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% Submitted to PSYCOLOGQUY 2.09.94
\hfill {\small 2.09.1994}

     A SOLUTION TO FUNDAMENTAL PROBLEMS OF COGNITIVE SCIENCES\\

     W{\l}odzis{\l}aw Duch \\
     Department of Computer Methods,\\
     Nicholas Copernicus University,  \\
     ul.  Grudziadzka 5, 87-100 Toru\'n, Poland.\\
     duch @ phys.uni.torun.pl\\

ABSTRACT: Problems laying at the foundation of cognitive sciences, such
as the mind-body problem, the existence of qualia or the symbol
grounding problem are all due to the lack of proper language to describe
mind events.  Such language is introduced in this paper.  Cognitive
systems are viewed primarily from the mentalistic point of view.
Concepts and symbols interact strongly forming ``mind objects".  Mind
states are identified with activations of these objects.  An abstract,
multidimensional ``mind space" is defined, with the axes (dimensions) of
the coordinate system corresponding to features of internal
representations, such as preprocessed sensory data and abstract
qualities.  This space serves as a theater for mental events.  A neural
network, or a brain, is a natural realization for such model of the
mind.  Activation of objects of the mind space is governed by
psychophysical laws and brings casual changes in the neural hardware.
From the neuropsychological point of view mind objects result from
activity of the neural cell assemblies.  No fundamental problems with
understanding of the real minds arise and the way to computational
models of artificial minds is clear.  The problem of free will and
consciousness is also discussed.

KEYWORDS: cell assemblies, cognitive philosophy, cognitive science,
consciousness, inner space, mental representation, mind, mind evolution,
mind-body problem, philosophy of mind, psychophysics, symbol grounding.

1.0 INTRODUCTION: FUNDAMENTAL PROBLEMS

1.1.  Foundations of cognitive sciences are plagued with many serious
problems.  Mind-body problem is one of the most difficult in the history
of philosophy.  How can the non-material mind, or the B-world in terms
of Popper (1972), have influence on the material body and vice versa?
In more technical terms: mind states appear to be ``causally inert"
(Fodor 1987) and yet they influence the brain and matter.  Mind-body
problem is basic to cognitive sciences: mental representations require
cognitive agents and cognitive agents require mental representations.
Some experts believe that the mind-body problem may never be solved and
no new ideas to solve it are in sight (Rakover 1993 and the discussion
following his paper).  Rakover (1990) claims that problems with
explanation of the mind-body relationship create discontinuity in
psychological research and prevent continuous progress in significant
part of psychology.

1.2.  The problem of qualia, or of the qualitative character of our
experiences, is another manifestation of the fundamental problems in
cognitive science.  Symbolic systems do not admit qualia and for many
people it seems obvious that artificial systems cannot have genuine
experiences, feel pain, have hopes or any autonomous purposes, that
something mysterious is missing in computational approach to cognition
(Penrose 1989).  Yet it is hard to pinpoint what that ``something"
should be.  I am not counting here the naive statements that quantum
mechanics has something to do with it since the concept of consciousness
based on quantum effects, advocated by some physicists, does not help to
explain the numerous psychological facts related to conscious human
behavior nor does it address the problem of qualia.

1.3.  The symbol grounding problem (Harnad 1990) captures the difficulty
of connecting the purely formal world of symbols and rules for creating
other symbols with the mental world.  ``How can the meanings of the
meaningless symbol tokens [...] be grounded in anything but other
meaningless symbols?" (Harnad 1990).  The frame problem may be treated
as one of the symptoms of the symbol grounding problem (Harnad 1993).
Most of the philosophical objections to the strong version of artificial
intelligence, like Searle's ``Chinese Room" argument (cf.  the
discussion in Hofstadter and Dennett, 1981), or formal arguments of
Penrose (1989), are problems with defining a meaning in symbolic
systems.  The Central Paradox of Cognition (Smolensky et al 1992) is yet
another statement of the same problem: how can the structure and
meaning, expressed in symbols and ideas at the mental level result from
numerical processing at the brain level?  How can the formal theories of
grammar, logical reasoning and higher mental faculties explain the full
richness of human behavior?

1.4.  Solution to all these problems, in full agreement with intentional
realism as well as causal efficaciousness of naturalistic properties, is
proposed below.  It is based on a better understanding of mind events
and their relation to symbols.  The price to be paid for this solution
is not high: contrary to the hypothesis of Newell and Simon (1976) the
physical symbols system is not a sufficient basis for intelligence.
Scientific theories require precise, realistic language to speak about
the objects and events in their specific domains.  The language of
rules, logic and symbol processing does not describe real mind events.
The language of neuronal excitations is also inadequate.  A language
more suitable for cognitive science is described below.

2.0 THE LANGUAGE OF MIND EVENTS

2.1.  Mindless bodies and disembodied minds are products of imagination.
There are many reasons to believe that minds are emergent properties of
brains rather than to believe that brains are just receivers of minds,
as Eccles (1994) does.  Paleoantropological data slowly uncovers the
origin of mental concepts (Sheets-Johnstone 1994) showing that the
living body was essential to generate, by tactile--kinesthetic
experiences, the basic concepts used in hominid thinking.  Computational
models of the infant's development (Rutkowska 1994) show little doubt
that basic human concepts emerge from complexity of neural systems
interacting with environment.  Experience is intimately connected with
bodily reactions that may be evoked by sensory stimuli or by
recollection of previous such events.  Memories are really ``chunks of
experiences" that left their imprints in the neural hardware.  Mind
develops in neuronal systems by ``en-folding its environment" (including
one's own body), therefore mind is a reflection of the Universe with its
unpredictable events.

2.2.  It is impossible to talk about mind events in terms of
computational processes of neural hardware, as it is impossible to talk
about properties of the liquid water in terms of properties of the
hydrogen and oxygen gases or even properties of the individual molecules
of water.  Cooperative properties in interacting systems are always
qualitatively different from properties of the constituents.  It is not
surprising that specific interactions of a large number of neurons give
rise to qualitatively new properties.  There is nothing strange in the
emergence of the higher cognitive functions from the complexity of the
brain.  At each level of description an appropriate language and theory
should be used.  The flow of water in a river is described by
hydrodynamics, the theory that uses specific concepts that cannot be
directly derived from quantum mechanics.  It is impossible to speak
about mental events using directly the language of neural events as it
is impossible to speak about turbulence in quantum mechanical terms.  An
appropriate language should be created for cognitive sciences.  Mind
events are given by direct experience while the existence of all other
events is inferred from this experience by reasoning.  Therefore
cognitive science has to be based on mind events and has to connect
these mind events with the computational processes in the brain.

