Chapter 2
Any experiment is based on a model. That model tells us what we are doing and the same model tells us what we get and what we see. Any model that makes the experiment just improves itself. An improved model will make better predictions and that is all. There is no guarantee associated with the knowledge except the confidence in our own structure of models.
Let's see another aspect. We saw that any experiment is based on a model. The model tells us what we did and what we get and see. If there are many persons who participate in an experiment, everyone will make his/her own model based on his/her own structure of models. What everyone gets and sees depends on one's own structure of models.
Example: up to around year 1500 everybody knew that the Earth was the center of the Universe. This idea was supported by direct observation of the sky but also by a powerful structure of models. So, in that period, the astronomers were able to calculate Sun and Moon eclipses, understand and calculate many parameters associated with the movement of the Moon, Sun and stars. Even the Holy Book supported this idea, at least in an implicit way. In that period, the idea that Earth is the center of the Universe was correct.
I want to emphasize again that the situation is generated by the work principle of the brain. It does not matter if we like or not this situation! The situation will be the same forever. For instance, Newton's Mechanics considers that there is a fundamental field of forces called "gravity". Everybody considers that the gravity exists. But Einstein says that there is no such a field of forces; what we see is just an effect of the distortion of the space due to mass. If Einstein is right, the idea that there is gravity is not correct anymore. See also the applications.
So, in every moment, the brain will consider as correct everything which is generated by its structure of stable models.
Some scientists could consider these assertions as unacceptable, but regardless of the fact that we like or not such a situation, the brain is able to do only what the hardware structure is able to do.
There is another term that has some associated problems. This term is "wrong". If a model makes wrong predictions, this usually does not mean that the model is wrong. It means just that the model is not suitable to the given external reality.
Faced with a new external reality, the brain will activate the model which makes the best predictions associated with that external reality. If a model makes wrong predictions, we have to change the model with another one or to modify the model.
Example: Newton's symbolic model of Mechanics makes wrong predictions associated with the objects moving at a speed comparable to the speed of light, but its predictions are good (correct) at lower speed.
In any situation, the terms "correct" and "wrong" must be associated with a model or with a structure of models.
We already described the first basic hardware facility associated with the brain (human or animal). It generates truth, reality, knowledge and consciousness. Now we will describe the second basic hardware facility of the brain. This is the action on the external reality.
We already saw that faced with a section of the external reality, the brain makes at least one ZM model. A ZM model works in association with any available (or several) M-model and with any other ZMs of that brain. The main ZM is able to predict in a correct way the evolution of a section of the external reality. Such a ZM is able to make a new class of long-range models called ZAMs.
ZAMs are artificial and invariant. An artificial model is made without any direct interaction with the external reality. An invariant model is a model, which cannot be changed by direct interaction with the external reality.
A ZM model will make a ZAM model in order to modify the external reality. Once a ZAM is made, it becomes a reference model in changing the external reality. To do this, the ZAM-model works in connection with a number of AZM models. An AZM is a model which is already connected to the execution organs of a being (for human beings these are legs, hands and so on).
Once a ZAM is activated, it will simulate the requested action using any information from all models of the brain. Based on simulations, ZAM will determine if it is able or not to meet the goal. If the simulation shows that the action is possible, then the ZAM will activate AZM models for action on the external reality. The ZAM will control the AZMs to act on the external reality exactly as in the successful simulation, with good chances of success. If by any simulation the objective is impossible to reach, the brain will be blocked to do that activity.
Example: if a person has to jump over an obstacle, that person will know very fast if the jump is possible or not. The person knows this, because a ZM makes a ZAM-model, which is associated to the external reality (the person itself, the supporting surface and the obstacle, as main elements). The ZAM then simulates the jump on the model. If the simulated jump fails, the brain is blocked to do the action. If the jump is done with success in the simulation, the ZAM will control the body during the jump exactly as it was in the simulation, with good chance of success.
