Mental Metaphors (Introduction)

 Mental Metaphors (Introduction)

The most recent technical advancement has been likened to the brain (and, by extension, the Mind) in every passing generation. At the moment, the computer metaphor is all the rage. Software metaphors and, more recently, (neuronal) network metaphors have supplanted computer hardware analogies. In every area of human knowledge, people often try to understand things by comparing them to other fields. The structural notion of "tensegrity" has recently been proposed by mathematicians and architects as an explanation for the phenomena of life. It is well-documented that humans have a survival benefit from their propensity to perceive patterns and structures everywhere, even when none exist.
There is also a growing tendency to disregard these metaphors as false, unimportant, or intentionally misleading. But it is the same Mind that generates these metaphors that will ultimately define it. Similar to how "brain-children" are the products of "brain-storming" and "minds" alike, the things or processes that the brain is compared to are likewise conceptual conceptions. If not a physical embodiment of mental processes, then what are computers, software programs, and communications networks?

Put simply, for ANYTHING that humans have made, there must be an essential and adequate link to the human intellect. A "mind-correlate" is required even in a gas pump. Also, it's possible that our minds contain representations of the "non-human" aspects of the cosmos, whether these representations are a-priori (not based on experience) or a-posteriori (depending on experience). The "excretions," "output," "spin-offs," and "products" of the human mind are closely related to the human mind, and this "correlation," "emulation," "simulation," and "representation" describes this relationship.
The claim that we may learn about an artist via their work, a creator through their creation, and, more broadly, about an origin through any of its derivatives, inheritors, successors, products, or similes is part of a larger category of statements.
When the product's nature is identical to that of its origin, this general contention becomes more stronger. If the father is human and the child is human as well, then there is a wealth of information that can be securely gleaned from the product that can be used to identify the father. The closer the point of origin is to the product, the more information about the origin can be uncovered. A "thinking machine"—whatever that may mean—that is mechanical, recursive, limiting, and simulated. In a similar vein, the brain is a "thinking machine"—albeit one that is considerably more nimble, adaptable, non-linear, and potentially even qualitatively distinct. Regardless of the disparity, which is sure to be substantial, the two must be intimately related. Both are "thinking machines" and, more importantly, the second is an offspring of the first, which explains their tight relationship. The computer metaphor is thus exceptionally powerful. The analogy will become more powerful in the event that an organic computer is developed. Some parts of the metaphor will definitely be improved if a quantum computer is really built.
By the way, it's not always the case that knowing where something comes from allows us to predict what it will be used for. The number of free variables is excessive. To use a metaphor from Bohr, the presence of a product "collapses" our probability set and expands our knowledge.
The starting point is a "wave function"—a set of hypothetical outcomes to which probabilities are associated; the outcomes are the products that are theoretically and logically feasible.
But what can be gleaned about the source by making a simplistic analogy to the end result? Structure- and function-related characteristics predominate. You can see these easily. Does it cover everything? Do we have any insight into the "true nature" of the beginning? No, it is not correct. Generally speaking, it's bad news: we have no right to seek or assume knowledge of the "true nature" of anything. Metaphysics, not physics, governs this domain. Quantum mechanics describes the universe and micro-processes amazingly well without stating anything substantial about either. Instead of elaborating on one viewpoint or another, modern physics aims to make accurate predictions. It only summarises; it offers no explanations. When attempts at interpretation (like as the Copenhagen interpretation of QM) are made, they inevitably encounter philosophical roadblocks and impassable problems. So, many metaphors abound in contemporary science, including "particles" and "waves," to name just two. The "thinking scientist's" toolbox includes metaphors, and they've shown to be effective in science.
Furthermore, a metaphor can evolve, and this evolution closely mirrors the stages of the origin. Consider the software metaphor in relation to computers:
The first software applications were written in a serial fashion using machine language, with data being strictly separated from instruction code in the form of "structures" and "functions" or "procedures" at the birth of computing. Similar to how the embryonic brain develops, this was a "biological" stage. The machine code was a near-perfect match for the hardware's actual wiring. Even in biology, the instructions are kept separate from the data, which consists of amino acids and other things essential to life, by means of DNA. An alphabetic order is an extrinsic order that exists only in the mind of the "imposer"; databases were serial, managed on a "listing" basis (sometimes called a "flat file"), and had no inherent relationship to each other. They had reached the substrate state and were prepared to be worked with. In order for functions to work on structures, they had to be "mixed" in the computer during program execution.
