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).