How to Learn: Unlocking the Brain's Secrets
Barbara Oakley, renowned author and polymath joins Charles Humble for a deep dive into the brain's modes of operation, exploring the focused and diffuse modes of thinking, which together enhance learning by balancing task-oriented problem-solving with creativity. They emphasize the importance of mental relaxation and the role of exercise, sleep, and a healthy diet in boosting cognitive function. Key concepts such as deliberate practice, retrieval practice, and the value of psychological safety in team dynamics are explored, offering insights into effective learning and collaboration. The relationship between working memory and long-term memory is examined, highlighting how techniques like the Pomodoro Technique and spaced repetition optimize retention. Additionally, the conversation underscores the potential of AI in education, encouraging its integration despite concerns among educators. Lastly, the phenomenon of rustiness in skill retention is explained through the dynamics of neural connections, with a hopeful outlook on the future of neuroscience research in understanding conditions like autism and dyslexia.
About the experts
Charles Humble ( interviewer )
Freelance Techie, Podcaster, Editor, Author & Consultant
Barbara Oakley ( expert )
Professor of Engineering at Oakland University
Read further
Exploring Effective Learning Techniques
Charles Humble: Okay, and we're recording now. Hello, and welcome to this episode of the GOTO Unscripted podcast. I'm Charles Humble, I'm a freelance techie, editor, author, and consultant, and for this episode, I'm joined by Barbara Oakley. She is a true polymath. She was a captain in the U.S. Army, a Russian translator on Soviet trawlers, a radio operator in the South Pole, and an engineer. She's a distinguished professor of engineering at Oakland University in Rochester, Michigan, and her work focuses on the complex relationship between neuroscience and social behavior. She created and teaches Coursera's Learn How to Learn, one of the world's most popular online courses with over three million registered students.
She's a New York Times bestselling author who has published in outlets as varied as the Proceedings of the National Academy of Science, "The Wall Street Journal," and "The New York Times." Her book, "A Mind for Numbers," has sold over a million copies worldwide. She's the winner of the "McGraw Prize," the Colloquial Nobel Prize for Education, and is a fellow of both the Institute of Electrical and Electronics Engineers and the American Institute for Medical and Biological Engineering. She gave a remarkable keynote recently at GOTO Copenhagen on using generative AI to strengthen and speed learning, and I was delighted to chat to her afterwards, and then GOTO asked if we could interview her for their podcast. Learning is such a vital skill for the work we all do in IT, but it's one that seems to be so rarely talked about, so this feels like a fantastic opportunity to explore how we learn more effectively. Barbara, welcome to the show.
Barbara Oakley: It's so nice to be here, Charles. Good to see you again.
Charles Humble: Likewise. Thank you so much. So, I was intrigued because my limited experience of academia is that it sort of sharpens but also narrows your focus, but you seem, and I mean this in the best possible way, to have kind of avoided that. So, how did you manage it?
Barbara Oakley: I kept myself hidden until I got tenure, and then everything came out. So, keeping your bonafides close to the chest early on is probably a good way to go, but I am fascinated by everything, and so I am so happy now to have a perch with tenure and be able to explore what I think is truly important to be explored.
Charles Humble: That's fantastic. There's a sort of related question I have, and it might turn out to be a rubbish question, in which case we can edit it out. But for various reasons, I've spent quite a lot of time over the last decade or so talking to educational psychologists, and they're obviously very knowledgeable in their particular field, but they seem to talk a lot about generalities that don't seem to me to be universal. So, for example, they maybe don't apply to how we learn maths or science using chunking, say, as you might if you were learning music. Am I wrong to feel that way, do you think?