No action on the external reality is possible without a successful simulation of that action. The action will be as in the successful simulation. Both in an immediate action and in an activity that has to be done in the future, any brain follows this procedure.
We shall add some considerations about the speed of action on external reality. So, when we walk on a plane surface, for each step there is at least one simulation before the step is done. Due to a large number of internal and external factors, any step is unique. Thus, if we walk on a raw surface (a stony trail in the mountains, for instance) not only every step in based on a simulation but even during the execution of a step, it is possible to make a new simulation based on new data and so a step in execution can be modified at all time to meet the goal as ZAM requires. Thus, a very complicated activity as walking on a mountain trail, can be done very easily and even elegantly, based on continuous predictions and simulations associated with every step.
As it was already emphasized before, this procedure to simulate in advance any activity on external reality is followed in all situations, regardless if the activity is immediate or it has to be done in the future.
We have already described the two main hardware facilities of the brain (human or animal). Here is a preliminary abstract of the main hardware models of the brain:
M-models: these models are associated to sense organs. The brain tries to make a preliminary model of the external reality. To do this, it uses a number of YM concept models. The main activity is to find the entities of the external reality and to associate to any entity a YM model. Then, by simulation on the model, M-models try to integrate any YM model in the structure in a harmonic way. That is, any simulation of interaction between a YM and any other YM- model must confirm the M-model, unaltered.
If, for instance, some predictions of an YM1 model in relation with an YM2 model are not compatible with the prediction of the YM2 model in relation with the YM1 model, then M has to change YM1 or YM2, or some relations, or some other YMs, so that the M-model is stable. M-models work in an automatic way, trying to be stable in interaction with the associated section of the external reality.
YM-models: they are concept models associated with all the entities, which have already been discovered by the brain by M-model activity. When a new being is born, there are practically no YMs. They are made by direct interaction with the external reality.
ZM-models: they are the main long-range models of the brain. They generate knowledge and consciousness. Also they make YMs, ZAMs and AZMs. They are able to take any information from any other model of the brain. ZMs can replace a YM-model with another if something is not OK after an advance prediction and simulation based on any available data. They also control ZAM-models during their activity.
ZAM-models: they are artificial and invariant models. An artificial model is not generated by direct interaction with the external reality. An invariant model is a model, which cannot be changed by direct interaction with the external reality. ZAMs are models, which act on the external reality. Once a ZAM was made and activated by a ZM, it will simulate the activity, using any information from any model of the brain. By one or more simulations, the ZAM will find the right solution. If it fails to find a solution, then the ZM will make another ZAM and the process continues.
AZM-models: they are associated in a direct way to the organs which can act on external reality. They are ready-made when a being is born, but, to be used, they have to be dynamically calibrated by the activity of the ZAMs. That is, a ZAM has to know everything is association with the external organs of a body (e.g. hands, legs for a human). When a ZAM has to make a simulation, it has to know all the parameters of the muscles, for instance. An AZM has to know and transmit such parameters. To do this, AZMs keep a model of any external organ of that being.
All these models are associated with the hardware implementation of the brain. We will see later some others types of models which are associated with the software implementation of the brain.
SOME PRINCIPIAL PROBLEMS
When an M-model is activated it does not know how many entities are in the external reality. Even more, it does not know which are these entities. The device will try to find them based on the facilities of the sense organs, but there is no guarantee that M-models have found all the entities and no guarantee that the right YMs are associated to such entities. This is a basic deficiency.
The camouflage and dissimulation are methods which use this deficiency. By camouflage an entity is not discovered and by dissimulation M-models associate a wrong YM to an entity.
Let's see another basic problem. Any model evolves to be harmonic with itself and so, to be stable. This means that, after any change in the model, it has to regain its stability. If a model has a disharmony, it has to correct itself based on IR or based on an internal change (IR is not available in any situation). Thus the model regains its stability, but in some cases the model could be not suitable anymore to reflect the external reality. There are many cases when a model is stable but its predictions associated with the external reality are wrong.
