1

SEMIOTIC FUNDAMENTALS OF INFORMATION PROCESSING

IN HUMAN BRAIN

Leonid I. Perlovsky

Air Force Research Lab., 80 Scott Rd., Hanscom AFB, MA 01731

Tel. 781-377-1728; e-mail: Leonid.Perlovsky@hanscom.af.mil

ABSTRACT

The paper discusses a mathematical nature of

signs and symbols, and relates it to information

processing and understanding, structure of the

mind and brain, learning, and pattern

recognition. I discuss past limitations of

algorithms and neural networks, combina-torial

complexity, the roles of concepts and emotions

in mind’s mechanisms, and various types of

logic underlying mathematical techniques. A

mathematical theory of semiosis, adaptive

processes of sign interpretation, is described; it

includes a similarity measure between signals

and internal representations and fuzzy dynamic

logic, a mechanism of the similarity

maximization. Mathematical mechanisms of sign

and symbol processing are presented and

related to the functioning of mind.

KEYWORDS: semiotics, symbols, fuzzy

dynamic logic, neural networks, emotions,

concepts, intelligent systems.

1. SEMIOTICS, MIND, AND BRAIN

Semiotics studies signs and symbols, which

are generally understood as entities designating

some other entities in the world or in the mind.

Using words like mind, thought, imagination,

emotion, concept represents a specific

challenge: people use these words in many

ways colloquially, but their use in science and

especially in mathematics of intelligence has not

been uniquely defined and is a subject of active

research and ongoing debates [

1

]. Whereas

standardized definitions come at the end of the

development of a theory (like “force” was

defined by the 2

nd Newton’s law, following

centuries of less precise usage) this paper

adheres to a following guidance: we need to

make sure that our definitions: (1) are

mathematically exact, (2) correspond to the

usage in scientific and mathematical community,

(3) correspond to the general usage. According

to a dictionary [2], mind includes conscious and

unconscious processes, especially thought,

perception, emotion, will, memory, and

imagination, and it originates in brain. These

constituent notions will be discussed throughout

the paper. Specific neural mechanisms in brain

“implementing” various mind functions constitute

the relationship between the mind and brain; we

will discuss how the mathematical descriptions

of mind are implemented in brain.

In mathematics and in “Symbolic AI” there

is no difference between signs and symbols.

Both are considered as notations, arbitrary nonadaptive entities with axiomatically fixed

meaning. But in general culture, symbols are

understood also as psychological processes of

sign interpretation. Jung emphasized that

symbol-processes connect conscious and

unconscious [3], Pribram wrote of symbols as

adaptive, context-sensitive signals in the brain,
2

whereas signs he identified with less adaptive

and relatively context-insensitive neural signals

[

4

].

In this paper I use “symbol” as a symbolprocess, corresponding to general notions of

symbol in culture and psychology. The symbolprocesses are closely related to the processes

of thinking, and a mathematical theory suitable

for the description of sym-bols is closely related

to the mathematical description of the working

of the mind.

A broad range of opinions exists on the

mathematical methods suitable for the

description of the mind. Founders of artificial

intelligence thought that formal logic was

sufficient [

5

] and no specific mathematical

techniques would be needed to describe the

mind [6]. An opposite point of view is that there

are few specific mathematical constructs, “the

first principles” of mind. Among researchers

taking this view is Grossberg, who suggests that

the first principles include a resonant matching

between lower-level signals [7] and higher-level

representations and emotional evaluation of

conceptual contents [8]; Zadeh develops theory

of granularity [9], Meystel develops hierarchical

multiscale organization with specific intra-level

closed-loop structures [

10]; and the author,

suggests similarity measures between lowerlevel signals and higher-level representations [11]

and the fuzzy dynamic logic [

12] among first

principles of mind.

2. MIND, LOGIC, AND COMPLEXITY

Understanding the meaning of signals

coming from sensory organs involves

associating the subsets of signals corresponding

to an object with internal representations. This

recognition activates internal brain signals

leading to mental and behavioral responses

involved in understanding.

