Transcript (2005). Integrating Language and Cognition

INTEGRATING
LANGUAGE AND COGNITION
NEW RESULTS IN COMPUTATIONAL INTELLIGENCE
International Joint Conference on Neural
Networks (IJCNN) 2005
31 July 2005
Leonid Perlovsky
Technical Advisor, AFRL
"The lecture material in this book is intended for strictly limited distribution to IJCNN2005
tutorial attendees only. This work is copyrighted by the tutorial speaker(s). No
duplication of this work is permitted without the written consent of the author."
OUTLINE
1. Cognition
2. Modeling Field Theory (MFT)
3. Integration of cognition and language
4. Prolegomena to a theory of the mind
5. Evolution of language and culture (future
directions)
DETAILED OUTLINE
Cognition – integration of real-time signals
and a priori knowledge
1.1. physics and mathematics of the mind
1.2. genetic argument for the first principles
1.3. the nature of understanding
1.3.1. “chair”
1.3.2. hierarchy
1.4. combinatorial complexity (CC) – a
fundamental problem?
1.5. CC since 1950s
1.6. CC vs. logic
1.6.1. formal, multivalued and fuzzy logics
1.6.2. fuzzy dynamic logic
1.6.3. Aristotle vs. Russell:
1.7. mathematics vs. mind
1.8. structure of the mind: concepts, instincts,
emotions, behavior
1.9. instinct for knowledge
1.9.1. need for learning
1.9.2. “knowledge emotion” = aesthetic emotion
1.
2.
Modeling Field Theory (MFT) of cognition
2.1. Instinct for knowledge = max similarity
2.2.
Similarity as likelihood, as information
2.3.
Dynamic Logic (DL)
2.4.
Exersices, applications
2.4.1 Recognition
2.4.2 Tracking and CRB
2.4.3 Fusion
2.4.4 Prediction
2.5.
Block-Diagrams
2.6.
Example: patterns in images
2.6.1. pattern with and without noise
2.6.2. models
2.6.3. finding pattern in noise, one object
2.6.4. finding pattern in noise, three objects
2.6.5. computational complexity: MFT vs. MHT
2.7.
Hierarchical structure
DETAILED OUTLINE
CONTINUATION
3.1. Language
3.1.1. Language acquisition and complexity
3.1.2. Language separate from cognition
3.1.3. Hierarchy of language
3.1.4. Application: search engine based on understanding
3.2. MFT of language
3.2.1 Differentiating non-differentiable qualitative
functions
4.
Integration of cognition and language
4.1. Language vs. cognition
4.2. Past: AI and Chomskyan linguistics
4.3. Integrated models
4.4. Humboldt’s inner linguistic form
5.
Prolegomena to a theory of the mind
5.1. Why mind and emotions?
5.2. From Plato to Lock
5.3. From Kant to Grossberg
5.4. MFT vs. Buddhism
5.5. MFT vs. biology
5.5. MFT dynamics: elementary thought process
5.6. Consciousness and unconscious
5.7. Cognition and understanding
5.8. MFT as multi-agent system
5.9. Semiotics: signs and symbols
5.9.1. MFT theory of symbols
5.9.2.
5.10.
5.10.1.
5.11.
6.
6.1.
6.2.
6.2.1.
6.3.
6.4.
6.5.
6.6.
6.6.1.
6.7.
6.8
7.
Symbolic AI
Aesthetic emotions
Beauty
Intuition
Evolution of Culture
Origin and evolution of language
Evolution of concepts
Split between conceptual and
emotional
Creativity
Disintegration of cultures
Terrorist’s consciousness
Synthesis
Mathematics of synthesis
Differentiation of emotions
Role of music in evolution of the
mind
Publications
COGNITION
Integration of real-time signals and
existing (a priori) knowledge
– From signals to concepts
– From less knowledge to more knowledge
COGNITION

Example: “this is a chair, it is for sitting”

Understanding signals (visual, acoustic, text)
– Identify objects in signals
• signals -> concepts; or sounds -> words
– Associate objects with prior knowledge and with behavior
• chair is for sitting

What in the mind help us do this? Representations,
models, ontologies?
– What are the nature of representations in the mind?
– There are wooden chairs in the world, but no wood in the brain
COMBINATORIAL COMPLEXITY
A FUNDAMENTAL PROBLEM?

