Metaphors of the Mind (Part I)

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 by: Sam Vaknin, Ph.D.

The brain (and, by implication, the Mind) has been
compared to the latest technological innovation in every generation.
The computer metaphor is now in vogue. Computer hardware metaphors were
replaced by software metaphors and, lately, by (neuronal) network
metaphors. Such attempts to understand by comparison are common in
every field of human knowledge. Architects and mathematicians have
lately come up with the structural concept of "tensegrity" to explain
the phenomenon of life. The tendency of humans to see patterns and
structures everywhere (even where there are none) is well documented
and probably has its survival value added.

Another trend is to discount these metaphors as
erroneous, irrelevant, or deceptively misleading. Yet, these metaphors
are generated by the same Mind that is to be described by them. The
entities or processes to which the brain is compared are also
"brain-children", the results of "brain-storming", conceived by
"minds". What is a computer, a software application, a communications
network if not a (material) representation of cerebral events?

In other words, a necessary and sufficient connection
must exist between ANYTHING created by humans and the minds of humans.
Even a gas pump must have a "mind-correlate". It is also conceivable
that representations of the "non-human" parts of the Universe exist in
our minds, whether a-priori (not deriving from experience) or
a-posteriori (dependent upon experience). This "correlation",
"emulation", "simulation", "representation" (in short : close
connection) between the "excretions", "output", "spin-offs", "products"
of the human mind and the human mind itself - is a key to understanding
it.

This claim is an instance of a much broader category of
claims: that we can learn about the artist by his art, about a creator
by his creation, and generally: about the origin by any of its
derivatives, inheritors, successors, products and similes.

This general contention is especially strong when the
origin and the product share the same nature. If the origin is human
(father) and the product is human (child) - there is an enormous amount
of data to be safely and certainly derived from the product and these
data will surely apply to the origin. The closer the origin and the
product - the more we can learn about the origin. The computer is a
"thinking machine" (however limited, simulated, recursive and
mechanical). Similarly, the brain is a "thinking machine" (admittedly
much more agile, versatile, non-linear, maybe even qualitatively
different). Whatever the disparity between the two (and there is bound
to be a large one), they must be closely related to one another. This
close relatedness is by virtue of two facts: (1) They are both
"thinking machines" and, much more important: (2) the latter is the
product of the former. Thus, the computer metaphor is unusually strong.
Should an organic computer come to be, the metaphor will strengthen.
Should a quantum computer be realized - some aspects of the metaphor
will, undoubtedly, be enhanced.

By the way, the converse hypothesis is not necessarily
true: that by knowing the origin we can anticipate the products. There
are too many free variables here. The existence of a product
"collapses" our set of probabilities and increases our knowledge - to
use Bohr's metaphor.

The origin exists as a "wave function": a series of
potentialities with attached probabilities, the potentials being the
logically and physically possible products.

But what can be learned about the origin by a crude
comparison to the product? Mostly traits and attributes related to
structure and to function. These are easily observable. Is this
sufficient? Can we learn anything about the "true nature" of the
origin? The answer is negative. It is negative in general: we can not
aspire or hope to know anything about the "true nature" of anything.
This is the realm of metaphysics, not of physics. Quantum Mechanics
provides an astonishingly accurate description of micro-processes and
of the Universe without saying anything meaningful about both. Modern
physics strives to predict rightly - rather to expound upon this or
that worldview. It describes - it does not explain. Where
interpretations are offered (e.g., the Copenhagen interpretation of
Quantum Mechanics) they run into insurmountable obstacles and
philosophical snags. Thus, modern science is metaphorical and uses a
myriad of metaphors (particles and waves, to mention but two prominent
ones). Metaphors have proven themselves to be useful scientific tools
in the "thinking scientist's" kit.

Moreover, a metaphor can develop and its development
closely traces the developmental phases of the origin. Take the
computer software metaphor as an example:

At the dawn of computing the composition of software
applications was serial, in machine language and with strict separation
of data (called: "structures") and instruction code (called:
"functions" or "procedures"). This was really a "biological" phase akin
to the development of the embryonic brain (mind). The machine language
closely matched the physical wiring of the hardware. In the case of
biology, the instructions (DNA) are also insulated from the data (amino
acids and other life substances). Databases were handled on a "listing"
basis ("flat file"), were serial and had no intrinsic relationship to
each other (an alphabetic order is an extrinsic order, imposed from the
outside and existing only in the mind of the "imposer"). They were in
the state of a substrate, ready to be acted upon. Only when "mixed" in
the computer (as the application was run) did functions operate on
structures.

This was, quite expectedly, followed by the
"relational" organization of data (a primitive example of which is the
spreadsheet). Data items were related to each other through
mathematical formulas. This is the equivalent of the wiring of the
brain, as the pregnancy progresses.