Developing mathematical descriptions of the

very first recognition step of this seemingly

simple association-recognition-understanding

process has not been easy, a number of

difficulties have been encoun-tered during the

past fifty years. These difficulties have been

summarized under the term combinatorial

complexity (CC) [

11]. The problem was first

identified in pattern recognition and

classification problems in the 1960s and was

named “the curse of dimensionality” [13]. The

following thirty years of developing adaptive

statistical pattern recognition and neural

network algorithms led to a conclusion that

these approaches often encountered CC of

learning requirements. Rule-based systems

were proposed to solve the problem of learning

complexity. An initial idea was that rules would

capture the required knowledge and eliminate a

need for learning. However, rule systems and

expert systems in the presence of variability,

encountered CC of rules. Model-based

systems were proposed to combine advantages

of adaptivity and rules by utilizing adaptive

models, but they encountered computational

CC (N and NP complete algorithms).

Combinatorial complexity has been related

to the type of logic, underlying various

algorithms and neural networks [

14]. Formal

logic is based on the “law of excluded third”,

according to which every statement is either

true or false and nothing in between. Therefore,

algorithms based on formal logic have to

evaluate every little variation in data or internal

representations as a separate logical statement

(hypothesis); a large number of combinations of

these variations causes combinatorial

complexity. In fact, combinatorial complexity of

algorithms based on logic has been related to

the Gödel theory: it is a manifestation of the

incompleteness of logic in finite systems [

15].

Multivalued logic and fuzzy logic were

proposed to overcome limitations related to the

law of excluded third [16]. Yet the mathematics

of multivalued logic is no different in principle

from formal logic. Fuzzy logic encountered a

difficulty related to the degree of fuzziness, if

too much fuzziness is specified, the solution
3

does not achieve a needed accuracy, if too

little, it might become similar to formal logic.

Another view on these difficulties can be

obtained by comparing mathematical techniques to human mind. An essential role of

emotions in the working of the mind was

analyzed from the psychological and neural

perspective by Grossberg [17], from the neurophysiological perspective by Damazio [18], and

from the learning and control perspective by the

author [

19]. One reason for engineering

community being slow in adopting these results

is the cultural bias against emotions as a part of

thinking processes. Plato and Aristotle thought

that emotions are “bad” for intelligence, this is a

part of our cultural heritage, and the founders of

Artificial Intelligence repeated it. Yet, as

discussed in the next section, combining

conceptual understanding with emotional

evaluations might be crucial for overcoming the

combinatorial complexity as well as the related

difficulties of logic.

3. MODELING FIELD THEORY (MFT)

Modeling field theory [

11], summarized

below, associates lower-level signals with

higher-level representations, resulting in

understanding of signals, while overcoming the

difficulties described in the previous section. It

is achieved by using flexible measures of

similarity between the representations and the

input signals combined with the fuzzy dynamic

logic. Modeling field theory is a multi-level,

hetero-hierarchical system. This section

describes a basic mechanism of interaction

between two adjacent hierarchical levels of

signals (fields of neural activation); sometimes, it

will be more convenient to talk about these two

signal-levels as an input to and output from a

(single) processing-level.

At each level, the output are concepts

recognized (or formed) in input signals. Input

signals X are associated with (or recognized, or

grouped into) concepts according to the

representations-models and similarity measures

at this level. In the process of associationrecognition, models are adapted for better

representation of the input signals; and similarity

measures are adapted so that their fuzziness is

matched to the model uncertainty. The initial

uncertainty of models is high and so is the

fuzziness of the similarity measure; in the

process of learning models become more

accurate and the similarity measure more crisp,

the value of the similarity increases. We call this

mechanism fuzzy dynamic logic.

3.1 Internal Models, Learning, and

Similarity

 During the learning process, new

associations of input signals are formed resulting

in evolution of new concepts. Input signals

{X(n), n ∈ N}, is a field of input neuronal

synapse activation levels, X = {Xd, d = 1,...

