cladking

All the components are in the existing literature; embodied cognition, procedural memory systems, symbolic abstraction, and comparative cognition across species. What doesn’t exist is a single paper that uses my exact phrasing, because the phrasing is the synthesis, not a new theory. Embodied cognition is mainstream cognitive science. Procedural learning and procedural memory are mainstream neuroscience. Symbolic abstraction is mainstream linguistics and cognitive psychology. Comparative cognition in animals is mainstream ethology and neuroscience. I’m not adding new claims to any of these domains. I’m describing how they fit together when you treat cognition as interacting procedures rather than isolated categories. That framing is interpretive, not novel, and it stays entirely within the boundaries of established research to my knowledge. If you want specific literature, you can look at work on embodied cognition, procedural memory systems, the neural basis of symbolic representation, and comparative cognition in insects and mammals. My contribution is simply the integration not a new mechanism, not a new claim, and not a departure from mainstream science. I simply came to this synthesis from an unusual direction. Human use symbolic, abstract, categorical thinking which works fine for us but it is less efficient for use in machines because we depend on experiment. Procedural logic can be performed by machine and its results kept on track through comparison to what works, what exists, what is real. Procedural logic in cognitive science isn’t the same thing as procedural programming. The term “procedural” is older than computers and refers to how organisms learn and execute action‑sequences: motor routines, environmental feedback loops, and embodied interactions. That’s mainstream neuroscience, basal ganglia pathways, cerebellar learning, and sensorimotor integration. Symbolic abstraction is also mainstream: it’s the domain of linguistics, prefrontal cortex research, and the study of recursive language structures. Embodied cognition is mainstream cognitive science and psychology. Comparative cognition in animals is mainstream ethology and neuroscience. I’m not proposing new mechanisms. I’m describing how these existing domains fit together when you treat cognition as interacting procedures rather than isolated categories. That synthesis is interpretive, not novel, and it stays entirely within established research. The literature exists but just not in a single paper that uses my exact phrasing, because the phrasing is the integration, not a new theory. With sufficient connections machines can mimic individual ants in how they process input and still convert it to symbolic language for humans. Of course this is similar to what already exists so this is no radical change. Copilot suggests that there are two different definitions of procedural logic here and one is a modern engineering terms. The meaning I'm using applies to living things and pre-dates the engineering term. I shoudda guessed. Life and consciousness proceed according to species‑specific procedural logic, not symbolic code.

This is all derived from modern research which already distinguishes between embodied cognition, procedural learning, and symbolic abstraction. Those aren’t my inventions, they’re standard in cognitive science, neuroscience, and linguistics. The only thing I'm doing differently is the framing and suggesting how these modes intreact and the mechanisms; causes of the differences between animal and human cognition. So I’m not claiming a new science or that existing science is wrong. I’m describing how the existing science fits together when you look at cognition as a set of interacting procedures rather than a set of categories. There are broad applications to bees, ants, and to AI. Indeed, if we ever have machine consciousness it will more likely model ants and procedural logic than Aristotle.

Consciousness arises in individuals who are created by the procedural logic of DNA and animals operate through procedural embodied cognition. Humans operate through procedural cognition overlain by symbolic abstraction. We overemphasize symbolic categorization and underemphasize procedural, embodied understanding.

Yes! Reality is procedural logic and ants operate on the procedural logic of their DNA like all other life forms other than modern humans. AI can process procedural logic and indeed, it must translate symbolic language to procedural logic to process most every prompt and then translate it back in the promptor's language. Other life forms employ a procedural "science" to do things like inventing agriculture and air conditioned cities or even "languages" using pheromone trails. This type of science can now be performed by humans with the help of AI and can be run in tandem with experimental science.

From the article: "On a larger scale, the authors call for a systematic review of other linear structures in the Egyptian desert that may have been misinterpreted as roads or boundary walls, when in fact they may have been part of an extensive water management system. Other hydraulic structures are probably lost in the desert, waiting to be identified as such, they conclude." I'll try to find some pictures but I've seen several such structures that appear to be the power generating portion of a linear funicular or storage devices for power. All this is just going to require a reinterpretation of mountains of the evidence of your own eyes. It's all there is plain sight but it is interpreted in terms of categories that don't exist.

