Key Points

It is no secret that progress in medical therapeutics in recent decades has been terribly disappointing. In spite of massive investment of resources, there have been on major therapeutic advances in the major categories of human illness, including diseases of the nervous system.

This failure is best explained by a critical flaw in the dominant treatment paradigm, where we’ve almost exclusively sought to find treatments (i.e. pharmaceuticals) that act at the level of biochemical processes, or that, to use the computing metaphor, intervene at the level of “source code.”

While this therapeutic approach is appropriate for acute, single variable illness, it is the wrong level of intervention for the chronic, multi-factorial mismatch diseases that characterize the vast majority of modern illnesses.

The only viable strategy for the treatment and prevention of mismatch diseases are holistic, systems based, “game level” interventions.

 

Links

Sean Carroll’s “The Big Picture”
“How To Win At Angry Birds” presentation at the Physicians for Ancestral Health Winter Retreat
Model #7: Mismatch
Model #1: Hardware & Software
Anatomy of an Epidemic: Magic Bullets, Psychiatric Drugs, and the Astonishing Rise of Mental Illness in America, by Robert Whitaker

Opening music – “Bug Catching” by Emily Sprague, “Rainbow Forest” by Quincas Moreira

Transcript

A long time ago, in a galaxy far, far away, an iPhone lands on an alien planet. On this planet, there are no iPhones, no computers, no electronics of any kind, but the aliens are intelligent. The iPhone is loaded up with the game Angry Birds, which they especially like. They also happen to be a competitive species of aliens, and they decide they’re going to hold an Angry Birds competition. They break into two teams, and decide they’re going to meet back in one month to hold the Angry Birds Championships, to declare one team the winner.

In preparing for the championships, however, the two teams take very different approaches. The first team prepares, as you might expect. They practice playing the game, itself. Through trial and error, they develop an intuitive sense of the game physics, where and how to aim their slingshots to hit the pig structures where they’re most vulnerable. And so, over time, the first team gets really good at playing the game. So we’ll refer to them as Team Game Level.

Team Two, on the other hand, they take an entirely different strategy. Their an analytical bunch of aliens. So they decide to take the game apart to see how it works. So they keep peeling back, layer by layer. Ultimately, they figure out that this game is written in a programming language, and that language is ultimately converted into a binary code, that then specifies the moment-to-moment state of transistors in the iPhone’s processor. So they essentially strip things down, all the way to the level of machine language.

At this point, they’re feeling pretty pleased with themselves, and are almost giddy with excitement. Given that they’ve cracked the code of how the game really works, they think the other team now has no chance whatsoever. So they decide they’re not going to waste time playing the game. Instead, their strategy for winning will be to manipulate the machine language, and we’ll refer to this team as Team Source Code to reflect that strategy.

Now the day of the Angry Birds Championship arrives, and the two teams face off. Of course, it’s a massacre. Team Game Level destroys Team Source Code. Even though these aliens are pretty sharp, they’re nowhere near clever enough to pull off such a feat. The team that practiced by playing the game racks up a super high score. Team Source Code, on the other hand, can’t even make it through a single round without crashing the game.

Why is this? Because even the smartest computer scientists alive couldn’t do this sort of thing. Just as no molecular biologist could look at the base parasequence in a set of chromosomes, and know what organism that was going to produce, no computer scientist can look at the machine language instructions for a software program, and grasp what it does, much less manipulate it in a way that would produce a desired outcome.

That’s not to say that understanding the source code wasn’t a useful kind of knowledge, it just wasn’t the right kind of knowledge for winning the game. Even though Team Source Code understood far more about how the game was made than Team Game Level, it didn’t make them better players.

Hi, I’m Dr. Josh Turknett, Founder of Brainjo and the Brainjo Center for Neurology and Cognitive Enhancement, and this is the Intelligence Unshackled Podcast. Join me, as we take a tour through the human brain, to explore and understand the true nature and scope of human intelligence, and to unlock the secrets of optimizing brain health and function.