2.3.  The question ``what is the mind" begs for a model, a language to
speak of the mind.  Is it possible to answer such question?  Ultimately
we may only explain what is the structure of the mind, as we can explain
what is the structure of the matter, even though we cannot define what
the matter is.  In physics we use with great success concepts such as
energy, space or time, concepts that are not reducible to simpler
concepts.  Ultimately the meaning of fundamental concepts should be
grounded in direct common sensory experiences.  The vicious circle of
self-referential definitions is stopped by pointing directly to real
events.  What is the taste of chocolate?  Only when you taste it you
will know.  What kind of an answer can we expect to the question: ``what
is the mind" ?  Will an answer based on physical concepts, such as ``the
mind is a result of zero point vibration vacuum fluctuation interactions
with the coherent states of microtubles" satisfy us?  Or an answer
claiming that the mind is a result of logical properties of some
mathematical structures?  Such answers are irrelevant to the
understanding of cognitive processes and to cognitive sciences in
general.  A meaningful language to describe mind events is needed.  It
should allow for more precise description and should lead to models of
the mind that can be tested in computer simulations.  The language
should be extendable but at the initial stage it should only cover the
basic features of the mind, in a similar way as the mathematical
language of classical physics allowed to describe planetary motions and
other simple physical systems.  The ``time" of classical physics has
little to do with the perception of time and yet proved to be a very
useful concept.  In the same way we should define a set of simple,
useful concepts that will allow for more precise description of mind
events.

2.4.  In the Platonic world there are only abstract concepts,
represented by symbols.  Since language is based on linear sequences of
symbols we tend to think that minds work by symbol manipulation.
Two-years old children may not yet use symbols and yet their minds are
already quite complex.  What are the real mind events made from?  They
are composed primarily from the preprocessed sensory data and are mostly
recognitions, recollections and memorizations.  The preprocessing stage
of sensory information is very important and much of the research in
cognition is devoted to understanding how the low-level cognitive stages
operate.  Direct sensory perceptions and iconic memories of past
experiences form primary mind objects, while secondary, more abstract
objects acquire their meaning through their relations with the primary
objects.  Once a primary object, an experience involving bodily
sensations as well as the internal mind states, is committed to the
long-term memory it can be recalled back and the experience repeated
(mental training, for example in music, really works).  Some mind
objects, even though conventionally designated by a single symbol, are
very complex.  They should be treated rather as a collection of
interrelated simpler objects, occupying relatively large volume of the
mind space, than as a single object.  Probably the representation of the
cognitive system itself, referred to as ``myself", is the most complex
of all mind objects.

2.5.  The process of formation of mind objects takes many months of
cognitive development.  In the ``inner space" infants reify very slowly
their experiences as combinations of shapes, colors, smells and
somatosensory data into fuzzy objects that are distinguishable from each
other and are committed to long-term memory.  My son once remarked when
he was 7 years old that ``words never end".  Even a child can see the
problem, as well as the solution: some words refer directly to sensory
experiences, reactions of organisms.  Words, labels for the concepts
that we use, acquire their meaning via mutual interactions and mutual
referencing, via context in which they appear.  Mind cannot be based on
the labels of objects only, as suggested by the formal symbolic approach
to artificial intelligence, but should be based on the multidimensional
mind objects.  If sufficient time is given for recollection or
contemplation of these objects they are experienced by the body.  For
example, thinking of an apple we recollect not just an abstract idea but
colors, shapes, smells and taste.  It is convenient to imagine that
these objects are in some space called here ``the mind space".  Mind
events are activations of entrained collections of mind objects.

2.6.  It is easy to imagine mind objects corresponding to some physical
object.  How about more abstract concepts?  Most of the symbols or
concepts that we use do not have separate existence, i.e.  if we had no
facts entangling these concepts with other concepts we could not define
them.  Any time we learn a new concept we have to define it in relation
to what we already know.  The more features (dimensions) are entangled,
the more relations to the existing facts are defined, the more precisely
defined the mind object becomes.  Children ask over and over questions
``what is this", ``is it like that" etc, and the formation of objects in
their minds requires many years.  The real grounding of the meaning of
these objects is in sensory experiences.  Even for blind and deaf people
like Helen Keller it was the sensory experience of water flowing on her
hand that opened the world of symbols to her.  A concept such as
``heavy" relates to the experience of trying to lift different objects
and is defined in reference to different life events; a concept of the
``even number" to experiences with division of numbers.  Any concept we
can think of is either related to direct sensory experiences or defined
in reference to other concepts.  Mind space is useful to accommodate
mind objects and model relations among these objects, as a stage for
mental events.  We are free to build models of the mind spaces starting
from a universal mind space that can 0 all imaginable mind events and
ending at a very small spaces, constructed for the problem at hand.
Every sensory or abstract feature or quality may define an independent
variable for representation of the data received by a cognitive system,
i.e.  define a new dimensions in the mind space used to accommodate the
data.  How to give adaptive structures ``feeling" of what is interesting
and what is boring, what is right and what is wrong?  How do we develop
such feeling?  In the learning process the input data and the symbols
appear in different context and relations among these symbols are
established, facilitating automatic associations (cf.  Miikkulainen 1993
to see how such associations are created in a natural processing
system).  These relations are subjective, depending on the data selected
for training and even on the order of presentation of the data.  The
sense of judgement developed in this way, purely subjective, is
sufficient to answer such questions as ``do you like it?", or determine
the author of a literary work from the ``feeling" of his style.

2.7.  The multidimensional space spanned by the axes corresponding to
the primitive features of inner representations is called the mind space
or the inner space.  Combination of these features define mind objects.
Some features of representation are of binary type:
existing-nonexisting.  Other features have various degrees, for example
numerical values or sizes.  A ``horse" has size, shape, head, tail,
mane, makes sounds, likes to run, eats grass and is defined by all these
and many other properties.  Each of these features is to some degree
fuzzy.  The symbol ``horse", as well as the few features of a horse just
mentioned, activates the corresponding object in our mind.  This
particular combination of features forms a unique category.  The meaning
of the object is grounded in the combination of all relevant features of
inner representation, some of them related to analog sensory data.  More
abstract information is represented as objects in form of kinestethic
image schemas (Johnson 1987; Lakoff 1987) and time-related information
is converted to spatial relations (Griniasty et.al.  1993).  One way of
modelling the mind objects is to represent them in the mind space as
fuzzy areas of non-vanishing density around the combination of features
that define them.  The shape of an object in the mind space has of
course nothing to do with its physical shape.  Ancient Indian and
Chinese philosophers understood the process of formation of the mind
objects quite well, for example in the Lankavatara Sutra (quoted from
Wilber 1985) the following description is given: ``By appearance is
meant that which reveals itself to the senses and to the discriminating
mind and is perceived as form, sound, odor, taste and touch.  Out of
these appearances ideas are formed, such as clay, water, jar etc, by
which one says: this is such and such thing and is no other - this is
name."

2.8.  Mind space is thus defined by the coordinate system based on all
features of internal representation.  Objects in the mind space are
defined by ``mind function" $M(X_i)$ for all relevant features $X_i$.
Nonzero values of the mind function define these objects as a fuzzy
regions in the mind space.  Topographical relations of objects in this
space are very difficult to imagine because of the large number of
dimensions involved.  Problems with understanding how the mind works are
to a large degree connected with the difficulties in imagining relations
in multidimensional spaces.  The mind function, defined in the mind
space, represents all objects that such system is able to recognize
(i.e.  correctly classify using partial description or distorted input).
Cognitive system is able to modify the contents of the mind space by
adding more objects (learning and remembering), modifying existing
objects or learning new associations (changing topographical relations
between existing objects).

