Universe: The Fabrics of Perception

https://i2.wp.com/www.historyforkids.org/learn/greeks/clothing/pictures/weaving.jpg

I am working with the Latin language and it is helping me to classify my thoughts more effectively by understanding historical correlations in meaning. For example matter was considered a fabric. The term for light, “lume”, comes from the term loom which alludes to textile manufacture. In fact all of the textile terms merge with geometry where they were practically applied.

WEAVE: a fabric
POINT: a intersection
LINE: a line
ANG: a cut
HEIR: an area
VOL: layers
QUAL: a bundle

These terms have influenced our thinking for literally thousands of years. We still talk of the “fabric” of space, the fabric of time and “material” or whatever. We are unintentionally applying a metaphor. Yet it is a metaphor that has served us well.

At this point I present a scale that I have arrived at for human sensory perception.

outsideness

– 8 , – 2 , – 1 , 0 , + 1 , + 2 , + 8

where

8 is infinity

2 is two

1 is one

0 is zero

+ is positive

– is negative

– 8 : WEAVE below perception: Datrice
– 2 : POINT: below acception: Sortrice
– 1 : LINE: below exception: Matrice
0 : ANGE: exception: Natrice
+ 1 : HEIR above exception: Patrice
+ 2 : VOL: above acception: Fratrice
+ 8 : QUAL: above perception: Satrice

1. WHO: Eyes: Occipital Lobe: Speciatation of Matter.

+ 8 , + 2 , + 1 , 0 , – 1 , – 2 , – 8

Standard prefixes with root ASTR for the night sky:

– 8 : WEAVISTER: below perception
– 2 : POINTISTER: below acception
– 1 : LINISTER: below exception
0 : ANGISTER: exception
+ 1 : HEIRISTER above exception
+ 2 : VOLISTER: above acception
+ 8 : QUALESTER: above perception

PhotonicPhotons, PhotonicElectrons, PhotoincIons, PhotonicGases, PhotonicLiquids, PhotonicSolids, PhotonicMolecules

2. WHAT: Ears: Temporal Lobe: Association of Matter

+ 8 , + 2 , + 1 , 0 , – 1 , – 2 , – 8

Standard prefixes with root FUL for Electricity or “Lightning” which is interesting because it means we hear events.

– 8 : WEAVIFUL: below perception
– 2 : POINTIFUL: below acception
– 1 : LINIFUL: below exception
0 : ANGIFUL: exception
+ 1 : HEIRIFUL: above exception
+ 2 : VOLIFUL: above acception
+ 8 : QUALIFUL: above perception

ElectronicPhotons, ElectronicElectons, ElectronicIons, ElectronicGases, ElectronicLiquids, ElectronicSolids, ElectronicMolecules

3. WHEN: Nose: Brainstem: Attibution of Matter

+ 8 , + 2 , + 1 , 0 , – 1 , – 2 , – 8

Standard prefixes with root FIED for Ions or burn which is interesting because it means we smell ions or things that are reactive.

– 8 : WEAVEFIED: below perception
– 2 : POINTFIED: below acception
– 1 : LINEFIED: below exception
0 : ANGFIED: exception
+ 1 : HEIRFIED: above exception
+ 2 : VOLFIED: above acception
+ 8 : QUALIFIED: above perception

IonicPhotons, IonicElectrons, IonicIons, IonicGases, IonicLiquids, IonicSolids, IonicMolecules

4. WHERE: Throat: Parietal Lobe: Domination of Matter

+ 8 , + 2 , + 1 , 0 , – 1 , – 2 , – 8

Standard prefixes with root AER for Gases

– 8 : WEAVIER: below perception
– 2 : POINTIER: below acception
– 1 : LINIER: below exception
0 : ANGIER: exception
+ 1 : HEIRIER: above exception
+ 2 : VOLIER: above acception
+ 8 : QUALIER: above perception

GasicPhotons, GasicElectrons, GasicIons, GasicGases, GasicLiquids, GasicSolids, GasicMolecules

5. WHY: Mouth: Frontal Lobe: Ingestion of Matter

+ 8 , + 2 , + 1 , 0 , – 1 , – 2 , – 8

Standard prefixes with root AEST for Liquids or “Sea” which is interesting because it means that the Sea is the surface of the water.

– 8 : WEAVIEST: below perception
– 2 : POINTIEST: below acception
– 1 : LINIEST: below exception
0 : ANGIEST: exception
+ 1 : HEIRIEST: above exception
+ 2 : VOLIEST: above acception
+ 8 : QUALIEST: above perception

LiquidicPhotons, LiquidicElectons, LiquidicIons, LiquidicGases, LiquidicLiquids, LiquiidicSolids, LiquidicMolecules

6. HOW: Body: Cerebellum: Deduction of Matter

+ 8 , + 2 , + 1 , 0 , – 1 , – 2 , – 8

Standard prefixes with root TER for Liquids or “Earth” because it means that the creators of the word Earth meant “water”.

– 8 : WEAVITER: below perception
– 2 : POINITER: below acception
– 1 : LINITER: below exception
0 : ANGITER: exception
+ 1 : HEIRITER: above exception
+ 2 : VOLITER: above acception
+ 8 : QUALITER: above perception

SolidicPhotons,  SolidicElectons, SolidicIons, SolidicGases, SolidicLiquids, SolidicSolidsSolidic, Molecules

HOW MUCH: Gut: brain region unknown

+ 8 , + 2 , + 1 , 0 , – 1 , – 2 , – 8

Standard prefixes with root DUCT for Counting which is interesting because this involves the digestive process.  Molecule literally means “soft stone”.  Another word for dung.

– 8 : WEAVIDUCT: below perception
– 2 : POINTIDUCT: below acception
– 1 : LINIDUCT: below exception
0 : ANGIDUCT: exception
+ 1 : HEIRIDUCT above exception
+ 2 : VOLIDUCT: above acception
+ 8 : QUALIDUCT: above perception

MoleculicPhotons, MoleculicElectrons, MoleculicIons, MoleculicGases, MoleculicLiquids, MoleculicSolids, MoleculicMolecules.

