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This document is a work in progress.
Last update: June 2020

Data vs. Architecture

Our mind, the human mind, contains our knowledge. It stores within it everything we know about our external world, our internal world, and everything in between.

But just like any other data-storage, some of these entities are data, while some are architecture. As some things we know because we learned them, and some we simply know because they are part of the structure that contains the data.

That means that, when looking at relational databases, for example, we have columns, rows, and tables. In those, we can store whatever data we see fit. But, even if there’s no data in the system, there will still be columns, rows, and tables. These not to represent something, but as a way to represent something. These are the primary entities of that specific data storage.

If you were to take your data out of a relational database and move it into a graph DB, for example, then you would lose all the columns, rows, and tables. Instead, the storage architecture will now give you vertices and edges to work with.

But, what does the architecture gives you when you move data into the human mind, and what do we lose when taking data out of the mind?
Well… that’s what we’re here to try and figure out.

The Patterns of a Primary Entity

Every data-storage needs primary entities in order to exist, and we can only store data using the primary entities we have available on any given system. These are the entities we lose when transferring information out of the mind – or the entities we gain when moving information into the mind.

These primary entities we’re looking for must have a few characteristics that make them different than all other “data” entities.

First, primary entities aren’t knowledge. They are the things which knowledge is made of. They aren’t data that’s in the brain, but they are the structure of the brain itself. It is something that you have simply because you use the system.

Therefore, primary entities must be fixed and independent of any definition.

Primary entities are the constants and they remain the same through the entire life-cycle of the system. Primary entities (1)do not need to be learned; (2)can’t be forgotten, and (3)can’t be changed or manipulated.

That would also entail that, since they are ingrained in the structure, they are (4)independent of any definition.

Unlike learned concepts, that need to be defined before use, primary entities can be used without defining them. Furthermore, any definition we might attempt to impose on them will serve only as a description of our subjective experience of them, as it cannot bind them or change the entities in any way.

These are the traits I used in order to hunt down the primary entities and set them apart from other entities.


Primary entities aren’t islands. They exist as parts of greater primary functional units of mind – unconscious sub-systems – that require these entities in order to function.

You’ll notice is that I’ve taken the liberty of already dividing the primary entities into 4 clusters. Each cluster dealing with a different entity type and containing a button-up and a top-down interpretation of it. 

Note that the followings are not definitions but merely descriptions of undefinable terms. Please bear with me as you try to connect with the meaning of the term rather than the words.

Primary Entities – Nodes

The first type of primary entity we would expect to find in a neural network structure would be the node.

Cluster 1 – Actions

Actions – Things that can be done (concrete)

Procedures – A monitored and prioritized sequence of actions.  (action 1, wait [for x], then action 2)

Cluster 2 – Things

Concrete Objects – a specific Implementation of a concept / the source of a concept.

  • This house

Abstract Concepts – a generalization of an entity type.

  • A house

Cluster 3 – One of

The pickers are invisible entities that have no properties to describe. We can only know about them by studying their function.

The X | First | Last |

Picker – returns a single entity out of a closed list

  • the biggest spoon in the drawer

Finder – returns a single entity out of the entire knowledge base (by certain conditions)

  • The president
  • The best thing to do right now

Cluster 4 – All (Collections of similars / Several of the Same)

Group – A collection of known entities

  • Plants (that are) in the pot
  • My family

Fetcher (query) – a collection of entities which qualify under certain conditions

  • All the spoons that fell on the floor
  • Everything that happened today


Primary Entities – Connections

The second type of entity we would expect to find in a neural network structure would be the connection. Primary connections seem to be slightly more complex than we might expect as they sometimes act more like junctions – containing 3 ends and possibly even more. These ends connect to other primary node entities or other primary connections in a combination that carries a certain meaning.

Much like the primary nodes, these can be divided into 4 clusters, each containing a top-down and a bottom-up interpretation.

Cluster 1 – Doing

Event – An Action that happened
(Subject did Action [on Object])

  • He was running
  • He went home

Task – A Procedures to be executed

Cluster 2 

Association – The mutual appearance of two separate entities.
(Entity1 [association type] Entity2)

  • Hammers go with nails (General Association – Correlation)
  • I can use that hammer (Current Association – Availability/Affordance)
  • Sleep influences alertness (Associated as Influence)
  • Dropping a mug causes the mug to break (Causal Association)
  • After the rain comes the sun (Temporal Association)

Comparison – difference between two entities  
(A is more/less/equals X than B)

Cluster 3 – Parts & Members

Reference – Spatial relation / Abstract Affiliation + Hierarchy as Position in Relation to Reference
(Entity [relation (on/in/near/away from// Wear/Hold…)] Reference Entity)

  • A member of a group
  • An action within a process
  • An object within a container (alienable)
  • An object/concept part of object/concept (inalienable)

Inheritance – classification of an entity to an existing concept
(Entity [type of] Concept)

  • This is a horse
  • A horse is an animal
  • This procedure Implements that method / What he’s doing is called reverse-engineering

Cluster 4 

(A is B | A’s R is B)

  • The couch is gray | The couch’s color is gray
  • This action is easy | This action’s difficulty is easy
  • This group is big | This group’s size is big

Mental Actions / Exec. Functions

(Still not sure how to describe these)