Shalom, brethren and sisthren! It’s quite nice to be writing another one of these. I’ve been working on four major projects over the last several months, so that’s been eating up a healthy chunk of my time. And frankly, I wasn’t sure that the utility all you schmeags derive from this blog was worth the effort of writing posts, so my incentive for writing has been fairly low.
However, I’ve actually spoken to a fair number of people over the last month or so that have expressed their enjoyment in partaking in XFA. To all of you who enjoy observing my mind soup, a most heartfelt thank-you.
Now then, let’s get down and dirty with the notion of Desire (not the sexual type, just your average “I want that car cause it looks cool” desire), and why I’ve boldly associated it with Tension. But before we really dissect this bad boi, I’d like to talk about why this is even on my mind in the first place.
Actually, a more honest answer than the one I’ll provide is that Desire is fundamentally tied to the notion of goal-formation, and I’ve actually been thinking a good deal about goal-formation in relation to the nature of human intelligence. While we typically take our ability to formulate goals (even subconsciously) for granted, attaining a bio-mechanical understanding of the neural dynamics of goal formation is comparable in complexity to achieving an understanding of the nature of consciousness, which anyone who is anyone agrees is pretty fracking hard problem.
Anyway, I’ve been extensively studying the formation of intelligence over the last months, and this has led to me to frequently contemplate the nature of goal-formation. However, the last two paragraphs actually have nothing to do with where I was originally going with the train of discussion in the third paragraph. Wow, how’s that for a cohesive narrative. Anyway, let’s get back to where I was originally going with this post.
Ahem.
The other main reason I’ve been really thinking about Desire is because of my current situation. Here’s what’s going on in my life. First, I’m almost finished creating a social media platform that I believe might actually fundamentally improve how people interact on the internet. If it works, it also might be incredibly lucrative. Second, next week I’m heading to Detroit to meet with some incredibly powerful individuals to pitch them on a project I’ve been working on with a couple of buddies. While the project is itself exciting, the more exciting aspect is that this could actually potentially provide me with an opportunity to successfully drop out and work full-time on an awesome project, and get paid, baby! Third, a good friend of mine has had an app idea that seems remarkably promising, and at this point, I’m pretty much confident in my ability to throw this new app together fairly quickly.
Ok, so why am I saying this? Am I trying to sloppily flex on the haters? Well, not quite. My current life situation is somewhat interesting because even though some of the stuff I have brewing is pretty exciting, there is absolutely no guarantee that any of it will pan out. And if it doesn’t, I’ll be back at square one.
But what does this have to do with Desire? Well, several times over the last couple months, I’ve been hit with such a strong confidence that my social network will make it big that my brain just assumes it’s going to happen. And in those moments, I’ve been possessed by some of the most potent anxiety I’ve ever experienced. And that’s weird.
Before we go any further, some of you might note that it’s incredibly presumptuous for me to assume a new social network is actually going to take off. You’d be absolutely right, of course. However, the aforementioned confidence I felt was more a remnant of my extremely turbulent emotional cycles, and less a function of the logical deduction. For reference, typically when I get extremely excited about a new idea working, I can almost guarantee that I’ll be hit with a wave of existential depression the following day. Isn’t life fun?
Anyway, the whole point of this lil story is that there frequently seems to be a strong correlation between Desire and anxiety in my life. Also, I’m only capitalizing Desire because it’s the subject of the post. Just thought I should make that clear.
So then, why are Desire and anxiety seemingly linked? An even more interesting phenomenon is that gratitude seems to have an opposite effect from Desire. Frack it, I’m going to stop capitalizing desire. It seems weird.
Specifically, as you’ve undoubtedly experienced, achieving a state of gratitude for the good aspects of life seems to assuage my anxieties tremendously. Gratitude journaling also seems to be rampantly taking over the self-health community, so I’m not the only one thinking about this.