D}; a set of concepts {h ∈ H} is characterized

by internal parameters {Sh} and by models

(representations) of the signals {Mh(Sh,n)}

corresponding to concepts {h}. For each

model h, the set of parameters is denoted as Sh

= {S

a

h

, a = 1,... A}. Learning process increases

a similarity measure between the sets of models

and signals, L({X},{M}). The similarity

measure is a function of model parameters and

associations between the input synapses and

concepts-models. A similarity measure is

designed so that it treats each model as an

alternative for each subset of signals

L({X},{M}) = 

n∈N

∏

h∈H

∑ r(h) l(X(n) | h), (1)

here l(X(n)|h) (or simply l(n|h)) is a conditional

partial similarity between signal vector X(n) and

model Mh (when mapping this terminology onto

its implemetation in the brain, n and h are neural

indexes numbering individual neurons or small

groups of neurons). For example, l(n|h) can be
4

selected as a probability density function. Then

L is a total likelihood (this interpretation does

not require statistical independence among

signal vectors n and n': dependencies are

accounted for by model dependencies on {n}).

In the process of learning, concept-models

are constantly modified. From time to time a

system forms a new concept, while retaining an

old one as well; alternatively, old concepts are

sometimes merged. Formation of new

concepts and merging of old ones require a

modification of the similarity measure (1); the

reason is that more models always result in a

better fit between the models and data. This is a

well known problem, it can be addressed by

reducing (1) using a “penalty function”, p(N,M)

that grows with the number of models M, and

this growth is steeper for a smaller amount of

data N. For example, an asymptotically

unbiased maximum likelihood estimation leads

to multiplicative p(N,M) = exp(-Npar/2), where

Npar is a total number of adaptive parameters in

all models (this penalty function is known as

Akaike Information Criterion, see [

11] for

further discussion and references).

In case, when a set of observations, N,

corresponds to a continuous flow of signals, for

example, a flow of visual stimuli in time and

space, it is convenient instead of eq.(1) to

consider its continuous version,

L = exp ?

N

 ln (

h ∈H

∑ r(h) l(X(n) | h) ), (2)

where N is a continuum, such as time-space. In

this case, models describe continuous modeling

fields and maximization of similarity L can be

compared to minimization of action in a physical

field theory.

3.2 Fuzzy dynamic logic and MFT

The learning process consists in estimating

internal parameters S and associating subsets of

signals with concepts by maximizing the

similarity (1). When likelihood is used as a

similarity measure, this is a problem of the

maximum likelihood estimation. Note, that (1)

contains a total of HN items; this is a source of

the combinatorial complexity in many algorithms

(called maximum hypothesis testing) which

attempt to maximize similar expressions by first

maximizing each item over the parameters and

then finding the maximal item.

Modeling field theory solves this problem

by utilizing fuzzy dynamic logic [

11,20]. Let us

introduce association variables f(h|n)

f(h|n) = r(h) l(X(n)|h) /

h '∈H

∑ r(h') l(X(n)|h'). (3)

Eq.(3) looks like the Bayes formula for a

posteriori probabilities, if l(n|h) are conditional

likelihoods. An internal dynamics of the

Modeling Fields (MF) is defined as follows,

df(h | n)/dt = f(h | n) 

h '∈H

∑ {[δhh' - f(h'|n)] ·

[?lnl (n|h')/?Mh'] ∂M'

h'/∂Sh' · dSh'/dt, (4)

dSh/dt={?

N

f(h|n)[?lnl(n|h)/?Mh]∂M'

h

/∂Sh, (5)

here

δhh' is 1 if h=h', 0 otherwise. (6)

Parameter t is the time of the internal dynamics

of the MF system (like a number of internal

iterations). A more specific form of (5) can be

written when Gaussian-shape functions are used

for conditional partial similarities,

l(n|h) = G(X(n) | Mh(Sh, n), Ch). (7)

where G is a Gaussian function with mean Mh

and covariance matrix Ch (this is not a

necessary assumption, but it will simplify some

discussions later, also, it is not same as usual

Gaussian limitation, in fact, it is not much of a

limitation at all, because a weighted sum of

Gaussians in (1) can approximate any positive
5

function). And let us specify the dynamics of the

MFT as follows,

dS

a

h

/dt = [Yh

-1

]

ab

Zh

b

 , (8)

dCh/dt = -0.5Ch

-2

?