The latest evolutionary phase has been the OOPS (Object
Oriented Programming Systems). Objects are modules which contain BOTH
data and instructions in self contained units. The user is acquainted
with the FUNCTIONS performed by these objects - but not with their
STRUCTURE, INTERNAL COMMUNICATIONS AND PROCESSES. Objects, in other
words, are "black boxes" (am engineering term). The programmer is
unable to tell HOW the object does what it does, how does external,
useful function arise from internal, hidden ones. Objects are
epiphenomenal, emergent, phase transient. In short: much closer to
reality as we came to describe it in modern physics.

Communication can be established among these black
boxes - but it is not the communication (its speed or efficacy) that
determine the overall efficiency of the system. It is the hierarchical
and at the same time fuzzy organization of the objects which does the
trick. Objects are organized in classes which define their (actualized
and potential) properties. The object's behaviour (what it does and to
what it is allowed to react) is defined by its very belonging to the
class. Moreover, a principle of "inheritance" is in operation: objects
can be organized in new (sub) classes, inherit all the definitions and
characteristics of the original class plus new properties which
distinguish it from its origin. In a way, these newly emergent classes
are the products and the classes that they derived from are the origin.
This process so closely resembles natural phenomena that it lends
additional credibility to the metaphor.

Thus, classes can be used as building blocks. Their
permutations define the set of all soluble problems. It can be proven
that Turing Machines are a private instance of a general, much
stronger, class theory (back to the Principia Mathematica). The
integration of hardware (computer, brain) and software (computer
applications, mind) is done through "framework applications" which
adjust the two elements structurally and functionally. An equivalent
must be found in the brain (a priori categories, a collective
unconscious?).

We use the term evolution because one phase replaces
another. Relational databases cannot be integrated with object oriented
ones, for instance. To run Java applets, a "virtual machine" needs to
be embedded in the operating system. These phases closely resemble the
development of the brain-mind couplet.

When is a metaphor a good metaphor? When it teaches us
something about the origin that could not have been gleaned without it.
That it must possess some structural and functional resemblance we have
already established. But this is not enough. This is merely the
"quantitative, observational" aspect of the metaphor. There is also a
qualitative one: it must be instructive, revealing, insightful,
aesthetic, parsimonious - in short, it must establish a theory and the
resulting hypotheses. A metaphor is a theory which is the result of
given logical and aesthetic rules. It must be subjected to the rigorous
testing demanded by science before it can be judged to be a reliable
one.

If the software metaphor is correct, the brain must contain the following features:

• Parity checks through back propagation of signals -
  the electrochemical signal in a neurone must move back (to its origin)
  and forward, simultaneously in order to establish a feedback parity
  loop

• The neurone cannot be a binary (two state)
  machine (a quantum computer will be a multi-state one, for instance).
  It must have many levels of excitement (representation of information).
  The threshold ("all or nothing" firing") hypothesis must be wrong

• Redundancy must be evident in all the aspects
  and dimensions of the brain and its activities: the hardware (different
  centres will perform similar tasks), communications (information
  transfer channels will be replicated and the same information will be
  simultaneously transferred over more than one as a basis for
  comparison), retrieval (data excitation will happen in a few spots at
  the same time) and usage of obtained data (through working, "upper"
  memory).

• The basic concept of the working of the brain
  must be the comparison of "representation elements" to "models of the
  world". Thus, a coherent picture is obtained which allows for
  predictions and for manipulation of the environment in effective,
  result producing ways.

• Many of the functions solved by the brain
  must be recursive. To a large extent, we could even half expect to find
  that we can reduce all the activities of the brain to computational,
  mechanically solvable, recursive functions. Should this happen, the
  brain will come to be regarded as a Turing Machine and the wildest
  dreams of Artificial Intelligence will come true. Until such time,
  however, a strong recursive streak should be evident in the operations
  of this magnificent contraption inside our heads.

• The brain must be a learning, self organizing, entity.
  

Only if these six requirement are cumulatively met - can we
say that the software metaphor is a strong one. Otherwise, we should be
forced to neglect it in favour of a stronger one.

The brain is a paranoiac machine governed by Murphy's
Laws. It assumes the worst, prepares for it and takes no chances.
Precariously balanced, materially delicate, in charge of life itself it
can - and does - take no chances.

About the Author

Sam Vaknin is the author of "Malignant Self Love - Narcissism Revisited" and the editor of mental health categories in The Open Directory, Suite101, and searcheurope.com.

His web site: http://samvak.tripod.com

Frequently asked questions regarding narcissism: http://samvak.tripod.com/faq1.html

Narcissistic Personality Disorder on Suite101: http://www.suite101.com/welcome.cfm/npd

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