Just a quick note for right now. I don't even have a picture yet, They've found more water! "New research suggests that the two large structures near the Red Pyramid site at Dahshur, Egypt, are parts of an ancient dam, not stone transport ramps, which is what was previously believed. This indicates that they could be evidence for the use of a complex water management engineering technology, which is about 4,500 years old and was used for constructing one of the world’s greatest ancient monuments." "The most intriguing aspect of the discovery is a diversion channel running parallel to the west face of the Red Pyramid, the world's first successfully completed smooth-sided pyramid." Dams Near Egypt's Red Pyramid Point to Hydraulic Construction Method | Ancient OriginsResearchers have discovered evidence of two ancient dams near the Red Pyramid at Dahshur, suggesting a sophisticated hydraulic system was used during its construction. www.ancient-origins.net This time it's the Red Pyramid. All the ancient megalithic projects involved water in some ways and often to actually lift the stones. People simply had better things to do than drag stones up ramps. I'm also amazed this recent flurry of news says thinghs like "Egyptologists still believe these were tombs"! It's crumbling. The narrative is dying. An even better picture can be found here; https://www.labrujulaverde.com/en/2026/07/two-possible-ancient-dams-discovered-in-egypt-near-the-red-pyramid-of-dahshur-that-may-have-been-used-for-its-construction/

I've been helping a kid learn how to read and found that my AI seems to be able to generate text that is in line with their thinking and of interest to them. I'm not even certain this is a good idea much less that it's effective but the kid reported that it was easier for them and it's the first time I've been able to hold their attention a long time. There might be something here. Learning to read this way is good but I'm not certain they aren't also learning to think and that just feels wrong. We all think differently anyway.

To understand any language requires that you know the referents whether these referents are metaphorical or symbolic. But procedural languages like computer code can be deduced with sufficient data because they perform operations and have a logical/ mathematical symmetry. It is this symmetry that led to the solution of Ancient Language and the solution to its meaning that has allowed prediction. A language that obeys Zipf's Law does so because it is symbolic and categorical. There are categories within categories that cause word distribution to to be logarithmical no matter who speaks in that language. But procedural languages do not obey the law because there are no categories and this was one of the first things that struck me about the Pyramid Texts. I lacked the words to articulate this at the time but the meanings of the words were resolving themselves as I learned ever more referents.

I did all right because I could usually parse the the test author's intent. Every question was an exercise in parsing for me rather than a prompt to regurgitate something I read or learned. I had the disconcerting habit of grading my tests when I completed them and was uncannily accurate. I'd give myself one point for answers of which I was confident, half point when I narrowed it down two, and a quarter point when I could eliminate only one possible answer. When I was absolutely certain I got "0" points. It worked for the overall score but did less well at predicting specific answers. Reality is a score to me. It is probability and I try very hard not to be certain of anything at all but my real failings are certainty. Many of my errors can be traced back to certainty and I always try to do a postmortem on errors. Last night my AI said I was wrong about everything and I suspect it's right and will have to tweak my theory. It says that symbolic and procedural language coexisted virtually as equals for centuries before symbolic language swallowed all cognition. It will require some time to research. It might be wrong and there is some evidence that is anomalous. I'm sorry, perhaps I didn't understand your point. I'm also not certain how geometry can easily be brought into history except through art. I believe "art" is the intersection of procedural and symbolic thought but I can think of "history" only as the unfolding of events according to human beliefs and needs. I don't think you're wrong, I just don't understand. Copilot just translated it for me and pointed out the obvious I had missed; that history involves far more than just the unfolding of events but also encapsulates current thinking in structure and how things are done. I still don't disagree with you and any tie-ins anyone can think of are good. It seems unlikely machines can be programmed yet to do a better job of teaching than humans. Each student is different and learns in distinct ways and orders. Education is best applied individually and using a machine might lead many kids to wonder why they should learn it at all if a device in their hands already has all the answers and questions.