Cognition and language involve evaluating
large numbers of combinations

Associate pixels or samples into objects
– A general problem
• Recognition, tracking, fusion, language…

Combinatorial Complexity (CC):
– Combinations of 100 elements are 100100
– This number ~ the size of the Universe
• all the events in the Universe during its entire life
COMBINATORIAL COMPLEXITY
SINCE the 1950s

CC was encountered for over 50 years

Statistical pattern recognition and neural networks: CC of
learning requirements

Rule systems and AI, in the presence of variability: CC of
rules
– Minsky 1960s: Artificial Intelligence
– Chomsky 1957: language mechanisms are rule systems

Model-based systems, utilizing adaptive models: CC of
computations (N and NP complete)
– Chomsky 1981: language mechanisms are model-based (rules and parameters

Current ontologies, “semantic web” are rule-systems
– Evolvable ontologies will lead to CC
CC AND TYPES OF LOGIC

CC is related to formal logic
– Law of excluded third
• every logical statement is either true or false
– There is no similarity measure between logical statements
• “between any two logical statement another one can fit”
– CC is Gödel's “incompleteness” in a finite system

Multivalued logic eliminated the “law of excluded third”
– Still, it was based on the math. of formal logic
– Excluded 3rd -> excluded (n+1)

Fuzzy logic eliminated the “law of excluded third”
– Fuzzy logic systems are either too fuzzy or too crisp
– Adapt fuzziness for every statement at every step => CC
FUZZY DYNAMIC LOGIC

Fuzzy dynamic logic
– based on a similarity between models and signals / data

Dynamic Logic unifies formal and fuzzy logic
– initial “fuzzy model-concepts” dynamically evolve into
“formal-logic or crisp model-concepts”

Overcomes CC of model-based recognition
– fast algorithms
ARISTOTLE VS. GÖDEL
forms, logic, and language

Aristotle
– Logic: a supreme way of argument
– Forms: representations in the mind
• Form-as-potentiality evolves into form-as-actuality
• Logic is valid for actualities, not for potentialities (Dynamic Logic)
– Thought language and thinking to be closely linked
• Warned not to use overly precise statements in logic
• Language contains the necessary uncertainty

From Boole to Russell: formalization of logic
– Logicians eliminated from logic uncertainty of language
– Hilbert: formalize rules of mathematical proofs forever

Gödel (the 1930s)
– Logic is not consistent
• Any statement can be proved true and false

Aristotle and Alexander the Great
OUTLINE
•
Cognition
•
Modeling Field Theory (MFT) of cognition
•
Language
•
Integration of cognition and language
•
Prolegomena to a theory of the mind
•
Future directions
- Logic did not work, but the mind does
- Structure of the mind
STRUCTURE OF THE MIND

Concepts
– Models of objects, their relations, and situations
– Evolved to satisfy instincts

Instincts
– Internal sensors (e.g. sugar level in blood)

Emotions
– Neural signals connecting instincts and concepts
• e.g. a hungry person sees food all around

Behavior
– Models of goals (desires) and muscle-movement…

Hierarchy
– Concept-models and behavior-models are organized in a “loose”
hierarchy
THE KNOWLEDGE INSTINCT

Model-concepts always have to be adapted
– lighting, surrounding, new objects and situations
– even when there is no concrete “bodily” needs

Instinct for knowledge and understanding
– Increase similarity between models and the world

Emotions related to the knowledge instinct
– Satisfaction or dissatisfaction
• change in similarity between models and world
– Related not to bodily instincts
• harmony or disharmony (knowledge-world): aesthetic emotion
REASONS FOR PAST LIMITATIONS

The basis of human intelligence is in
combining conceptual understanding with
emotional evaluation

