The Brain, A Decoded Enigma

Copyright (C) 2004 by Dorin T. Moisa

THE BRAIN, A DECODED ENIGMA

Dorin T. MOISA

Warning

This book contains a symbolic model associated to the basic hardware function of the brain.

A symbolic model is a model based on logic only. So, this book is not recommended to individuals who has the tendency to understand the external reality based on imagination.

The book can be understand by persons between 12 and 20 years old who have special abilities in the field of positive sciences.

Also, the book is recommended to persons who already work in the field of positive sciences (mathematicians, phisicists, engineers and so on).

CONTENT

Introduction
Abstract
Fundamental Terms (keywords)
Definitions associated with the basic terms
The basic hardware elements
Some principial problems
How M-ZM models are build
The human brain (introduction)
The human brain versus animal brain
Human brain: evolution or external intervention
Basic design deficiencies of the human brain
The structure of the brain, the PSM-model
The structure of the brain: functional facilities and types of models
Paranormal phenomena
The normal human brain
The abstract of the functional facilities of the brain
The personality (human only)
The conciousness
Abstract: model dictionary

Example, Tests, Aplications (ETAs)
ETA 1: The model
ETA 2: Truth, reality, communication
ETA 3: Fundamental problems associated to scientific knowledge
ETA 4: General communication language (GCL), dictionary of terms
ETA 5: NULL model
ETA 6: Time
ETA 7: Music
ETA 8: Cinematography
ETA 9: The fundamentalisms of the world we live in
ETA 10: Terrorism
ETA 11: Problems of the human brain evolution
ETA 12: Rattlesnake
ETA 13: The main psychiatric illnesses: paranoia and schizophrenia
ETA 14: Suicide
ETA 15: Normality tests
ETA 16: Dreams
ETA 17: The history of the evolution of the human species, based on MDT
ETA 18: The organization of the human society
ETA 19: The schizophrenic-paranoiac complex (XSPC)
ETA 20: Induced paranoia (XIP) and paranoiac-schizophrenic complex
(XPSC)
ETA 21: Disharmonies of the functions of the brain
ETA 22: Direct demonstration of the function to build image models
ETA 23: Some basic parameters of the brain for measuring the
performances
ETA 24: Animals
ETA 25: Very complicated operations on image models (walk, jumps,
climbing trees) of humans
ETA 26: The brain evolves under our eyes
ETA 27: Principial negative effects associated with the functioning of
the brain
ETA 28: Free-masonry
ETA 29: Problems associated with movie-making
ETA 30: Optical perspective and the quality of construction of image
models
ETA 31: Something agressivity may fight XS1-type schizophrenia
ETA 32: Sex
ETA 33: The internal body
ETA 34: The european spirit
End notes
Bibliography

Introduction

This book contains my original theory, called MDT (Modeling Devices
Theory) on the basic hardware function of the brain (human or animal).

As any scientific theory, it is a symbolic model. Any symbolic model is based on a limited number of basic terms and a limited number of basic relations between the basic terms.

For the basic terms and only for them, there are accepted descriptive definitions. All the others terms are generated by the model, together with their normal definitions. These definitions are generated by the model by logical and mathematical procedures.

These are the basic characteristics of any scientific theory and so, I follow the procedures described above, to make a theory on the basic hardware functions of the brain.

This theory is in a total opposition with all the actual sciences associated with the functions of the brain. The present sciences, associated with the functions of the brain, are not based on a single fundamental model. In this way, as my theory will be accepted, all what it was already written in the actual sciences associated with the functions of the brain, have to be re-written or forgotten.

This attempt of total revolution is necessary because the actual sciences on the brain don't use normal definitions of the terms; there are only descriptions associated with them. Because the definitions of the terms are not generated by a single fundamental model, the logical corelation between them is not possible. So, the actual sciences associated with the brain cannot evolve to become positive science anymore.

In psychology, for instance, about any scientist has his/her own list of descriptive definition associated with the terms used by him/her. In this way, it is not possible to make a logical structure to integrate all the terms used in that field. So, the psychology, for instance, is not a positive science.

Another example: Let's consider a classical positive science, as Newton's Mechanics is. In this symbolic model, all terms have exactly the same definition for all the scientists. These definition are not changed since 340 years ago when they were generated by the symbolic model of Mechanics. For instance, the term "speed" is defined as v=s/t. That is, "speed" means that the "space" is divided by "time". This definition is a normal definition generated by the symbolic model of Mechanics not a descriptive definition.