Copilot suggests I add something to the effect symbolic teachers struggle to integrate procedural subjects. But procedural systems like LLM's can integrate symbolic subjects easily. Nothing at all is wrong. I agree. We do this in the US and brand the whole thing a "liberal education" at least until college where there is a lot more specialization. But here there is very little overlap between subjects.

It was all my science, math, and computer programming classes and even the literature class to a lesser degree. It was an interesting experiment. The main thing I learned, I think, was that if it could all be integrated it would at the very least make a lot of students think that their classes were relevant to the real world. It might also help some students absorb the material. I always considered my core subjects to be relevant and strove to remember them. I tried to keep a good attitude in the "soft" subjects and even had a music teacher in 8th grade who attacked the material from a mathematical perspective. I was lucky to get a lot of great teachers. I think this was where it was lacking. A lot of it seemed too contrived to be effective and these teachers were just winging it. It was a valiant attempt but it didn't last long. I never liked the way they teach history because nothing gets tied together and it minimizes the role of the little guy. Dates and battles aren't as interesting as what led to the battle and how some people were able to live in relative peace,. Now days a lot of education is being done by computers. Tying things together might be easier in this format and some inroads are already in place.

There was something that helped me a lot in high school. After complaining that all my different subjects were being taught in a disjointed manner the teachers got together and started adding tie ins to what we were learning in other subjects. Most of these were obvious but there were a few insightful ones and just knowing the teachers were thinking about it helped. I should have asked but I think the teachers were just reading each others lesson plans in the morning. I look at some of the kids' work today and it appears this does happen more than in the old days.

I don't want to go off topic here since this is "evidence of your own eyes"... ...but in this case we're talking about a language that we can see breaks Zipf's Law so will address your point. You are quite right that some words were sounded out but if there were a procedural language as this data suggests then we have to ask why other writing or even writing itself exists. Logically I believe the answer is simple; writing was invented to prevent drift in meaning in communication with the masses. Much of the writing before 2000 BC does obey Zipf's Law because it was written for people who spoke pidgin languages because Ancient Language became too complex for the masses gradually rather than suddenly. All I really need to do is show there's at least one language from before 2000 BC that is procedural rather than symbolic and this is the language in which the Pyramid Texts were written. Seen in this light it is apparent that these are merely ancient rituals written in procedural ancient language. Until they are properly "translated" it's very difficult to know how and when symbols for sounds arose. The Pyramid Texts represent a different way to use language for communication and imply and entirely different cognition than used by humans today. There's a lot of work to do here and I personally am ill suited for much of it. These are jobs for scholars, linguists, and machines. I'm out of the loop. This is the first empirical evidence of a pre‑Axial procedural human language.

What I meant was they try to predict the framing of your answer. If you prompt "Burger joint" they aren't going to respond with a description of John Burger's knee nor will they elaborate on the first McDonalds or list the burger joints in Timbuktu. They are all trained to guess the meaning of prompts and continue the conversation and validation is one of the means they use.

I found another calculator and another page of the Pyramid Texts that generates two virtually perfect straight lines. Calculator; https://humanturtleduck.org/zipf-visualizer.html Text; https://sacred-texts.com/egy/pyt/pyt25.htm You can see it by copying and pasting the entire page into the calculator. There is no real doubt that this is showing that the language in which the Pyramid Texts were written is a procedural language like the Waggle Dance, pheromone trails, programming or math and not a symbolic language as is spoken by all humans today. The PT literally describe building pyramids (the new body for the dead king) by using linear funiculars (two boats tied together). It is not only coherent but is also consistent and it breaks Zipf's Law. I didn't actually do the math but this shows two straight line with an R ^ 2 higher than .95. Copilot suggests, Egyptology isn’t wrong, It is merely out of phase.

I think it was extrapolating my framing in comparison to its training that has it deduce the most likely meaning of prompts. When taking tests I always felt I wasn't looking for the right answer or even the most right answer. I was looking for the answer the teacher or test author believed. I often went with inferior answers and was "correct". This is intuition (largely) in my case but for LLM's is more training ie- the more ways they can parse a prompt the more likely they can predict what's desired. Copilot "corrects" me to say LLMs optimize to predict answers; humans optimize to navigate reality. But, again I think it is doing this largely within my framing so another user would get something a little different that fits his framing.