Note: The seven International System Units are:

– 8 : WEAVE: below perception: Candela
– 2 : POINT: below acception: Ampere
– 1 : LINE: below exception: Kelvin
0 : ANG: exception: Metre
+ 1 : HEIRabove exception: Second
+ 2 : VOL: above acception: Kilogram
+ 8 : QUAL: above perception: Mole

I posted all of the above, because I believe that classification is underrated. If we spent more time thinking about the aesthetics of our classification language, which is presently total crap, we might make more discoveries.

How much do we conceal from ourselves because we deceive ourselves into thinkng a dogmatic classification system won’t bear fruit.

Have you ever seen this guy?

https://relationary.files.wordpress.com/2007/11/mendeleevphoto.jpg

He beat his brains out letting the data talk to him and came up with this:

Periodic Table

When Dmitri Mendeleev created this table to describe periodic behaviour of the elements, many of the elements had not been discovered. However, the table projected what the properties of those elements would be making the search much easier.  Dmitri also was very good at making Vodka.

As I have discussed there are Satrice, Fratrice, Patrice, Natrice, Matrice, Sortrice and Datrice networks.  Each of them classify in different ways.  Understanding these networks and their classification are the road to new discoveries.  Networks are classification systems.

I just saw this in the New York Times:

knowledgemap

It is called a “Knowledge Map”.  It is a plot of the link clicking behaviour of a scientific community.  Not what they say is important, but where they are going that they think is important.  From this information it may be possible to reorganize knowledge to make it more accessible to everyone.

And that is what we are all here for getting and giving access.

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The Brain: ZenUniverse 1.0

zencircle01

“Tao can Tao not Tao”

Lao Tzu

Since reading the work of Clare W. Graves of Spiral Dynamics fame, reflecting on the work of all the people mentioned in my Blogroll as well as my recent foray into Zen I attempted to review and revise my work on the assortment of frameworks I had come up with. As I was making revisions it dawned on me that nature had done all the work already.

http://upload.wikimedia.org/wikipedia/en/c/c6/Boyd56.jpg

“Outside this office, Business as Usual;

Inside this office, Thunder and Lightning.”

Colonel John Boyd

I decided to take another angle of attack.  I realized I was dealing with entities, hierarchies, attributes and relationships and one thing Boyd overlooked, results, in two dimensions not one.  You may remember this graphic:

theboydpyramid

I realized I would have to take the Boyd Pyramid a bit more seriously.  And I have.  I compared Boyd’s work to Einstein’s, saw the correlations and what I think is a flaw.

albert-einstein

“The only real valuable thing is intuition.”

Albert Einstein

ZenEntity

The first thing I want to address is a misconception regarding solids.  It was one Plato made as well as R. Buckminster Fuller.  There are not five stable solids.  There are six.

The mistake Plato and R. Buckminster Fuller made was to demonstrate the stability of a triangle composed of three rods to their students while saying that the simplest solid in three dimensional space is the tetrahedron.  He didn’t realize the triangle in his hand was the simplest solid.  The triangle is a two sided three vertex solid that is the simplest enclosure of space.  Our eyes use two of them to locate an object and calculate distance.

Considering the above solid and the Platonic Solids we have six three dimensional closed network structures as illustrated below:

zensolids5002

Take note of the stability of each of the solids.  What this means is that the triangulated solids are able to support themselves structurally, while the non-triangulated solids collapse.

What I realized regarding the work of Einstein and other physicists is they did not regard the various phases of matter as important.  However the states of matter are important.  Each state from the triangle up to the icosahedron as illustrated above are higher states of order.  Yet, each state of order is fundamental to the universe in which we live.  And all are simply phases of what I call the “ZenEntity”.

ZenAssociations

I decided after looking at what I had found regarding the solids to reject contemporary empirical conventions and simply address one thing.  We have six fundamental ordered states.  After several billion years of evolution would not all organisms have what they require to function in response to all of the six states in their niche?

My next question was, “How do I represent the phenomena I had encountered as a network?”

In my profession there are data architects, database designers, data modelers, database administrators, data entrists, data analysts, database developers, database programmers database analysts, data warehouse architects, data warehouse analysts, data warehouse developers, Extract-Transform-Load architects, ETL analysts, ETL designers, ETL developers, ETL programmers, Business Intelligence architects, BI analysts, BI designers, BI developers and so on.  However, I was never satisfied with any of these position titles.  So, I coined one myself: data designer.  I was of the opinion no matter how much data was out there, it was finite.  Zero and Infinity were very useful, but they violated the laws of thermodynamics.  I saw seven distinct phases of order in the universe and only saw transitions from one state to another.  I could design according to those states.

This led me to explore how I could represent the six states.  I studied and applied a variety of project lifecycles such as System Development Lifecycle, Extreme Programming and Rapid Application Development, joint application development.  I had learned various enterprise frameworks such as Zachman and TOGAF, modeling techniques like UML, the various generations of programming languages, data structures, network topologies, organizational concepts, rule based systems, event based systems, data based systems, user centered design, goal directed design, location based services, pattern languages, service oriented architecture, hardware architectures and many more.  I studied English, Greek, Latin, Anglo-Saxon, German and French to see how I could develop a consistent taxonomy as well.

Ultimately I concluded that a majority of the people out there working on these problems had abandoned the basics for pet concepts.  They had no idea how many entities there were.  They had no idea how those entities should be related.  So I took it upon myself to identify all the relations that were applicable and came up with the following:

zenassociations5003

The associations are as follows:

  1. Pattribute: a triangle entity
  2. Battribute: a one to many relationship describing the association between a triangle and an tetrahedron
  3. Attribute: a one to one relationship describing the association between a triangle and a hexahedron
  4. Nattribute: a many to one relationship describing the association between a triangle and a octahedron
  5. Lattribute: a recursive many to one relationship describing the association between two icosahedrons and one icosahedron
  6. Mattribute: a recursive one to one relationship describing the association between two dodecahedrons

As you can see, the network is asymmetrical and allows for Node, Lattice, Tabular, Lattice, Linear; Lattice arrangements.  Note that since all of the entities are simply states of a single “ZenEntity” none of the states are independent from each other in the network.

