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Speaker 1: Hey, Welcome to Stuff to Blow your Mind. My name

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Speaker 1: is Robert Lamb and I'm Joe McCormick, and it's Saturday.

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Speaker 1: Time to go into the vault. This episode originally aired

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Speaker 1: on June eleventh, nineteen, and it was called Brain Soup

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Speaker 1: and Liquid Brains. Uh. I don't know what to say

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Speaker 1: about it beyond that it's about liquid brains. Huh. Yeah, yeah,

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Speaker 1: I mean it's it's there is kind of this um,

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Speaker 1: you know, yucky horror vibe to our sort of selling

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Speaker 1: of the concepts certainly, but it really gets into like

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Speaker 1: what we can figure out via the liquefocation of brains

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Speaker 1: um regarding like the nature of intelligence in an organic brain.

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Speaker 1: This one was one that I was inspired to pick

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Speaker 1: up after the twenty nineteen World Science Festival in New York.

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Speaker 1: So yeah, so yeah, it's a pretty good one. Let's

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Speaker 1: jump right in. Welcome to Stuff to Blow Your Mind,

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Speaker 1: a production of I Heart Radios has to ports. Hey,

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Speaker 1: Welcome to Stuff to Blow your Mind. My name is

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Speaker 1: Robert Lamb and I'm Joe McCormick. Joe, what what kind

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Speaker 1: of image enters your head when I say the words

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Speaker 1: brain soup? Brain soup? That's a Green Day song. Isn't it?

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Speaker 1: Is it? I'm having trouble making soup. I don't know.

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Speaker 1: I'm not I'm not I'm not not dukie, right, I

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Speaker 1: only know like green Green Day songs. I think I'm

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Speaker 1: getting this wrong. H No. I also think of there's

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Speaker 1: a horrible scene in Indiana Jones and the Temple of

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Speaker 1: Doom where they're they're eating all kinds of weird foods

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Speaker 1: that are parts of animals that you shouldn't eat, but

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Speaker 1: why not. But one of those parts is they eat

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Speaker 1: a monkey brain. I don't think that's supposed to be soup.

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Speaker 1: By the way, one of my favorite things on the

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Speaker 1: Internet is a total tangent is uh is franchise specific

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Speaker 1: WICKI entrees for inanimate objects. Allow me to explain, So,

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Speaker 1: there is like an Indiana Jones wiki that's just a

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Speaker 1: website out there. It's got entries on it for all

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Speaker 1: the characters minor characters in Indiana Jones movies, all the

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Speaker 1: adventures he went on. But then also they're just entries

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Speaker 1: because it's a wiki of just objects that appear in

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Speaker 1: the Indiana Jones movies. So this will include like individual

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Speaker 1: pages for each course of the meal in Indiana Jones

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Speaker 1: and the Temple of Doom, and one of them, the

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Speaker 1: one that's got all the snakes in it, is called

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Speaker 1: coiled Wrigley's. So it is it just it's just about

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Speaker 1: a fictional food, or is it attempting to draw lines

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Speaker 1: between these outrageous and you know, like shock oriented foods

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Speaker 1: and in some actual traditions, because that if they're not

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Speaker 1: doing it, that would actually be kind of a fun

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Speaker 1: thing to do to sort of take the shock that

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Speaker 1: they're that they're trying to to utilize in that scene

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Speaker 1: and say, we'll hold on. Actually, here are some actual

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Speaker 1: culinary traditions that are not really that shocking. Uh no,

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Speaker 1: it's not at all that informative mind opening. Just let

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Speaker 1: me allow me to quote directly from the page for

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Speaker 1: coiled Wriggles. Coiled Wriggles, also known as snakes Surprise, was

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Speaker 1: a dish served at the Guardian of Tradition dinner given

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Speaker 1: at Pancock Palace in n five As the second course.

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Speaker 1: It was live baby eels stuffed inside a moist boa constrictor.

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Speaker 1: One of the guests at the dinner, a merchant, was

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Speaker 1: very pleased when the dish was served. Another guest enjoyed

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Speaker 1: the eels with great gusto. I have not seen that

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Speaker 1: film in a very long time. I really don't know

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Speaker 1: how it would hold up. Some elements certainly do not.

