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.