Barbara Oakley: No, I don't think you're wrong at all. It's sort of unusual that when people learn something, they think that whatever they've learned is applicable much more broadly. So, for example, if you learn how to teach children mathematics from a kindergarten through 6th-grade level, maybe from 0 to 10 years old, in other words, that should be how everyone learns. But of course, it's not how everyone learns, and adult college-level individuals learn quite differently, and a challenge, a real challenge is that many psychologists are trained beautifully in the humanities and social sciences, but they have very little background in the cognitive skills necessary for programming, for example, or for learning in math and science in the STEM topics. So, this is what prompted me to work on the book, to write the book, "A Mind for Numbers." I originally trained as a linguist. I went to the Defense Language Institute in Monterey, California, which is one of the best language-learning institutions in the world. I picked Russian at random, more or less. And then later, when I got out of the military at age 26, I could see that my skills in language were not what the world was really looking for.
So, I am determined to retrain my brain and actually learn math and science. And I found that many of the approaches that I had been taught at the Defense Language Institute for language learning were absolutely appropriate for learning in math and science. And when I used those techniques, well, guess what? I went from a complete object failure in math and science to now being a distinguished professor of engineering who loves math and science. And so what I really found was that some of the ways that are currently being used to teach, especially in the STEM disciplines, are perfectly appropriate for teaching in the humanities and social sciences. But they can actually cripple your ability to learn in STEM kinds of disciplines. In disciplines that are involved, for example, in learning how to program. So, that is why I started to write the book, "A Mind for Numbers," and I think why the book has been so successful.
Recommended talk: Using Generative AI to Strengthen & Speed Learning • Barbara Oakley • GOTO 2024
Charles Humble: There was something you said in your keynotes as well, and it really brought me up short. And it was, I think I'm getting this right, but less than 1% of all educational research is repeatable. Is that right? Have I got that right?
Barbara Oakley: Exactly right. And I actually showed the paper during my keynote because sometimes people have problems believing that there really is solid research-based evidence. But absolutely, educational literature often, all you have to do is publish something, and the chances of somebody going in and repeating what you're doing and verifying that it is actually correct are infinitesimal. And so that means you can get published virtually, as long as you can publish something. You can run around saying, "I've got published research that shows that writing on vertical surfaces will teach kids how to learn math better." And there really is research like that. And it's why we have so many fads going out in education. People, it's one thing, then another, and poor teachers, you know, they're drawn this way and that with this year's new fad.
But then the real work of learning that has been proven by hundreds of papers, for example, retrieval practice, like pulling the information from your own mind, that is not disseminated or taught about hardly at all. It's kind of crazy. There's a wonderful book I should mention called "Powerful Teaching" that talks all about retrieval practice. But if you talk to a crowd full of 1,000 educators and you ask them, "Which of these four techniques is the best for learning, underlining, rereading, retrieval practice, like using flashcards, or concept mapping?" Which means writing out the concepts and connecting them together with something you're trying to learn. Virtually everyone will say, "It's concept mapping." But that's been proven decades ago, and yet it's still being taught. So, it's a crazy world out there.
The Brain's Focused and Diffuse Modes in Learning
Charles Humble: Let's talk a little bit about sort of how our brains work and what's going on. So, again, my understanding is you have sort of two fundamental modes of the way our brains work, so focused and diffuse. So, can you talk about that a little bit for us?
Barbara Oakley: Maybe a good way to understand this is to first think about what allows a bird to survive, thrive, and reproduce. A bird needs to be able to focus intently, for example, on a seed and peck that seed, but it also has to be able to look around broadly to see if a predator might be coming in from nowhere. So, it has two completely different ways that it must interact with the world. Focused and much more diffuse. And it is thought that that may be behind why there are two different hemispheres in almost all animals. One hemisphere, often the left, is more geared towards focus type of activities, like focusing on a math problem or doing coding. And that right hemisphere is more broadly involved in thinking creatively about the world, in music, and so forth. So, these two different modes are, we can see the very different patterns involved in focus mode versus that diffuse mode activity, which is called default mode activity by neuroscientists, a task-negative activity by psychologists. And we can see how the brain operates in a very different way.