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Speaker 1: You know, I should point out that, like you know,

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Speaker 1: talking about brain soup, actual brain soup does exist. Do

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Speaker 1: you have you know, various pork brain soups, etcetera. And

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Speaker 1: even though I'm a I'm a pet scutarian these days,

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Speaker 1: I wouldn't order pork brain soup at a restaurant. I

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Speaker 1: looked at some photos and it looks perfectly delicious and

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Speaker 1: not at all weird. Uh So I just want to

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Speaker 1: drive that home regarding the consumption of of brain based

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Speaker 1: soups before I bring up, uh something that comes to

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Speaker 1: my mind when I when I hear brain soup and

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Speaker 1: what came into my mind when I heard it recently,

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Speaker 1: I instantly think back to the film. It came from Hollywood. Oh, yes,

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Speaker 1: so this was this is a film. It's it's I

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Speaker 1: think you can watch most of it on YouTube these days.

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Speaker 1: But as Dan Ackroyd, John Candy Cheech and Chong Gilda Radner,

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Speaker 1: that whole crew, and sort of before Mystery Science Theater,

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Speaker 1: there was this movie it was just a clip show

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Speaker 1: of like bad sci fi films and stuff, with the

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Speaker 1: hosts of the movie making jokes about them. Yeah, and

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Speaker 1: so a lot of trailer clips, a lot of weird clips.

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Speaker 1: And there's a whole section that I that I loved

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Speaker 1: as a kid about brains. You know, all these weird

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Speaker 1: fifties and sixties brain monster films, which which I think

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Speaker 1: are probably expressing a certain amount of anxiety regarding like

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Speaker 1: post war advances in neuroscience and the sort of existential

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Speaker 1: conundrums that are raised. You know, like, am I just

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Speaker 1: a brain? What does it mean to be just a brain? Well,

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Speaker 1: a brain jump out of someone's ed and attacked me

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Speaker 1: with its tentacles. There was a lot of revolutionary neuroscience

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Speaker 1: going on in the nineteen fifties, and yeah, I can

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Speaker 1: see exactly why people would be concerned about this kind

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Speaker 1: of thing, though some of them are just more like

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Speaker 1: I think there's one called like the Brain from Planet

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Speaker 1: Rouse or something that, yeah, just giant brains swarming at people.

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Speaker 1: So I don't know exactly how much that plays on

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Speaker 1: anxieties about neurology, but I mean I get that what

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Speaker 1: I really get a sense of it when I see

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Speaker 1: clips from Fiend without a face because as we have

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Speaker 1: the fabulous stop motion brains and kind of like spinal

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Speaker 1: column tentacles people, yeah, all over the furniture. We just

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Speaker 1: sprayed for brains last week. But anyway, this particular segment

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Speaker 1: on brains was introduced by Dan ackroyd Uh and he's

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Speaker 1: he's he's in this this scene. It's like he's in

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Speaker 1: a butcher's shop and he's playing a maniacal Perhaps he's

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Speaker 1: supposed to be like a mad brain scientist, or a

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Speaker 1: brain butcher, or like a brain addict cannibal. It's it's

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Speaker 1: kind of hard to say, it's not entirely clear, but

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Speaker 1: it's it's silly and it's gross, and it's I was

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Speaker 1: tempted to play a clip from it here, but it's

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Speaker 1: just so over the top that I don't I don't

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Speaker 1: think it would translate well to an audio only environment.

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Speaker 1: You are correctly capturing here the strangeness of the premise

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Speaker 1: for this sketch in the movie. I don't just blithely

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Speaker 1: throw around the phrase cocaine fuel. But there there are

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Speaker 1: some cases where you get you get a suspicion. But

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Speaker 1: also in that scene, I remember Dan Ackroyd is doing

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Speaker 1: a voice that sounds a lot like a Monty Python voice.

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Speaker 1: It's he sounds like the leader of the Knights who

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Speaker 1: say knee, Yes he does. It's that kind of like

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Speaker 1: ridiculous cartoonish voice. Uh yeah, the whole segment is is fabulous,

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Speaker 1: all right, But all this sort of these sort of

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Speaker 1: visceral reactions to the idea of brain soup. Aside, we

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Speaker 1: are not going to be talking about eating brains today

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Speaker 1: on the show. We're going to to talk about the

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Speaker 1: very real practice, the scientific practice of making brain soup

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Speaker 1: to better understand the neural power of animal brains. Well,

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Speaker 1: I am very intrigued already now, Robert, I know this.

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Speaker 1: The subject is not coming out of nowhere. This is

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Speaker 1: inspired by something that you just saw when you were

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Speaker 1: at the World Science Festival in New York City last week.

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Speaker 1: That's right, Yeah, I just got back and uh yeah.

