Related to anticipation by simulation on a model, I have seen a photo of a
baseball player that was trying to catch a fast ball in flight. The high-speed
photo showed the ball flying directly into the glove of the player. The
sensational in this picture is that the eyes were looking at the place where
the ball had been some time before. The model for catching the ball has
anticipated the position of the ball based on the previous information coming
from the eyes. The action was a success, even if the eyes were unable to give
the real time information, and in spite of the fact that the hand can not move
very fast. This example illustrates the huge advantage of the construction and
operation of models. The effective speed of action can be considerably higher
than the intrinsic speed of action of the components.
Hikers who go on difficult treks with heavy backpaks, which change slightly
their position, know that hiking can be done with exceptional stability.
Personally, the immense capacities of the brain to process information, and
the speed of execution of these incredibly complicated operations, have always
amazed me.
Associated with the above issue, I had a situation on a trek, carrying a heavy
backpack, when a stone slipped from under my foot and I fell. The active
models were not able to anticipate this possibility. In this case, the
capacity to build a new model suitable to the external reality is reduced. As
we know, PSM is activated instead. However, I am still amazed that I was not
hurt during that fall (it was practically a controlled fall, but outside my
consciousness). Usually, PSM tries to save what can be still saved, and it is
even possible, that it will accept sacrificing an arm, to save what is more
important.
Connected with this specific problem, there is also another variant that a
parallel model to the main model of walking was created. This parallel model
predicted that the stone will slip and built a saving model outside the line
of the PSM. However the theory predicts only two lines: one of the PSM and one
of the ZAM which controls the global activity. Possibly, the ZM could let the
main ZAM act, and build in parallel other ZAMs models for new situations,
which would be activated in special cases. This type of behaviour is not
specifically excluded by the theory, but in reality it is not met sufficiently
clearly, so that it can be sustained. Building a parallel model is an easy
operation, but the question is, how does the main ZM know what other ZAM to
activate, when the active ZAM does not correspond anymore. The implementation
of this facility could be done if there were a 'pipeline' built by the main
ZM, so that a specific order of activation of parallel ZAMs existed in special
cases. But this would imply the existence of a new hardware. As I already
said, the existence of this facility (pipeline of ZAMs) cannot be sustained
yet, due to insufficient data, but could be a line of further hardware
development of the brain.
The issue of walking, jumping and running is inimaginably complicated and I do
not believe that in predictible future, robots will come close to the
performance of a chicken a few days old, running on a difficult terrain.
Climbing trees is an even more complicated activity, than walking and jumping.
The basic information is related to the lack of precise information about the
resistance of the branches. The models are able to make an evaluation of the
resistance of each branch, but the model will have enough simulations in which
the branch will break. ZM will need to take this into account, based on
various local models, in order to build a good strategy (the best ZAM
reactualised very often). In this case, the stability in the tree will be
given by the capacity of building alternative models, which could be
activated, if a branch broke. The brain effort needed to ensure the stability
of the person in a tree is huge. Not all brains have this capacity. Moreover
the ZM should also build a 'saving' model, in which there should be at least
three points of support at any moment, in the ideea that if at least two will
behave as in the simulation, the system will have an acceptable level of
stability.
Walking on a difficult terrain, jumping and the stability in tree climbing are
tests, which can show global performance of humans in the domain of image
models. In animals these functions can be even more efficient.
ETA 26: The brain evolves under our eyes.
Generally all ETAs refer to the behaviour and evolution of the brain of a
normal average human.
In 1900 Quantum Mechanics appeared. It marks the highest level attained up to
now, in the brain evolution. However, people, who reached this extreme advance
of knowledge, are ordinary people in everyday life. Independent of the level
in the professional field, in everyday life, the brain continues to act to a
large extends based on image models.
I have an example, in which one can see clearly, and above any doubt, the
evolution of the brain towards more and more advanced symbolic models, at the
level of the common person, in an issue always associated with image models:
nutrition.
In all times, people have eaten based on analysis on image models. Associated
terms to nutrition are taste, smell, colour, aspect etc. The decision to eat
or not a certain food, is based on image models. It can be said that the whole
being, with its whole structure, participates at solving the nutrition
problem.
We have an explicit situation, when the process of evolution towards symbolic
models in nutrition is clear. I have seen a person in a shop, who wanted to
buy a certain product. The person had at hand a list of dangerous food
additives (the so-called "E"s). A product was rejected because it contained
such an "E".
Taste, smell, aspect and natural drive were overrun and a logical decision
taken, totally independent of any image model. "E"s cannot be sensed based on
image models. When a decision is taken exclusively on symbolic models, we have
clearly a progress. This phenomenon, according to MDT, will increase on all
levels.
Important note: the elite has imposed that any food should be labeled with the
exact contents and other data, so that evolved people can take their own
decision. This type of information is useless for the image-type public. The
real problem is not the information, but the technical capacity of the brain
to collect information and decide based on symbolic models.
As I mentioned, the attitude of the majority of consumers continues to be
strongly anchored in a complex structure of image models. There is an infinite
number of cases, in which the image models from one's mind, make the products
more expensive, and also more dangerous.
Example: "good quality" butter might contain a substance, which confers it a
Yellow commercial colour. Between butter with colorant and the same without
it, many consumers choose the one with colour. There are several soft drinks
(some very famous) which, without colorants, would have less success.
The brain follows its ascending course towards the increase importance of
symbolic models, so that, as we have seen it already happening, the decision
in nutrition will be taken with more and more contribution from symbolic
models.
The guarantee exists on symbolic model, that, within the laws and the level
attained by science and technology and the laws of economy, food is possibly
the safest. Nutritional regulations can only impose what a certain product may
not contain.
The evolution of the brain is, however, slow on the scale of human active
life, even if the evolution accelerates. We are on a level development of the
brain on which, in domains where image models were leading (e.g. nutrition),
symbolic models are gaining ground.