At this point, you might be wondering, “…Hey Danny? Are you just trying to say greed makes us sad, and gratitude makes us glad?” (couldn’t resist the rhyme) “Isn’t that totally obvious?” Well, yes, reader, I suppose you’re right, but I’d like to take a deeper dive into this phenomenon from a quasi-rigorous neurological perspective. Prepare yourself for a classic Danny Boi G stream of consciousness, because I haven’t really fully fleshed out my thoughts on the subject.
Ok, let’s start with what our brain is even doing in the first place. One of the most important jobs of the brain is to form an efficient representation of reality. Let’s take that statement apart, shall we?
First things first, what do I mean by an efficient representation? A representation is a structure that exhibits similar characteristics and behaviors as some other structure. So a lego human being is a representation of a human being, because it shares some of the same rudimentary characteristics of a full-fledged biological human being. So what do I mean by efficient? By efficient, I mean that the representation is able to evolve far more rapidly than the structure it’s representing.
For an example of an efficient representation, think of a billiards video game on your computer screen. If the person who programmed the game did a good job, then the computer game should be a good representation of a real, physical game of billiards. Now then, unlike in real life, we can speed up the computer game, which allows the state of the computer game to evolve faster than actual game. Thus I would call the billiards video game an efficient representation of an actual billiards game.
Aside from being merely convenient for the sake of entertainment, why might the computer game be helpful? Well, let’s say that you want to figure out where the eight ball will go if you hit the cue ball in a certain way in the actual game of billiards. You basically have two options. First, you could just hit the ball in real life and see what happens. Second, you could set up an analogous situation in the video game and watch the video game play out. If you go with the second option, you reap all sorts of benefits. First things first, you can speed the video game up, which allows you to figure out what’s going to happen faster. Not only that, you also figure out what’s going to happen without ever hitting the cue ball in real life. That’s big. Basically, the efficient representation of the pool table (ie, the video) gives you “knowledge of the future” before the future even happens.
Ok, so let’s go back to my original statement about the brain. I’m essentially asserting that one of the brain’s principal functions is to construct efficient representations of perceivable reality. And just like in the billiards game, this efficient representation allows the brain contemplate and simulate future situations before they’ve even occurred.
Why is this a big deal? I use this example all the time, but bugger me bloody, here we go again. Let’s say you look up, and you see an asteroid falling toward your head. If this asteroid hits you, you are going to die. Let’s carefully take apart what happens next.
For your entire life, you’ve been watching objects fall. Whenever an object is unsupported it falls towards the ground. This happens in such a consistent fashion that our brain is able to form an efficient representation of the process. This efficient representation of falling objects allows our brains to simulate situations faster than they actually evolve in real life. This ability to simulate allows our brains to make predictions about the future state of reality with remarkable accuracy. So if your cousin Davy throws a football, you have a pretty good idea of where the football is going to land before the football actually lands. While this sort of cognitive process probably doesn’t sound that exciting, I’d like to humbly submit that this is one of the most miraculous aspects of our present reality. Ok, but let’s get back to the asteroid, because it’s actually quite important.
So there you are, looking up at the asteroid. If you were a jelly fish or a rock, the perception of an asteroid above your head isn’t going to change your behavior. However, because you’re a human with a brain that has formulated a good representation of falling objects, your brain can rapidly play out what is going to happen in the next several moments, before it happens. So basically the brain can determine that a sizeable object is about to fall on your head, and if you don’t get out of the way, you’re going to die. And thus, you get out of the way, and you don’t die. Ta-da.
It seems like people frequently refer to “knowledge” as this nebulous entity that grows and changes over time. If we’re getting technical, however, “human knowledge” is basically just the sum total of efficient representations of reality the brain has acquired over its lifetime. And as we’ve just seen, the reason why our brains even form efficient representations in the first place is because it greatly benefits our ability to contend with threats, thus markedly improving our stability as individuals and as a species.
Ok, I certainly could go off on some unreasonably long tangents about this, but let’s try to get back to the subject at hand. What does all this business about efficient representations have to do with desire and/or gratitude?