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Speaker 1: This is held every the last week of May every year,

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Speaker 1: and as usual I got to hear some of the

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Speaker 1: world's leading scientists and thinkers discussed a number of exciting topics,

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Speaker 1: and one of the talks I attended was rethinking thinking

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Speaker 1: how intelligent are other animals? This is always a great

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Speaker 1: subject and something we've revisited quite a few times on

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Speaker 1: the podcast before. One time we talked with friends devol

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Speaker 1: here on the podcast in an interview about his book.

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Speaker 1: His book, I remember had a kind of awkward title.

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Speaker 1: It was something it was something like, are we smart

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Speaker 1: enough to know how smart animals are? Doesn't really roll

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Speaker 1: off the tongue. But it's actually a really good book. Uh.

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Speaker 1: And and this has come up with reference to the

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Speaker 1: intelligence of birds, and and quite a few cases. Actually, yeah,

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Speaker 1: and so this particular talk was loaded with interesting participants,

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Speaker 1: but the participant that got me thinking about brain soup

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Speaker 1: was Susanna or Kolana Uzel, PhD, biologist and neuroscientists at

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Speaker 1: Vanderbilt University in Nashville, Tennessee, where she is Associate professor

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Speaker 1: in the Department of Psychology and Biological Sciences, and her

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Speaker 1: research focuses on what different brains are made off. Now, Robert,

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Speaker 1: I envy you getting to see a panel that involved her,

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Speaker 1: because I've actually watched a TED talk that she did

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Speaker 1: back from I think it was twenty thirteen. She's she's

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Speaker 1: a fantastic public speaker, So yeah, she seems like a

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Speaker 1: really good public science communicator. Yeah, Like when I say

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Speaker 1: you know that you have a panel of of leading

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Speaker 1: scientists there on the stage, like, don't don't imagine something stuffy,

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Speaker 1: because you often have some just really wonderful science communicators

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Speaker 1: up there that you know, are experts in their field,

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Speaker 1: but are also able to talk about that in a

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Speaker 1: way that people at you know, various levels of expertise

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Speaker 1: can can jump in on. And that's certainly the case

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Speaker 1: with Susanna her Colono Ozel. Now I do want to

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Speaker 1: mention you're probably you're hearing this last name. Sometimes you

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Speaker 1: will hear people pronounce it her colono hose l um.

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Speaker 1: If you're trying to look it up, it's h e

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Speaker 1: r c u l a n O dash h o

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Speaker 1: u z e l. Trying to to do the name

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Speaker 1: justice here with our bungling mouths. So, as she tells it,

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Speaker 1: her Colona Hoose was working as a science educator um

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Speaker 1: in Rio de Janaro, Brazil, and uh and she kept

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Speaker 1: running into an old myth that I'm sure many of

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Speaker 1: you have run into before as well. Now this is

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Speaker 1: not when we use the word myth too. In the

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Speaker 1: non derogatory sciences foundational story maybe involving supernatural elements. Yeah,

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Speaker 1: this is the derogatory myth, and that myth is we

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Speaker 1: only use ten percent of our brain. And this has

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Speaker 1: been used really overused in I FI movies, especially over

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Speaker 1: the years, as recently as Teens Lucy and twenty eleven

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Speaker 1: is Limitless Um, which by the way, decided to double it,

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Speaker 1: but still but still stuck to this idea that there's

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Speaker 1: only a small percentage of the human brain that is available. Uh.

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Speaker 1: And of course, all these these films and pretty much

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Speaker 1: anytime you see this trouted out, it's it's with the

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Speaker 1: idea like there's there are ways to open up the

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Speaker 1: rest of the brain. There's like this dormant brain. Uh,

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Speaker 1: these dormant brain portions that can be utilized if only

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Speaker 1: you have the key. And the key, of course is

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Speaker 1: left of the cage, right, and it's supplied by you know,

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Speaker 1: some sort of mad science. If you're in a fiction

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Speaker 1: or if you're encountering it saying some sort of self

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Speaker 1: help scenario, then they're going to sell you the key

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Speaker 1: or yeah, if you're encountering it, and uh, it's say

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Speaker 1: nutritional supplement marketing. I mean, this is a big thing

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Speaker 1: out there. Yeah, but we say it's a myth because

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Speaker 1: and we'll break this down a little bit here, but

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Speaker 1: but basically, there's nothing to these numbers. Now, we we

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Speaker 1: all use of our brains. Well, when you instart, when

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Speaker 1: you start to look at the claim, it becomes less

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Speaker 1: and less clear what it even means. What does it

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Speaker 1: mean we only use ten percent of our brain at

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Speaker 1: a time? Do we only use ten percent of it

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Speaker 1: ever in our whole lives? Like what even counts as

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Speaker 1: quote using the brain in the first place. Yeah, I

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Speaker 1: feel like we've talked about about neuroscience on the show enough,

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Speaker 1: you know, and talking about various f m r I

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Speaker 1: S studies and like what parts of the brain are

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Speaker 1: lighting up, what parts of the brain, and what networks

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Speaker 1: of the brain are associated with different thoughts and behaviors, etcetera.