N

f(h|n)[Ch-Dnh Dnh

T

]; (9)

Dnh = ( X (n) – Mh' ) , (10)

Yh

ab

 = ?

 N

f(h|n)[M

;a

hCh

-1

M

;b

h

], (11)

Zh

b

 = ?

 N

f(h|n)[M

;b

hCh

-1

Dnh], (12)

here superscript T denotes a transposed rowvector; summation is assumed over repeated

indexes a, b; and (;) denotes partial derivatives

with respect to parameters S with

corresponding indexes:

M

;b

h

 = ∂Mh/ ∂S

b

h .

(13)

The following theorem was proven.

Theorem. Equations (3) through (6) (or (3)

and (8 through 12)) define a convergent

dynamic system MF with stationary states

defined by max{Sh}L.

It follows that the stationary states of an MF

system give the maximum similarity solution of

the model-based pattern recognition problem.

When likelihood is used as similarity, the

stationary values of parameters {Sh} are

asymptotically unbiased and efficient estimates

of these parameters [

21]. A computational

complexity of the MF method is linear in N.

3.3 MFT hierarchical organization

The previous sub-section described a single

processing layer in a hierarchical MFT system.

An input to each layer is a set of signals X(n),

or in neural terminology, an input field of

neuronal activations. An output are the

activated models Mh(Sh, n); it is a set of models

or concepts recognized in the input signals.

Equations (3-6) or (3) and (7-12) describe a

loop-process: at each iteration (or internal-time

t) the l.h.s. of the equations contain association

variables f(h|n) and other model parameters

computed at the previous iteration. In other

words, the output models “act” upon the input

to produce a “refined” output models (at the

next iteration). This process is directed at

increasing the similarity between the models and

signals. It can be described as an internal

behavior generated by the models.

The output models initiate other actions as

well. First, activated models (neuronal axons)

serve as input signals to the next processing

layer, where more general concept-models are

recognized or created (internal behavior within

the MFT system). Second, concept-models

along with the corresponding instinctual signals

and emotions may activate behavioral models

and generate behavior directed into the outside

world (a process not contained within the

above equations).

MFT describes an intelligent system

composed of multiple adaptive intelligent agents:

each concept-model is an agent, which is

"dormant" until activated by a high similarity

value. When activated, it is adapted to the

signals, so that the similarity increases. Every

piece of signal may activate several concepts,

which "compete" with each other, while

adapting to the new signals.

3.4 MFT theory of mind

MFT dynamics, (3) and (4-6) or (7-12),

describes an elementary process of perception

or cognition, in which a large number of modelconcepts compete for incoming signals, modelconcepts are modified and new ones are

formed, and eventually, connections are

established among signal subsets on the one
6

hand, and model-concepts on the other.

Perception refers to processes in which the

input signals come from sensory organs and

model-concepts correspond to objects in the

surrounding world. Cognition refers to higher

levels in the hierarchy where the input signals

are concepts activated at lower levels and

model-concepts are more complex and

correspond to situations and relationships

among lower-level concepts.

A salient mathematical property of this

processes ensuring a smooth convergence is a

correspondence between uncertainty in models

(that is, in the knowledge of model parameters)

and uncertainty in associations f(h|n). In

perception, as long as model parameters do not

correspond to actual objects, there is no match

between models and signals; many models

poorly match many objects, and associations

remain fuzzy; this can be described more

specifically, if Gaussian functions are used for

l(X|h): for poorly matched models, the

covariances, Ch, are large (that is, model

uncertainties are large), which in turn prevents

f(h|n) from attaining definite (0,1) values.

Eventually, one model (h') wins a competition

for a subset {n'} of input signals X(n), when

parameter values match object properties, Ch'

becomes smaller than other Ch, and f(h'|n)

values become close to 1 for n∈{n'} and 0 for

n∉{n'}. Upon the convergence, the entire set of

input signals {n} is divided into subsets, each

associated with one model-object, Ch become

small, and fuzzy a priori concepts become crisp

concepts. Cognition is different from perception

in that models are more general, more

abstracts, and input signals are the activation

signals from concepts identified (cognized) at a

lower hierarchical level; the general

mathematical laws of cognition and perception

are similar in MFT. Let us discuss relationships

between the MFT theory, theory of solitons in

non-linear systems and concepts of mind

originated in psychology, philosophy, linguistics,

aesthetics, neuro-physiology, neural networks,

artificial intelligence, pattern recognition, and

intelligent systems.