2.9.  Mind states may have symbolic label as one of the features in the
mind space.  Symbolic name is not different from other features of
representation except that it is less fuzzy than most other features,
thus allowing for faster retrieval of mind objects.  We usually think
about symbolic names as some unambiguous (sharply defined) codes, string
of letters.  The low-level preprocessing may remove some ambiguities
from the sensory data reaching our minds but in general sounds or
written words are also fuzzy.  This fuzziness, expressed in different
styles of handwriting or different pronunciations leads sometimes to a
confusion.  In such cases symbolic names do not match the labels of mind
objects and additional properties are needed for correct activation of
``what we have in mind" in minds of other people.  Words have meaning
defined only by the objects they activate in minds.  A symbolic name
assigned to a combination of representation features increases stability
of the mind object by adding a sharply defined feature.  Many primitive
features used by the human mind, such as kinesthetic, visual or auditory
features, do not have symbolic names.

2.10.  At any given moment of time $t$ some of the features of
representation $X_i(t)$ have well defined values, pointing to certain
regions of the mind space.  These features of representation $X_i(t),
i=1,2...$, change with time.  A collection of these features is called
``mind state".  An object corresponding to a given mind state is
``activated" - the system recognizes this object and may respond to it.
The process of activation of mind objects, or the evolution of mind
states, has its dynamics.  States in the regions of high M-function
values slow the rate of changes of the mind state, ``trapping" these
states for some period of time.  It makes sense to talk about energy and
momentum of this dynamics.  Changing from one state of mind to another
requires energy.  The system receives this energy from the environment.
The external stimuli are driving changes in the features of
representation $X_i(t)$ leading to the recognition and learning
processes.  The internal dynamics leads to activations of the entrained
mind objects (Laughlin et.  al.  1990) and includes a stochastic
component influencing the momentum of changes of mind states.  The
dynamics of the whole system is a mixture of this internal and external
dynamics.  The balance between the two determines the level of
intro/extroverticity of the system.

2.11.  The probability of finding the system in a given mind state at a
given time $t$ depends on evolution of the mind state and on the value
of mind function for that state.  Some mind objects are more
``attractive" than others, they hold the mind states for a longer time
and are activated more frequently during the evolution of the mind
state.  Creation of new objects in the mind space requires time and
energy.  Frequent presentation of certain features of representation,
i.e.  frequently repeated states of mind, increases the value of the
mind function in the corresponding region, creating new or stabilizing
the existing objects in the mind space.  New objects or memory traces
have a tendency to decay if the same regions (the same states) of mind
space are not activated many times and are not linked via some
associations to other mind objects (i.e.  are not overlapping in some
dimensions with these objects).  Objects are connected via their shared
features (i.e.  similar projections on some subspaces of the mind
space), therefore an entrainment of objects occurs (Laughlin et.  al.
1990).  Transition probability $p(A->B)$ from the mind object A to the
mind object B may be expressed in agreement with the conditional
expectancy hypothesis of Sommerhoff (1990): ``The brain's internal
representations of the world consist of linked sets of conditional
expectancies of the what-leads-to-what kind, which are in the main based
on the past what-leads-to-what experiences of the world".  The
mathematical formalism useful for the description of actual dynamics
resembles quantum-mechanical formalism, with $p(A->B)=<M_A|P|M_B>$ where
$M_A$ is a certain mathematical representation of the mind object $A$
and $P$ is the momentum of the mind state in a given moment of time.
Simple recognition and learning processes activate only one object at a
time.  Once activated, an object is easily accessible due to the
short-term memory.  Therefore various objects in the mind space may be
active, depending on the state of the input variables (sensory data and
meta-node outputs).  In this way complex objects, composed from many
simpler ones, are analyzed in the recognition process and created in the
learning process.  The states of mind that lead to the strongest values
of the mind function leave memory traces and are remembered as ``an
experience", enabling feedback (reflection).

2.12.  Expressions such as ``to have in mind, to keep in mind, to put in
mind" and many others refer directly to the mind space.  It is not an
accident that popular ``mind maps" (Buzan 1989) are effective in
improving learning since they reflect, albeit only in a crippled,
2-dimensional way, real entrainments of objects that should be formed in
the mind space in the learning process.  In the mind maps method of
learning one identifies keywords, while other objects, such as words,
drawings, iconografic symbols or numbers, are linked to these keywords
creating a kind of semiotic net.  Multiple links partially reflect the
complexity of the multidimensional mind space containing objects
corresponding to these symbols.  Mind should be regarded as an open
system.  The number of dimensions in the mind space may change with
introduction of new features.  The topology of the mind space is time
dependent.  New mathematical tools are needed to deal with such spaces
and such functions.

2.13.  Human mind space, a container of concepts, ideas and memories, is
almost empty.  Although millions of mind objects are stored in it, many
sharing the same features, forming richly connected structures
(entrainments), most of regions of the mind space are empty.  Assuming
only 100000 features of representation (axes, dimensions), each taking
only two values (present-not present), the number of all possible
distinct objects in the mind space (in this case a binary hypercube) is
equal to $2^{100000}$ or about $10^{30103}$, an unimaginably large
number that has nothing to do with the number of objects stored or even
recognizable by the cognitive system.  Not all features are relevant for
each object, but even assuming that a combination of no more than 5
features defines an object still the number of all possible objects is
of the order of $10^{23}$.  The number of distinguishable objects that
may be stored in the mind space is almost unlimited (in practice
hardware realizations impose some limits).  The number of thoughts,
memory recalls and other mind objects that are activated during human
life is much smaller.  The duration of conscious human life is of the
order of $10^9$ seconds.  Assuming one mental event every second gives
around 1 billion events.  Thus the mind space is almost empty since only
a very small fraction of potentially distinguishable objects are
realized.  The number of concepts an average human uses is perhaps in
the range of $10^4-10^5$.  The number of other objects in the mind space
is even larger, including many iconic memories.

2.14.  The language introduced here allows to speak about various mind
events without leading to paradoxes.  The relation of this language to
the ``real" mental events is of course metaphorical.  The model is
useful for discussion of evolution or development of minds through
unsupervised and supervised learning in terms of the mind function and
of the topography of the mind space, discussion of recognition,
recollection, association, dynamics and many other mental processes.
This language is based on purely abstract, mathematical constructs, and
does not refer to anything physical.  We are free to build various
models of cognitive systems but if they should have any similarity to
real minds they should be based on real mind events.  The view of the
mind presented here is very different from conventional: it is a
dynamical system with evolution of the mind state leading to a series of
activations of various mind objects.  Symbols and language are of
secondary importance and logic can at best be used to approximate
evolution of the mind state in some circumstances.  Such system is
purely subjective and reflects the nonalgorithmic nature of its
environment.  Before discussing the cognitive paradoxes some remarks
about hardware realizations of the abstract concepts introduced here and
about the relations to neurobiology are presented.