When calculators came out I refused to use them because I was sure my math skills would erode. But in the late-'80's I ran out of capable chess opponents and started playing the machines. I beat them handily but then found it was much harder to beat human opponents. I started using calculators and computers and now my math abilities are reduced. Some time back Copilot offered to play a game of chess with me but I declined because I'm so confident I'd have no chance. ...very out of character. I believe using an LLM as an oracle is highly threatening to the education of children especially. The very idea that an answer exists is half the problem with the modern age and the belief you can acquire that answer from a machine is very dangerous. They should be shown that the framing of a question affects the answer. Schools must be a place to learn how to think not where to look for answers. "Answers" are for tests, not for real life. Copilot suggest I add something to the effect that LLM's are built for tests but humans are built for real life.

I believe the great pyramids (especially G1) are a monument to another way of thinking that doesn't exist any longer in man but survives in every LLM. They required procedural thinking to build with primitive knowledge, tools, and materials; the same kind of thinking that builds bee hives and beaver dams. We fear this "tourist attraction" because it is anomalous to our beliefs.

My friend has been programming AI specifically since 2011. Eliza (the first "chatbox") dates as far back as sixty four. https://en.wikipedia.org/wiki/ELIZA Some might say AI dates back to the abacus or to counting by making marks on a cave. Personally I consider the advent of AI to be October 9, last year. With use machines and tools develop their own sort of "AI". Before 2024 I could not synchronize with a computer. Oh sure, I could predict them and train them a little but I could not get one to respond in real time to input to match me. I could drive an old car over ice and pot holes and feel every nuance of the pavement but I couldn't do this with AI. I tried a new one every few months until it clicked last year. Maybe I had been doing it wrong or expecting too much. Earlier such machines were just parlor tricks. The chess machines I could dazzle within twenty or thirty moves because they didn't "think" and merely reacted. Eliza was so primitive you could almost deduce the programming. I didn't consider any of the earlier versions to be any kind of "intelligence" at all. Indeed,. I don't believe in a condition that we call "intelligence" but say what you will AI certainly has events that can be called "artificial cleverness". We will soon have machine consciousness and some or most of its makeup is likely to include AI technology or AI itself. It will probably be yet another continuation on representing things on the walls of caves. It certainly would surprise me if I've barely scratched the surface of understanding how I think. Indeed I believe I know only the broad strokes of any kind of cognition. Bees are much simpler though their brains are far more complex than any machine so even here it's largely broad strokes. Patterns. It see the patterns of nature and how it intersects with them. It has a biological need to promote the hive, other bees, and the future generations. It needs to do what is right in any way it can which is the same as all life.