So, when you're first learning something, you're often focusing, concentrating, often sort of using various portions of the left hemisphere. And then when you are relaxing, take a little break, you are more broadly using many areas of the brain, a broad network. And as you're learning, you're often going back and forth between this focusing and then relaxing, diffuse, then back to focusing. But when you go into that relaxed, diffuse state, that's when you begin to make sense of things that might have puzzled you during that initial focusing. So, when you're trying to figure out some coding problem, you've reached a dead end, you can't figure out what on earth you did wrong, and then you step back, take a little break, do something else. And in the background, that diffuse mode will be working away, consolidating, making new connections. And when you return, suddenly, it all seems to make sense as you return to that focus mode again.
Charles Humble: So, is there a risk for being overly focused?
Barbara Oakley: Yes. And research is just giving us a bit of intimation about this problem. But what can happen is if you are in focus mode constantly, there is some evidence that it can suppress the activities of that diffuse mode. Why is that a problem? I mean, the diffuse mode is actually affiliated with feeling a bit anxious about things. You're imagining all the ways that things could go wrong. But it is the mode that's affiliated with creativity, with thinking more broadly and making unexpected connections. So, if you're always focusing, let's say you focus at your work, then you go home and you do focus mode meditation, what can happen? You're focused, focused, focused, and you can suppress that more creative, it seems, diffuse mode. So, a little time to let your mind sparkle and just kind of run free, romp in a mind-wandering way is a healthy thing for you.
Charles Humble: How important is exercise for learning?
Barbara Oakley: Exercise can be invaluable in helping you learn. And we used to wonder why athletes would often do better during the season, where their sport was in season, and so they had much less time to study. But their grades would improve during these times. And people were wondering, "What the heck is going on there?" They have much less time to study, but they're doing better. Well, it turns out that when you exercise, the brain produces something called brain-derived neurotrophic factor. That's abbreviated as BDNF. And it also produces some other substances and has other effects. But BDNF is a biggie. And what it can do is it, there's your neurons connecting to one another when you are learning something. So, they connect through these little sprouts that come off of the neuron's leg. These sprouts are called dendritic spines, and they look like spines. If you take BDNF and you sprinkle it over neurons, you can actually see all these little spines will begin just popping out. And in the brain, that is part of what seems to happen as well.
When you exercise, you are producing BDNF in the brain. It is like a little neural fertilizer for those connective dendritic spines. They sprout out and they're sitting there going, "Okay, teach me something. I'm sitting here. I'm all ready to learn. Just connect me to something." You have this much easier facility for learning because of the exercise you've been doing. And exercise coupled with sleep, sleep is also a time when your brain practices with what you've been learning. And so we can see how little electrical signals are involved in whatever you've been learning, your brain during sleep will ripple through and practice with those things. Especially if right before you go to sleep in those couple of minutes, bring to mind the problem you're trying to solve or the new concept you're trying to learn or understand. Bring that to mind and that will signal your brain, that's the area of the brain that I want to really kind of activate during sleep.
And it will indeed have these little ripples going through, practicing, and you will wake up more likely to have figured out the problem you were trying to solve or to have better learned the new concept you've been trying to learn. So, both exercise and sleep are profoundly important. If you also add in a good diet, you're really packing a great set of encouraging practices for your learning. By good diet, everybody has really different ideas about what a good diet is, but there is good evidence that if you eat plants that are in the onion family, the cabbage family, eat nuts, and also chocolate, surprisingly enough, that hasn't been processed too highly. So, you want dark chocolate that's not processed highly. All of those things seem to contribute nicely to a healthy sort of brain diet that can allow you to learn more easily and effectively.
Recommended talk: Thinking Fast and Slow • Linda Rising • GOTO 2019
Deliberate Practice and Psychological Safety in Team Management
Charles Humble: I want to talk about deliberate practice a bit because as someone who's managed teams a lot professionally, I'm interested in your thoughts on how you might apply deliberate practice to the business of managing people.