ZenPhases

Now, that we have established the solids and how they are interconnected we can look at what the actual phases of the ZenEntity are.  Each of these phases are recognized in physics, however I have not come across any discussion of the possibility that they are together a set of fundamental phases.

zenphases5001

Usually, we see Space, Time, Energy and Mass described in Einsteinian classical physics.  We also have discussions of Ions, Gases, Liquids and Solids as states of matter.  But we don’t see them together.

  1. Energy: a three dimensional coordinate system
  2. Time: a connection between one three dimensional coordinate system and two four dimensional coordinate systems
  3. Ion: a connection between one three dimensional coordinate system and one six dimensional coordinate system
  4. Gas: a connection between two three dimensional coordinate systems and one eight dimensional coordinate system
  5. Liquid: a connection between two twelve dimensional coordinate system and one twelve dimensional coordinate system
  6. Solid: a connection between two twenty dimensional coordinate systems

Next, we will see how these states are all very important to our sensory systems.

ZenStates

As well as the phases there is another way to look at the six solids.  This is in the Latinate language of the six states.  The states differ from  the phases in that they deal with the essence or source of each of the states.

zenstates5006

The essence of each of the states is as follows:

  1. Pattern: Father
  2. Battern:  Hold
  3. Attern: Give
  4. Nattern: Birth
  5. Lattern: Milk
  6. Mattern: Mother

ZenSensors

Now, I am going to introduce you to some friends of mine.  I call them “Zen Sensors”

zensensors5001

As you can see each ZenEntity State has a coresponding human sensory organ:

  1. Eye: detect events
  2. Ear: detect pressures
  3. Nose: detect plasmas
  4. Throat: detect molecules
  5. Jaw: detect organics
  6. Body: detect inorganics

ZenInterrogatives

Next, we have for your viewing pleasure the standard interrogatives and how they correlate:

zeninterrogators5001

I found this interesting, because I spent a great deal of time resisting the order of these interrogatives.  Finally, I just went along and found ultimately the order does make perfect sense.  It is an acquired taste.

  1. Eye: Who: Identification
  2. Ear: What: Objectification
  3. Nose: Where: Location
  4. Throat: When: Chronation
  5. Jaw: Why: Rationation
  6. Body: How: Function

If you read enough Anglo-Saxon it makes sense.

ZenHemisphere

Having considered the Entities, Associations, States and Sensory Organs, let us now look at how this relates to a hemisphere of the brain:

zenhemispheres5001

The above illustration shows the left hemisphere of the brain and the major regions.  They are color coded to correspond to the fundamental states I have described.  You can also see the corresponding sensory organ as well as the corresponding network structure in the region:

  1. GREEN: EYE: OCCIPITAL LOBE: visual center of the brain
  2. YELLOW: EAR: TEMPORAL LOBE: sensory center of hearing in the brain.
  3. SKY: NOSE: BRAINSTEM: control of reflexes and such essential internal mechanisms as respiration and heartbeat.
  4. BLUE: TONGUE: PARIETAL LOBE: Complex sensory information from the body is processed in the parietal lobe, which also controls the ability to understand language.
  5. RED:  JAW: FRONTAL LOBE: control of skilled motor activity, including speech, mood and the ability to think.
  6. ORANGE: BODY:  CEREBELLUM: regulation and coordination of complex voluntary muscular movement as well as the maintenance of posture and balance.

ZenBrain

Everything is great so far, but there is the fact that there are two hemispheres to the brain and they interact through the Corpus Callosum which I claim is where the self resides.  One of the interesting things about my study of Latin is that I discovered most questions actually required a two part answer.  This answer would be composed of an Archetype and a Type.  After reading Jill Bolte Taylor’s book, My Stroke of Insight and listening to her account of her perceptions while the left hemisphere of her brain was being shut down by an exploded blood vessel, it became apparent to me that the left hemisphere of the brain contained the Types the Latin language required and the right hemisphere of the brain contained the Archetypes.  It was necessary to create a two axis model to accomodate a brain with two hemispheres:

zenuniverse5008

Each of the light colored cells in this table represent a connection between one coordinate system association (row) and another coordinate system association (column).  This accounts for the broad variety of properties we encounter making the states we experience.

There are actually not one or two, but four directions you can take on the above table.    Top to Bottom is right hemisphere deduction.  Bottom to Top is right hemisphere induction. Left to Right is left hemisphere deduction.  Right to Left is left hemisphere induction.

This is a physiological model of human perception that I have arrived at.  Our current definitions of dimensionality are incorrect.  Each state has its own dimensionality, its own associations, its own sense organs, its own region of the brain and the brain two hemispheres connected by the corpus callosum.  If the work of Dr. David Bryson on Physical, Decisional and Perceptual Learning is right, then deduction happens during waking and induction happens during sleeping.

This is not a complete model by any means as it does not deal with scale-free networks.  Or does it?

But to this point, that is the Zen Universe.

Link:

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DemocraNet: Scale-free CPUs, HFGW Networks, Associational DBMSs, Iconic Languages, AlwaysOns and Laypeople

fractal

I came across this article http://tinyurl.com/58envr in Infosthetics.com regarding a medical iconic language. This lead me to think about iconic languages in general.

What would happen if we developed non-text languages where icons were not just “terms” but were used as “definitions” as well?