A long-standing cultural belief that emotions
are opposite to thinking and intellectually
inferior
– “Stay cool to be smart”
– Socrates, Plato, Aristotle
– Reiterated by founders of Artificial Intelligence [Newell,
Minsky]
OUTLINE
•
Cognition – integration of real-time signals and a priori
knowledge
•
Modeling Field Theory (MFT) of cognition
•
Integration of cognition and language
•
Prolegomena to a theory of the mind
•
Future directions
- Mathematics of MFT
- Examples and applications: recognition, tracking, fusion,
prediction
Modeling Field Theory (MFT)

A mathematical construct modeling the mind
– A loose hierarchy of more and more general concepts
– At every level: concepts, emotions, models, behavior
– Top-down and bottom-up signals
MODELING FIELDS
one layer in a MF hierarchy: from signals to objects

Bottom-up signals
– Pixels or samples (from sensor or retina)
x(n), n = 1,…,N

Top-Down, concept-models (objects…)
Mm(Sm,n), parameters Sm, m = 1, …;
– Models predict expected signals from objects

Goal: learn object-models and understand
signals (knowledge instinct)
– associate signals n with models m and find
parameters Sm
– learn signal-contents of objects (and object properties)
THE KNOWLEDGE INSTINCT

Knowledge instinct = maximization of
similarity between signals and models

Similarity between signals and models, L
– L = l({x}) =  l(x(n))
n
– l(x(n)) =  r(m) l(x(n) | Mm(Sm,n))
m
– l(x(n) | Mm(Sm,n)) is a conditional similarity for x(n) given m
• {n} are not independent, M(n) may depend on n’

CC: L contains MN items: all associations of
pixels and models
DYNAMIC LOGIC (DL)
non-combinatorial solution

Start with a set of signals and unknown object-models
– any parameter values Sm
– associate object-model with its contents (signal composition)
– (1)

f(m|n) = r(m) l (n|m) /  r(m') l (n|m')
m'
Improve parameter estimation
– (2) Sm = Sm + a  f(m|n) [ln l (n|m)/Mm]*[Mm/Sm]
n
• (a determines speed of convergence)
– learn signal-contents of objects

Continue iterations (1)-(2). Theorem: MF is a
converging system
- similarity increases on each iteration
- aesthetic emotion is positive during learning
OUTLINE
•
Cognition
•
Modeling Field Theory (MFT)
•
Integration of cognition and language
•
Prolegomena to a theory of the mind
•
Future directions
- Mathematics of MFT
- Examples
EXAMPLE
FINDING PATTERNS IN IMAGES
DIFFICULTY:
SIGNAL BELOW NOISE
Object Image + Noise
y
y
Object Image
x
x
PRIOR STATE-OF-THE-ART
Computational complexity
Multiple Hypothesis Testing (MHT) approach:
try all possible ways of fitting model to the data
For a 100 x 100 pixel image:
Number of Objects
Number of Computations
1
1010
2
1020
3
1030
Y
DL FINDS OBJECT BELOW CLUTTER
X
SNR = -2.0 dB
DYNAMIC LOGIC EXAMPLE
DL starts with uncertain knowledge, and similar to human mind does not sort through
all possibilities, but converges rapidly on exact solution
• Object invisible to
human eye
• By integrating
data with the
knowledge-model
DL finds an object
below noise
x (m) Cross-range
MULTIPLE OBJECT DETECTION
Three objects in noise
object 1
object 2 object 3
SNR
- 0.70 dB -1.98 dB -0.73 dB
3 Object Image + Clutter
y
y
3 Object Image
x
x
MULTIPLE TARGET DETECTION
DL WORKING EXAMPLE
DL starts with uncertain knowledge, and similar to human mind does not sort through
all possibilities like an MHT, but converges rapidly on exact solution
x
COMPUTATIONAL REQUIREMENTS
COMPARED
Dynamic Logic (DL) vs. Classical State-of-the-art Multiple
Hypothesis Testing (MHT)
Based on 100 x 100 pixel image
Number of Objects
Number of Computations
DL
vs.
MHT
1
108
vs.
1010
2
2x108
vs.
1020
3
3x108
vs.
1030
• Previously un-computable (1030), can now be computed (3x108 )
• This pertains to many complex information-finding problems
OTHER ASPECTS OF COGNITION
•
Many applications were developed
-
•
Recognition
Signal Processing
Spectrum Estimation
Inverse Scattering
Tracking
Fusion
Search Engines
Bioinformatics
Prediction
Each application requires appropriate models
MFT/DL SENSOR FUSION