3.0 HARDWARE AND BIOLOGICAL REALIZATIONS OF MIND

3.1.  In practice mind function $M(X_i(t))$ has to be computed by some
hardware.  It is natural to realize the mind function in a form of a
modular neural network based on localized representation of the mind
objects, each node processing a separate object.  Other realizations,
based on nonlocal (distributed) representations, are also possible.  The
model does not relay on distributed representations for generalizations
and associations, it is sufficient that features are represented as
fuzzy areas in the mind space.  Massively parallel computers with analog
inputs/outputs are most suitable for the hardware realization of the
mind function.

3.2.  As first approximation models of modular neural networks, such as
GaRBF models (Poggio and Girosi 1990, Platt 1991), CALM (Murre 1992),
ALCOVE (Krushke 1992), SONNET (Nigrin 1993) or FSM (Duch 1994; Duch and
Diercksen 1994) may be adopted to model the mind function.  Mind space
objects are organized in a hierarchical way: simple objects are created
in the subspaces connected to sensory features of representation.  These
primary objects have their linguistic labels and are formed from classes
of combinations of invariant features of representation (given by
low-level visual, auditory and somatory processing).  Metaobjects are
formed from abstractions of these primary objects and include measures,
numbers, simple physical concepts such as ``mass", ``force" or
``energy".  At the meta-metaobject level more abstract concepts, such as
variables, fields, wavefunctions and quantum objects are defined.  They
are not close to the primary objects and thus their meaning is hard to
understand or interpret since there is no direct relation to experience.
In mathematics even higher order metaobjects are created.

3.3.  Feature Space Mapping (FSM) is a neural network model based on the
separable processing functions of the network nodes.  Such network may
be trained using supervised and unsupervised learning procedures and is
equivalent to an expert system that uses fuzzy logic (Duch 1994; Duch
and Diercksen 1994).  The mind function and the objects of the mind
space are created using the unsupervised learning procedures and a
growing network of processing nodes.  Topology of the objects in the
mind space may be constrained (genetic conditioning).  Such networks are
capable of natural language processing (cf.  Miikkulainen 1993).  FSM is
a neurofuzzy system constructed from the mind space point of view.  The
systems has some features characteristic to the biological cognitive
systems, although it was not designed to be the model of such systems.
For example, humans use linear evaluation in many situations, using
terms such as `worse' or `better' for comparison of frequently quite
incompatible data or multidimensional data that cannot be linearly
ordered.  In the mind space this tendency may be linked to the measuring
of the distance of different mind objects relatively to some point of
view.  Another aspect of this linearized thinking is expressed in the
way problems are solved by focusing on one aspect of the problem at a
time.  The brain uses topographical maps for auditory, motoric, somatic
and visual actions, probably also for more abstract reasoning, so
mapping to the low-dimensional spaces is frequently done.  Low-level
processing and topographical mappings (Kohonen 1989), in particular
semantic topographical mappings can explain some aspects of
psychophysical relations among objects of mind.  In the mind space model
these mappings are related to dimensionality reduction allowing for data
compression.  Another interesting feature is the ability to focus on
general rather than specific and return to details after general
understanding has been reached.  Such defocusing and focusing operations
are used for classification in FSM (a dynamical version of the bumptree
structures is used to speed up searches in multidimensional spaces).
People can reach the same conclusion or express the same fact in many
alternative ways.  Mind space admits many pathways since there are very
many dimensions and possibilities of associations.

3.4.  Although in cognitive modeling neurobiological plausibility should
not be as important as psychobiological plausibility a few remarks about
relations with neurobiology are in order.  The low level sensory data
processing and formation of the feature detectors are described best
using the attractor neural networks (Amit 1989).  Assemblies of neurons
may specialize in recognition of certain mind objects or representation
of objects in some regions of the mind space.  For the symbol grounding
problem it is important to note that the forward cortical projections
are accompanied by prominent projections back to the original sites.
The signals in neuroanatomical networks may flow back and forth to
sensory receptors and subcortical brain structures, making the
activation of an object in the mind space by an internal dynamics almost
indistinguishable from the external activation (Damasio 1989).  Results
of recent experiments on processing words and pseudowords (Pulvermueller
et.al.  1994) support the hypothesis that transcortical cell assemblies
are involved in recognition of mind objects.  These cell assemblies are
large groups of neurons, with strongly reciprocal internal connections,
binding parts of the cortex in which different sensory modalities are
processed.  Transcortical cell assemblies are sufficient to create
objects of the mind space, i.e.  to bind different sensory modalities in
one experience, without any central place in the cortex where all
information is gathered.  Interactions among various wave patterns in
these cell assemblies are modeled in a probabilistic way at the mind
space level.  The state of mind may become fuzzy, many objects partially
activated, many things ``going on" in the mind -- if there are too many
the state of the mind becomes chaotic (formally a ``mental temperature"
global parameter controls the overall activation).  Experiments with
monkeys show that neural activity persist for as long as 16 seconds
after the removal of visual stimulus.  Such experiments may be analyzed
in details using models based on attractor neural networks (Grianiasty
et.al.  1993).  From mentalistic point of view they may be described as
creation of new objects (visual memories) in the abstract mind space
with immediate access to most recently activated objects.  In the
experiments with monkeys trained to recognize visual patterns
temporarily correlated reinforcement stimuli are changed into spatially
correlated attractors (Grianiasty et.al.  1983); in the mind space
temporal correlations are creating entrainments of objects.

3.5.  The state of mind is constantly changing because of the incoming
sensory stimuli, including the proprioreceptive stimuli (reptilians do
not have such strong proprioreceptive stimuli and their minds dwell in
one state for a long time).  In some cases (sensory deprivation) one can
observe the dynamics of mind state without sensory stimuli, driven by
the internal noise of the system.  Activation of a mind object is
equivalent to a wave front of activations of hundreds of thousands of
neurons.  Assuming about $10^{5}$ cells in each assembly and $10^{11}$
neurons in the whole brain the number of independent cell assemblies
available for representation of mind objects would be of the order of
one million; since many assemblies may share the same neurons it may be
even larger.  Sensory stimuli signifying potential danger requiring
urgent attention demand computing power of a good part of the brain,
integrated reaction of the whole organism.  Storing such experiences in
long-term memory and recollecting or reliving them has strong
evolutionary advantage.  This could have been one of the major factors
in the development of consciousness.  Biological solution is not
necessarily the simplest realization of the mind function.  Since cell
assemblies specialize in recognition of different mind objects there is
no need for more detailed modeling at the actual distributed neural
network level.

3.6.  Specific ``mind landscape" is formed in the infant and
developmental period primarily by the unsupervised learning process, in
which many new synaptic connections are added, enabling new neuron
assemblies to be formed and store new mind objects.  In the childhood
and later periods more supervised learning is involved.  In adults mind
space objects become more rigid, entrainments of these objects more
stable and the mind states have stronger tendency to repeat themselves.
New mind objects are created with greater and greater difficulty.  Near
the end of life no new objects are created (including memory traces) and
the old, if not activated frequently enough, are forgotten - even if
they are not completely decayed the entrainment leading to their
activations may be lost (hence the tendency to repeat the same stories
many times).  To understand the complexity of developed, conscious mind
one should understand the development process, animal minds, hominid
minds before language was invented and the stages of personal,
especially infant development.  Transformations of consciousness (Wilber
et al 1986) start from pre-personal, sensorimotoric level.  In the first
two years general features of the mind's space topography are formed.
Most of the primary mind objects are formed between the second and
seventh year of life and metaobjects start to form only later.  The
self-reflection stage is reached only between eleventh and fifteenth
year.  Modelling cognitive system of an intelligent human is certainly a
very difficult task but good models of less developed minds (animal and
handicapped people) should not be so hard.

