HOW M-ZM MODELS ARE MADE
For a given external reality, the brain makes a structure of models, using information taken from the external reality or from other models.
We will see how this function works in a specified situation: how a new M-ZM is made in interaction with a new external reality. This function is described for a normal and mature brain. The term "normal brain" will be treated later. Here, a "normal brain" is a brain, which is able to work as it was already described in the section of hardware facilities. A mature brain is a brain, which has enough YM and ZM models made during a long time of interaction with the external reality.
An image is an information which is received as it is, in the same way as it would be generated by a TV-camera for instance. This kind of information, without any meaning in fact, has to be integrated by the brain as an image- model.
As we already know, M-models have to find some entities in that image. They start by making a 3D-image. This is possible in a rather easy way because almost all beings have two eyes. So there are two plane images and M-models will make a 3D-image. Now, the basic problem is that from a 3D-image it is not an easy task to identify the entities. M-models will use any supplementary information associated with this 3D-model, as color, contrast, brightness, the movement of some entities and so on. Anyways, M-models have to associate entities to YM-models. This process could be affected by mistakes, but, because M is a model, there will be a lot of crosschecks that will allow to discover and correct some of the mistakes.
For instance, if something round is discovered, it could be an apple (YM- apple) or a ball (YM-ball) or anything else.
Once a possible entity is associated with a YM, the M-model will predict how this YM interacts with the other YMs of the model.
For instance, there is a YM-apple. It has a relation (it is very close to) with a YM-table. So, from the predicted properties of the table, based on simulation, it results that it can support an apple, and from the predicted properties of the apple, it results that it can stay on that table. So, this relation seems to be good and thus, maybe the YMs are OK.
Now another example: an apple is on a thin branch of a tree. From the predicted properties of the branch, it results that it cannot support that apple. So, the choosen YM-apple or YM-branch is not good. M-models have to change something or to add something (maybe there is no gravity there…) to be stable.
The exact procedures and methods can be different. Anyway, MDT is a basic theory and it is not concerned with the technological implementation of the functions of the brain. It is enough to say that there are basic methods to solve the problems and also that the methods are not 100% safe, as everybody knows from his/her direct interaction with the external reality.
What is obtained by this interaction is a preliminary M-model associated with the external reality. This M-model is in interaction with, at least, one ZM- model, which develops the M-model based on any other information available in the brain.
These two processes happen almost simultaneously. As an M-model is made, a ZM- model takes some information from the M-model and improves itself. Also, ZM can change or add some information into the M-model, based on information obtained from other M-models or ZM-models. These two processes are performed, in fact, almost simultaneously due to this very close communication. They are called M-(YM)-ZM processes. The aim is to make a better and better ZM-model associated with a given external reality. As we know, such processes generate the knowledge and the consciousness.
Faced with the same external reality, every brain makes and operates its own structure of M-ZM models and so its own reality. For everyone, the reality is generated by his/her own structure of harmonic/logic models. From this mode of interaction, it does not result that faced with the same external reality, everyone makes the same structure of models.
Example 1: If a painter and a forest ranger look at a tree, each will make another M-ZM-model, and each will think and act based on one's own reality.
Example 2: When we drive a car in the city, M-models transmit the full information on what is around, but ZM-models, which control the car, will use only part of it. As the speed increases, ZM will process a smaller and smaller part of the M-model, to drive the car. This phenomenon can be called the narrowing of the consciousness field. It occurs every time when the brain is overloaded.
Basically speaking, everything what was already presented up to now is about the same for human and animal brains.
The exceptions are associated with symbolic models (which are based on logic).
The animals cannot make any symbolic models.
As we know, the basic function of any brain (human or animal) is to make and operate image-models. Let's continue with the basic differences between the human and animal brain.
THE HUMAN BRAIN (Introduction)
The basic difference between the animal brain and human brain is the capacity of the human brain to make and operate symbolic models. The animals are not able in any way or form to make and operate symbolic models.
We already analyzed how a human or animal brain interacts with an image to make an image-model. For the symbolic models the interaction is different.
A symbolic model, as we know, uses as elements letters, words or numbers. When a human brain interacts with such elements, the M-models will contain such elements as specialized YM-models. Such YM-models contain all the shapes of the letters, for instance. It is not necessary to discover the elements, because they are there in an explicit way.
All the symbolic elements are contained in a symbolic model called General Communication Language (GCL). There is a spoken language and a written language, as directly interacting symbolic models. This is true only for cultural zones which use alphabets. There is a specific application which treats this problem.
For a given written text, we have all the elements and all the relations between the elements, in an explicit way, as words. Usually, the elements are the nouns and the relations between them are the verbs. Any sentence is a symbolic model, for instance.
Example: the sentence: "I go home" has two elements "I" and "home" and a relation between the elements as "go".
The stability of the symbolic models is based on logic. When a symbolic model is stable we call it a logical model. A logical (stable) model can be understood by anybody who can make and operate symbolic models.
Sometimes there is a correspondence between image-models and symbolic-models as in the following example.
Example: Let's analyze the sentence "An apple falls from an apple-tree". We have two elements and a relation between them. On the other hand, we can make an image-model that describes the same situation: an apple falls from an apple-tree.
So, the logic could have been born in the process of translation from an image model to a symbolic model (when the translation is possible). As an image- model is stable based on laws of harmony, a symbolic model is stable based on the laws of logic.
Here we have in an implicit way the definitions of harmony and logic, as the rules and methods to ensure the stability of an image-model (harmony) or a symbolic-model (logic). An implicit definition means that we are able to recognize the effect of harmony or logic in a structure of data.