Difficult part: association of data among sensors
– Which sample in one sensor corresponds to which sample in
another sensor?

MFT/DL for sensor fusion requires no new
conceptual development

Multiple sensor data require multiple sensor
models
– Data: n -> (s,n); X(n) -> X(s,n)
s -> M (s,n)
– Models Mm(n)
m

Note: this solves the difficult problem of
concurrent detection, tracking, and fusion
FINANCIAL PREDICTION
 Data
are available for free
 Difficulty: too many parameters
– Markets * instruments ~ 100 to 1000
– Time intervals ~ *10
– Predictions in shortest time, estimate hundreds of
parameters from few data points, impossible?
 Select
few best variables for each single prediction
 Predicted
crash following 9/11
MFT/DL FOR COGNITION
SUMMARY

Cognition
– Integrating knowledge and signals

Knowledge = concepts = models

Knowledge instinct = similarity(models, data)

Aesthetic emotion = change in similarity

Emotional intelligence
– combination of conceptual knowledge and emotional
evaluation

Applications
– Recognition, tracking, fusion, bioinformatics, prediction
MFT HIERARCHICAL STRUCTURE

At every level of hierarchy
– Bottom-up signals are lower-level-concepts
– Top-down signals are concept-models
– Behavior-actions (including adaptation)
Action/Adaptation
Similarity measures
Models
Similarity measures
Action/Adaptation
Models
OUTLINE
•
Cognition
•
Modeling Field Theory and Dynamic Logic
•
Integration of cognition and language
•
Prolegomena to a theory of the mind
•
Future directions
LANGUAGE
WORDS, CONCEPTS AND GOALS

Language is a hierarchy
– Sounds or letters, words, phrases,…

Models (e.g. phrases made up of words)
– Simplistic “bag”-model
• a set or collection of words
– More complex real-language models

Modeling Fields and DL of language
LANGUAGE
AND COMPLEXITY

Chomsky: linguistics should study the mind
mechanisms of language (1957)

Chomsky’s language mechanisms
–
–
–
–
In forefront of mathematical ideas at the time time
1957: rule-based
1981: model-based (rules and parameters)
1995: minimalist, language and cognition

Pinker: language instinct (the 1990s)

Kirby: language evolution (2002)

Combinatorial complexity
– For the same reason as all rule-based and model-based
methods
INTEGRATION OF
COGNITION AND LANGUAGE

Human mind is the only example of language and
abstract cognition
– Possibly they are only possible together
– Cognition does not exist without communication?

Past attempts based on logic did not work
– We are conscious about “final results” of language
mechanisms ~ logical concepts
– We are not conscious about fuzzy mechanisms involved
in language

Future: sub-conceptual, sub-conscious integration
INTEGRATED
LANGUAGE AND COGNITION

Where language and cognition come together?
– A fuzzy concept m has linguistic and cognitive-sensory models
• Mm = { Mmsensory,Mmlinguistic };
– language and cognition are fused at fuzzy pre-conceptual level
• before concepts are learned

Understanding language and sensory data
– Initial models are fuzzy blobs
– linguistic models have empty “slots” for cognitive model (objects
and situations) and v.v.
– language participates in cognition and v.v.