Let's suppose now that a symbolic model associated with the basic hardware function of the brain is already created or it will be created. The first consequence is that all the terms used in association with the functions of the brain will be generated by the model by logical and mathematical procedures, together with their normal definitions. There is no reason to suppose that any descriptive definition which is already used in the present sciences of the brain will be compatible with the definitions generated by that symbolic model.

So, all what is already created in the present sciences associated with the brain has to be re-written or forgotten.

Regardless of the fact that MDT theory will be accepted or not, a symbolic model which covers the basic hardware function of the brain will produce this total revolution in all the sciences associated with the brain, including psychology, psychiatry, gnoseology, epistemology, many parts of social sciences and so on.

Let's consider that a symbolic model to cover the basic functions of the brain is created and is already accepted. The persons who already work in these fields have to re-start everything about from zero. Their opposition will be enormous and I have no illusion in this direction.

This theory was created about 10 years ago. Based on my personal experience, the theory is easily understood by persons with special orientation on positive sciences, including children's over 12 years old. Also, the persons who already work with symbolic models (mathematicians, physicists, engineers and so on) have a high capacity to understand it.

Let's see what MDT offers.

First of all, MDT treats the brain as a device which processes the information. In this way, MDT has no direct connection with the medicine.

MDT is concerning only with the basic functions associated with information processing and it is not interested by problems as "what part of the brain is doing something" or "how a function is implemented". MDT is a theory associated with the processing of the information and so it has no direct interference with the hardware implementation of the physical brain.

As a symbolic model associated with the basic function of the brain, it generates precise definition, based on logic, of all the terms used in association with the basic hardware functions of the brain. In this way, all the terms are logical correlated between them.

Examples: MDT generates normal definitions for: knowledge, consciousness, to imagine, to think, intelligence, emotion, to be irritate, love, happiness and many others. E.g. the "consciousness" is the facility of a brain to make and operate a model which contains the being itself as an element. MDT defines, than, 6 different types of consciousness which are, of course, defined in the same precise way. Even more, based on MDT, it is possible to design a logical structure to implement the function called "consciousness".

MDT explains the basic functions of the brain up to the level to make a logical design to synthesize all the brain's functions (human or animal). Of course, a technological implementation of that functions is not possible now because the computers, for example, have yet a very low power to process the information and this situation will last, I think, at least in the next 25 years.

In fact, the brain is treated by MDT as a technological product. So, there are defined the main design goals and also, the main deficiencies (by design or given by technological implementation).

There is analyzed the problem if, by evolution, it is possible or not to evolve from animal brain to human brain.

There are analyzed the design and technological problems, including the functional illnesses of the brains.

The theory treats also the paranormal phenomenon and suggest some methods to develop such activities.

The Application section (ETAs) contains also many items as a history of the evolution of the brain, the evaluation of the problems of psychological tests and performance tests for a brain, some problems associated with the present and future levels of evolution of the brain, some long range problems associated with the development of the human society (including the terrorism) and many others.

The basic elements of MDT occurs in 1993 and the first written form (on
WEB) in 1997. In 2003 a printed edition of the theory was published (in
Romanian language) by the Romanian Publishing House "Editura Albastra"
and in 2004, in the frame of Gutenberg Project, a new edition also in
Romanian. The process of developing is continuing.

Dorin T. MOISA moisa@zappmobile.ro

THE BASIC FUNCTIONS OF THE BRAIN

ABSTRACT

This theory, called by me as MDT (Modeling Devices Theory) considers that the basic hardware function of any brain (human or animal) is to make and operate image models (or analogic models) which are associated with the external reality. In this frame, for the human brain only, there is an additional hardware facility: to make and operate symbolic models.

FUNDAMENTAL TERMS (KEYWORDS)

Image model (or analogic model), symbolic model, simulation on model, information, truth, reality, input reality (IR), external reality, image, harmony, logic, general communications language (GCL), logical and mathematical language.

DEFINITIONS ASSOCIATED WITH THE BASIC TERMS

This theory is a symbolic model. Any symbolic model has a limited number of fundamental terms. For these terms and only for them, there are accepted descriptive definitions. A descriptive definition is, usually, not precise enough for a scientific approach. This lack of precision is due to the fact that it uses terms which must be defined before. The terms used in the definition must also be defined using already defined words. This process seems to be endless.

In any positive science, the descriptive definitions are accepted only for a very limited number of terms. These are called "fundamental terms".