Consider having:

Iconic vocabularies.
Iconic grammars.
Iconic syntax.
Iconic linguistics.
Iconic dictionaries.
Iconic thesaurii.
Iconic wikis.
Iconic semiotics.
Iconic animation.
Iconic context.
Iconic databases.
Iconic functions.
Iconic organization.
Iconic networks.
Iconic events.
Iconic fonts.
Iconic classics.
Iconic metrics.
Iconic audio.
Iconic video.
Iconic mechanio (pressure)
Iconic olfio (smell)
Iconic gustio (taste)
Iconic thermio (heat)
Iconic nocio (pain)
Iconic equilibrio (balance and acceleration)
Iconic proprio (body position)

Such languages already exist. Chinese Hanyu for example. But what if a new global iconic language were developed?

In my reading I am discovering that even words are treated by our minds iconically as symbolic clusters. If the first and last letter of a word is correct the remaining letters in the word can be in any order. In fact, we do the same things with words themselves. We create word clusters and shuffle them around to create sentences. I think language does not have the formula Chomsky came up with using random sets of words arranged syntactically. Words are symbols and sentence fragments are symbols that we connect together. We do the same thing with lists which are basically paragraph fragments. All these fragments are are arranged according to the rules of a scale-free network not a hard wired linguistic structure. I think that would shake Steven Pinker up.

The thing that is necessary to point out is literacy and numeracy does not make us any more or less intelligent. It is a symbolic system like any other that trains us to think in certain ways to process language and quantities. Whatever we do we are simply learning another, perhaps more efficient way of processing symbols representative of reality. Plato thought that literacy was dumbing down his students because they did not memorize and meditate on what they learned, choosing to write it down and put it on the shelf instead. Are our children any different if they choose to let computers deal with the mechanical aspect of literacy and numeracy so they can concentrate on higher order operations? Do we agonize over our children being unable to weave cloth and tailor clothing?

If Marshall McLuhan is right, we are not past the point where we are pumping old media through the new internet media pipe. Text will always be with us, I think because it is just too darned useful. But we will utilize it differently as we become able to record, replay, produce, publish, communicate and collaborate using non-textual, non-numeric media and move beyond linear and tabular networks and into netular scale-free networks.

Something that occurred to me about phonetic language like English and syllabic language like Arabic versus iconic language like Hanyu Chinese is a phonetic or syllabic language enable you to encode or decode words according to their sound and store and retrieve them based on a simple index. Hanyu on the other hand provides no association between code and sound. You are dependent on the person you hear the word from to provide the association making coding and decoding author dependent. Iconic storage and retrieval indexes are not always obvious either although they do exist based on the subordinate symbols from which words are composed. The internet poses the remedy to this by enabling the automation of the association between sound and icon and definition.

It seems to me that iconic languages as a technology are undergoing a major evolutionary change that could not be achieved without the internet.

Computing is going through an interesting process:

Note: PL means programming language

Nodular Computer: Mainframe: Priesthoods operate
Nodular Network: ARPANET: Priesthoods connect
Nodular Data: Variable: Noun: Priesthoods Query
Nodular Language: Variable PL: Assembler: Priesthoods Manipulate
Nodular Communication: Variable Packet: TCP/IP Priesthoods Communicate
Nodular Schedule: Sequential Batch

Linear Computer: Minicomputer: Scribes operate
Linear Network: Ethernet: Scribes connect
Linear Data: String dbms: Verb: Scribes Query
Linear Language: String PL: 3GL: Scribes Manipulate
Linear Communication: String Packet: HTML: Scribes Communicate
Linear Schedule: Multi-Tasking

Tabular Computer: Microcomputer: Educated operate
Tabular Network: Internet: Educated communicate
Tabular Data: Relational dbms: Noun Set: Educated Query
Tabular Language: Relational PL: SQL: Educated Manipulate
Tabular Communication: Relation Packet: XML: Educated Communicate
Tabular Schedule: Multi-Threading

What is over the horizon and will accompany Iconic Languages I call “DemocraNet”

Netular Computer: Scale-free CPUs: Laypeople operate
Netular Network: High Frequency Gravity Wave Network: Laypeople communicate
Netular Data: Associational DBMS: Verb Set: Laypeople Query
Netular Language: Assocational PL: Iconic Language: Laypeople Manupulate
Netular Communication: Association Packet: XMPEGML: Laypeople Communicate
Netular Schedule: AlwaysOn

Scale-free CPUs will be solid state computers.  There will be no moving parts at all: Solid State Storage, no fans, no boards and a Network Processor.

High Frequency Gravity Wave Networks will make available bandwidth several factors larger.

Associational DBMSs will allow us to modify databases on the fly without concerns regarding referential integrity or normalization.

Iconic Language will Internationalize visual communication.

XMPEGML as new form of markup language for the standardization of iconic language exchange awaits development.

AlwaysOn would mean that you are always connected to democranet and always processing data.

Everything is in the mix to varying degrees, but each successive community is larger.

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Bureaucracy: The Olympic Torch Bearer

petro-canada-torch1

Last year I attended a gathering where a gentleman, let’s call him Chuck, delivered a speech to us about an accomplishment he had made.

In 1988 Canada hosted the Winter Olympics in Calgary, Alberta.  As part of the celebration Canada’s state owned oil company Petro-Canada decided to sponsor the Olympic Torch Relay across the country.  How would the relay team be assembled?  By lottery.  All you had to do to participate in the Olympic Torch Relay was go to your nearest Petro-Canada Gas Station and fill out an entry form.  Relay participants would be drawn from among the entrants.  You could enter as many times as you wished.

Chuck lived in a small rural community, but it turns out our he was ambitious.  He was determined as a grade school student that he would participate in the Olympic Torch Relay.  He went down to the Petro-Canada Gas Station and picked up as many entry forms as the gas station attendant would allow, went home and began filling out entry forms by hand, one at a time.  Then he would go back to the Petro-Canada Gas Station and stuff all his completed entry forms into the entry box.

Chuck was determined.  Every day he would go to the Petro-Canada Gas Station and collect a ream of entry forms.  Everyday he would spend all his spare time filling out the entry forms one at a time by hand.  When other kids his age were spending their time with their families and friends, enjoying leisure time or participating in extra-curricular activities or sports, our speaker was filling out forms.