I’ve said before that one of the reasons I entered the field of neurology almost two decades ago, was because of the promise of therapeutic breakthroughs over the course of my career. At the time, there had been tremendous investment in resources in understanding the brain, and we’d made major advances along those lines, along with billions of dollars that have been poured into pharmaceutical research.

And it seemed like we were on the cusp of discovering transformative treatments in neurological illness. And yet nearly two decades later, not only have there been no major breakthroughs, there haven’t really been any incremental improvements on any of the major categories of neurological illness. To say that this has been disappointing is a huge understatement.

In the field of neurology, I think you’d have to cite the last breakthrough therapeutic treatment as occurring in 1991, with the discovery of sumatriptan for migraine headaches. And in retrospect, it’s debatable whether that should even be considered a major breakthrough, or an improvement, as it has had many unintended consequences with arguably more downsides than upsides. And may have even created more problems than it solved, and this has been true across the board in medicine, whether it’s diabetes, obesity, heart disease, kidney failure, cancer, and so on.

Most of the major drug discoveries, the ones that produced a transformative change from what had existed previously rather than something merely equivalent to existing treatments. Almost every major drug discovery of that kind occurred many decades ago, and it’s also worth noting that almost everyone of those major drug discoveries occurred by accident. With rare exception, we don’t even understand the body well enough at this level to predict what compounds are going to influence physiology in ways that we desire.

And I would argue that the reason for this spectacular failure is because we’ve been taking Team Two or Team Source Code’s approach to winning the game. In this case, the game of human health and disease. The reductionist methods of science like what Team Source Code used to break the game down into its lower levels, have been tremendous in many respects. They’ve greatly enhanced and expanded our understanding of human physiology, they’ve greatly enhanced and expanded our understanding of the pathy of physiology of disease, increasing our knowledge of the biological mechanisms of illness.

But where we’ve been mistaken is in thinking that this level of description, at the level of molecular biology, of proteins, and neurotransmitters, is that that’s the right level for us to intervene in this system. Just like it did to our alien team Source Code, it feels like because these levels of description are hidden from us, and that we have to work to find them. It feels like when we do, that we’ve peeled back the curtain and it seems intuitively that we’ve discovered some deeper, more privileged and more useful form of knowledge.

It also feels like intervening, or acting at this level, which is what we do with pharmaceuticals is a way of hacking into the system, or is a more effective means of influencing our biology. I think that intuitively, it feels like that’s true to most people. But as the allegory of the Angry Birds story hopefully illustrates, that’s a misconception. Different levels of description and analysis just have different utilities, or different domains of application. No one level is inherently better because it ultimately depends on what you’re using that knowledge for.

Now there are instances in biology, where intervening at this source code level is more appropriate for solving a particular problem. For example, for a disease caused by a single-based paramutation in DNA, correcting that through some sort of gene editing process would be an appropriate application of a source code intervention, or an illness caused by a single identifiable thing, like a bacterial infection of the lungs, that’s also amenable to source code level intervention, like an antibiotic.

And it’s no coincidence that modern medicine has succeeded almost exclusively in these sorts of acute single variable problems, where its therapeutic paradigm fits. The problem is that this is the wrong domain of application when it comes to intervening on chronic multifactorial conditions, which describes the vast majority of things we physicians see day in and day out, the vast majority of conditions that affect humans in this day and age, and the ones that are responsible for our out of control healthcare costs.

And that’s because, as discussed a couple of episodes ago, those chronic conditions arise from mismatches between our modern and ancestral habitat. So for chronic multifactorial conditions like these, the only viable approach is to play the game, to intervene at the game level, rather than trying to manipulate things at the level of the source code.

And there are two major problems that arise from the source code approach, whether it’s with a computer or a biological system, which is even more complex, and that’s number one, that we have a very limited understanding of how changes at that level will impact the entire system. And number two, that interventions at this level are typically going to be further downstream than ones at the game level.

So that means that these kind of interventions have less influence over the thing we want to influence, and more unintended consequences. Furthermore, interventions at the game level worked by activating, supporting, or amplifying existing homeostatic mechanisms for repair, recovery, and growth. So they’re facilitating the body’s natural abilities, which are tremendous.