L & C help learning and understanding each other
– help associating signals, words, models, and behavior
INNER LINGUISTIC FORM
HUMBOLDT, the 1830s

In the 1830s Humboldt discussed two types of
linguistic forms
– words’ outer linguistic form (dictionary) – a formal designation
– and inner linguistic form (???) – creative, full of potential

This remained a mystery for rule-based AI,
structural linguistics, Chomskyan linguistics
– rule-based approaches using the mathematics of logic make
no difference between formal and creative

In MFT / DL there is a difference
– static form of learned (converged) concept-models
– dynamic form of fuzzy concepts, with creative learning
potential, emotional content, and unconscious content
OUTLINE
•
Cognition
•
Modeling Field Theory (MFT) of cognition
•
Language
•
Integration of cognition and language
•
Prolegomena to a theory of the mind
•
Future directions
WHY MIND AND EMOTIONS?

A lot about the mind can be explained: concepts, instincts,
emotions, conscious and unconscious, intuition, aesthetic ability,…
– but, isn’t it sufficient to solve mathematical equations or to code a
computer and execute the code?

A simple yet profound question
–
–
–
–
–

the answer is in history and practice of science
Newton laws do not contain all of the classical mechanics
Maxwell equations do not exhaust radars and radio-communication
a physical intuition about the system is needed
an intuition about MFT was derived from biological, linguistic,
cognitive, neuro-physiological, and psychological insights into
human mind
Practical engineering applications of DLA require
biological, linguistic, cognitive, neuro-physiological,
and psychological insights in addition to mathematics
MF DYNAMICS

A large number of model-concepts compete for incoming signals

Uncertainty in models corresponds to uncertainty in associations f(m|n)

Eventually, one model (m') wins a competition for a subset {n'} of input signals

Upon convergence, all input signals {n} are divided into subsets, each
associated with one model-object

Fuzzy a priori concepts (unconscious) become crisp concepts (conscious)
– dynamic logic, Aristotelian forms, Jungian archetypes, Grossberg
resonance

Elementary thought process
CONSCIOUSNESS AND
UNCONSCIOUS

Jung: conscious concepts and unconscious archetypes

Grossberg: models attaining a resonant state (winning
the competition for signals) reach consciousness

MFT: fuzzy mechanisms (DL) are unconscious, crisp
concept-models, adapted and matched to data are
conscious
SYMBOL

“A most misused word in our culture”
(T. Deacon)

Cultural and religious symbols
– Provoke wars and make piece

Traffic Signs
SIGNS AND SYMBOLS

Signs: stand for something else
– non-adaptive entities (mathematics, AI)
– brain signals insensitive to context (Pribram)

Symbols
– general culture: deeply affect psyche
– psychological processes connecting conscious
and unconscious (Jung)
– brain signals sensitive to context (Pribram)
– signs (mathematics, AI): mixed up
– processes of sign interpretation

MFT: mathematics of symbol-processes
– elementary thought process
MFT OF SYMBOLS
 Example: a written word "chair"
– The mind interprets it to refer to an entity in the world, or the concept
"chair" in the mind
 This
is a simplified description of a thinking process
 Elementary thought process is a symbol process
– involves consciousness and unconscious, concepts and emotions
– much more complicated than envisioned by founders of “symbolic AI”
 Two parallel hierarchies of Cognition
– A dog can learn word-object connection
– A chimp can learn elements of 2 levels?
– Only human mind has multi-level hierarchy
and Language
SYMBOLIC ABILITY

Integrated hierarchies of Cognition and Language
– High level cognition is only possible due to language
– Language is only possible due to cognition
cognition
Action
Similarity
language
M
M
Similarity
Action
M
Action
Similarity
Similarity
Action
M
SYMBOLS and “SYMBOLIC AI”

Founders of “symbolic AI” believed that by using
“symbolic” mathematical notations they would
penetrate into the mystery of the mind
– But mathematical symbols are just notations (signs)
– Not psychic processes

This explains why “symbolic AI” was not successful

This also illustrates the power of language over
thinking
– Wittgenstein called it
• “bewitchment (of thinking) by language”
AESTHETIC EMOTIONS

Not related to bodily satisfaction

Satisfy instincts for knowledge and language
– learning concepts and learning language