The entry form completion and submission routine went on for months.  Chuck’s family thought he was crazy, his friends thought he was crazy, his teachers thought he was crazy, the attendants at the Petro-Canada Gas Station thought he was crazy.  Then the day of the draw for the Petro-Canada Olympic Torch Relay participants finally arrived.  The draw was made and about a week later a letter arrived at Chuck’s home.  He had been drawn to carry the Olympic Torch as a relay participant.  Everyone was overjoyed.

The Olympic Torch Relay began during a Canadian Winter and it finally arrived at the point where Chuck would take the Olympic Torch from the previous Torch Relay participant, bear the Olympic Torch for a kilometer or two and pass it to the next Torch Relay member.  Chuck was dressed in the red and white Olympic Torch Relay uniform with the Calgary 1988 Winter Olympics logo,  the Canadian Flag logo, the Olympic Torch Relay logo and the Olympic Torch Relay logo emblazoned on it.  However, it was bitterly cold, the relay schedule was very tight and physically Chuck was not only unfit, but considerably overweight.  However, no matter, Chuck received the Olympic Torch and jumped on the back of a snowmobile driven by a Torch Relay volunteer.  They crossed the snowy Canadian winter wilderness with God speed with Chuck holding the Olympic Torch high.  Finally, they arrived at the next relay point and Chuck jumped off the back of the snowmobile and passed the Olympic Torch to the next Torch Relay participant, who in turn jumped on the back of the snowmobile and continued onward.  Victory had indeed been sweet.

Now, let’s return to 2007 on the day this speech was being delivered.  Chuck completed his story and proudly displayed the Olympic Torch Relay uniform he had worn during his leg of the relay.  We all looked admiringly at it and thought about our own desire to carry the Olympic Torch that we had not attempted to realize.  And looked at a man who had had the courage to realize a dream.

Chuck stood before us proud, reserved and two hundred pounds overweight.  He now worked for one of Canada’s provincial governments as a senior bureaucrat.  He was a senior elected member of the organization of which his audience belonged.  He was also a member of the subdivision of the organization to which the audience belonged.  He does not believe in new members or in fact any members of the organization receiving a copy of the organization’s constitution, but knows it intimately.  He studies Robert’s Rules of Order intensely during organization meetings, but does not share this knowledge with the members, instead waiting to be called upon in an advisory role as Parliamentarian deciding for everyone what due process is.  Instead of rationally debating motions, he bellows out bombast like profanity.  When asked about ethics, he says his is winning.

So, what did Chuck learn from the example of Olympic Torch Relay?  First, he learned that sport and sportsmanship had nothing to do with the Olympic Torch Relay.  Second, he learned that the Olympic Torch Relay was a lottery, not based on merit.  Third, he learned that he could manipulate the outcome of the Olympic Torch Relay selection process by stuffing the ballot box.  Fourth, he learned to be a good bureaucrat legalistically filling out the same Olympic Torch Rleay entry forms day in and day out, neglecting family, friends, liesure, extra-curricular activities, sport and physical health.  Fifth, he learned that the Olympic Torch Relay had no physical fitness requirements at all.  He simply sat on the back of a gas poewered, internal combustion engine, polluting snowmobile so the organizers of the event could meet their schedule.  It’s a wonder that Chuck had the strength to hold the torch up for the length of his leg of the relay.

Chuck had learned a lot of lessons from the Olympic Torch Relay.  I believe that the Olympic Committee, Canada, the Petro-Canada Corporation and the Canadian Olympians should all be proud of what they accomplished.  They have produced an immoral, misleading, scheming, complex, inefficient, ineffective, inadequate, over-indulgent, imprecise and inaccurate bureaucrat who could die of any number of self-inflicted chronic health problems the next moment.  Although I’m sure he has a redeeming trait or two. They all deserve a medal.

Live the Dream.

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Pablo Picasso: Learning

pablo-picasso

I am always doing that which I can not do, in order that I may learn how to do it.
Pablo Picasso

The Brain: Hardwiring and Softwiring

I’m just finishing a very fine book by Steven Pinker, The Languange Instinct: How the Mind Creates Language

and several years ago I read Donald D. Hoffman’s book, Visual Intelligence: How We Create What We See. Both books deal with the same subject: What part of our minds are hardwired–instinct–and what parts of our minds are softwired–reason. It is a truly fascinating exploration.

Stephen Pinker in The Language Instinct very thoroughly explores all the aspects of spoken language. He discusses how broken pidgin languages are turned into grammatically rich creoles by children. He explains that whether a person learns a language or not they can have complex thought he calls Mentalese. He explains Chomsky’s concept of a Universal Grammar and how, with language, learning does not cause mental complexity, but mental complexity causes learning. He reveals that children have an acute sense of the morphology of words and rapidly acquire vocabulary as listemes because of the nature of the relationship between child, adult and reality. The perception of speech as well as the physical production of speech is explored. How we derive meaning from language rejects the technical concept of packets being transmitted and received for a much more subjective process of interpretation. The ability of children to learn language is treated as an evolutionary trade off existing only long enough to adopt the tribes language and then shutdown to make way for other special priorities. The “Language Organ” or region of the brain that is responsible for speech is narrowed down. The chain of being is pushed aside for the bush of evolution to reveal that hundreds of thousands of generations existed for language and homo sapiens sapiens to evolve separate from all our other primate cousins. The difference between living spoken language is separated from living written language, the discipline required for each and the fact that language is never in decay. Finally the relativism of the Standard Social Science Model (SSSM) or tabula rasa as proposed by Margaret Mead is rejected, Pinker takes sides with the Evolutionary Psychologists stating that environment alone cannot create the complexity of the mind, the mind must have many complex modules to be able to learn from the environment at all. He discusses Donald E. Brown’s Universal Person (UP) inspired by Chomsky’s Universal Grammar (UG). Finally, Pinker tries to define the modules of the human mind and here I get excited as I find I am able to fit them easily into the Six Hats, Six Coats model. Pinker says that language is a system and extrapolates to say humans are a system of both hardwiring and softwiring.

