce a lot of negative effects, including induced psychiatric disorders.

We define XD3B as a design deficiency, which means that a brain is not able to reconstruct a model, once the model is detected as a wrong model in association with a new external reality. We can express this also as the impossibility of a brain to correct a XD3A deficiency, once it was discovered.

XD3-deficiencies are widespread in the current activity of human beings. There is no reference to know that all the entities of the external reality are associated with the right YMs in the associated model. For us, the external reality exists only if it is associated with a model. Once we activated such a model, the reality is what the model says. We cannot be outside of our active model.

Once we have a model associated with a specific external reality, the model is considered as a good model based on the predictions which are already done. There is no guarantee that the model will continue to be good in any situation and any time. A good quality brain has to know this and to predict some negative effects associated with such a situation. So, this deficiency can be controlled by software (education, for instance).

XD4: This is a deficiency associated only with image-models. It does not exist in a symbolic-model environment.

For an image-model there is no possibility to know the importance of an element or relation. The brain will choose in a more or less arbitrary way the importance. A model can be harmonic (stable) for any importance which is associated with its elements and relations.

A "lightly" negative consequence of this deficiency is the fact that, faced with a given external reality, almost any person makes a personal image-model associated with that external reality. We will see later that, for extreme situations, such deficiency is associated with the psychiatric disorder called "paranoia".

The symbolic models do not have such problems. Once a symbolic model is made in a mathematical environment, the "law of the propagation of the errors" is able to predict the importance of any element or relation.

For instance, if we have a complex mathematical formula, the law of the propagation of the errors will tell us how much the result is changed if an element is changed with, let's say, 1%.

We already used the term "correct" associated with the importance of an element or relation in an image model. If there is an external reality and two associated models, one image-model and one symbolic-model, and if the two models have the same predictions, then the importance associated with the elements and relations of the image model is correct. If not, the right importance is that of the symbolic model.

The above method is not good in any practical situation. In fact, there is no method to know if we associated the right importance to any element or relation of an image-model. This is XD4.

XD5: this deficiency is a technological one. It means that there is no hardware or software method to erase a model of the brain. A model is made forever. It can be destroyed only in an uncontrolled way due to the biological deficiencies or the brain.

The consequence of this deficiency is huge in many practical situations. The problem is developed more in another section of this book.

THE STRUCTURE OF THE BRAIN, THE PSM MODEL

The Protection and Surviving Model (PSM) is the basic image-model of any brain. When a new being is born, it has only this model in its brain. This model is very complex and it will be described in this chapter.

There is a section of the PSM, which contains a collection of short-range models. They have to act very fast to provide minimal protection for that being (including newborn beings) in some specific dangerous situation. These activities are called "reflex actions".