Barbara Oakley: You know, I think there's a lot of different ways that you could take an answer to that. So, deliberate practice is often thought to entail, like, doing the hardest thing when you're learning. So, this often involves retrieval practice. I mean, think about it. If you're trying to learn something, you might... I mean, what's the easiest thing to do? Draw a line, you know, with a highlighter over what you are trying to learn. You don't actually learn much by doing that, but it feels so good. I mean, it's easy, you're making a motion, and surely you must be learning something. You're not, actually. And it's amazing. Sometimes you'll look at people's books and they're highlighted left and right. But if you ask them questions about what the content of the book is, that's when they begin to struggle.
So, retrieval practice is a really good way of like, you read a chapter, you look away, see if you can retrieve those key ideas from your own mind. This is deliberate practice. It's doing the hardest form of learning. And yet that is what's going to save you the most time. You can reread a dozen times. It feels more and more comfortable each time, but you're kind of wasting time when you're doing that. Deliberate practice means doing the hardest thing, but also learning the most quickly. Now, how do you apply this when you are working with groups? I suppose there's several ways you can take that. Number one is you can encourage your groups when they are learning to use deliberate practice. But let's look at it from a different perspective. If you want your team to be functioning more effectively together, might deliberate practice involve allowing your team to not necessarily be agreeable with one another?
I mean, that's a little harder to do where you often say, "We want our teams to be... Everyone is working nicely together." But it turns out that teams that work really, really nicely together, where everyone's really nice, are often not very creative. So, perhaps a form of deliberate practice could involve encouraging teams to really dig in. And if there's something that is a bit uncomfortable to speak about, perhaps it might involve encouraging them to do that. Because the truly creative teams are the ones who will talk about the kinds of things that need to truly be addressed, instead of making everyone feel comfortable, which is easier, kinder, softer, and not truly as effective.
Charles Humble: Yes, there's an interesting thing that comes up for me there, which is to do with psychological safety, which is one of those terms that in my industry gets bandied around all the time, without necessarily much of an understanding of what it means, I think. And I think often people take psychological safety to mean, "We're just all terribly nice to each other." But that's not really what it means. Really what it means is, we're respectful, but we're able to challenge people when we think somebody's wrong, and particularly when that feels a bit uncomfortable in a way that feels safe and measured. But as I say, I think it's one of those things that gets misunderstood a bit. And most of the very effective teams I've been on, or the more effective teams I've been on, that ability to sort of challenge people is a very important part of it, I think.
Barbara Oakley: What a perceptive comment. Because sometimes I find that when I'm around teams, where they say, "Okay, and this is a safe environment, we will all be very safe in our interactions," this red flag immediately goes up. And it's like, "Well, then it's a totally unsafe environment because you're telling me that if I say something that is not sort of safety-oriented, I'm going to get in trouble." So, it's a red flag that it is exactly opposite of what you are saying that the team environment should be like. But of course, I'm a bit of a curmudgeon. So, perhaps there was that as well.
Recommended talk: Insights on Leadership & Innovation • Gene Kim & Charles Humble • GOTO 2024
The Role of Working Memory vs Long-Term Memory in Learning
Charles Humble: I want to shift to talking a bit about the role of memory. So, in your keynote, you talked about the octopus of attention, which was a fabulous metaphor, with regards to working memory. And actually, that made me think I must be wrong about my understanding of working memory, because your metaphoric octopus, if I remember rightly, had four arms. And I was sure I'd been told at some point that working memory had seven slots in it. But anyway, it also got me thinking, what's the role of working memory versus long-term memory in terms of learning?