Not just what artists do

Guide every perception and cognition process

Perceived as feeling of harmony-disharmony
– satisfaction-dissatisfaction

Maximize similarity between models and world
– between our understanding of how things ought to be and how they
actually are in the surrounding world; Kant: aesthetic emotions
BEAUTY

Harmony is an elementary aesthetic emotion

The highest forms of aesthetic emotion, beauty

Beautiful “reminds” us of our purposiveness

Beauty is separate from sex, but sex makes use of all our abilities,
including beauty
– higher aesthetic emotions are involved in the development of more
complex “higher” models
– related to the most general and most important models
– models of the meaning of our existence, of our purposiveness or intentionality
– beautiful object stimulates improvement of the highest models of meaning
– Kant called beauty “aimless purposiveness”: not related to bodily purposes
– he was dissatisfied by not being able to give a positive definition: knowledge
instinct
– absence of positive definition remained a major source of confusion in
philosophical aesthetics till this very day
INTUITION

Complex states of perception-feeling of unconscious
fuzzy processes
– involves fuzzy unconscious concept-models
– in process of being learned and adapted
• toward crisp and conscious models, a theory
– conceptual and emotional content is undifferentiated
– such models satisfy or dissatisfy the knowledge instinct before
they are accessible to consciousness, hence the complex
emotional feel of an intuition

Artistic intuition is about
– composer: sounds and their relationships to psyche
– painter: colors, shapes and their relationships to psyche
– writer: words and their relationships to psyche
INTUITION: Physics vs. Math.

Mathematical intuition is about
– Structure and consistency within the theory
– Relationships to a priori content of psyche

Physical intuition is about
– The real world, first principles of its organization, and
mathematics describing it

Beauty of a physical theory discussed by physicists
– Related to satisfying knowledge instinct
• the feeling of purpose in the world
OUTLINE
•
Cognition
•
Modeling Field Theory (MFT) of cognition
•
Language
•
Integration of cognition and language
•
Prolegomena to a theory of the mind
•
Future directions
- Evolution of culture
EVOLUTION

Genetic evolution simulations (1980s - )

Cultural evolution
– Using basic genetic mechanisms to explore genetic evolution
– Artificial Life, evolution models: Bak and Sneppen, Tierra, Avida
– Genes and memes (cultural concepts)
– Evolution of languages
– Evolution of concepts vs. evolution of genes
• Culture evolves much faster than genetic evolution (~10,000 years)
– Concepts are propagated through language
• Cognitive-models are not transferred to the next generation
• Language-models are

Each individual
– receives language models from culture
– has to develop cognitive models from language models
MECHANISMS OF
CULTURAL EVOLUTION

Differentiation and synthesis

Differentiation
– Fuzzy concepts become detail and clear

Synthesis
– Integrate cognition and language {MC, ML}
– Language evolved toward
• crisp, unemotional
• compare to animal cries: undifferentiated concept-emotion
– Emotions integrate L & C
– This is why music is so important
• Bach and Eminem integrate collective consciousness
SPLIT BETWEEN
CONCEPTUAL AND EMOTIONAL

Dissociation between language and cognition
– Might prevail for the entire culture

Words maintain their “formal” meanings
– Relationships to other words

Words loose their “real” meanings
– Connection to cognition, unconscious, and emotions

Conceptual and emotional dissociate
– Concepts are sophisticated but “un-emotional”
– Language is easy to use to say “smart” things
• but they are meaningless, unrelated to instinctual life
CREATIVITY

At the border of conscious and unconscious

Archetypes should be connected to consciousness
– To be useful for cognition

Collective concepts–language should be connected to
– The wealth of conceptual knowledge (other concepts)
– Unconscious and emotions

Creativity in everyday life and in high art
– Connects conscious and unconscious
DISINTEGRATION OF CULTURES

Split between conceptual and emotional
– When important concepts are severed from emotions
– There is nothing to sacrifice one’s life for

