2006%20Evol%20Cog%20Lan%20Music,%20Mazo - SlideFinder - PowerPoint search engine with thumbnail results
JOINT EVOLUTION OF COGNITION, CONSCIOUSNESS, AND MUSIC
Leonid Perlovsky
Technical Advisor
Air Force Research Lab
Ohio State University
Columbus
24 April 2006
2500 years old QUESTION
Aristotle, 2300 ya:
Why music, being just sounds, reminds states of soul?
Kant, 1790s:
Among fine arts, in their aiding our cognitive abilities, music will have the lowest place… it merely plays with senses
Steven Pinker, 1990s:
The icing on the cake, it merely plays with some sensitive spots…
OUTLINE
Algorithms & neural net-s for Cog. and Lang. => combinatorial complexity (CC)
Similar to Gödel’s incompleteness of logic
Mathematics of Dynamic logic overcomes CC
Evolves from vague and fuzzy => crisp
Psychologically = the knowledge instinct
Cognitive role of music, brain neural mechanisms
Language differentiates concepts
Music differentiates emotions
Music creates synthesis (wholeness) of psyche (soul)
Cultural evolution
Differentiation and synthesis – symbiotic and antagonistic
In Eastern cultures synthesis dominates
In Western cultures differentiation dominates
Synthesis of differentiated consciousness is maintained by music
History: examples from Isaiah to rap
ALGORITHMIC DIFFICULTIES of computational intelligence
Cognition and language evaluate large numbers of combinations
Combinatorial Complexity (CC)
A general problem (since the 1950s)
Pattern recognition, rule systems, AI, neural networks, …
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, with adaptive models: CC of computations
Chomsky 1981: language mechanisms are model-based (rules and parameters)
Current ontologies, “semantic web” are rule-systems
Evolvable ontologies : present challenge
CC AND TYPES OF LOGIC
CC is related to formal logic
Law of excluded middle (or excluded third)
every logical statement is either true or false
Gödel proved that logic is “illogical,” “incomplete,” the 1930s
CC is Gödel's “incompleteness” in a finite system
Multivalued logic eliminated the “law of excluded third”
Excluded 3rd -> excluded (n+1), CC not resolved
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
Logic pervades all algorithms and neural networks
rule systems, fuzzy systems (degree of fuzziness), pattern recognition, neural networks (training uses logical statements)
OUTLINE
Combinatorial complexity (CC) of algorithms
Mathematics of Dynamic logic overcomes CC
Psychologically = the knowledge instinct
Higher cognitive functions
Cognitive role of music, brain neural mechanisms
Cultural evolution
DYNAMIC LOGIC
Dynamic Logic unifies formal and fuzzy logic
initial “vague or fuzzy concepts” dynamically evolve into “formal-logic or crisp concepts”
Dynamic logic
based on a similarity between models and signals
Overcomes CC of model-based recognition
fast algorithms
ARISTOTLE VS. GÖDEL logic, mind, and language
Aristotle
Logic: a supreme way of argument (rhetoric for Alexander)
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 are 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
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
Mathematically described by dynamic logic
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
Three objects in noise OBJECT RECOGNITION y y x x 3 Object Image + Noise 3 Object Image
OBJECT RECOGNITION
DL WORKING EXAMPLE x y DL starts with uncertain knowledge, and similar to human mind does not sort through all possibilities, but converges rapidly on exact solution
HIGHER COGNITIVE FUNCTIONS
Similarity measures Models Action/Adaptation Models Action/Adaptation Similarity measures objects abstractions purpose Abstract models at higher levels of hierarchy are less conscious
At every level
Bottom-up signals are lower-level-concepts
Top-down signals are concept-models
Behavior-actions (including adaptation)
IMAGINATION
Close eyes
Imagine a chair
Fuzzy vague image
Imagination is a part of thinking
Top-down neural model-signals
Perceived by visual cortex
Recognition (and cognition)
A match or resonance
Sensory signals <–> imagination signals
Crisp => more conscious; vague => less conscious
BEAUTIFUL AND SUBLIME
At the bottom of the mind hierarchy
Harmony, an elementary aesthetic emotion
At the top of the mind hierarchy
Concepts of the meaning of life
Beauty
Emotion
Beautiful objects stimulate improving the highest models of meaning
“Reminds” us of our purposiveness
Kant called beauty “aimless purposiveness”: not related to bodily purposes
he was dissatisfied by not being able to give a positive definition
The knowledge instinct
absence of positive definition remaines a major source of confusion in philosophical aesthetics till this very day
Spiritual sublimity
Emotion
Models of behavior (realizing the highest meaning)
PUBLICATIONS
OXFORD UNIVERSITY PRESS
www.oup-usa.org
OUTLINE
Combinatorial complexity (CC) of algorithms
Mathematics of Dynamic logic overcomes CC
Evolution of language = differentiation of consciousness
Evolution of music = synthesis of consciousness
Cultural evolution
ANCIENT FUSED CONSCIOUSNESS
Pre-human consciousness was “fused”
Concepts, emotions, and actions were one
Undifferentiated, fuzzy psychic structures
Monkey, when seeing a leopard
Perceives danger (concept)
Fears (emotion)
Cries “danger” (word)
Jumps on a tree (behavior)
Undifferentiated, fused concept-emotion-word-behavior
Monkey’s word-cry is connected to its deepest instincts
Ancient human consciousness was less “fused,” still
Concepts multiplied, but connection to instincts was automatic
Possibly, until 6,000 years ago
Psychic conflicts were unconscious and projected outside
Gods, other tribes, other people
LANGUAGE DIFFERENTIATE CONCEPTS
Fused consciousness was differentiated due to Lang.
Concepts, since 2 million year ago
Concepts-emotions-behavior, since 6,000 years ago
How language and cognition interact in the mind?
A fuzzy concept has linguistic and cognitive models
Model-concept = { cognitive-model, language-model};
Language and cognition are fused at fuzzy pre-conceptual level
before concepts evolved
Joint evolution (in history and in the mind)
Initial models are vague fuzzy blobs
language 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
Comments