Our brain’s ability to construct efficient representations of reality not only allows us to better comprehend the state of the present moment, but it also allows us to simulate hypothetical scenarios. Our brain also has functionality to evaluate the quality of these real or hypothetical scenarios. Our brain might attempt to determine if a hypothetical scenario might involve pain, or if it might trigger pleasure receptors. Basically, the brain can determine if a hypothetical scenario is better or worse for us than the present scenario. Finally, the brain can determine if a hypothetical scenario is actually in sync with the present state of reality.
Ok, I’m kinda running out of steam here, but I’ll just try to finish my train of thought. In terms of evolutionary fitness, it would make sense that whenever the brain simulates a hypothetical scenario that is of higher perceived utility than the present situation, the brain induces a certain amount of anxiety. This anxiety would be the impetus for us to attempt to manipulate the state of the present to become more like the hypothetical, “simulated” state of reality. This mechanism would allow the brain to help move a human being towards states of greater utility.
On the contrary, if the brain determines that the present state of reality sufficiently meets the requirements of stable survival, it would make sense for the brain to “turn off” anxiety so that the human doesn’t make any changes to their situation to compromise the state of stability.
Ok, so what about desire and gratitude? Well, under this reframing, desire can be thought of as the brain attempting to move the human being toward a state of greater perceived utility. And in order to do that, the brain would naturally want to induce some level of discontent/anxiety about the present situation. Likewise, gratitude is basically the process of recognizing the beneficial aspects of the present state of reality. If the brain is able to decide that the present state of reality is sufficiently beneficial, then the brain should remove feelings of discontent and anxiety to preserve the current state.
So yeah, not particularly difficult to see, but here’s a reasonable explanation of why desire induces anxiety, and gratitude induces peace. So yeah, give gratitude a try.
To finish out, let’s get a bit soppy. Basically, yeah, there are a couple things in my life that are kinda exciting at the present moment. However, I’ve found that it’s imperative to my mental health to recognize that even if all of them don’t pan out, there’s so much about my present state that allows for stable survival, and thus tremendous cause for gratitude.
So yeah, in case you were wondering why gratitude is so important, these are my two cents. Peace.
The Orchid Project aims to replace a wide swath of modern mathematics with a set of digital structures that can be understood and manipulated by humans and computers alike. To understand how Orchid will fulfil this goal, it is fruitful to first consider the nature of mathematics. Mathematics is the study of abstract entities with stable characteristics and behaviors. Over the past millennia, human beings have developed a set of written symbols used to describe the characteristics and behaviors of these abstract entities. These written symbols allow humans to reference properties of the abstract entities being studied and perform symbolic manipulations on these entities in accordance with well-defined rules.
Modern mathematics is a symbiosis between the human mind and the aforementioned symbol set. Without the human mind, the symbols are essentially worthless and only interact with reality in accordance with physics of their constituent physical materials. Without the symbols, humans would have to reason about abstract entities without any outside assistance, and therefore would suffer from the limitations of human memory and intelligence. Together, however, there is a beautiful symbiosis. The abstract structures live within the human mind, but they can be compressed and stored compactly within the symbol set of mathematics.
However, this brings us to an interesting philosophical question: why is mathematics useful? Mathematics is useful simply because there is stable structure in reality as perceived by humans. The term "stable" is defined here to simply mean "existing for a non-trivial duration of time". While there are, of course, no guarantees about the stability of the different entities perceived within reality, there nonetheless seem to exist a very large number of entities that exhibit some degree of stability.
One particularly fundamental reason contributing to humanity's evolutionary fitness is the human mind's ability to create a model of the stable structures in the human’s environment, and act according to this model. If, for instance, a human learns there is a meteor directly overhead, the human will use their internal model of the world to reason that they must run away in order to survive.
While the importance of the brain's ability to harbor an internal representation of reality cannot be understated, humanity has progressed even further by creating spoken language. Spoken languages allow groups of humans to translate the representations stored within their brains into a set of stable auditory signals. These auditory signals are then decoded by other humans and translated into neural representations of the world.