For instance, in the symbolic model of Newton's Mechanics, the fundamental terms are mass, space and time. None of these terms have a normal definition (i.e. generated by the model). They have only descriptive definitions.

Once the fundamental terms are introduced by description, all the other terms have normal definitions, which are generated by the symbolic model, by logical and mathematical operations.

Let's see the definitions of the terms used by the MDT theory.

Model: this is a term used on large scale in science and technology. The MDT theory accepts the definition used there.

A model means some fundamental elements and some fundamental relations between the elements.

The elements could be of any type (physical objects, the representation of any object in any form, including pictures of any type or images of any type or mathematical symbols of any type and so on). In fact, an element could be associated with anything which can be considered as an entity. The elements have some properties, which must be specified somehow. There are a number of relations between the elements, which must also be specified.

An image model (or analogic model) contains an unspecified number of elements and an unspecified number of relations between the elements. An image model is just given as it is. It is not possible to specify in explicit and precise ways which are the elements and which are the relations.

Examples of image models: maps, models of an object of any type, an assembly of such models including any material elements (water, air and so on), any representation in any form of such elements.

A symbolic model uses as elements letters, numbers or words. The relations are of logical or mathematical type.

The most important symbolic model is the General Communications Language
(GCL). The elements are usually nouns and the relations are usually verbs.

Warning: GCL is not really a symbolic model. The GCL just contains all the elements and all the relations. When a symbolic model is made (a sentence, for instance), elements and relations from GCL are used. Thus, because there is no available word, I decided to consider, by extension, the GCL as a symbolic model. In this frame, GCL has to be considered as "symbolic model".

Once a model given, it is possible to simulate some situations on it. For simulation, a change must be made to the model. After that, the entire model will be changed because all the elements have some relations between them.

Any implicit or explicit information which is generated by simulation by a model, is called "truth". Any truth must be associated with the model, which generated it. This is the definition of the term "truth" in the MDT theory.

All the information, which is or could be generated by a model by simulation, is called "reality" associated to that model. This is the definition of the term "reality" in the MDT theory. We also see here that before declaring the reality, one needs to declare the model which generated it.

We already used the term "information". This term is a fundamental term. It has no normal definition. MDT accepts the descriptive definition from common life and from science. The same situation is for the term "entity".

Warning: in connection with the term "information", something is considered as information after that "something" is processed somehow by a device which takes and processes that "something".

This somehow confuse situation is normal for any fundamental term. Just think, for instance, how one can explain what is "time". The only possibility to explain what is "time" is to use examples that already use the term "time". In fact it is impossible to define terms as "mass", "time", "space", "information" or "entity".

Let's introduce two new terms: "harmony" and "logic".

Once a model is given, it is possible to make simulations on the model, as it has already been explained. By simulation, it is necessary to change an element or a relation. The model goes into a temporary unstable situation because all the elements are connected between them. The model will evolve to a new stable situation. For an image model, the evolution to stability is based on harmony laws. For a symbolic model, the evolution to stability is based on logic. Thus, a stable model is a harmonic or logic model and, after a perturbation, the model will regain the stability based on the laws of harmony (image models) or logic (symbolic models). The evolution of any model toward stability (to become harmonic or logic) is also a basic hardware facility of the brain.

Because some situations from external reality can be associated, sometimes, with both types of models, there can be a corespondence between harmony and logic.

Thus, the implicit definitions of the terms "harmony" and "logic" are associated with the methods to regain the stability of an image model (harmony) or symbolic model (logic). An "implicit definition" means that we are able to recognize the effect of harmony or logic in an informational structure.

We are now in the situation to present the basic hardware function of any brain, based on the terms, which have already been defined.

The basic hardware function of any brain (human or animal) is to make models associated to external reality and to predict, by simulation, the possible evolutions of the model. Because the model is associated with external reality, it is possible to predict by simulation some probable evolutions of the external reality.

We already used the term "external reality" which is not defined yet. This fundamental term is considered as a source of information, which is not localized in the structure of models of the brain. I want to emphasize that the external reality is not a source of information, but is just considered so by any brain.

Thus, one of the main hardware functions of the brain is to make models of the external reality and to predict, by simulation on the model, the possible evolution of the associated external reality.

We already defined the reality as all the information which is or could be generated by a model. This means that we understand the external reality by the reality, which is generated by a model, which is associated with the external reality.