The "relational" structuring of data, of which spreadsheets are a basic example, followed suit, as one might assume. By means of mathematical formulas, relationships were established between the data pieces. This happens at the same rate that the brain's wiring does throughout pregnancy.
The Object-Oriented Programming Systems (OOPS) represent the most recent stage of development. Data and instructions are contained in self-contained units called objects. These things' FUNCTIONS are known to the user, but their STRUCTURE, INTERNAL COMMUNICATIONS, and PROCESSES remain a mystery. What this means is that things are essentially "black boxes" (an engineering phrase). The object's hidden, externally beneficial functions are created by its internal, invisible ones, and the programmer has no idea how it achieves it. Things are fleeting, new, and epiphenomenal. Just put, it's a lot more in line with what contemporary physics has come to describe as reality.
The system's total efficiency is not determined by the communication (its speed or efficacy), but rather by the fact that it can be established among these black boxes. The key is in the objects' hierarchical and simultaneously fuzzy arrangement. The attributes of objects, both actualized and potential, are defined by the classes into which they are arranged. Being a member of the class defines the object's behaviour, including its actions and the conditions under which it can react. Additionally, the "inheritance" principle is in play: objects can be arranged into new (sub)classes, which allow them to retain the original class's definitions and traits while also acquiring additional qualities that differentiate them from their parent. Essentially, the classes from which these new ones emerged are the source, and the classes from which they emerged are the products. The procedure is so realistic in its depiction of real-world events that it strengthens the metaphor.
Classes are thus useful as foundational elements. The set of all solvable issues is defined by their permutations. Returning to the Principia Mathematica, it is possible to demonstrate that Turing Machines are an instance of a more general and robust class theory. By modifying the structural and functional aspects of both the hardware (the computer) and software (the computer applications), "framework applications" allow for the integration of the two. There must be a mental analogue somewhere (coherent categories, the collective unconscious, etc.).
We call it evolution because one stage gives way to another. Consider the impossibility of integrating relational databases with object-oriented ones. An operating system's built-in "virtual machine" is required to execute Java applets. These stages are very similar to the maturation of the mind-brain pair.
At what points does a metaphor work well? When its inclusion sheds light on the genesis that would have been impossible to discover otherwise. That it has to be similar to what we've already found in terms of structure and function. However, that is insufficient. This is just the metaphor's "quantitative, observational" part. As for the qualitative one, it needs to lay the groundwork for a theory and its hypotheses by being illuminating, informative, artistic, and sparse. Metaphors are theories that emerge from predetermined standards of logic and aesthetics. To be considered trustworthy, it needs to undergo the kind of extensive testing that is required by scientific inquiry.
The brain needs to have these characteristics if the software metaphor is true:In order to set up a feedback parity loop, the electrochemical signal in a neuron must travel both forward and back (to its origin) at the same time. This allows for parity checks to be performed.Contrary to what one might expect from a quantum computer, which has many states, neurons cannot be binary (having only one). It needs to be exciting on multiple levels (information representation). This "all or nothing" firing threshold theory has to be incorrect.Everything about the brain and how it works needs to be redundant. This includes the hardware (so that different parts of the brain can do the same things), the communications (so that different parts of the brain can receive and send information to each other, and vice versa), the retrieval (so that data can be excited in multiple places at once), and the usage of the data (through working memory, or "upper")."Representation elements" and "models of the world" must be compared as the fundamental idea of how the brain functions. Predictions and effective, result-producing manipulation of the environment are made possible by obtaining a coherent picture.The brain must solve many recursive functions. Reducing all brain activity to computational, mechanically solvable, recursive functions is something we may reasonably anticipate finding, at least to some degree. The most fantastical visions of AI will materialize if this occurs, elevating the brain to the status of a Turing Machine. Until then, though, this incredible machine in our brains should be behaving in a strongly recursive fashion.The brain ought to be an entity that can learn and organize itself.

To claim that the software metaphor is robust, all six of these conditions must be satisfied simultaneously. Otherwise, we'll have little choice but to ignore it in preference of a more formidable opponent.
Paranoid machines controlled by Murphy's Laws make up the brain. It is risk-averse, always ready for the worst, and never takes any chances. Dangerously poised, materially fragile, and master of its own destiny, it can and will not take any chances.
Wow, that's cool!

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