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Speaker 1: That it's it's clear that like there's there is definitely

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Speaker 1: a lot of stuff in play, and that virtually and

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Speaker 1: then really everything is in play. Um I guess, I

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Speaker 1: guess the confusion could be if you're looking at various

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Speaker 1: images of fMRI I s and you're thinking, oh, well,

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Speaker 1: that looks about like ten percent. That looks about like

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Speaker 1: ten percent. That's a really good point when people talk

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Speaker 1: about f m r I studies, sometimes they will loosely

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Speaker 1: and casually try to explain the findings of an fMRI

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Speaker 1: I study by saying, hey, when somebody was doing this

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Speaker 1: to ask with their brain, you know, they were trying

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Speaker 1: to tie a knod or they were you know, whittling

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Speaker 1: a whittling a horsey out of soap. When they're doing

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Speaker 1: this task, this part of their brain lights up. That's

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Speaker 1: the phrase this lights up, which implies to the entire

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Speaker 1: rest of the brain is dark until it just lights up,

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Speaker 1: as if it's like totally dormant. And that's not true.

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Speaker 1: I mean brain imaging studies like you know, PT scans

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Speaker 1: and fm r I that they show relative activity, so

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Speaker 1: that they're charting activity and seeing what areas, uh get

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Speaker 1: get surges of activity relative to what they were doing

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Speaker 1: at other times. Right there, the areas that are not

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Speaker 1: lit up are not lifeless. Right. But I mean, even

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Speaker 1: back to this, I want to know again what what

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Speaker 1: would actually mean to use your whole brain or to

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Speaker 1: use certain percentages of your brain. I was reading around

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Speaker 1: about this and I found one pretty interesting thing. So

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Speaker 1: the neuroscientist Gabrielle and Torrey. I think she's at Boston

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Speaker 1: University now. She wrote an interesting post about this myth

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Speaker 1: that I found on a website Knowing Neurons, and in

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Speaker 1: one section of her article, she discusses difficulties determining what

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Speaker 1: it would actually mean to use quote a hundred percent

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Speaker 1: of your brain, Like, there's no single way to measure

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Speaker 1: what portion of the brain is being used at any

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Speaker 1: given time. The default mode network, of course, is in

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Speaker 1: operation throughout the brain pretty much all the time. But

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Speaker 1: does that not count as the brain being used? I

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Speaker 1: mean that even when you're at rest, the default mode

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Speaker 1: network is whirring away all throughout the brain. Ye, quite

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Speaker 1: unfortunately in some cases. In some case I don't know

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Speaker 1: if you'd want to be without your default Well now

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Speaker 1: I wouldn't want to give it up, but it it

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Speaker 1: is kind of thorn in my side at times. It

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Speaker 1: certainly can be. But then interesting, I really like this part.

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Speaker 1: So she tries to take this claim seriously, like this

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Speaker 1: ten percent of the brain is being used claim, and

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Speaker 1: she's like, what would that actually mean? Well, she offers

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Speaker 1: one interpretation of what it would mean to use a

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Speaker 1: dred percent of your brain, and it would mean a

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Speaker 1: grand moll seizure, So it would be like limitless except

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Speaker 1: Bradley Cooper takes a magic and it just makes his

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Speaker 1: brain like like we almost explode. Yeah, exactly. So, to

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Speaker 1: read from tories where quote, seizures are defined by excessive

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Speaker 1: and synchronous neural activity. If we wanted to use a

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Speaker 1: hundred percent of our brains to stimulate each of the

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Speaker 1: brains one hundred billion neurons, this is funny. We should

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Speaker 1: come back to this in a second, to maximum capacity

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Speaker 1: firing would result in a likely fatal physical experience. To

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Speaker 1: hope for synchronous exit. Tory activity across the cortex is

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Speaker 1: in many ways synonymous to a Grand Mall seizure. Uh.