Hoffman’s book deals with an aspect of mind that more easily subscribes to the module concept than language because it is a much more detached, empirical exercise to test for the visual hardwiring that humans have through the use of visual illusions. Hoffman takes us through many aspects of vision such as facial recognition, edge and shadow and color and the perceptual development of children to reveal what appears to be hardwired and softwired. He concludes with a relativistic statement, but I think that he chooses this because of the political desire of scientists to distance themselves from the eugenics of the first half of the 20th century instead of an objective conclusion that, yes, we have a complex module in our brain specifically hardwired and softwired for vision as used by our species. In other words, when presented with the depth of Steven Pinker’s work compared to the breadth of Donald Hoffman’s work, I believe that we do have a vision instinct.

All in all I believe that Steven Pinker’s and Donald Hoffman’s work is revealing that humans minds are far more than just an empty neural net at birth. That in fact there is an evolved complex predefined structure that humans make use of through the learning stages of childhood to understand their environment that diminishes to adult levels at puberty. Consequently, no form of Artificial Intelligence will succeed unless it also comes with a robust collection of Artificial Instincts.

Related Article:

Science: Know “Why”

avoid-boring-people-cover.jpg

I am currently taking a break to read James D. Watson’s new book, Avoid Boring People.

Here’s a quote dear to my heart and core to effectively using the Six Hats, Six Coats metaphor:

Knowing “why” (an idea) is more important than learning “what” (a fact)

World Almanac facts, such as the relative heights of mountains or the names of British kings, go you nowhere at Hutchin’s college. The essence of its educational mission was the propagation and dissection of ideas, not the teaching of facts often best left to trade schools. Why the Roman Empire had risen and fallen was much more important than the birth date of Julius Caesar. And why the European cathedrals were built mattered much more than their relative sizes. Equally unimportant were the details about the French Revolution when contrasted to the philosophical ideas of its eighteenth-century Enlightenment, whose emphasis on reason as opposed to theological revelation greatly accelerated the development of modern science. Likewise, details of Linnean taxonomy paled in significance to the idea of biological evolution, whereby all life-forms have a common ancestor. Better simply to know which books hold the details you will need than to overload your neurons with facts that later will never need to be retrieved.”

I’d like to add the following corollaries:

  1. Knowing “why” is more imortant than learning “who”
  2. Knowing “how” is more important than learning “what”
  3. Knowing “when” is more important than learning “where”

All this considered, James recognizes the importance of new facts leading to new ideas. He gives as an example Darwin’s journey on the HMS Beagle that led him to discover the geographical patterns of the distribution of species and the fossil record that led to his theory of the evolution of the species. “Sometimes a new idea can flow from old facts rearranged, but more typically it comes when new things previously unknown and unaccountable for under the old theory are introduced.” Induction has its place.

The Brain: Physical, Decisional and Perceptual Learning

I have recently crossed paths with David Bryson M.D. and through our discussions he brought to my attention an article he felt complements my work

  1. Physical: Regulate, Repose
  2. Decisional: Record, Report
  3. Perceptual: Relate, Revise

and which with his permission I am reproducing here. Dr. Bryson is currently working on a concept he intends to present during the 2009 celebration of the 200th anniversary of Darwin’s publication of Evolution of the Species. –Grant Czerepak

BEHAVIORAL ANALYSIS OF MAMMALIAN SLEEP AND LEARNING

Perspectives in Biology & Medicine, Autumn Issue,1969, pages 71-79
University of Chicago Press

David Bryson, M.D. and Stephen Schacher, M.D.

Theories of mammalian learning and theories of mammalian sleep have developed with scant interaction. This article states that mammalian sleep and learning are related fundamentally. Our approach is theoretical; no new experimental data are presented. Aspects of human behavior which are distinct from those of mammals in general will not be considered here.

Synopsis

Much of the mature mammal’s behavior is devoid of any significant decision making, due to the familiarity of most of the inputs with which the mature mammal deals. Familiar inputs are associated with the execution of routine behavioral acts, and during such periods mammalian record keeping is insignificant. In contrast, the record of the mammal’s activities which is kept is directly related to episodes of decision making. A decision is made when the mammal is confronted with an unfamiliar input, and this input, the resulting decision, and the outcome of the decision are recorded. For example, a hungry mammal may suspect that some unfamiliar substance is edible, may decide to sample it, and may find the taste unacceptable. This input, this decision, and this consequence are recorded.

The decisional record thus grows as the waking state progresses, at a rate related to the frequency of unfamiliar inputs. The decision record may be accessed as soon as a decision and its consequences have occurred, enabling a mammal to learn within a single waking period. Should some unfamiliar input recur, the current need for decision making may be reduced considerably if the previous decision for the same input had and acceptable consequence. If not, the probability of invocking this decision again is decreased, and decision making yields another decision and perhaps a more acceptable consequence.

Learning of this type is inadequate in one important respect. Access to the decisional record is limited to one decisional cluster (unfamiliar input, decision, consequence) at a time; decisional clusters are not themselves compared. For those inputs which do recur, the original problem of input unfamiliarity is improved empirically on a case-by-case basis. However, improved decision making for each case still fails to exploit any generic relationships which may exist between the individual decisional clusters.

During sleep, the information in the decisional record is utilized for a further purpose. The individual decisional clusters are combined into informational sets, the combinatorial rule being to group those unfamiliar inputs in which the decisions and consequences were similar. For example, suppose a mammal in an unfamiliar environment has spent various portions of the current waking state in trying to establish reliable landmarks for navigational purposes. During sleep, those environmental features which were selected to be–and in fact turned out to be–reliable landmarks are grouped into one informational set. Inductive analysis of this set of unfamiliar inputs may reveal recurrent similarities in their characteristics, a finding with implications for the input classification (perceptual) system. If so, the perceptual system is revised as the mammal sleeps. Sleep thus enables the mammal to classify unfamiliar inputs having the same significance as the same perception. Thus previously unfamiliar inputs subsequently are perceived with increased familiarity.