Solitons and MFT resonances. The

physical nature of concepts of mind in MFT is

similar to that of solitons. If the data X(n) are all

given from the very beginning, equations (3-6)

or (7-12) converge to a fixed point of MFT

system. This fixed point is comprised of a

number of resonances [22] between the field of

models and field of data, in other words, the

models come into a resonance with the data,

and the system stays in this resonant state.

Formation of a resonance takes different time

(number of iterations) for various models, and it

is more proper to talk about each model

coming into a resonance with a corresponding

data subset. If there is a continuous flow of

data, X(n,t), a resonance is a long-living state

(long comparative to a single iteration cycle).

The nature of this resonance between the

modeling fields and the data field is such that a

particular subset of data (corresponding to an

object h) "drives” the modeling-field to a

specific value (or pattern) Mh(Sh,n,t), and

these modeling-field values "drive” the

association-fields, f(h|n), to {0,1} values. It

follows that concepts of mind in MFT theory

are resonant states, or solitons of a highly

nonlinear MFT system. It is interesting to note

recent results [

23] establishing relationships

between solitons in certain nonlinear systems

and theorems of inversive geometry. More

research is needed to establish general

relationships between concepts of mind as longliving resonant states in a nonlinear system and a

body of results obtained in the theory of

integrable systems and solitons [24].

Elementary thought-process, consciousness, and unconscious. A thought-process or

thinking involves a number of sub-processes

and attributes, including internal representations

and their manipulation, attention, memory,

concept formation, knowledge, generalization,

recognition, understanding, meaning, prediction,

imagination, intuition, emotion, decisions,
7

reasonining, goals, behavior, conscious and

unconscious [7,10,11].

A “minimal” subset of these processes has

to involve mechanisms for afferent and efferent

signals [22], in other words, bottom-up and topdown signals coming from outside (external

sensor signals) and from inside (internal

representation signals). According to Carpenter

and Grossberg [

22] every recognition and

concept formation process involves a

“resonance” between these two types of

signals. In MFT, at every level in a hierarchy the

afferent signals are represented by the input

signal field X, and the efferent signals are

represented by the modeling field signals Mh;

resonances correspond to high similarity

measures l(n|h) for some subsets of {n} that are

“recognized” as concepts (or objects) h. The

mechanism leading to the resonances is given by

(3-6) or (7-12), and we call it an elementary

thought-process. The elementary thoughtprocess involves elements of conscious and

unconscious processes, imagination, memory,

internal representations, concepts, instincts,

emotions, understanding and behavior as further

described later.

A description of working of the mind as

given by the MFT dynamics was first provided

by Aristotle [

25], describing thinking as a

learning process in which an a priori form-aspotentiality (fuzzy model) meets matter (sensory

signals) and becomes a form-as-actuality (a

concept). Jung suggested that conscious

concepts are developed by mind based on

genetically inherited structures of mind,

archetypes, which are inaccessible to

consciousness [3]; and Grossberg [7] suggested

that only signals and models attaining a resonant

state (that is signals matching models) reach

consciousness.

Understanding. In the elementary thought

process, subsets in the incoming signals are

associated with recognized model-objects,

creating phenomena (of the MFT-mind) which

are understood as objects, in other words

signal subsets acquire meaning (e.g., a subset

of retinal signals acquires a meaning of a chair).