Forming and reasoning about internal neural representations of the world takes time and effort, and frequently humans form the same sorts of representations. For example, even without communicating, multiple humans could easily learn that a meteor overhead probably means grave danger. Spoken language is an incredibly tool because it decentralizes the effort required to formulate internal representations of reality. As a simple example, a person entering India for the first time several millennia ago might never have interacted with a tiger before. The person could either learn about the dangers of tigers by experiencing one for himself/herself, or he/she could communicate with the locals and learn that the big striped orange and white cats ought to be avoided. The latter option obviously better lends itself to human survival.
Spoken language is a miraculously useful tool, but it suffers from the fact that audio signals decay rapidly, thus requiring two humans to be in immediate contact while communicating. This limitation is addressed by written language, which is next in the chain of incalculably useful human developments. The written word can last for centuries or even millennia, and therefore allows humans beings to share neural representations of reality across wide swaths of time.
So where does mathematics fit into all of this? Languages like English or Mandarin allow humans to describe a large portion of either their perceived reality or even hypothetical situations, and typically rely on some context for understanding. “How fast did Blake run?” I might ask. “Fast,” or “slow,” or “faster than Usain Bolt,” you might answer. Given my current context, I’ll probably form an internal representation of the events you witnessed that isn’t too far off from what you actually perceived. However, the words “fast,” “slow,” and even “faster than Usain Bolt” are all very imprecise, and effectively lose all meaning without the ill-defined context I described.
Mathematics attempts to address this lack of precision by describing both the structures and context in terms of abstract imagined entities with infinitely precise characteristics and infinite stability. To my question of how fast Blake ran, you could instead answer “44.83 ± 0.9 km/h.” You could also provide me with a mathematical model of Blake’s trajectory using polynomials and describe the Blake’s physics using Newtonian mechanics. Given the definition of kilometers, hours, and real numbers, someone 300 years from now would still be able to form a highly accurate internal representation of Blake’s speed, were they to desire that knowledge.
Mathematics therefore gives humans the ability to describe reality with far greater detail, precision, and accuracy than languages like English or Mandarin. The toolset of mathematics has also informed the development of some (if not all) of humanity’s most impressive modern technologies.
Why, then, has mathematics not replaced the languages of the world? If mathematics can provide such superior descriptions of reality, why don’t we entirely replace “the old technologies,” like English? The reason we haven’t done this is because mathematics comes with a high cost. It’s really, really hard. The reason why “Einstein” has become synonymous with “genius” is because Einstein formulated a mathematical description of the world that was more consistent with reality than previous attempts. Mathematics isn’t nearly as forgiving as world languages. While humans can rely on mutual context to convey information with language that would otherwise be imprecise or inaccurate (think metaphors or sarcasm), well-defined mathematics doesn’t allow for any of that behavior.
Before I continue, I’d like to once again emphasize how critical both language and mathematics have been to the improvement of the human condition. Even though I will talk about how Orchid aims to achieve a superior technology, the importance of both mathematics and language should never be taken for granted.
So where does all this leave us? Spoken/written languages can describe a broad swath of reality, but they are imprecise and typically rely on ill-defined and brittle context to actually convey meaning. Mathematics can describe some aspects of reality in incredible detail, but it is difficult to use and struggles at generalizing to complicated systems.
The Orchid Project aims to move beyond language and mathematics for formulating representations of reality by utilizing advances in computers to significantly lower the costs associated with mathematical descriptions of reality. As previously described, right now mathematics is a symbiosis between the human mind and a symbol set, wherein the actual mathematical structures live in neural representations within the human mind but can be represented compactly within a set of written symbols.
Put in high level terms, Orchid transforms neural representations of mathematical entities into computer data structures, which can be created and manipulated by computers. While this is easily said, the ramifications of this statement are enormous. By making this translation from the brain to the computer, Orchid allows computers to “think” about mathematics like humans do.