Example: For a given external reality, any person makes an associated model. Any person has his/her own model associated to the same external reality. We think and act based on our own reality and not based directly on the external reality.

In fact, external reality is rather an invention of the brain to explain its structure of models.

THE BASIC HARDWARE ELEMENT

Let's see what is the basic hardware element of a brain (human or animal). There are some image-type models called M-models, which are associated with the sense organs (eyes, ears and so on). M-models work in association with some YM-models, which already exist in the brain. YM-models are concept models. A concept-model is a simplified model which, in this way, fits a large class of similar models.

Example of YM models: "dog", "table" and so on.

M-models have to discover as many as possible entities in the external reality and to associate a YM model to any entity. Once an entity was firstly associated with a YM, M-models will predict its evolution based also on that YM.

Example: if an entity was associated with a YM-dog, the M-model is able to predict how this YM performs in connection with all the other YMs of it.

Any prediction of M with that YM included is compared with the information obtained by M from external reality. The information obtained by a M-model from outside during the comparison process, is called "input reality" (IR).

We just introduced a new term as "input reality" or IR. IR is the information obtained by an M-model from outside (from external reality or from other models) to improve its predictions.

If the prediction meets IR, then M will try another prediction to improve its quality. If one or more predictions do not meet IR, then M will replace that YR with another, and the process will continue. This process will continue so that all the entities which are discovered by M-models will be associated with some YMs and all the predictions of M must confirm the M-model, unchanged. Such a model is, thus, a stable model. When M is stable, all YMs are integrated in M in a harmonic way.

The main function of M-models is to make a preliminary harmonic model (stable model) associated with an external reality.

Conclusion: a M-model interacts with a section of the external reality. M will be a model made in an informational way by analogy with that section of the external reality. Because M is a model, all the elements are connected between them in a harmonic way, so that the model is stable. This stability is verified on and on in an automatic way, as long as a specific external reality is in interaction with the specific M-model.

M-models interact with some other type models, called ZM-models. ZM-models take some information from one or more M-models and continue the construction of models associated with the corresponding external reality. To do this, ZM- models interact with the other ZM-models of the brain to improve M-models.

M-models are just preliminary models based on YM-models. A ZM model will take any information from any other M and ZM models of the brain, to improve it.

Example: an M-model is associated with a bus that transports people. A ZM- model takes this information and tries to see if this bus transports tourists or is a public transport vehicle. To do this, it will use information taken from any other ZM-models and M-models. The aim is to make a ZM-model, which reflects as well as possible a section of the external reality. Because ZM is a model, it is stable and because this model is integrated in a structure of other ZM-models, the structure of ZM-models is stable too. This problem will be treated later in details.

ZM-models are long-range models. This term will be explained later. Here, the "long-range model" is understood as a model, which already developed its elements as self standing models.

ZM models are the main models, which reflect the external reality.

We define now two very important terms: knowledge and consciousness.

Knowledge is associated with the facility to predict the evolution of the external reality based on a structure of harmonic/logic models. This structure was made by a large number of interactions with many sections of the external reality and so it already generated a large number of good predictions. This means that the only guarantee of the correctness of the knowledge is the confidence in that structure of models. This issue will be developed in details later in the book.

The consciousness is the facility to make and operate a model, associated with the external reality, where the person itself is an element of that model. When such a model is activated, it will also find the position of the person in the model and so it will predict the position of the person in the external reality. This issue will also be developed in detail in another part of the book.

We will now develop some issues associated with the term "knowledge". We already defined knowledge as the capacity to predict in a correct way the evolution of the external reality.

Here we use the term "correct". Let's see what it means. This term has two definitions. One situation is when a model makes a prediction and the prediction is compared with IR. If the prediction meets IR, then the prediction is "correct". Unfortunately, there are very few situations when the comparison between prediction and IR is possible.

For instance, building a bridge. A problem is, for instance, if the bridge will be stable or not in case of an earthquake. Here we need a guarantee that the bridge is properly built and there is no possibility to verify this based on IR.

The second definition of the term "correct" is: the brain will consider as "correct" any prediction based on a harmonic/logic structure of models. To be harmonic, the structure was already verified, based on IR in many other situations. So, the only guarantee of a "correct" prediction is the confidence in that structure of models.

MDT is associated with the basic hardware functions of the brain. Once we described the hardware structure, everything what the MDT predicts is based on what the hardware is able to do. What MDT says about knowledge is not another theory on knowledge but what the hardware is able to do.

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.

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.