We already defined reality as all the information that is or could be generated by a model by simulation. The guarantee of a correct reality is the stability of the model but the stability of the model is not a guarantee that the model is capable to accurately reflect the associated external reality.
That is, there is no guarantee that all the entities of a given external reality are discovered, there is no guarantee that the right YMs are associated with these entities and so on. The stability of a model is just a guarantee that all the available information is correlated in the right way.
There is another class of basic problems associated with the changes in a model. If a model has to be changed, sometimes there are small chances to do that. In fact, the only possibility is to make a new model from scratch, using or not elements and relations from the old model. This activity could be sometimes so complex that it can exceed the technical capacity of the brain.
Indeed, a new model must be accepted by the whole structure of models. That is, any other model of the structure must accept any prediction of the new model, so that the new structure is stable.
If the new model is good in interaction with the external reality but the structure of the models is not good enough, then some other models of the structure have to be changed too. As I said, this process can exceed the brain's technical capacity of processing. This can be considered as a design deficiency too.
This explains a lot of situations in common life, when logical arguments or facts taken from external reality cannot change wrong models some people have.
As we know, a stable model is a model which correlates in a right way all the available information. But, there is no guarantee that we gain enough information to make the right model. This basic deficiency is attenuated by the fact that there is a structure of models. The structure of models helps a lot when we interact with a new external reality because it can make predictions based on the previous interaction with other external realities. On the other hand, the structure of models is like a brake for evolution if the structure has problems.
Example: The astronomer Copernicus made a model of the Universe based on the idea that the Sun is the center of the Universe, not Earth, as everybody knew at the time. Around the year 1543, very few persons were able to change the whole structure of models, based on this new model.
We continue with other basic problems and features.
In the normal activity of the brain, any ZM-model has full access to any model of the brain. That is, a ZM model can correlate information from many M-type models and from any other ZM of the brain. This is true for any ZM of the brain.
In the complex interaction between a brain and the external reality, there is a single ZM at a time, controlling that being. This ZM is called a local-ZM or an active-ZM. A ZM can be changed to another in a dynamical way, so that the being does many activities in time-sharing.
This activity is not simple. So, when a local-ZM is deactivated, it has to store the conditions, to be able to resume when it takes control again. There are problems associated with this activity. Some of the information can be lost or the external reality may evolve in the mean time so that the stored information will be of no use. In this way, any model, which takes control of the being, has to initialize before being able to regain full control. This activity of initialization is very complex and in some situations it might contain errors. Thus, it is rather difficult to do many activities in time- sharing.
There is also a basic problem associated with the term "knowledge". As we know, the knowledge is associated with the predictions of a structure of models.
So, the knowledge is associated with the structure of models and not with the external reality, as we'd like it to be. We should never ever forget this thing. Even more, knowledge is a non-sense if we do not declare the structure of models.
Example: in any positive science, it is usual to say that something is true based on a specified theory (model).
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.
THE HUMAN BRAIN VERSUS ANIMAL BRAIN
MDT is a theory that treats the human and animal brain in the same framework.
I present here a possible evolution of the brain, from animal brain to human brain. It is very important to specify that the theory is like a tool: it does not support and also does not reject the evolutionist theory. MDT just describes the situation.
For any external reality, the brain (human or animal) will make an image- model. This function is basically the same for human and animal brain.
In a given external reality many similar elements could exist. For any element, the brain has to make a YM-model.
For instance, a dog has to make a YM for any dog which it meets. Such a big number of models use a lot of the brain resources.
When there are many similar elements, a solution is to make a concept-YM. Such a YM will fit a big number of similar elements. This reduces the quantity of data to be processed by an animal brain, and so, the brain becomes faster and more efficient.
Thus, the first level of evolution of the brain (level 1) is the extensive use of the concept models. This level is, probably, reached by all animals.
Observation: the human and animal beings continue to use, for some special situations, pure image models. A pure image model is a YM-model associated with a single entity of the external reality. For instance, a cub has a pure image model of its mother.