While you could probably see the importance of this concept after a moment or two of thought, let me perhaps provide some motivation for why this could be revolutionary. Modern computer architectures are capable of roughly a billion operations per second. The spiking rate of a neuron in the human brain is about 200 spikes/second. If we equate a single spike as being approximately equal to a single clock cycle, then a computer processes information more than 5 orders of magnitude faster than the human brain.
What this means, in more accessible terms, is that a task that would take the human mind 1,000 years could be completed by a modern computer in under 4 days. This example obviously makes some irresponsible assumptions about the similarities between the human brain and computers, but the point being made remains valid.
With that said, by drastically reducing the cost associated with mathematical descriptions of reality, Orchid aims to give human beings a tool that robustly addresses the limitations of both languages and mathematics in formulating representations of reality. In accordance with the historical trend outlined throughout this text, one can hope that the tools provided by Orchid can aid in both a drastic improvement of the human condition and the progression of organized complexity within reality as a whole.
Goodnight! I, of course, mean that as a greeting rather than an adieu in a highly purposeful floutation of linguistic norms, seeing as it is 11 at night as I’m writing this. No need to dally, let’s jump right in to where we left off. If you haven’t read my last post, I would encourage you to do so, even though I doubt this post will be inaccessible for those of you who refuse. To easily get to the last post, go to the bottom of the page where you’ll find a link to the previous post shaped like a bra (I am referring to the “bra” of Dirac’s “bra-ket” notation for Quantum Mechanics, not the garment. If you think I’m pulling your metaphysical leg, I would encourage you to look up “Dirac bra-ket notation,” and I believe you will find all the answers for which you have ever sought).
In the last post, I (somewhat exhaustingly) took you on a trip through a rough picture of how the brain works. To summarize, the brain well and truly is a wonderfully complex pattern recognition system. There. Now you know how the brain works. Take that to the teacher at the front of the room and get a golden frikin star.
Given this rudimentary understanding, I would like to now explore our human notion of “Understanding.” I am specifically referring to the term within the context of someone saying “…to get a better understanding of [you fill in the blank] ….” Specifically within the research community, you will frequently hear researchers throw this phrase around, usually when they’re trying to convince other people that their research is worthwhile. In my current line of work, you will frequently hear people say something to the tune of “We do [blank] in order to get a better understanding of the early universe.” But what the blue heck does that even mean? I understand you might think I’ve taken some cuckoo pills, but answer me this, cynical reader, can you tell me, in clear language, what researchers mean when they say “…to get a better understanding?” “Sure,” I can hear you saying through the walls of time and space, “here ‘understanding’ basically means broadening our knowledge about a particular subject.” But, oh great reader, what is knowledge? Really think about that for a second. And if you answered “truth” then I’ve got you cornered.
The fact of the matter is that we do not have access to fundamental truth. There’s really no way around that. Now then, I imagine some Christian readers may be slightly flaring up at that distinction. After all, didn’t Jesus purport to be the way, the truth, and the life? Even if Christianity is the absolute fundamental truth of the universe, I still firmly claim that we do not have access to it. If you’re still doubtful, let me pose this question: if Christianity was the fundamental truth in the universe, and human beings did have access to this truth, then why isn’t everyone on the planet a Christian? Surely that would be the only logical option. So then, I think I’m perfectly correct in asserting that as Christians, you in some way or another believe that the tenets of Christianity are associated with fundamental truth, even though you yourself do not have access to the fundamental truth of the universe.
As a brief side note, I’m only mentioning Christianity here instead of other religious traditions and practices because I myself was a very serious Christian for the better part of 20 years, and it was my attempt to forcibly associate Christianity with fundamental truth that caused me a great deal of mental health problems. If you take issue with anything I’m asserting on the basis of any other religious tradition, feel free to email me, as I would love to hear your thoughts. It would also be a wonderful change to not get a spam email from XFA for once.