On the other hand, pharmaceutical interventions almost exclusively work by disrupting those mechanisms. And so whenever we’re advocating for their use, we’re essentially saying that the disruption that they cause is preferable to the status quo. Presumably because a regulatory system has broken beyond repair.

And just to elaborate a bit more on this concept, every phenomenon in our world has multiple levels at which we could describe it. For example, you could say that humans generate energy by extracting the energy from sunlight that’s trapped in the food we eat. Or, you could say that humans generate energy by creating an electrochemical gradient in the mitochondria, that’s then used to add a phosphate bond to identicine diphosphate.

Both of these explanations are correct. They’re just different levels of description of the same phenomemon, with different utilities. In the book, The Big Picture, physicist Sean Carroll illustrates this idea of levels of description with the example of the letters on the pages of a book.

Now one way we could describe the letters on that page is to perform a chemical analysis of the ink. We could break the page of text down into its component elements, and the various atoms that comprise it. And if our goal was to understand the chemical composition of the printed letters, then that atomic level would be the appropriate level of description.

On the other hand, if our goal was to read the text, to decode the information that the author wanted to convey to the reader, then that’s absolutely the wrong level of description. And the appropriate level is, of course, the level of the words. For those needs, the chemical composition of the ink is irrelevant to our interests. And in my view, this explains exactly why we’ve failed so spectacularly in finding treatments for the most common diseases of our time.

Because of the nature of these conditions, we’ve been looking to intervene at the wrong level. To use the analogy, “We’ve been looking to intervene at the level of source code, rather than at the level of the game.” And when it comes to improving human health, game level interventions are far more powerful and far less likely to do harm than source code interventions.

So on the one hand, the past few decades have been an endless barrage of one disappointment after another. On the other hand, because we’ve been barking up the wrong tree for so long, we’ve barely scratched the surface of what’s possible if we fully explore and utilize the potential of game level interventions.

And through the implementation of this new paradigm for human health and disease, where we combine our understanding of modern illness as ones of mismatch, with the use exploration and refinement of game level interventions, we have the ability to make significant headway into the major diseases of our time.

As I said, the last mental model that I introduced was that of mismatch, the idea that mismatches between any living things present, and ancestral environment places strains on its homeostatic system, which leads to dysfunction and disease. And the greater the mismatch, the greater the amount of strain. And this model is the most useful framing device for understanding how chronic diseases emerge, and how best to prevent and even treat them.

And thanks to the significant mismatches in the average human’s life nowadays, because of the major technological changes that have occurred in the last 10,000 years, we experienced significant mismatches in our day-to-day life. And as a result, most of the chronic health conditions that doctors see in this day and age, are mismatched diseases, and are the result of breakdown in those homeostatic systems because of that accumulated strain.

And as I mentioned, we’ve made virtually zero progress in treating these conditions, despite incredible amounts of investment, and this allegory of Angry Birds helps to demonstrate why we failed so spectacularly and what needs to happen in order for us to course correct. And like I said, there’s incredible potential in applying this framework.

As some of you know, I published a book several years ago called The Migraine Miracle that uses this sort of game level approach to minimizing mismatches for the treatment of migraines, and the results of that have been incredible. We have thousands of people from around the world who’d been suffering for decades with chronic migraines, despite the best that modern medicine had to offer, and have now achieved results using this framework that they’d never previously thought possible.

And results that I could never achieve when I was using the standard source code-based approach to care, and there are many other practitioners now, largely outside of the conventional healthcare system, who are achieving results that you just don’t see with the standard pharmaceutical-based treatments, including in disorders of cognition. And so in this sense, the current therapeutic paradigm of modern medicine, which is looking primarily for source code solutions is mismatched to our current problems in healthcare, which require game level interventions.

And like I said, this failure to find effective treatment isn’t unique to neurological illness, by any means. In most every specialty in medicine, our breakthrough pharmaceuticals are many decades old, and when you consider the scale of the modern healthcare and drug industry, how much has been invested, and by contrast, the technological advances that have transformed human life over the same time interval, these failures become all the more remarkable.