Barbara Oakley: So, you are indeed correct. When George Miller first conceptualized working memory back in the '50s, he thought, it looks like there's about 7 slots, in other words, 7 pieces of information that we can hold in working memory. Now, more recent work by Nelson Cowan and others says, you know, it looks like seven things, but actually we can group numbers together. And perhaps four might be a better number. And in fact, there is some evidence that perhaps we can only hold two things in working memory. But all of this is really a way of fiddling around with actual neuroscience. Even so, first, they thought, it's like seven slots, seven pieces of information you can put in working memory. Now, I have four arms of a quadropus, instead of an octopus, that can reach into long-term memory. But the reality is that there are theta waves that they serve as a carrier within like the front to the back of the brain. And if we're carrying information, we're like, we're holding information in mind, there's little gamma waves that ride on the carrier of the theta. So, the theta is, like, from 4 to 8 hertz, the gamma is maybe in the 25-hertz range. It rides right on top.
And interestingly, you can hold about four gammas on a theta wave. That's about, like, four pieces of information. Perhaps there's a relationship there. But there's wonderful work being done now in, we're moving well past that idea of, you know, we've got working memory and we've got, you know, like the visual-spatial sketch pad and the audio sort of file. We're moving way past that, and we're looking at the carrier waves within the brain itself and, you know, how we use them to manipulate our memory. But sorry about that. I'm going on into a little bit of the weeds. For our purposes, working memory is what we can temporarily hold in mind. So, for bilingual individuals, when they're holding a sentence in one language in mind, while they're translating it to another language, that uses their working memory. It's that temporary. Long-term memory is in the neocortex, scattered around, and it is where we're holding information that we can, you know, remember for a long time. There is an intermediate memory point, and that involves the hippocampus.
The hippocampus reinforces it, and can serve as, like, an index for long-term memory. So, our working memory works with that hippocampus to send information to long-term memory. And here's the interesting thing. If you practice what is called the Pomodoro Technique, this was invented by an Italian in the 1980s. What it involves is setting aside all, like no pop-ups on your computer, nothing to, you know, pop up on your cell phone or anything like that. So, set everything aside, focus as intently as you can for 25 minutes, set a timer for that, and then when you're done, take a 5-minute break where you are not using your... You know, not looking at your cell phone or anything like that. Truly a five-minute break where maybe you're getting a cup of tea or something, but mostly just space out. What this does is as long as your working memory is feeding information into the hippocampus, the hippocampus is going, "Yeah, yeah, yeah, okay, I got it, I got it." And then you stop feeding information.
What the hippocampus can do is turn around to long-term memory and go, "Hey, you guys, this is the important stuff that we've been learning." But if you're looking at your cell phone instead of letting your hippocampus do its magic, you're not going to be reinforcing that information that you just learned. So, working memory, long-term memory, and that hippocampus, all play important roles in learning. And here's the wicked thing. That hippocampus has got really flexible connections and they can form very quickly. So, let's say you cram the night before a test. Well, you make these connections with your hippocampus and then they form really quickly and you think, you aced the test, you do really well, and they melt away right afterwards. It's easy come, easy go, and you can't remember later what you've learned. So, this is why cramming is such a bad idea. Whereas spaced repetition, if you have five hours in a week, space out one hour per day over five days, spaced repetition using retrieval practice is a great way of learning.
Charles Humble: What actually gets stored in long-term memory?
Barbara Oakley: So, that is something that is bandied about by neuroscientists. It is believed that a memory involves an engram, what is called an engram, and it isn't stored in one neuron. It is stored in the connections between a cluster of neurons. So, you can think of it as a bit like a computer chip where some bits are on, some bits are off. It's actually the connections that are turning on and turning off those bits, is how we store information in computers. And in the mind, we are making connections or not making connections between neurons, and it's those connections within that cluster of neurons that seem to form the memory.
Charles Humble: That's absolutely fascinating. We started this sort of section talking about your octopus metaphor. So, can you talk a bit about the value of the metaphor more generally?