Split may dominate the entire culture
– Occurs periodically throughout history
– Was a mechanism of decay of old civilizations
– Old cultures grew sophisticated and refined but got severed from
instinctual sources of life
• Ancient Acadians, Babylonians, Egyptians, Greeks, Romans…
– New cultures (“barbarians”) were not refined, but vigorous
• Their simple concepts were strongly linked to instincts, “fused”
TERRORIST’S CONSCIOUSNESS

Ancient consciousness was “fused”
– Concepts, emotions, and actions were one
• Undifferentiated, fuzzy psychic structures
– Psychic conflicts were unconscious and projected outside
• Gods, other tribes, other people

Complexity of today’s world is “too much” for many
– Evolution of culture and differentiation
• Internalization of conflicts: too difficult
– Reaction: relapse into fused consciousness
• Undifferentiated, fuzzy, but simple and synthetic

The recent terrorist’s consciousness is “fused”
– European terrorists in the 19th century
– Fascists and communists in the 20th century
– Current Moslem terrorists
SYNTHESIS

Creativity, life, and vigor requires synthesis
– Emotional and conceptual, conscious and unconscious
– In every individual
• Lost synthesis and meaning leads to drugs and personal disintegration
– In the entire culture
• Lost synthesis and meaning leads to cultural disintegration

Historical evolution of consciousness
– From primitive, fuzzy, and fused to differentiated and refined
– Interrupted when synthesis is lost

Individual consciousness
– Combining differentiation and synthesis
– Jung called individuation, “the highest purpose in every life”
MATHEMATICS OF SYNTHESIS

Integrating a wealth of concepts
– Undifferentiated knowledge instinct “likelihood
maximization”
– Differentiated knowledge instinct
• Likelihood involves “local” relationships among concepts
• Highly-valued concepts

Highly valued concepts acquire properties of
instincts
– Affect adaptation, differentiation, and cognition of other
concepts

Differentiated forms of knowledge instinct
– All concepts are emotionally related
– This requires “continuum” of emotions (music)
– Differentiated emotions connect diverse concepts
ROLE OF MUSIC IN EVOLUTION
OF THE MIND

Melody of human voice contains vital information
– About people’s world views and mutual compatibility
– Exploits mechanical properties of human inner ear
• Consonances and dissonances
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Tonal system evolved (14th to 19th c.) for
– Differentiation of emotions
– Synthesis of conceptual and emotional
– Bach integrates personal concerns with “the highest”
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Pop-song is a mechanism of synthesis
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Integrates conceptual (lyric) and emotional (melody)
Also, differentiates emotions
Bach concerns are too complex for many everyday needs
Human consciousness requires synthesis immediately
Rap is a simplified, but powerful mechanism of synthesis
– Exactly like ancient Greek dithyrambs of Dionysian cult
PREDICTIONS AND TESTING
of MFT/DL theory of the mind

General neural mechanisms of the elementary thought process
– confirmed by neural and psychological experiments
– includes neural mechanisms for bottom-up (sensory) signals, top-down
(“imagination”) model-signals, and the resonant matching between the two
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Adaptive modeling abilities
– well studied: adaptive parameters are synaptic connections
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Instinctual learning mechanisms
– studied in psychology and linguistics
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Emerging method: simulation of multi-agent evolving systems
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Ongoing and future research will confirm, disprove, or suggest
modifications to
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–
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similarity measure as a foundation of knowledge and language instincts
mechanisms of model parameterization and parameter adaptation
dynamics of fuzziness during perception/cognition/learning
mechanisms of language and cognition integration
mechanisms of differentiation and synthesis
THE END

Can we describe mathematically and
build simulation models for evolution of
all of these?
PUBLICATIONS
OXFORD UNIVERSITY PRESS
www.oup-usa.org
Future
together with Fernando
Fontanari:
Emotional mechanisms
in evolution;
Creation of meanings in
evolution
PUBLICATIONS
 New
book is coming:
–The Knowledge Instinct
BACK UP
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MFT and inverse problems
Cognition and understanding
From Plato to Locke
From Kant to Grossberg
Mechanisms of evolution
MFT and Buddhism
MFT VS. INVERSE SCATTERING