And that should raise major alarm bells for anyone interested in improving human health, and anyone operating inside of that system, if we haven’t even made incremental progress after decades throwing enormous resources at these things, I think that’s a call to stop and reexamine our fundamental approach to recognize that there’s something very wrong with our dominant therapeutic paradigm, and to adopt one that’s better fitted to the world we live in.

And so when it comes to the things that we consider to improve the health and function of the brain, and protect it from disease, I will almost always prioritize game level over source code interventions for reasons that are now hopefully clear, and I’ll also continue to use that terminology in talking about them.

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While we’re on this topic of levels of description, there are a couple of other areas where this applies that relate to the themes of this podcast. The first is in the relationship between the brain at the level of cellular and neuronal interactions, and our resultant subjective experience and consciousness. As I discussed in the episode on hardware and software, the mind-body illusion is a powerful one. It doesn’t feel like, nor will it ever feel like that our thoughts and experiences are the direct result of a physical entity, which is our brain.

Now there’s still much work to be done on understanding the relationship between brain activity and consciousness, as it’s the last great mystery in science, but what’s important to keep in mind, is that even if we were to develop a robust theory of how consciousness arises from the signaling of neurons in the brain, it is not something that’s ever going to make intuitive sense to us.

In the same way that it’s impossible to look at the machine language for a piece of software and understand what it does, or look at the base pairs in a strand of DNA, and understand what their resultant organism it will produce will be, it’ll never be possible for us to map our experience of consciousness onto a model at the neuronal level.

And we shouldn’t expect or demand that our model do that because it’s an entirely unrealistic expectation, just as it would be unrealistic for a model of how DNA produces living things, or how machine language produces a computer program. And the danger there is in thinking that our inability to have that intuition means that the model itself is not true. That consciousness cannot arise from physical stuff, but the point being that our model will never provide that kind of intuition.

And the second way this concept of levels of description relates is along similar lines with how we explain the science of human behavior. So you’ll rarely hear me talk about neurotransmitters, and neuromodulators when discussing the neuroscience of human behavior and cognition. Not only are we still very much in the dark when it comes to our understanding of those relationships, it’s also the wrong level of description for most scenarios.

On the other hand, our models of behavior and cognition that have come from psychology, cognitive neuroscience, and behavioral neurology, are far more useful largely because those explanations are at the proper level of description for the kind of understanding that we’re interested in.

Of course, there’s no shortage of people these days, describing human behaviors, in terms of dopamine, and serotonin, and the like. Again, another reflection in part of the seduction of that reduced level of description in giving the appearance that it’s a more privileged or useful form of knowledge.

Yet, we only need to look to the era of psychopharmacology to see how limited our understanding is in that area, and how dangerous that illusion of knowledge can be because there’s no better example of the dangers in monkeying with our source code, and our manipulations of brain chemistry through psychopharmaceuticals. It’s an area that’s been a huge net negative, where we’ve done far more harm than good.

This is an area where our understanding of how interactions at the source code level produce game level phenomemon is the most limited, and so likewise, the area where influencing that source code is the riskiest. But it’s also an area where game level interventions have tremendous potential.

Okay, that’s it for this episode. Just a reminder, that the Learn to Play the Ukulele Brain Fitness Challenge remains in full swing for members of the Brainjo Collective, but there’s still plenty of time to take part in it. You can learn more about that by going to elitecognition.com/fitness. And it is, of course, no coincidence that learning to play a musical instrument as a means of improving brain health and function is a game level intervention.

Remember, too, that show notes and transcripts for each podcast are available on the site by going to elitecognitition.com and just clicking podcast at the top menu. And if you’re enjoying this podcast, it would be wonderful if you left a rating in iTunes, it really helps others to discover it, which helps me spread these ideas that I really care about, and hopefully that you care about spreading, too.

And lastly, remember that the best approach to winning the game of health, of affording yourself the best chance of a long life well-lived, is not to try to hack into the source code, but to play the game.

Model #8: Game Level VS. Source Code

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