3.7.  Logical reasoning and problem solving is rarely used by people.
Even medical doctors, after many years of studying, relay mostly on an
intuitive knowledge acquired during their practice.  In the model
presented here intuition results from topography of objects in the mind
space.  In intuitive response there is no reasoning involved, just a
look-up of associative data in the mind space.  Reasoning requires
access to various theoretical concepts and may be done only by small
steps.  Many people will not even try to answer a question if they
cannot do it intuitively.  Folk medicine was always based on
observations with little theoretical justification.  The knowledge
stored in the mind space is almost static, changing slowly with
observations or with the new training data coming in.  Intuitive
knowledge, hailed through centuries in many cultures, for example as the
tao principle, may be identified with the quality of the inner
representations of the environment formed in the process of unsupervised
learning.

3.8.  Complex objects that do not have short symbolic name require
dynamic analysis.  Translating from English to my mother's tongue single
words or short idiomatic expressions come up in my mind immediately,
they are ready associations, objects ``in place".  Longer expressions or
sentences must be restructured according to the grammatical rules of the
target language.  Complex, temporary mind object has to be constructed
in the short term memory from available simpler elements, i.e.  known
objects that have linguistic labels.  This complex object is now labeled
by an appropriate expression or a sentence in the target language.  This
linguistic ``deep structure" is not reducible to purely symbolic objects
and therefore linguists have failed in their search for it.  Language
does not have to be verbal, for example language of the movies is
primarily visual.  Although linguistic processes are very important for
understanding of human cognition they are irrelevant in context of the
fundamental problems of cognitive sciences discussed in this paper.

3.9.  It is obvious that some conscious phenomena experienced by humans
may be explained only by taking into account the structure of real
brains.  For example, conscious perception of reality is distorted by
slow operation of neurons.  One of the most interesting illusions is the
color Phi phenomenon (cf.  Dennett 1991): two lights, red and green, are
placed close to each other and switched on and off in a sequence every
0.2 seconds, creating an illusion of a moving light.  The color of this
moving light changes right between the two lights.  It looks as if the
mind synthetising this illusion from the sensory data could predict
future: how do we know that the green light will be turned on in 0.2
second so that the red light seems to move towards it?  The neurons
operate slowly and the brain needs time to put the mind object together
from different features of representation identified by the low-level
subsystems.  The subject of conscious experience is not the reality
itself but the picture of reality build from mind objects and
combinations of primitive features.  The visual cortex contains separate
subsystems for the color and for the motion detection.  Once a
recognition by these systems is made the brain network structures stay
excited for a few tenth of a second; blinking of the other light
activates the motion subsystem and the combination of these two
features, the color and the motion, gives the illusion of moving color
light.  The time frame of 0.1 second is the shortest quanta of time for
processes consciously perceived because at least that much time is
necessary to form a mind object from combination of different sensory
features.

3.10.  The evolution of the mind states leads in a natural way to the
dreaming and day-dreaming.  During the night dreaming a number of mind
objects may be activated or created from various primitive objects.
Since there is no overall guidance of the senses correcting badly formed
objects in the short-term memory strange combinations of features and
mind objects are possible.  In sensory deprivation situations (for
example in the ``samadhi tanks") mind space is explored.  If mental
activity is suppressed in deep concentration certain granularity of
experience should be expected, due to the wandering of the mind state
through many dimensions of the mind space.  Symbols, feelings, trains of
thought and moments of attention arise, are noticed (by the reflective
feedback mechanism) and vanish.  This is exactly what is reported during
intensive meditation and concentration practices (Laughlin et al 1990).
Regression during hypnosis leads to very detailed description of events
from the distant past.  The details recalled seem to be frequently
imagined rather than remembered (O'Connel et.al.  1970).  This is what
one should expect: there is a slow decay of memories and substitution of
averaged values for forgotten values.  If a few features of some mind
object are recalled the state of mind points to a subspace of the mind
space containing similar objects and the missing features are obtained
from the similar (neighbor) objects completing the mind object recalled.

4.0 SOLUTION TO THE FUNDAMENTAL COGNITIVE PARADOXES

4.1.  The mathematical language introduced in the previous sections
tries to capture the essence of operation of the real mind.  The mind
function is defined in the mind space and created via process of
unsupervised learning and genetic evolution.  The natural hardware
realization of this abstract model is in the form of modular neural
network.  Nodes of such network model activity of the transcortical
neural cell assemblies in biological brains.  Evolution of the mind
state and activation of the mind objects are mental, nonphysical
processes.  Parallel changes in the supporting hardware are physical
processes.  Physical and mental are just two sides of the same
phenomenon and the operation of the mind system may be viewed from two
sides.

4.2.  Since mind objects are reified experiences there is no mind body
problem.  How can a state of mind, such as a ``low gas" observation,
lead to an intentional, physical action?  If the mind is in the state
represented by the ``low gas" mind object the next probable train of
states (only probable, because many factors may push the next state of
mind in another direction) is ``car will stop" and ``look for the gas
station".  These mind objects were created in the learning process and
once they are in the mind space the motoric actions associated with
these objects will follow.  The brain processes (or other hardware
processes) have to follow this particular entrainment of mind objects
because the only stable patterns of neural excitations correspond to the
objects that are entrained.  These objects are close to each other in
the mind space (i.e.  transition probabilities among these objects are
high) and at the hardware level corresponding attractors in the
recurrent neural network (Amit 1989) are close to each other.  The
presence of a non-physical concept in the mind has thus direct influence
on the hardware states.  My belief, via the entrainment process, leads
to a series of states of mind.  Since each state is grounded in the
kinestethic image and sensoro-motoric schemes it has direct influence on
hardware actions - the ``mind over matter" mechanism is quite simple.
On the other hand any disturbance in the natural functioning of the
neural hardware (especially at the level of neurotransmiters in the
brain) will strongly influence the dynamics of the mind states and thus
experiences of the system.

4.3.  The symbol grounding problem (Harnad 1990) is solved in an
straightforward way, together with the problem of qualia.  Each mind
object, such as a memory object, is created from a combination of many
features of internal representation.  An activation of the object,
equivalent to some state of the mind, is done using a subset of these
features.  Since it brings the state of mind into a specific region of
the mind space other qualities associated with this object are
immediately accessible, and the back-projection paths to the sensors
activated.  The experience is repeated, with vividity dependent on the
strength of the back-coupling and the level of activation of the object.
What do I mean by ``sweet"?  Something sweet!  The system, like an old
Zen master hitting with his stick, points directly at the sensory
experiences as the ultimate source of grounding the mind in reality.
The problem arises only when the linguistic labels are separated from
other qualities of the objects of mind and when someone insists that
such artificially created subsystem should act as a cognitive system.
We do not ground symbols in experiences since experience comes before
symbols, we label experiences.  The label ``sweet" corresponds to a
projection of all sensations, all objects that we have associated with
it.  The existence of qualia has observable consequences: the
probability of the next mind state obviously depends on them.  From the
sensation ``sweet" memories of things sweet spring up.

