Most explanations of human performance lean on psychology or philosophy, but best-selling author Steven Kotler argues that these frameworks are only metaphors. If you want repeatable, measurable science, you need to use biology.

By understanding brain networks, Kotler explains how we can engineer peak states like flow, creativity, and focus for peak performance

STEVEN KOTLER: When I think about why a physiological explanation for human behavior is more interesting to me than a philosophical one, I always say that the philosophical, or as it evolves in the 20th century, you get the psychological, and they're sort of the same thing for a little while. Psychology's incredibly useful science, but in a lot of cases, it's an outside-in science. The brain is actually an inside-out mechanism. And psychology as a result and philosophy, they're metaphor. They're great metaphors. They really help us think about things, but they're still metaphor. If you're interested in human performance, what you want is something that's reliable and repeatable, and thus you want neurobiology 'cause neurobiology gives you mechanism.

- [Narrator] The biology of peak performance.

- Not always, but often, the advantage in neurobiology, when you're trying to train human performance from the psychological level, personality often gets in the way. And personality is very individual. When you get down to the neurobiological level, and we're talking about things that evolved across the entire species, what works for me will work for you. Whereas at the level of psychology when personality is involved, because personality is shaped by both nature and nurture, right, genes and the environment we grew up in, what works for you probably isn't gonna work for me. So at the level of neurobiology, we get mechanism and we get something that's reliable and repeatable and most likely will work for everyone, or at least has a much better chance of working for everyone. Technology, as many of us know, is now kind of accelerating exponentially. This includes neurotechnology. Brain imaging technology's been advancing on exponential growth curves for quite a while, and over the past 20 years, starting in the late 1990s with spec scans and things like that, and moving into the early thousands with FMRI, which has only gotten better and better and better. And yes, there are still all kinds of problems, but better and better and better. So we're now able to kind of see where in the brain things are taking place. This is what we get out of FMRI for example. This gives us location, sometimes not just neural anatomy, but networks of the parts of the brain that are working together at once. And then we have technologies like EEG and MEG that move very, very quickly and allow us to look at things that happen in really short, quick timescales in the brain. FMRI is like usually over the order of seconds. And with EEG, you're talking about milliseconds. So the traditional understanding of the brain, and you know, this was a bad idea in a sense from go 'cause this idea emerged out of phenology, right? The study of like various bumps on the head, which were supposed to relate to psychological characteristics and it was absolute nonsense. But out of that thinking, we came up with the idea of certain areas of the brain are optimized for certain functions. Some of this happened, this happened usually through like people would have strokes or a different part of the brain would get injured. And you know, some characteristic would go away and we'd figure out, oh, Broca's area does a language for example. Some of that is true, it is not that certain functions aren't kind of localized in certain regions. Some of that is true. But as a general rule, the brain works in networks. It's a lot of different parts of the brain working together at the same time. Now when people hear the term networks, they think, oh, hardwired together. And that is sometimes true in the brain and there are hardwired connections in the brain and they're myelinated, which is basically a way of insulating kind of the neuronal connections for faster information transmission. But as a general rule, we'll have what are known as functional networks where a bunch of different neurons are organized into what's called a cell assembly. And basically they're all active at the same time. They're all doing work at the same time. And this could be very, very quick, these cell assemblies are like the order of, they come together like one 100th of a millisecond and they can, you know, be together for very quick periods at a time. We call that functional connectivity. And more and more the recent thinking on the brain is, you know, it mostly comes down to networks and how the different parts interact and work together. You know, some of the more famous ones are the ones that are, you know, there's a fear network. There's fear circuitry in the brain, there's the executive attention network, right? When you have to pay attention to something that you're not particularly interested in, you're listening to a boring lecture, but you're gonna be tested on it later. That's the executive attention network. The executive attention network also lets you override kind of more instinctive behavior, right? You have an impulse to eat, you know, all the ice cream in the refrigerator 'cause it's sugary, it's sweetened, you want the calories, but the executive attention network goes, no, no, no, let's think about this. Let's think about the future. Let's think about your waistline. You know, it can suppress those instincts. So there's tons of different networks and new ones are discovered all the time. But those are a couple of the big basic ones. So it's not that kind of this localized neural anatomy ideas are wrong, it's just that they don't take us far enough. And you know, so again, some of the problem here is that we started, when kind of neuroscience started, it actually started with one of my great heroes, you know, William James, he wrote a book in called Principal Psychology, the first psychology textbook. And he lists, oh, here's perception and here's time perception, here's memory. And this is basically like an outside in perspective. We think this is what the brain is doing. We think memory and perception is a thing and where does it happen in the brain? But you know, it turned out the brain is inside out. And you know, our vague psychological terms sort of relate to what the brain does, but not exactly. So the new thinking is inside out and kind of networks at the heart of this. When I talk about peak performance, I often define peak performance as getting our biology to work for us rather than against us. This, by the way, this is not a new idea, this is not my idea at all. In fact, William James, in the very first psychological textbook ever written about 120 years ago, said, the great thing in all education is to get our nervous system to be our ally and not our enemy. And by our nervous system, right? He meant our brain and our biology. The reason that is so useful is, in a sense, it takes the genetics out of it, right? We're all born a little different from the other person, but the biology underneath it tends to work the same. And when we talk about our biology, especially in the work that I do, which is on cognitive peak performance predominantly, that's actually a limited skillset. So I'm saying our biology, but what I'm really talking about are the systems underneath what we call motivation, learning, creativity and flow. And flow is an optimized state of consciousness where we feel our best and we perform our best. To put this in kind of slightly different terms, each of those headings is sort of a catchall term, right? When psychologists or scientists or neuroscientists say motivation, they're often talking about extrinsic motivation, things in the world we're gonna work hard to get, intrinsic motivators, things that drive us from within like curiosity or passion or purpose. They're also talking about goal setting and grit. So these are catchall terms when you're talking about cognitive human performance, these are the four categories. And the way I sort of like to think about it is motivation is what gets us into the game. Learning allows us to continue to play. Creativity is how we steer. And this is especially true if we're sort of going after high hard goals, we don't quite know how to get there, right? We know where we want to go, but we don't know the path. Creativity is really crucial and flow, which is optimal performance, is how we amplify all the results kind of beyond all reasonable expectation.