In order to continue in any meaningful fashion, I believe I should attempt to define “fundamental truth.” The dictionary says truth is “that which is true in accordance with reality.” However, I would like to take this a step further. My conception of fundamental truth is untouched by human constructs, particularly human knowledge and understanding. I will talk more about these two entities shortly, but hang tight for the time being.
Furthermore, if there are any aliens in the universe that are at all similar to us humans, then I would imagine that fundamental truth should be untouched by any of their constructs, or what they might consider “knowledge” or “understanding.” With this in mind, it’s actually quite difficult to define what fundamental truth even is.
When I talking to other people about this sort of thing, I usually define fundamental truth as a “piece of knowledge that would allow us to make predictions and claims about reality with 100% certainty.” But even that is somewhat wrong because it assumes that fundamental truth can take the form of “knowledge” as we know it.
So then, while I can’t give you a precise definition of what I mean by fundamental truth, I hope I’ve sort of cultivated a connotation for what I’m trying to describe. In many ways, I feel that fundamental truth is equivalent to the fundamental structure of reality. You may have noticed in some previous posts that I have an obsession with order and structure, and this is really where it comes from. With this in mind, we actually don’t have any guarantees that our reality actually even possesses fundamental truth (or structure, or whatever you feel you ought to call it).
At this point you may be asking yourself, “But what about things that I know are true, like the fact that the object in front of me is a computer, or that the big fiery ball above my head is called the sun?” That is an excellent point, intellectually gifted reader, and it provides a wonderful Segway back to the original discussion about the brain.
At the beginning of this post, I asserted that the brain is a pattern recognition system. If that is the case, then I imagine that you would probably agree that our conceptions of “knowledge” and “understanding” are intimately connected with the notion of a pattern. I would like to take this a step further by asserting that what we think of as “knowledge” and “understanding” are simply patterns themselves.
I think the best way to explore this is through an example. Let’s say that a couple millennia ago, there was a cave man called Danny schmeaging around the mountains. Danny looks around him and sees a bunch of hard looking objects with generally similar brown and grey appearances. Danny doesn’t have anything better to do with his time, so he picks up one object, and hits it against a different one. When he does this, the two objects make a distinct “ckk” sound. This greatly amuses Danny, so he does it again. Danny soon realizes that he can actually make the sound “ckk” using his own mouth. He practices it for a couple minutes until he can confidently make the same sound as the two objects being hit against one another.
Pretty soon, another cave man walks by, lets call him Elon. Danny looks excitedly at Elon, points around him to all the different hard objects around him and makes the sound “ckk.” Pretty soon, Elon too knows that all the objects around Danny make the sound “ckk” when they are hit against one another.
Ok, let’s take a step back. What just happened here? Without even realizing it, Danny made an implicit association between the sound “ckk” and the objects around him. In the centuries to come, other humans learn to instead refer to the objects as “rock” instead of “ckk,” simply because many objects make a similar sound when hit against one another. So then, the auditory sound “rock” is now associated with an object that makes a “ckk” sound when it’s hit against another such object.
Let’s take another step back. The only way the word “rock” is useful to other cave men is if all the objects that are rocks make the sound “ckk” when they are hit against each other. This implies that there must be consistency for this piece of “information” to be useful. In other words, the only reason that the term “rock” is useful is because all rocks are characterized by a series of patterns, i.e. all rocks look the same, all rocks feel the same, all rocks hurt when someone else throws them at you.
Through this example, we see that what humans think of as “information” is simply a series of classifications of systems with consistent behavior. These classifications can themselves represent the consistent behavior of the interactions between other classifications. I would also like to firmly stress that this “information” is entirely a human conception. As far as we know, there’s no inherent connection between objects that make the sound “ckk” and the word “rock.”
So then, you are absolutely correct in saying that it’s true that you’re looking at a computer, and it’s true that the fiery object overhead is called the sun, but these are only true within the scope of truths manufactured by human beings. If you define the term “computer” to represent a system of hardware and software that performs logical operations on data, then it tautologically follows that it is true that the object in front of you is in fact a computer.