4.4.  The problem of the mental content is a particular form of the
qualia problem connected with the mental objects.  Thoughts must be
about something since they are activations of mind objects containing
reified experiences, are non-decomposable mixtures of many features of
representation.  Cognitive systems must have subjective states which
results from their past experiences.  Chomsky's ``poverty of the
stimulus" argument is also easily refuted if the mind space is taken
seriously.  A single sensory stimulus or a single word may put the state
of mind into a region of the mind space where associations abound.
Words, or abstracts symbols, are particularly effective in activation of
mind objects because - in contrast with analog features of
representation - they are more specific, they provide labels uniquely
identifying the regions of the mind space where ``chunks of our
experiences" are stored.

4.5.  The language of mind events allows also to understand from a new
point of view the problem of free will.  The discussion presented here
agrees with Crick's approach (1994), although is more specific.  Suppose
that the system ``has two choices", i.e.  there are two different
reactions possible in response to the data presented.  This means that
two objects in the mind space may become activated, each of them
associated via some common features with motoric or other actions.  The
evolution of the mind state may be indeterministic even though it may be
predictable.  There are two sources of randomness, external and
internal.  The evolution is indeterministic if the environment,
reflected in the topography of mind objects, is indeterministic.
Complicated interactions with other objects in the mind space, among
them with the representation of the self, depend on random events in the
environment and have an influence on the final decision of the mind.
Biological realization of the mind has also internal source of
randomness: neuronal computations are subject to rather strong noise in
the nervous system.  Our ability to solve complex optimization problems
is connected with the fact that some NP-hard algorithmic problems are
greatly simplified if the search for the best solution is given up in
favor of the search for the sufficiently good one.  Randomized
computing, with chaotic components, allows to solve problems of high
complexity in suboptimal way.  The decision may be predictable with high
confidence if the topography of the mind space is known and the internal
noise is low.  Predicting decisions of some people is not hard, although
usually the initial conditions for such predictions are not know
precisely enough.  From the mental point of view the mind has a free
choice since the system has no direct access to its own hardware; from
computational point of view decisions are made by the hardware (to some
degree randomly, if the internal noise is taken into account) and are
based on a non-algorithmic process (if the environment states are
non-algorithmic).  Each decision creates a new mind object that
interacts through various associations with other objects leading to new
states of mind that may lead to quite different decision.

4.6.  Symbol processing systems cannot reach real understanding, even if
they could pass the Turing test or solve the frame problem (I doubt that
it is possible).  Thermostats do not have qualia nor are qualia present
in the Chinese Room example of Searle (Hofstadter and Dennet 1981).
Object processing systems, grounded in sensory experiences, are not
reducible to purely symbolic computations.  In fact symbols may not be
necessary at all for mental processes.  Early hominids, before language
was invented, had minds.  Children that grew up in a jungle fed by
animals also had minds, and there is no reason to suppose that infants
or animals do not have minds, although sophistication of their minds is
far from that of a mind of a philosopher.

4.7.  In short, the root of the mind-body and related problems in
cognitive science lies in the attempt to use a wrong language for
description of mind events, language based on a separation of the
linguistic labels from the real mind objects.  There is no way of
communication that would use mind objects directly.  We are fooled by
our use of language, of verbalization distorting the totality of our
experience: words are like Platonic shadows on the wall, linearized
descriptions that cannot do full justice to the multidimensional
reality.  We are inventing more words, increasing the number of
linguistic dimensions, but still we deal only with the shadows.  Poper
(1972) calls the world of culture and concepts the C-world: it is based
on the B-world, the world of psychological events.  The mind space is
the B-world while the collection of many mind spaces defines the
C-world.  These worlds require cognitive systems and their inner (mind)
spaces.  Information stored in the books and films has by itself no
meaning - this meaning is in the mind spaces of cognitive systems.

5.0 TOWARDS A THEORY OF CONSCIOUSNESS

5.1.  Consciousness seems to be a very elusive subject partially because
most people have not really observed their own mental processes and
relay on myths and illusions.  The study of consciousness became an
interesting subject of hard science only quite recently.  Many
institutes and organizations involved in such studies devote their
energy in pursuit of mysterious properties of mind, such as the
paranormal phenomena.  Why should such phenomena have any bearing on the
question of consciousness?  They are interesting in their own right but
it is very unlikely that they are necessary faculties of the conscious
mind.  We should examine necessary conditions to consider someone to be
conscious.  Are infants conscious?  Observations of the development of
infant's brains show close correlation between the density of neuronal
connections and the ability to represent complex sensory data and form
internal representation of the world.  Kant was to a large degree right
claiming that we see only those forms that our perception allows us to
see.  For each one of us the world is something different, something
created by our own mind.  Looking at the brain tissue of the developing
mind one can see it quite clearly: we are born with many neurons that
are only potentially available, while complexity of the environment and
repetitive sensory patterns slowly ``wire themselves" in the brain.  If
the feature detectors are not developed the mind does not perceive.  For
example, one can train the auditory system to recognize the phonemes of
Vietnamese language, but without such training Vietnamese words will not
be remembered nor recognized.

5.2.  The brain constructs what we call ``reality" using massive
computational power.  Even the brain of a fly makes $10^9$ computations
per second.  Human brain performs on the order of $10^{16}-10^{18}$
operations per second.  This huge number of computations creates an
illusion of continuous reality.  In fact reality is reconstructed from
discreet events, for example visual awareness is reconstructed from the
rapid eye movements.  Time resolution of the visual awareness is of the
order of 0.1-0.2 seconds.  If the mind state is trapped for a shorter
time in a given area of the mind space short term memory traces are not
formed and the experience does not become conscious.  The brain performs
many complex computations in parallel.  Most of the actions and stimuli
do not reach our awareness.  What makes some processes conscious and
other not?  In the development of complex organisms the emerging mind
must have had a center, a concept of self that should be preserved,
influencing reactions of the whole organism in case of a danger.  Such
reactions were memorized and reflected upon.  Peripheral computations
were done in parallel, leading to automatic behavior that does not leave
traces in the memory.