There are several aspects to understanding and

meaning. First, object-models are connected

(by emotional signals [8,11,19]) to instincts that

they might satisfy, and also to behavioral

models that can make use of them for instinct

satisfaction. Second, an object is understood in

the context of a more general situation in the

next layer consisting of more general conceptmodels, which accepts as input-signals the

results of object recognition. That is, each

recognized object-model (phenomenon) sends

(in neural terminology, activates) an output

signal; and a set of these signals comprises input

signals for the next layer models, which

‘cognize’ more general concept-models. And

this process continues up and up the hierarchy

of models and mind toward the most general

models a system could come up with, such as

models of universe (scientific theories), models

of self (psychological concepts), models of

meaning of existence (philosophical concepts),

models of a priori transcendent intelligent

subject (theological concepts).

Imagination. Imagination involves

excitation of a neural pattern in a visual cortex in

absense of an actual sensory stimulation (say,

with closed eyes) [

7

]. Imagination was often

considered to be a part of thinking processes;

Kant [26] emphasized the role of imagination in

the thought process, he called thinking “a play

of cognitive functions of imagination and

understanding”. Whereas pattern recognition

and artificial intelligence algorithms of recent

past would not know how to relate to this [5,

6

],

Carpenter and Grossberg resonance model [22]

and the MFT dynamics both describe

imagination as an inseparable part of thinking:

imagined patterns are top-down signals that

prime the percepting cortex areas (priming is a

neural terminology for making neural cells to be

more readily excited). In MFT, the imagined

neural patterns are given by models Mh. MFT

(in agreement with neural data) just adds details
8

to Kantian description: thinking is a play of

higher-hierarchical-level imagination and

lower-level understanding. Kant identified this

“play” [described by (3-6) or (7-12)] as a

source of aesthetic emotion; modeling aesthetic

emotion in MFT is described later.

Mind vs. Brain. Historically, the mind is

described in psychological and philosophical

terms, whereas the brain is described in terms

of neurobiology and medicine. Withing scientific

exploration the mind and brain are different

description levels of the same system.

Establishing relationships between these

description is of great scientific interest. Today

we approach solutions to this challenge [

27],

which eluded Newton in his attempt to establish

physics of “spiritual substance” [

28]. General

neural mechanisms of the elementary thought

process (which are similar in MFT and ART

[

22]) have been confirmed by neural and

psychological experiments, this includes neural

mechanisms for bottom-up (sensory) signals,

top-down “imagination” model-signals, and the

resonant matching between the two [

29].

Adaptive modeling abilities are well studied with

adaptive parameters identified with synaptic

connections [30]; instinctual learning mechanisms

have been studied in psychology and linguistics

[

31].

Instincts and emotions. Functioning of the

mind and brain cannot be understood in

isolation from the system’s “bodily needs”. For

example, a biological system (and any

autonomous system) needs to replenish its

energy resources (eat); this and other

fundamental unconditional needs are indicated

to the system by instincts, which could be

described as internal sensors. Emotional signals,

generated by this instinct are perceived by

consciousness as “hunger”, and they activate

behavioral models related to food searching and

eating. In this paper we are concerned primarily

with the behavior of recognition: instinctual

influence on recognition modify the objectperception process (3) - (6) in such a way, that

desired objects “get” enhanced recognition; it

can be accomplished by modifying priors, r(h).

Aesthetic emotions and instinct for

knowledge. Recognizing objects in the

environment and understanding their meaning is

so important for human evolutionary success

that there has evolved an instinct for learning

and improving concept-models. This instinct is

described in MFT by maximization of similarity

between the models and the world, (1).

Emotions related to satisfaction-dissatisfaction

of this instinct are perceived by us as harmonydisharmony (between our understanding of how

things ought to be and how they actually are in

the surrounding world). According to Kant [32]

these are aesthetic emotions.

Intuition includes an intuitive perception

(imagination) of object-models and their

relationships with objects in the world, as well

as higher-level models of relationships among

simpler models. Intuition involves fuzzy

unconscious concept-models, which are in a

state of being learned and being adapted

toward crisp and conscious models (a theory);

such models may satisfy or dissatisfy the

knowledge instinct in varying degrees before

they are accessible to consciousness, hence the

complex emotional feel of an intuition. The

beauty of a physical theory discussed often by

physicists is related to satisfying our feeling of

purpose in the world, that is, satisfying our need

to improve the models of the meaning in our

understanding of the universe.