So then, going back to my original question, what does it mean for us to “gain a better understanding” of something? The something in question is simply a human-constructed classification, so “gaining a better understanding” of that classification is simply finding more patterns associated with that particular classification. For example, once you classify green, fuzzy plants as “moss,” then one example of gaining a better understanding of something would be to state “most rocks are covered in moss.”
Ok, I think I should probably wrap this boi up. I suppose the main takeaways of this post is that what we think of as knowledge is entirely a human construct. Furthermore, people generally talk about research as a field of discovery, but I would like to assert that research is just as much about creation as it is about discovery. But, to get meta on you, even that depends on how you define the term “knowledge.”
Finally, this is a topic I actually care a great deal about, so if you have any of your own thoughts on the matter, or disagree with me on any of these points, then for the love of Alexandria, can you email me? Like, please?
Well whatever. Let me try to regain the air of aloofness I’ve so desperately been attempting to cultivate. Deep breath in, deep breath out.
Ok, I just hit seven pages, and its 12:34 AM, so I feel the strong desire to perform a swan dive directly into my sheets. I love you all dearly. Geisz out.
A most aggressive “Shalom” to all you wonderful readers. Actually, now that you mention it, it has been a wonderful morning, thanks for asking. I was up until around 1 last night working on the app, and I had a lovely breakfast with my family. Afterwards, I did my laundry while listening to Flume’s self-named album and dancing about the laundry room wildly. I am now in my swamp room (if you don’t get the reference, it’s your own fault you aren’t on my email list), still listening to Flume at volumes that would make Percy Granger weak in the knees.
Now then, I had a very interesting discussion with my mother this morning about the nature of the mind, and what we know and don’t know about it. This discussion reminded me that I have had at least three thoughts in the past two weeks, and roughly two of them have to do with this very concept. I’m going to now pretend as though you are contractually obligated to read this post in its entirety, and I’m going to now launch into a pompous expose on my own personal thoughts regarding the nature of the mind.
To begin my civil diatribe, I would like you all, treasured readers, to think about what it means for a human being to learn something. Perhaps the accumulation of knowledge comes to mind, or perhaps, alternatively, the accumulation of experience. I would now like to humbly, yet domineeringly launch into my own meta-physiological understanding of what it means to “learn.” And to do that, we necessarily must have a one-way Socratic seminar regarding the brain.
The brain is gloriously dense pattern recognition system. That’s really what it is. I watched a TED talk on it, so I’m pretty much a professional. I’m sure most of us who’ve at least somewhat recently been in school have some notion of the brain being a collection of neurons in which “connections are formed.” Sure. That’s all fine and good, except that it’s too abstract to actually mean much of anything.
In order to understand how the brain actually learns, it’s best to cultivate a slightly more rigorous formulation of brain functionality than the statement “connections are formed.” Also, I’m only talking about the part of the brain that learns stuff over time. I’m not talking about the part of the brain that’s responsible for keeping all your internal systems in check.
Now then, I want you to think of the brain as a computer with a ton of USB ports. Your optic nerve plugs into one of the ports, your auditory nerves plug into another, your olfactory (smelling things) nerves plug into yet another, and so on. So basically, we have a computer hooked up to all of your sensory nerves.
All of these connections are constantly sending information into the computer, and the computer’s only job is to try to find patterns between the various inputs. Let me give you an example. Let’s say you’re about one year old. Your dad has placed a piece of paper and a box of crayons in front of you. Your dad picks up the red crayon, and says the word “red.” You’re one, of course, so you don’t have a flip shack frack what’s he’s talking about, but let’s look at what’s happening in the computer that is your brain.
When your dad says “red” and picks up the red crayon, your vision is being directed on the crayon in front of you, and hence your optic nerve is sending the visual information corresponding to a red crayon into your computer-brain. At the same time, your ears are sending the auditory information corresponding to the word “red” into your computer-brain.
Now then, your brain can’t tell one information source from another, so it does is form a correlation between the visual information corresponding to the red crayon and the auditory information corresponding to the word “red.”