5.3.  Why consciousness exists?  Because our cognitive system is
sophisticated enough to form a representation of itself and, as it is
the case with all mind objects, it has qualia connected with this
representation.  The representation of ``myself" is done by an unusually
complex set of objects connected with very strong survival reactions.
In the brain some of these self-related objects are realized by
transcortical cell assemblies connected to subcortical brain structures
controlling emotions.  Therefore qualia related to these objects are
unusually strong, with the mind state resonating between the ``I" and
all nonverbal qualia connected with it.  Consciousness seems to be
strange because it is impossible to verbalize such states of mind.  It
has to exist as a consequence of the fact that the mind space is
composed of real experiences, not just their linguistic labels, and
these mind objects are not decomposable into artificially separated
components.  Selective damage of the brain may lead to dissociation
between the concepts and their labels, observed in some forms of
aphasia.  Since features of representations for different sensory
modalities are determined by different, spatially separated parts of the
cortex their integration by transcortical cell assemblies to form a
total mind object is not always perfect.  Short and long-term memory
mechanisms contribute to the feeling of awareness.  Perhaps the degree
of being conscious should be measured by the degree of concentration.
Focusing on something brings the subject to awareness only if we can
hold to it for a while.  This is a resonance process, mediated by the
short-term memory, visual or other inputs and the long term memory,
necessary for recognition of the input patterns.  Sustaining this
resonance for a longer time creates patterns of excitations in the
brain, influencing subcortical brain structures responsible for bodily
changes, hormone secretion, feelings.  Conscious states are created when
the activity of the brain leads to the mind states trapped in the
objects of mind space for sufficiently long time (in case of humans
0.2-2 seconds).  Consciousness emerged together with the growing
sophistication of the mind space.

5.4.  What is subconsciousness?  Not all objects are activated strongly
and long enough to become conscious, i.e.  subject to reflection leaving
memory traces.  There are many low-level mental processes (for example,
detecting features of sensory data, internal day-dreaming states) taking
place at the same time.  Many sensory experiences also form weak memory
traces.  The mind does not reflect on them, but since they are stored in
memory they may be activated and reflected upon later.  Such
subconscious objects have nevertheless influence on the mind-state
dynamics and thus on the qualia and the behavior.  More interesting
subconscious processes are connected with repressed desires and
feelings.  In the hierarchy of objects and metaobjects of the mind some
higher order metaobjects are controlled by others that may prevent their
full activation.  In psychological terminology they are suppressed by
the ego, are shadows of the persona.  Mind states that activate objects
close to some central objects - like the mind objects representing the
ego - are controlled by these central objects.  Transition from these
mind objects may be so quick that their activation is never sufficiently
long to leave memory traces and become conscious experience.  Ego
concepts are very hard to modify, because any such attempt is considered
to be threatening and via connections to the subcortical structures
other mind objects with strong fear component are activated, changing
completely the state of mind.  This mechanism was created by the basic
survival instinct.  Very similar mechanisms are used for brain washing.
Central idea (a symbol, an object) is deeply implanted in the mind of a
brainwashed victim and it becomes very important and attractive to all
mind states in this region of the mind space.  It controls the access to
other mind objects connected via common features with this central idea
mainly by coupling to fear subcortical centers.

5.5.  Abhidharma, the early Buddhist psychology, is the most detailed
analysis of consciousness that exists.  It is not based on speculations
but on logical analysis of mental observations.  Introspection may
reveal the true nature of consciousness.  Experience in Zen and
Vipassana meditation techniques has produced understanding of the mind
expressed already more than 1600 years ago in the ``Heart sutra", the
holy text of the Mahayana Buddhism tradition, to this day repeated every
day by millions of Buddhists: ``Form does not differ from emptiness,
emptiness does not differ from form.  That which is form is emptiness,
that which is emptiness form.  The same is true of feelings,
perceptions, impulses, consciousness." From the Western perspective
translation of the Sanskrit word ``sunjata" as ``emptiness" is
problematic: a better word (Wilber 1985) would be ``unboundedness" or
``inter-relatedness".  The objects of mind, when reflected upon in
details, dissolve into features and than emptiness or unboundedness and
from this emptiness they are reconstructed.  A good analogy is provided
by the microscope: matter dissolves into atoms finally into emptiness
under very high magnification.  The final answer of the introspection
masters repeatedly stated since ancient times is: there is noone who
sees and noone who hears, the self is just the sounds, sights and
thoughts.  The meaning is found at the level of total integration of the
mind and body - the features of the mind objects cannot be decomposed
into the separate sensory/motoric and abstract thought subspaces.

5.6.  Some scientists, notably the brain experts W. Penfield and J.
Eccles (1994), came to the conclusion that the brain may be just a
receiver, rather than creator, of consciousness.  This is a fascinating
possibility, but not far from the idea that everything happens as it
does not because of the physical laws but because of the God's will.
The idea that consciousness is the emergent property of the complex
brains is the simplest and the most fruitful.  It allows us to formulate
scientific and engineering projects that in the end will either show
that some mysterious element is missing or that consciousness results
from the complexity and organization of the brain-like structures.  Of
course even creation of an artificial conscious system will not convince
dualists that the world of spirit is a product of brain's activity:
dualists will claim that a receiver has been built.  Mind develops as a
result of interaction with the environment.  Although this is a trivial
statement, it is very important and often forgotten.  Disembodied minds
may of course be imagined but they lead to many artificial philosophical
problems.  Psychological literature contains many descriptions of those
unfortunate beings that were deprived of healthy interactions in the
critical infant and childhood periods and as a result did not develop
conceptual categories and abilities important in human life.  We know
very little about complex systems and their emergent properties.  Why
should an explanation of consciousness require non-material or
non-physical elements?  Cognitive science and neuropsychology can answer
many precisely posed questions.  The question about the nature of
consciousness brings us very quickly to the question about the nature of
the self.  This is one of these questions that people avoid facing by
all means, including creation of very elaborate theories.  In essence it
leads to genuine mysticism, although transpersonal psychology writers
make it more acceptable to the Western mind (cf.  Wilber 1985; Wilber at
al 1986).

6.0 CONCLUSIONS

6.1.  From the point of view of cognitive philosophy mind is of primary
importance, realization of the mind in the form of a brain of secondary.
Various physical realizations of mental processes are possible.  Since
mind states are described in purely mentalistic terms they are not only
epiphenomenal or identical to some material (brain) processes.  The
structure of mind determines its function more than the supporting
hardware does (provided that it allows for forming sufficiently complex
structures in a stable way).  An object in the mind space may have
motoric components as well as purely symbolic components.  In simple
cases it is possible to find experimentally correlations between the
activation of an object of mind (such as a visual or an auditory object)
and coherent excitations of neurons assemblies necessary to support this
activation (Griniasty et.al 1993, Pulvermueller et al 1994).  In more
complex inner representations many spatially separate regions of the
neocortex cooperate and therefore such correlation will be much more
difficult to find (Damasio 1989).  It is more natural to talk about
mental events in psychological terms rather than in a low level
neurological terms.  The mind-body problem is created by false
identification with the mind only instead of the whole mind/body
organism (Wilber 1985).  Linguistic labels cannot be separated from
other features (including motoric features) of the objects of mind,
although many educated people, blinded by the power of language, strive
to do so.  Artificial intelligence based on symbolic systems does not
lead to artificial minds (and probably also not to real intelligence).

