The Brain: ZenUniverse: Seven Senses

zensevenuniversebrain1

  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.
  7. GREY: SELF: CORPUS CALLOSUM: The arched bridge of nervous tissue that connects the two cerebral hemispheres, allowing communication between the right and left sides of the brain.

zensevenuniversecolumn2

If you look at my first ZenUniverse post, you will see a six column model.  However, the System International Units require seven columns.

Here is a table of two hemisphere intersections.  I am using Latin roots, but you will recognize many of the terms:

zensevenuniversetable2

Here is a blank table you can print out and experiment with correlations and intersections of your own:

zensevenuniversecolumnblank

zensevenuniverseblank1

zensevenuniversebrain1

Link:

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.

Posted in 2. Science, 7. Quality. Tags: , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , . Leave a Comment »

Systema: Seven Hats, Seven Links

watch-parts1

Parable of the Watchmakers

There once were two watchmakers, named Hora and Tempus, who made very fine watches. The phones in their workshops rang frequently; new customers were constantly calling them. However, Hora prospered while Tempus became poorer and poorer. In the end, Tempus lost his shop. What was the reason behind this?

The watches consisted of about 1000 parts each. The watches that Tempus made were designed such that, when he had to put down a partly assembled watch (for instance, to answer the phone), it immediately fell into pieces and had to be reassembled from the basic elements.

Hora had designed his watches so that he could put together subassemblies of about ten components each. Ten of these subassemblies could be put together to make a larger sub- assembly. Finally, ten of the larger subassemblies constituted the whole watch. Each subassembly could be put down without falling apart.

sevenhats2.jpg

For the longest time I have been playing with interrogatives and associations.  Now, I think I finally have a complete representation and taxonomy.

Abstractly, it looks like the following:

enterpriseabstract3

Concretely, it appears as follows:

enterpriseseven5

As I mentioned in my earlier post, I was not satisfied with a six interrogative, four association model.  Consequently, I worked to resolve this and came up with the table above with the interrogative columns (seven hats) and the associative rows (seven coats).  I also came up with the data model below:

enterprisefact1

My hypothesis is, used correctly, the above data model can address all relational/dimensional requirements.

Related Posts: