013a-Connecting, disconnecting, substitution - Representation making
The simple function of representation is connecting and disconnecting. this process is arbitrary...until it's not. All connections between objects are initially arbitrary, but what makes connections between objects, what makes representations real, is that the connections and disconnections "make sense". That is, connections and disconnections become something not arbitrary, but something sensible. That "sensible" representation is when connections/disconnections between objects become something expected. That is, the representations made become part of another representational construct.
We may think of sensibility and arbitrariness as being context dependent, but connecting objects to a context is a key process of representation making. What does it mean for something to have a context? It means that a context becomes more sensible and less arbitrary.
If a context continues to be arbitrary, then any other context is equally useful/valid/usable. Arbitrariness never vanishes, because all representations are deeply arbitrary. It is only as representations accrete meaning, or sensibility in relationships to other representations that the arbitrary nature of representation making get obscured.
When we have AW:X = X, we are making a connection between AW:X and X. When we say X;Y, we are making a connection between X and Y and more than that, connecting the AW:X to the AW:Y through the X;Y representation. We make a connection between two object of awareness through representations.
The experience of sensible or arbitrary connection making (representation) is the result of how complex a representational structure develops in a representation making system(a repper), and how various representation structures get connected together by a representation making system(a repper). That is, representations get built upon, and create structures. These structures of association and meaning themselves get associated and connected or disconnected to other structures through representation making. And the elements of various structures are connected (associated or represented) to each other. These complex structures of connection/representation imply an ordinary development of certain kinds of connection making, that diverge from other kinds of representation or connection making.
At the most basic level, meaning, value, and processes of association are constructed.
For instance, set making (a, b, c) is a simple form of connection making. The construction of a set begins with a representational form of x (an association, concept, or abstraction) where x;a, x;b, x;c exists. The abstraction of x connects a, b, c to each other. such that they form a set (a, b, c). The set is a new representation (or abstraction), [in logical terms, X may be considered "the set (a, b, c)"] thus:(a, b, c);a, (a, b, c);b, (a, b, c);c or logically, c as c∈(a, b, c). Where c is an element of (a, b, c). c of (a, b, c) -> c:(a, b, c) -> c;c:(a,b,c) as opposed to some other set such as where c is an element of (c, q, r) - > c:(c, q, r) where c = c;c:(c, q, r) vs c; c:(a,b,c).
These two instance of representation and set membership/belonging (of) form at minimum two dimensions for the representation of c. Examples of this sort of bi-association are: tall men (ie. the set of men the set of tall things), long-haired cats, snowy terrain, cold days, even numbers, are bi-associative set examples. But there are more than those two associations taking place. There is c;x and c;y and c;c:(a, b, c) and c;c:(c, q, r).
Sets represent both grouping and multiplicity. The use of the association "of" where c is c:(a, b, c) is a representation. Awareness of X (AW:X) shows X belonging to the totality of awareness. X is not the only object of awareness but one of many objects of awareness. Awareness is an un-described collection of elements. c:(a, b,c) shows the same condition of membership, with the order reversed and the collection of elements, the set, described c:(c,b,a)
In the example, a, b, and c are all associated to X and this association is the basis for forming a set. Association is ALWAYS the basis for forming a set. Even a random grouping of objects is making an association where the random grouping (itself an association representation) forms the set. For example, c; c:(c, bat, square_root_of_2). c= c;(c:some_arbitrary_grouping), bat;(bat:some_arbitrary_grouping), square_root_of_2;(square_root_2:some_arbitrary_grouping) where some_arbitrary_grouping is substituted for (c, bat, square_root_of_2). c is an element of that set, and some_arbitrary_grouping is the representation of that set.
some_arbitrary_grouping is the name we give this random set (c, bat, square_root_of_2). The name is not the set. Rather the name is an association, a representation itself. ie. some_arbitrary_grouping ; (c, bat, square_root_of_2).
Making a representation to connect to elements, to make a set is what happens when we use definitions to determine sets. The definition is a substitution for the set itself. A set is a collection of members. A set is not a function that verifies what members belong to the set, any more than a set can be a name or description which verifies what members belong to a set.
When we use a definition, such as "positive integers" or "white things" to describe a set, we must distinguish between the set itself, and our description of membership requirements. The set is an arbitrary representation. A function or descriptions which tests an object for membership is a separate representation. That is, the set exists and we use a definition to test membership after the fact. This is how definitions for sets can easily lead to paradoxes. (sets can be unicorns).
If we jump ahead a bit and consider making an AI, this sort of arbitrary set making must be shown in the system design. As a hypothetical example, the node or network nodes which represent c must be related to the network nodes of this spontaneously created set (c, bat, square_root_of_2). This arbitrary set is not an input source, or an output source but a construction of the representation making process alone. A data model, or a cellular automata model must be able to make these same kinds of arbitrary constructions.
Whatever kind of system instantiates representations, that makes representations, must account for this kind of association making and make associations across different dimensions of representation. Whether it is a network model, a data model, or a cellular automaton model these connections must be accounted for in both meaningful and arbitrary ways. The computer AI must be able to arbitrarily create meaning, arbitrarily create sets of objects.
This way of discussing relationships with symbols and words lends itself to conceptualizing a data model. But these connections look like networks of connections. And a cellular automata would form these connections as chains from rules.
In a cellular automata, the rule must be bottom up, so that values of cells output other values arbitrarily. There is no pre-ceding automata rule. the rule is only "sensible" over time as it becomes used for those kinds of automata. automata rules would appear to be post-hoc, after the fact of arbitrary cellular automata action.
Neural like networks would do this naturally, but the inputs and representations explode the number of nodes and connections. Neural networks would also suffer from timing issues as representations become larger and larger.
data models must store and associate the variety of data, associations, and dimensions, and hierarchy of representations. But like cellular automata, the associations or rules must be developed from the bottom up, not programmed from the top down, because associations are deeply arbitrary.
Each model has it's own advantages and disadvantages for modeling representations. But each type is a way to model representations and associations. What is important is that each type of system is built upon a more fundamental process of representation making and connection/dis-connection. That is, representations are built up from representational precursors. either input atomic data elements, or input portions of maximal data sets.
Identity expressions can be replaced by flow expressions. X = X;Y X;Y = Y becomes X -> X;Y -> Y
identity and flow are both expressions of a relation or representation between objects. We can replace identity or flow expressions with representations directly. Thus X ; X;Y or X; (X;Y)
or (X ; (X;Y) ; Y)
(X ; (X;Y) )
( (X;Y) ; Y)
When representations are expressed in this nested fashion, the idea of identity or flow is masked. But our use, our conception of a flow or an identity must itself be some kind of representation that we impose on experience. Identity and flow are not pre-representational, they are representational. That is, the most basic functions of all representation are made up at the very beginning of experience.
Identity, similarity, difference, not, consistency, sequence. These are fundamental relationships, fundamental representations of objects. What do these structures actually look like?
These 'conceptual' arrangements of objects, of experiences, are representational primitives.
A representational primitive is a manifestation, or an abstraction, a physical representation or substitution for something else. For instance, a voltage charge, a binary value, a connection between cells, a chemical signal, etc. are representational primitives.
In a physical object like a brain, the brain is substituting neural networks and chemical/electrical signals for representational objects, or experiences. In a computer system, the substitution or primitive could be data or an algorithm stored in a database. Or it could be rules and structures of cells a cellular automata system.
Whatever the process of substitution is, the representations that are created are the objects of experience. A neural structure and signaling is not the experience, seeing a blue sky is the experience. The awareness is of a blue sky. The awareness is not about neural connections in the brain and eye.