6.2.  Fundamental models in cognitive psychology should be based on
psychological and biological facts and cannot be deduced from
speculations on disembodied forms of mind.  They are also hard to deduce
from neurological data.  Applications of models starting from first
principles to really complex situations may not be fruitful at all.  The
reductionist program in biology and even in chemistry has not been very
successful.  Phenomenological concepts in chemistry are still widely
used, concepts that cannot be deduced from quantum mechanics.  The model
of the mind presented in this paper is phenomenological.  It allows to
introduce concepts of self-organization of mind objects by supervised
and unsupervised learning, and it enables direct modeling of objects in
the mind space.  Fundamental approach, i.e.  trying to create a model of
the mind starting from the neuronal level is much more difficult.

6.3.  Cognitive modeling based on neural networks explains the details
of many processes, such as distributed memory storage and associative
recall of memories, resistance of the system to damage and noise,
independence of the retrieval time on the number of facts, formation of
cognitive topographical maps (such as somatory and tonotopic maps),
various kinds of aphasia resulting from selective damages of the brain
areas, formation of concepts in children, specific phenomena like the
color Phi effect and lots of other processes.  The solution to the
cognitive science paradoxes presented in this paper indicates, that it
should be possible to explain all phenomena using cognitive modeling.
There is no need for quantum mechanical explanations on the cognitive
level.  Quantum mechanics is important at the neurotransmiter level, but
we are not able to applied it with success even to a single protein
molecule not to mention such complex systems like the brain of a snail.
Why should consciousness emerge from some mysterious quantum mechanical
principles only at certain level of complexity of the brain if there is
no need for quantum mechanical explanations for simpler brains?

6.4.  The details of the model presented here are not yet fully
developed.  The model has been already useful to create new, universal
adaptive systems that can learn and reason (Duch 1994; Duch and
Diercksen 1994), but the real challenge is to understand human minds.
Not all features of internal representations are known, methods of
elucidation of topography of the mind space objects should be found,
detailed connections with neuropsychology worked out.  As a first
approximation stationary or almost stationary features should be
considered.  Some features of the semantic memory should be recovered in
this way.  Minds of very different types should be possible, depending
on the way the mind space is formed and the objects constructed from
experiences.  One can imagine strange virtual minds that would be
different from human minds in all respects and yet should deserve the
name ``mind" by virtue of having intentionality and genuine goals.  In
more distant future some coupling of human and artificial minds may even
be possible, with exploration of the inner space objects created from
the real sensory experiences replacing exploration of the physical
space.

This paper is available by anonymous ftp at the
address class1.phys.uni.torun.pl, file /pub/papers/kmk/m-blong.tex or
via WWW at the same address.

REFERENCES

Amit D.J, (1989) Modeling Brain Function.  The word of attractor neural
networks.  (Cambridge University Press, UK)

Buzan T, (1989) Use your head.  (BBC Books: London)

Crick F, (1994) The Astonishing hypothesis.  The scientific search for
the soul.  (Charles Scribner's Sons: New York)

Damasio, A.R.  (1989) The brain binds entities and events by
multiregional activations from convergence zones.  Neural Computation 1:
123-132

Dennett, D.C.  (1991) Consciousness explained (Little Brown, Boston)

Duch W. (1994) Towards Artificial Minds, Proc.  of I National Conference
on neural networks and applications, Kule, April 1994, pp.  17-28; Duch
W. (1994) Floating Gaussian Mapping: a universal adaptive system.
Neural Network World (in print)

Duch W. and G.H.F.  Diercksen (1994), Feature Space Mapping as a
universal adaptive system.  Computer Physics Communications (submitted)

Eccles J.C.  (1994) How the self controls its brain (Springer Verlag,
Berlin)

Fodor, J. (1987) Psychosemantics (MIT Press, Cambridge, MA)

Gallant S.I (1993), Neural network learning and expert systems (Bradfor
Book, MIT Press)

Griniasty, M, Tsodyks, M.V, and Amit, D.J (1993) Conversion of temporal
correlations between stimuli to spatial correlations between attractors.
Neural Computation 5: 1-17

Harnad, S. (1990) The symbol grounding problem.  Physica D 42: 335-346

Harnad, S. (1993) Problems, problems: the frame problem as a symptom of
the symbol grounding problem.  PSYCOLOQUY 4 (34) frame-problem.11

Hofstadter, D.R, Dennett, D.C.  (1981) The mind's I (Basic Books, New
York)

Johnson, M. (1987).  The Body in the Mind: The Bodily Basis of Meaning,
Imagination, and Reason.  Chicago: The University of Chicago Press.

Klir G.J.  \& Folger T.A.  (1988) Fuzzy Sets, Uncertainty and
Information (Prentice Hall, NJ)

Kohonen T. (1989) Self-organization and Associative Memory.
(Springer-Verlag, New York, 3rd edition).

Kruschke, J.K.  (1992) ALCOVE: An exemplar based connectionist model of
category learning.  Psychological Review 99: 22-44

Lakoff, G. (1987).  Women, Fire, and Dangerous Things: What Categories
Reveal about the Mind (Chicago: The University of Chicago Press)

Laughlin Jr, C.D, McManus J \& d'Aquili E.G.  (1990) Brain, symbol and
experience (Columbia University Press, New York)

Miikkulainen, R. (1993) Subsymbolic natural language processing: an
integrated model of scripts, lexicon and memory.  (MIT Press, Cambridge,
MA)

Newell A, Simon H. A. (1976) Computer Science as empirical inquiry:
symbols and search.  Communic.  of the ACM 19: 113-126

Nigrin, A. (1993) Neural Networks for Pattern Recognition.  (Cambridge
MA: The MIT Press).  O'Connell, D.N, Shore, R.E, \& Orne, M.T.  (1970)
Hypnotic age regression: an empirical and methodological analysis.  J.
of Abnormal Psych.  76: 32-43

Penrose, R. (1989) The Emperor's new mind (Oxford University Press)

Platt, J. (1991) A resource-allocating network for function
interpolation.  Neural Comput.  3: 213-225

Poggio, T. \& Girosi, F. (1990) Networks for approximation and learning.
Proc.  of the IEEE 78: 1481-1497

Popper, K. (1972) Objective knowledge (Oxford University Press)

Pulvermueller F, Preissl H, Eulitz C, Pantev C, Lutzenberger W, Elbert T
and Birbaumer N. (1994) PSYCOLOQUY 5(48) brain-rhythms.1.pulvermueller

Rakover, S.S.  (1993).  Precise of Metapsychology: Missing Links in
Behavior, Mind, and Science.  PSYCOLOQUY 4(55) metapsychology.1.rakover.

Rutkowska, J. (1994) The computational infant (Oxford University Press)

Smolensky P, Legendre G. and Miyata Y. (1992) Principles for an
integrated connectionist/symbolic theory of higher cognition.  Report
CU-CS-600-92, Computer Science Department, Univeristy of Colorado,
Boulder, USA.

Sommerhoff, G. (1990) Life, brain and consciousness (North Holland:
Amsterdam)

Wilber, K. (1985) No boundary.  Eastern and Western approaches to
personal growth.  (Shambala: Boston \& London)

Wilber, K, Engler J. and Brrown D.P.  (1986) Transformations of
consciousness.  (Shambala: Boston \& London)

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