Two categories of mammalian learning are thus proposed: decisional learning, a waking activity in which output selection for the same input is improved; and perceptual learning, a sleeping activity in which the functional reorganization of input classes reduces the number of decisional involvements required previously. Decisional learning is akin to “stimulus-response” learning; perceptual learning is akin to “gestalt” learning. All perceptual learning is the result of recent decisional learning. When the inputs associated with a series of decisions with inadequate consequences can be related and thus lead to perceputal learning, a considerable behavioral advancement may occur from one waking state to the next.

The first section of the article develops a model of the informational operations between initial input (attention) and final output (manifest behavior). The model describes how decisional learning and perceptual learning are related to the overall control of behavior. The setting of the model is ethological. The second section confronts learning research in the laboratory setting. The research section is referenced; the model section is not.

The Model

The model is presented at the level of a simple behavioral analysis. Our prototype mammal is always in one of two output modes when interacting with the environment: decision making, or decision execution. During decision making, the mammal is obviously inspecting or choosing; during decison execution, the mammal is obviously operating on the environment as part of some goal-directed act. Decison making and decison execution are to us thus a matter of seconds rather than milliseconds.

Two sequential processes are invovled on the input side of mammalian behavior: attention (input sampling) and perception (input classification). Attention and perception are components of both decision making and decison execution. The sequence of attention-perception-output-attention-perception-output may thus represent either ongoing decision making or ongoing decision execution.

We classify input into three types: potential input, attended input, and perceived intput. The process of perception converts attended input into perceived input.

Potential input is the totality of the physical environment to which the mammal theoretically may attend at any time. Potential input is thus a range of input information, reflecting the theoretical limits of the mammal’s sensory equipment. Attended input is that constellation of physical variables (light, sound, chemical concentration, etc.) to which the mammal actually attends at any time. The dimensions of potential input and attended input are always physical. During decison execution, the dimensions of perceived input becomes those of the utility and survival of a particular mammal at a particular time.

During decision execution (peeling a fruit, copulating, grooming), the mammal actively attends to its own behavioral output and in so doing excludes the remainder of potential input. Attended inputs here are thus an extremely narrow and gradually shifting sample of potential input. As long as attended inputs remain within prescribed tolerances, decision execution continues toward completion with no interruption for decision making.

Decision execution may be interrupted by sudden changes in potential input (as sudden noise or shadow). Attention automatically is diverted from behavioral monitoring. If the environmental change, now represented in attended input, is perceived as obviously irrelevant to the mammal (as a familiar “false alarm”), attention is returned to consummating the current goal. If the change is perceived as obviously relevant (a familiar opportunity or danger of greater significance than the current activity), the mammal quickly decides upon the more appropriate behavioral act and devotes its attention to the execution of this decision rather than the previous decision.

When the relevance of attended input is not immediately apparent, the mammal switches to the decision making mode. During decision making, attention is determined by a complex interaction between the mammal and its environment. The mammal attends to increasingly diverse aspects of potential input, making active searches for suspected environmental features and also being passively drawn to unexpected environmental features, and continues to do so until it discovers or is confronted with some environmental feature of obvious relevance. The mammal then returns to the decision-execution mode.

Every attended input is transformed into a perceived input. The product of perception is a perceived input of variable familiarity. Perceived inputs of high familiarity are perceived as action oriented (output oriented) and are associated with ongoing decision execution or the initiation of decision execution. Perceived inputs of low familiarity are perceived as feature oriented (input oriented) and are associated with ongoing decision making or the initiation of decision making.

During decision execution, two mammals may attend to similar features of the environment but have quite different perceptions. For example, suppose two mammals are attending to certain physical features of an edible plant which only grows near an open water source. Attended input A of mammal A is very similar to attended input B of mammal B. However, perceived input A is high in a dimension reflecting the accessibility of food, which mammal A happens to be looking for, while perceived input B is high in a dimension reflecting the accessibility of water, which mammal B happens to be looking for. During decision execution, perception reflects the current functiona requirements of the mammal. During decison making, because of the difficulty in classifying unfamiliar attended inputs, perception reflects the more constant structural features of physical reality.

On the output side of the model, every perceived input is transformed into an output signal. During execution, the output signal furthers the behavioral action represented in the precediing perceived input. During decison making, the output signal is related to obtaining more information relevant to the precedinng perceived input. During both output modes, the output signal directs attention in relation to the formation of the next attended input.

Within a single waking state, decision making results in decisional learning. Decisional learning can directly reduce the duration of subsequent decision-making episodes, and can indirectly reduce the number of subsequent decision-making episodes by improving the quality of decision making. During sleep, decisional learning results in perceptual learning. Perceptual learning can directly improve decision execution by improving input classification, thereby reducing the number of unnecessary interruptions. Perceptual learning sets the tolerances for variation in inputs at a functional level. Improved decison execution thus directly reduces the number of decision-making episodes.

Wide variations exist for the relative role of perceptual learning among different mammalian classes. Rodents are born with a number of input classes already fixed, and relatively few input classes are added as life proceeds. Rodents are nearly always in the decision-execution mode, since they have so few behavioral acts to decide between, and since their tolerance for input variation during decision execution is relatively broad. Most of the input classes of primates are learned, and the complex behavior of an adult primate requires a considerable repetoire of pereceptual categories. Primates are often in the decision making mode, since they have so many behavioral acts to decide between, and since their tolerance for input variation during decision execution is relatively narrow.

Such variation is related to the phenomena of play and curiosity. These are common features of primate behavior, especially during development, and are virtually absent from rodent behavior. We consider play and curiosity as phenomena which maximize decison making. Increased decision making leads to increased decisional learning, decisional learning to perceptual learnining, and perceptual learning to improved decision execution. As a result of play and curiosity, the behavioral repetoire of a mammal is increased, and thereby future waking-state activities which now have a goal-fulfilling function may be performed more effectively.