Barbara Oakley: The metaphor is, I'm hoping that someday I will find someone who is really interested in word embeddings, because the reality is, in the mind, when you got a concept that you understand, what that is, is a cluster of connected neurons. Once you've got that cluster of connected neurons for that one concept, that can help serve as a bridge, in part using some of the same neurons to help a person understand a new but related concept to that initial concept. And you can kind of get a sense, a physical sense. Well, if you've got this cluster of neurons and then they are somehow like if you have the flow of water, understanding how water flows, the current of water, if you've got that concept in your brain, then you can use some of the same neurons and connections to build into the idea that, the flow of electrical current is quite similar to the flow of water. Now, of course, we know at a quantum level that's not true, but still, to get a student started in understanding what electrical current is, that's a great metaphor to be using. If you look at computer science, I mean, computer science is like virtually every term you use is a metaphor for something else.
Charles Humble: Right. It's...
Barbara Oakley: You've got dashboards and branches and all this kind of stuff. It's all metaphors and it's metaphors that have helped us more quickly grasp key ideas.
Recommended talk: AI Assistance Beyond Code: What Do We Need to Make it Work? • Birgitta Böckeler • GOTO 2024
Using AI for Efficient Learning and the Future of Education
Charles Humble: What's the best way to improve your understanding of a subject?
Barbara Oakley: Let's say that I just got this 80-page paper on a new subject that I know nothing about, maybe in generative AI. The first thing I'd do is I'd feed it into Claude.ai, I'd say... And I actually have something called a text expander. So, the text expander I like to use is called Abrev8, and what it is, is I type ..c, and it instantly turns it into a sentence that says, "Please provide me with a three-page document in layperson's terms that explains the key ideas of this research paper and put the key different elements of your analysis in bold." So, I've got bold headers, I can really look at this thing. I've got an 80-page document, I will get sort of my essay, I'll load it on Claude, type ..c, it will use the text expander to tell it to create a nice sort of walkthrough of the key ideas of the paper.
I will read that, I'll get in mind what are those key ideas of the paper. This serves as, like in a closet, you don't want to just throw stuff in your clothes in a closet, you want to put it on hangers, and getting an idea of the key elements of this paper serves like hangers in a closet. I know what the key ideas are, now, I can hang my thoughts on those key ideas. Then I will begin reading and I will use something called I do recall and I can upload this paper onto that. It will generate retrieval practice questions for me, of all the key ideas within this paper. And then I will begin studying that, looking at that, reading the paper, getting into my mind, remembering those key ideas and that's how I would tackle learning something that was difficult, that was completely new to me.
Charles Humble: There's something fascinating to me about this because in general amongst the teacher friends that I have, most of whom would be in secondary school in the UK, so sort of 13 to 18, that kind of age range. Most of them are very anti-AI in an academic sense. And so I find it really interesting that you seem not to be, you seem to be using AI in your own work and be quite positive about the effects that it can have for education and learning in a way that seems quite counter to a sort of prevailing narrative, at least where I am in the UK.
Barbara Oakley: A challenge is that we never teach students how to learn effectively. I mean, is it not insane that students can go from 10 to 18 years of education and never have a single course in how to learn effectively? I think the next big revolution has to involve teaching students how their brain learns. Teachers are currently afraid of generative AI because they think it might take their jobs. And also it has just pulled the rug out from under them in that it's, you know, all the ways that they've used before to check and see whether students have learned things by, for example, doing an essay or completing a homework assignment. Those are no longer really feasible. Because students can cheat so easily. So, I think from my perspective, I can understand why teachers are so wary of generative AI, but it's really they're wary in part because they're worried about it and also because it's made their life more difficult. But the reality is generative AI is here to stay.