You can probably see some issues with this. Does the word “red” correspond to the wavelength (color) being emitted from the crayon, or does it refer to the short waxy thing your dad is pointing to? At this point, your brain has no way to tell.
Now then, you turn your attention away from the crayon in your dad’s hand to the blue crayon lying on the table. When you look at this new crayon, your optic nerve sends the visual information corresponding to the blue crayon to your computer-brain. While the image isn’t exactly the same as the image of the red crayon in your father’s hand, it’s similar enough that the image of the blue crayon turns on the connection that your brain-computer formed between the image of the red crayon and the word “red.”
So then, while you don’t have a clue what’s happening in your computer brain, when you look at the blue crayon, the word that pops into your head is “red.” And because you’re a semi-useless one-year-old and you have nothing better to do, you confidently point to the blue crayon and say “red” out loud. Now then, because your dad has no interest in having his child think “blue” is “red,” he says “No.” He then picks up the red crayon, and again says “red.” He then points to your red shirt and says “red.” He finally points to your red couch and says “red.” Each time he does this, your computer-brain creates a new connection between the visual information of each object and the auditory information corresponding to the word “red.”
Now here’s the real kicker. Your dad then picks up the blue crayon and says the word “blue.” The exact same correlation happens as before, but this time, the auditory information corresponds to the word blue.
Now then, you look across the room and see your mom also wearing a red shirt. While the shape of the shirt itself isn’t similar to that of a crayon or a couch, the wavelength being emitted from the shirt is the same, so when the visual information corresponding to your mom wearing a red shirt is sent into your computer brain, the aspect of the image that corresponds to the color “red” is fired, and once again, the word “red” pops into your head. So then, once more, your proudly proclaim “red” to everyone who’s around to hear. This, of course, makes your parents excited, and they say “Yes! Red!” which only serves to strengthen the connection between the visual information corresponding to the red wavelength of light with the auditory information corresponding to the word “red.”
There’s no universal connection between the word “red,” and the color red. This exchange could have just as easily occurred in a Spanish or French speaking household with the words “Rojo” or “Rouge.” All the brain is therefore doing is forming connections between different stimuli, which can be strengthened or weakened over time. In other words, it’s an incredibly efficient pattern recognition system.
How does it actually work? Instead of actually being a computer with bunch of USB ports that’s been programmed to find patterns in different sources of data (which is literally just machine learning, btw), your brain is a collection of around 86 billion neurons that are connected to one another. All neurons do is fire an electric stimulus to the other neurons it connects to when it has itself received enough electric stimulus from the other neurons that connect to it. So basically your brain is just a chain reaction of neurons causing other neurons to fire. Now here’s the kicker: once a neuron fires, it actually becomes easier to fire again. So then, going back to the previous example in less gruesome detail, when you see a red crayon and hear your dad say “red,” your optic nerve fires a huge amount of neurons corresponding to the visual information of the red crayon, which sends a huge chain reaction cascading all throughout the brain. At the same time, your ears fire auditory neurons corresponding to the word “red” throughout your brain, which causes a similar blossoming chain reaction.
Now here’s what’s super cool. Some neurons are fired by both the visual and auditory chain reaction. Because it’s easier to fire neurons after they’ve already been fired, this forms a strong “pathway” between the visual and auditory information in the sense that neurons along this pathway are more likely to fire in response to similar visual and auditory stimuli.
So then, even though your brain can’t tell the difference between visual data and auditory data, it can find patterns between the two sources, which translates to you knowing that the color of the red crayon is red.
At this point, I was going to launch into a greater discussion of what this means in terms of human knowledge, and what it means to “understand” things. However, being a decent human being, I can quite clearly see that I have just broken the 6-page mark, which is usually my sign that I should probably wrap things up. I think I will continue this discussion in the next post.
In closing then, contrary to Justin McElroy’s incessant pleading, I would urge you to not kiss your dad square on the lips, because that seems like a great way to spread coronavirus, which is generally inadvisable.