Inverse Scattering in Physics
– reconstruction of target properties by “propagating back” scattered fields
– usually complicated, ill-posed problems (exception: CATSCAN)

Biological systems (mind) solve this problem all the time
– by utilizing prior information (in feedback neural pathways)
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Classical Tikhonov’s inversion cannot use knowledge
– regularization parameter () is a constant
– Morozov’s modification can utilize prior estimate of errors for 

Inverse Scattering using MFT
– can utilize any prior knowledge ( became an operator)
– utilizes prior knowledge adaptively ( depends on parameters)
COGNITION AND UNDERSTANDING

Incoming signals, {x,w} are associated with model-concepts (m)
– creating phenomena (of the MFT-mind), which are understood as objects,
situations, phrases,…
– in other words signal subsets acquire meaning (e.g., a subset of retinal signals
acquires a meaning of a chair)

Several aspects of understanding and meaning
– concept-models are connected (by emotional signals) to instincts and to
behavioral models that can make use of them for satisfaction of bodily instincts
– an object is understood in the context of a more general situation in the next layer
consisting of more general concept-models (satisfaction of knowledge instinct)
• each recognized concept-model (phenomenon) sends (in neural terminology: activates)
an output signal
• a set of these signals comprises input signals for the next layer models, which ‘cognize’
more general concept-models
• this process continues up and up the hierarchy towards the most general models:
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)
FROM PLATO TO LOCKE

Realism:
– Plato: ability for thinking is based on a priori Ideas
– Aristotle:
• ability for thinking is based on a priori (dynamic) Forms
• an a priori form-as-potentiality (fuzzy model) meets matter (signals)
and becomes a form-as-actuality (a concept)
• actualities obey logic, potentialities do not
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Nominalism
– Antisthenes (contemporary of Plato)
• there are no a priori ideas, just names for similar things
– Occam (14th c.)
• Ideas are linguistic phenomena devoid of reality
– Locke (17th c.)
• A newborn mind is a “blank page”
FROM KANT TO GROSSBERG

Kant: three primary a priori abilities
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Reason = understanding (models of cognition)
Practical Reason = behavior (models of behavior)
Judgment = emotions (similarity)
“We only know phenomena (concepts), not things-in-themselves”
Jung
– Conscious concepts are developed based on inherited structures of the
mind, archetypes, inaccessible to consciousness
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Chomsky
– Inborn structures, not “general intelligence”

Grossberg
– Models attaining a resonant state (winning the competition for signals
and becoming crisp in this process) reach consciousness
MECHANISMS OF
CONCEPT EVOLUTION

Differentiation, synthesis, and language transmission

Differentiation
– Fuzzy contents become detail and clear
– A priori models, archetypes are closely connected to unconscious needs, to emotions,
to behavior
• Concepts have meanings

Cultural and generational propagation of concepts through language
– Integration of language and cognition is not perfect
• Language instinct is separate from knowledge instinct
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Propagation of concepts through language
– A newborn child encounters highly-developed language
– Synthesis: cognitive and language models {MC, ML} are connected individually
– No guarantee that language model-concepts are properly integrated with the adequate
cognitive model-concepts in every individual
• And we know this imperfection occurs in real life
• Meanings might be lost
• Some people speak well, but do not quite understand and v.v.
MFT AND BUDDHISM

Fundamental Buddhist notion of “Maya”
– the world of phenomena, “Maya”, is meaningless deception
– penetrates into the depths of perception and cognition
– phenomena are not identical to things-in-themselves
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Fundamental Buddhist notion of “Emptiness”
– “consciousness of bodhisattva wonders at perception of emptiness
in any object” (Dalai Lama 1993)
– any object is first of all a phenomenon accessible to cognition
– value of any object for satisfying the “lower” bodily instincts is
much less than its value for satisfying higher needs, knowledge
instinct
– Bodhisattva’s consciousness is directed by knowledge instinct
– concentration on “emptiness” does not mean emotional emptiness,
but the opposite, the fullness with highest emotions related to the
knowledge instinct, beauty and spiritually sublime