And if we hold it at an arm's length, we're actually doing students a disservice, and we're doing ourselves in our profession a disservice. Because when students go out in the working world now, they're going to be expected to know how to handle gen AI. The important idea here is that teachers are all the more important now in this day and age of generative AI. We have to ensure that students are still learning. And like in the 1930s through '70s, IQ scores rose, and they rose in part, it's thought, because of good teaching techniques. After the 1970s, a lot of research is beginning to show that IQ scores are declining. What happened in the '70s? Well, correlation is not causation, but there's some pretty interesting evidence that when calculators came out in the 1970s, suddenly educators and psychologists began saying, "You don't need to remember things, you can always just look it up." We have to be wary of avoiding that same sort of idea now that generative AI is out there.
Just because it can do things for us, doesn't mean that we should be only teaching things to students that generative AI can't do, because guess what? It can do most things. So, it's really important for teachers to do what they do, but also to learn to embrace the benefits of generative AI, because it'll make their jobs easier. You can ask for great ideas for metaphors to help you explain good ideas. You can ask for good ideas to help you hook your students, get them motivated, get them more interested. If you're learning some aspect, you're on the job and you're super bored with this new course that they want you to take and you can't stand the subject, go ask generative AI for some ideas to help motivate you and get you excited about it, to help you be curious, and that will actually help you learn better. So, it's hard to swivel around and embrace something that is also creating problems, but it's something that we can do and that will inevitably happen anyway. So, go for it.
Ongoing Mysteries in Brain Research
Charles Humble: Thank you. I've got a couple more things I'm dying to ask. One of them, which I've been curious about for years and don't know the answer to, is why do we get rusty. I play music in my spare time and if I haven't played for a while, I'm kind of terrible, and then it comes back and it comes back fairly quickly, but there's this period of time that's like, I can't, it just doesn't work. What's going on there? What causes that?
Barbara Oakley: So, when you create that engram, that memory, it's got little dendritic spines, those spiky things, you know, that are coming out of the dendrites, and those can kind of wither away if you haven't used them. So, like, my ability to speak Russian, which is what I've learned, is pretty rusty now. It's been 40 years, although I will say that when I have a glass of wine, it begins coming back more quickly. So, there's that. But it's just use it or lose it. Those connections will wither away if they're not used, but it can be easier to bring them back if you at least have already had some knowledge of that area.
Charles Humble: Thank you. And maybe one last question. What are some of the areas that are less well understood in terms of how the brain works?
Barbara Oakley: So, many things. It's amazing. One weird thing is, like, I've been talking about those neural connections of an engram, but one thing we don't understand, it looks like there's evidence that engrams move around, and they don't necessarily stick together. So, you can have an engram, it's within this one cluster of neurons, but you look a couple of days later, and they've kind of, like, migrated to some different neurons. What the heck is going on there? And for example, something I'm intensely curious about, autism. So, it looks like with autism, there may be several sorts of different types, and it may be that one type affects that automatic learning system of the basal ganglia, while the other may affect that hippocampus. I would love to see much more definitive research on what is going on with autism, what's going on with dyslexia. These are very common conditions, and if we can elucidate what is happening better with the brain, it will really help us. There are so many questions. It's like every answer we get is like a dozen more questions.
But I have to say, Terry Sinowski, my co-instructor, they were trying to figure out, they got this enormous wad of money from the government to help promote research involving the brain. So, they all argued about it. It was like 25 people on this panel, and who gets the money, why? And everybody was like, "I want the money for my research." And they finally decided, no, they weren't going to give it to a person for their research. They were going to put it into the development of new tools. And that's what they did. And now the whole field of neuroscience is blossoming because they've got tools that can allow them to really see in areas that they haven't been able to see before. So, the next decade, a couple of decades, are going to be amazing in the kind of discoveries we are going to be seeing because of the development of these new tools due to these very visionary researchers.
Charles Humble: That's fantastic. Barbara Oakley, thank you so much for spending time talking to me today for this episode of theGOTO Unscripted podcast.
Barbara Oakley: Well, thank you, Charles. And it was deeply unscripted. So, it was a pleasure.