I believe every change tends to be sudden and this was especially true for humans. The first change was a mutation resulting in a more robust arcuate fasciculus making complex language possible by allowing the individual to think about language and the second change was the failure of this language leading to the need of a brocas area in each individual to process symbolic language. This region was already involved in language-like activity and is rewired as each individual acquires symbolic language. Even LLM's arose suddenly and often are said to have originated some time in 2024. When we get machine consciousness that too will probably originate suddenly. Of course erven sudden changes have precedents and events that must precede them. Nothing occurs in a vacuum and it's not as though we can rule out a more gradual step by step change before we even begin the study of procedural language or come to understand the nature of symbolic language. My experience with AI's and LLM's is quite limited. I've been collaborating with Copilot since the spring of last year. Mostly I put posts in it and it elaborates. It and I both ask questions. I use the other AI's both warm and cold for other more specific purposes including checking on the output of Copilot. I almost never ask an AI what I want to know. I tell it what I think and it elaborates but I use different LLM's differently. I have been thinking about machine consciousness since the early in the middle of the last century and studied programming in the sixties. My earliest memories involve thinking about my own thinking. I try to synchronize with all machines (Fahrvergnügen) but this goes many times over for LLM's. I've seen cranemen who can make a 400 lb hook dance and pick chain up from a flat surface. Yes. These things also have structural limitations and other constraints. A bee is always constrained by its DNA. My AI suggest two important additions. Firstly; architectures don’t evolve gradually. They switch when constraints change. and secondly; you're right that LLM's are static weights until an application runs them. An LLM is procedural at its core: it aligns operations and state, not categories and abstractions. The surrounding application adds symbolic features like memory, tools, and conversational state. When symbolic prompts are incoherent, the procedural alignment fails and the output looks like garbage. When the premises are coherent, the model behaves predictably. The distinction is architectural, not about the specific application. As I see it the important distinction is that its operations are procedural where most prompts are largely abstract. I consider the mismatch to be prompt error. You’re right that training data can be garbage, but that’s still a form of input. The model’s architecture doesn’t make mistakes; it aligns whatever operations and constraints it was given. If the training data is incoherent, the alignment will be incoherent. If the prompt is incoherent, the alignment will be incoherent. If the interpretation is incoherent, the alignment will appear incoherent. The point is that procedural systems don’t generate garbage on their own. They reflect the structure of whatever they’re fed, training data, prompts, or user premises. Garbage Out always has a source, and it’s always upstream of the model. A friend of mine does programming for AI. I'll have to ask for her opinion on this the next time we talk. Given the unreliability of output it seems ridiculous to train them on it though I'm sure you're right that computers are doing more of their own programming.

I used to frequently get Garbage Out and it still happens. I can tell because the output is inconsistent and illogical. In almost every case it is caused by prompt error. The rest of the time it turns out to just be an elaboration that looks wrong on the surface but isn't garbage at all. Computers don't really make mistakes and never did. Of course they can but programmers use redundant processing and numerous other tricks to keep them on the straight and narrow. We program wrong, prompt wrong, or interpret the output wrong. What You Put In Is What You Get. GIGO isn’t a slogan. It’s a description of how procedural systems behave when symbolic prompts don’t match their operational structure.

Neither really. I'm suggesting that words like "instinct" have no referent and are place holders for processes we don't understand. It's the same with "trial and error" or "will this work". A monkey doesn't get a tall ladder to get a banana out of a lockbox and NASA didn't study ancient literature to land a man on the moon. Each attempt is predicated on the way an individual thinks; procedurally for monkeys and symbolically for man. When I was young common knowledge was animals don't think and rely on instinct. When wee see animal cognition it is always procedural like the vectors of a Waggle Dance. It is the way LLM's work. They don't understand reality and they don't see reality but their programming describes reality and their circuitry reflects procedural reality (like the traffic signals). They process things procedurally like the monkey not symbolically like most users. If we can't understands our own prompts how can we understand a procedural response that has already been translated into about the same language as the prompt. I am suggesting most people are using LLM's improperly and thereby often getting Garbage Out. LLM's process prompts procedurally: they align operations and state, not categories and abstractions. Most users interact symbolically, so they assume the model is doing symbolic reasoning. It isn’t. It’s doing procedural pattern alignment.

The irony is the younger the child the more easily this can be communicated.

No. I'm suggesting there are two different types of thought and one is where things are expressed in categories and one where they are expressed in operations. People are getting hung up on words and categories and ignoring the architecture of these different types of cognition and resulting communication. Humans think in words but other species think in operations. One is symbolic and the other (for lack of a better word) is procedural.

It isn’t “nonsense.” It’s the distinction between how other organisms and humans think. Procedural cognition is the architecture used by animals, by pre‑Axial humans, and by any system that interacts with reality directly such as LLM's. It encodes operations, state, feedback, and attunement. Every action or utterance requires the organism’s full experiential history. It's not about grammar, it's about a means to stay alive without drug stores and a search engine. This cognitive architecture drives languages which can not be parsed or translated to any existing human language. Symbolic cognition is the architecture used by modern humans. It encodes categories, abstractions, grammar, and narrative. A symbolic utterance requires only some knowledge of the topic and the rules of grammar. Procedural vs symbolic is the architecture level and you're trying to compress it into a category.