Implications for Research

Our model suggests that the unusual interaction of decisional learning and perceptual learning results in gradual, quantitative improvements in mammalian performance. For behavioral acts of which the mammal is already capable, improved output selection can simulate revised input classification before the latter occurs. Therefore sleep is not required for quantitative improvement in performance.

In order to demonstrate the role of sleep in mammalian learning criterion performance should represent a qualitative improvement over current performance. Such performance corresponds to the “problem-solving” class of previous experiments. While perceptual learning usually results in the modification of a previous input class, problem-solving behavior requires the formation of a new input class corresponding to the problem solution. We thus consider problem solving as a special case of perceptual learning. Since a new input class is required, decisional learning cannot simulate perceptual learning, allowing the role of sleep to be tested.

That sleep is not required for quantitative improvements in performance is obvious from experiments showing improved performance from the first trial outward [1]. We analyze such performance as improved output selection for the same perceived input (decisional learning). That sleep is required for qualitative improvements in performance is not obvious from previous research. To our knowledge, no experiment in mammalian problem solving has studied sleep as a performance variable. The following analyses of previous experiments are therefore inferential.

Chimps which did not use a hoe to reach for food placed outside their cages did so “three days later,” after intervening ad lib experience with sticks [2]. We suggest that primate curiosity resulted in the formation of a new input class in which sticks became perceived as a functional extension of the arm. A de novo decision execution thereby was made possible when the chimps were reconfronted with the original situation.

The “learning to learn” experiments with primates all take more than one training session [3]; improvement within a single training session is negligible [4]. We suggest that such experiments can also be explained as a special case of perceptual learning, with the problem solution representing a new input classification.

The perception of “size constancy” (learning that stimulus properties are distance invariant) takes about ten days for rats previously reared in darkness [5]. While not a classic problem-solving paradigm, we suggest that here again the formation of a new perceptual category was based on recent decisional learning, and the intervening sleep was a necessity.

Dogs and cats were trained to move around and behind a screen on the side to which food disappeared [6]. The problem here is to learn that the same food continues to exist after visual representation of the food is no longer a part of attended input. No animal achieved problem solution (moving to the side of the food, sometimes to the right, sometimes to the left) on the first of daily training sessions. Furthermore, in the data as published, problem solution tended to appear on the first trial of a daily session (presumably the only trial after intervening sleep). We suggest that intervening sleep was, in fact, required for criterion performance. Before solution, both dogs and cats exhibited “position habits,” that is, always choosing the same route (for some always the left, for others always the right), resulting in 50 percent rewarded trials for chance alone. We suggest that high chance-based reward rates tend to preserve a decisional basis for behavior, and thus that decreasing the incidence of chance-based payoff will accelerate the appearance of problem solution.

Because sleep has been neglected variable in problem-solving experiments, the first trial of a daily training session usually has had two features which must be dissociated: it is usually only trial which follows sleep, and it is usually the trial which ends the longest intertrial interval. When waking time and sleeping time are equated, we predict that normal sleep is more favorable to problem solution than is any waking activity of equal duration, including continued exposure to a problem. Furthermore, we predict that in problem-solving experiments, as the above examples, criterion performance cannot be established in the first training session (no intervening sleep) regardless of the number of trials within the first training sessions. However, compression of trials within a few training session, as opposed to the same number of trials distributed over many training sessions, should favor the apperance of problem solution. Compression of relevant trials favors inductive operations in the following sleep state.

All decisional learning is time based (the cotemporality of unfamiliar input, decision and consequence within a decisional cluster). In the informational rearrangements of sleep, temporal relationships within a decisional cluster are reduced, and categorical relationships between decisional clusters are enhanced (perceptual learning). Since our model assigns decisional learning to a given waking state, we cannot rigorously approach the effect of sleep on decisional learning. We do suggest that the more a performance represents a unique temporal association devoid of significant implications for input classification, the more that intervening sleep will revert this performance toward the prelearning level. We therefore suggest that time-based performance is better retained by distributing a given trial over many training sessions, as opposed to compressing the same number of trials within a few training sessions.

A general finding for all mammals is a decrease in sleep as life proceeds[7]. In terms of our model, increasing comprehensiveness of input classification increases input familiarity, increased input familiarity reduces decision making, reduced decision making reduces new record keeping, and reduced record keeping reduces the informational rearrangements of sleep. We thus suggest that an unfamiliar environment should cause increased sleep, regardless of the age of the mammal. In particular, we suggest that “sensory deprivation” is familiar to a newborn and thus should cause decreased subsequent sleep, while sensory deprivation is unfamiliar to an adult mammal and thus should cause increased subsequent sleep.

Mammalian sleep is composed of alternating periods of low and high brain metabolism [8]. We speculate that sleep is a recurrent ABABAB phenomenon in which an A period (low metabolism) is a necessary preparation for the following B period (high metabolism). If true, specifically depriving a mammal of B periods (rapid eye movement sleep deprivation) should interfere with recent learning more than total sleep deprivation, even though the latter is of greater clock time.

We repeat what we think is the most important implication of our model for future research. Problem-solving learning is a special case of perceptual learning. We predict that normal sleep is more favorable to problem solution in mammals than is any waking activity of equal duration, including continued exposure to the problem.

References:

  1. E.E. Smite. Psycol. Bull., 69:77, 1968.
  2. H.G. Birch. J. Comp. Psychol., 38:367, 1945.
  3. H.F. Harlow. Psychol. Rev., 56:51, 1949.
  4. D.R. Meyer. J. Comp. Physiol. Psychol., 44:162, 1951.
  5. D.P. Heller. J. Comp. Physiol. Psychol., 65:336, 1958.
  6. L.V. Kruschinsky. In: N. Wiener and J.P. Schade (eds.) Cybernetics of the nervous system, p. 280. Amsterdam: Elsevier, 1965.
  7. E. Hartmann. The biology of dreaming. p. 19. Springfield, Ill.: Thomas, 1967
  8. E. Aserinsky and N. Kleitman. Science, 118:273, 1953.