Beauty. Harmony is an elementary aesthetic

emotion related to improvement of objectmodels. Higher aesthetic emotions are related

to the development of more complex “higher”

models: we perceive an object or situation as

aesthetically pleasing if it satisfies our learning

instinct, that is the need for improving the

models and increasing similarity (1). The highest

forms of aesthetic emotion are related to the

most general and most important models.

According to Kantian analysis [32], among the

highest models are models of the meaning of
9

our existence, of our purposiveness or

intentionality, and beauty is related to improving

these models: we perceive an object or a

situation as beautiful, when it stimulates

improvement of these highest models of

meaning. Beautiful is what “reminds” us of our

purposiveness.

Theory testing and future directions. The

general neural mechanisms of the elementary

thought process, which includes neural

mechanisms for bottom-up (sensory) signals,

top-down “imagination” model-signals, and the

resonant matching between the two [33], have

been confirmed by neural and psychological

experiments (these mechanisms are similar in

MFT and ART [

22]). Adaptive modeling

abilities are well studied and adaptive

parameters have been identified with synaptic

connections [34]; instinctual learning mechanisms

have been studied in psychology and linguistics

[

35]. Ongoing and future research will confirm,

disprove, or suggest modifications to specific

mechanisms of model parameterization and

parameter adaptation (5) or (8), reduction of

fuzziness during learning (9), similarity measure

(1) as a foundation of aesthetic instinct for

knowledge, relationships between psychological

and neural mechanisms of learning on the one

hand and, on the other, aesthetic feelings of

harmony and emotion of beautiful.

Differentiated forms of (1) need to be

developed for various forms of the knowledge

instinct (child development, language learning,

etc.) Future experimental research needs to

study in details the nature of hierarchical

interactions: to what extent the hierarchy is

“hardwired” vs. adaptively emerging; what is a

hierarchy of learning instinct? theory of

emerging hierarchical models will have to be

developed (that is, adaptive, dynamic, fuzzy

hierarchy- heterarchy).

4. THINKING PROCESS AND

SEMIOTICS

Semiotics studies symbol-content of culture

[

36]. For example, consider a written word

"chair". It can be interpreted by a mind to refer

to something else: an entity in the world, a

specific chair, or the concept "chair" in the

mind. In this process, the mind, or an intelligent

system is called an interpreter, the written

word is called a sign, the real-world chair is

called a designatum, and the concept in the

interpreter's mind, the internal representation of

the results of interpretation is called an

interpretant of the sign. The essence of a sign

is that it can be interpreted by an interpreter to

refer to something else, a designatum. This

process of sign interpretation is an element of a

more general process called semiosis which

consists of multiple processes of sign

interpretation at multiple levels of the mind

hierarchy.

In classical semiotics [37] words sign and

symbol were not used consistently; in this

paper, a sign means something that can be

interpreted to mean something else (like a

mathematical notation, or a word), and the

process of interpretation is called a symbolprocess, or symbol. Intepretation, or

understanding of a sign by the mind according

to MFT is due to the fact that a sign (e.g., a

word) is a part of an object-model (or a

situation-model at higher levels of the mind

hierarchy). The mechanism of a sign

interpretation therefore involves first an

activation of an object-model, which is

connected to instincts that the object might

satisfy, and also to behavioral models that can

make use of this object for instinct satisfaction.

Second, a sign is understood in the context of a

more general situation in the next layer

consisting of more general concept-models,

which accepts as input-signals the results of

lower-level sign recognition. That is, recognized

signs comprise input signals for the next layer

models, which ‘cognize’ more general conceptmodels.

A symbol-process of a sign interpretation
10

coincides with an elementary thought-process.

Each sign-interpretation or elementary thought

process, a symbol, involves conscious and

unconscious, emotions and concepts; this

definition connecting symbols to archetypes

(fuzzy unconscious model-concepts)

corresponds to a usage in general culture and

psychology [3,4]. As described previously, this

process continues up and up the hierarchy of

models and mind toward the most general

models. In semiotics this process is called

semiosis, a continuous process of creating and

interpreting the world outside (and inside our

mind) as an infinite hierarchical stream of signs

and symbol-processes.

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