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Speaker 1: This is the most severe type of seizure and leads

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Speaker 1: to loss of consciousness and severe muscle contractions, not the

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Speaker 1: unlocking of superhuman abilities. So I think obviously we don't

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Speaker 1: just want universal synchronous patterns of excitation of every neuron

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Speaker 1: in the cortex. That's stupid. That's not something to wish for, right.

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Speaker 1: It's like, you don't want the brain working that hard universally.

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Speaker 1: It's like you don't want your your you don't want

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Speaker 1: your xbox to be clearly just just heating up and

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Speaker 1: that the fan is about to explode like that, you know,

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Speaker 1: something's wrong with it. Yeah, my CPU is constantly And

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Speaker 1: she also mentions the default mode network. You know, it's

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Speaker 1: this diffuse, interconnected set of activity notes that show activation

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Speaker 1: when the brain is otherwise at rest. You know, it

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Speaker 1: shows the brain is pretty much never dormant. There's activity

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Speaker 1: throughout most of the brain most of the time. So

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Speaker 1: where did this clearly false claim come from? The answer

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Speaker 1: actually isn't known for sure, but I found one commonly

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Speaker 1: cited early example is a quote falsely attributed to the

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Speaker 1: American psychologist William James, the author of a great classic text,

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Speaker 1: The Varieties of Religious Experience, which is still interesting to

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Speaker 1: read parts of today. Yeah, we've cited him a few

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Speaker 1: different times on the show. Yeah, but apparently James did

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Speaker 1: write something generically kind of like this, but without a

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Speaker 1: number sited uh, in a piece called The Energies of Men,

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Speaker 1: where you wrote, quote, we are making use of only

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Speaker 1: a small part of our possible mental and physical resource

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Speaker 1: is which is a very different kind of statement, right. Yeah,

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Speaker 1: he's not putting a fine number on it or saying like,

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Speaker 1: you know, I mean that that statement alone is not

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Speaker 1: not implying that the portions of the brain are inactive,

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Speaker 1: just that, you know, we're not taking full advantage of

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Speaker 1: our neurological potential. And I want to mention a bit

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Speaker 1: more about that in a second. Uh So, as to

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Speaker 1: the origin of the phrase, the ten percent figure is

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Speaker 1: also cited in the nineteen thirties, and this has been

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Speaker 1: pointed out by quite a few investigators by the author

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Speaker 1: of an introduction to Dale Carnegie's classic self help book

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Speaker 1: How to Win Friends and Influence People, which possibly little

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Speaker 1: known fact Charles Manson's favorite book Really Yeah, a classic

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Speaker 1: of business leaders and serial killers alike. But this explains

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Speaker 1: it a bit though. Right We have here an introduction

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Speaker 1: to a widely read popular book. Yeah, and the author

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Speaker 1: of the introduction says, we only act, you know, we

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Speaker 1: only use ten percent of our brains. The vast amount

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Speaker 1: of our mental potential is untapped. And I mentioned this

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Speaker 1: minute ago, but I just want to hammer home again.

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Speaker 1: I feel like I've seen this nonsensical ten percent fact

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Speaker 1: invoked by people selling some of the various like brain

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Speaker 1: booster pills and supposed new tropics supplements. Uh. I would

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Speaker 1: advise people to be cautious about that kind of stuff.

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Speaker 1: And I'm not ruling out the possibility that there are

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Speaker 1: some nutritional supplements or drugs that might in some cases

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Speaker 1: have a small effect on mental performance. But if somebody

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Speaker 1: is trying to sell you pills that will unlock the

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Speaker 1: untapped godlike potential of the you know, the paid access

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Speaker 1: part of your brain, the brain is premium content. As

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Speaker 1: a general rule, be suspicious of this. I would I

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Speaker 1: would advise in general, don't trust them and don't give

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Speaker 1: them your money, because they're certainly tapping into something I

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Speaker 1: think we all know to be true, and we all

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Speaker 1: certainly want to be true, the idea that there is

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Speaker 1: room for improvement exactly know that we can learn new things,

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Speaker 1: that we can we can take on new patterns, and

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Speaker 1: all of these things are true. You don't need a

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Speaker 1: phony number to back that up, exactly right. I mean,

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Speaker 1: I think a large part of the basis for false

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Speaker 1: fact about ten percent lies not in how much of

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Speaker 1: our brains we use as a as a ratio, but

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Speaker 1: in the way that we use our brains. And the

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Speaker 1: most salient example that comes to my mind is the

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Speaker 1: psychologist Daniel Konomon is really useful metaphors of system one

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Speaker 1: versus system to thinking. You know, so system one we've

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Speaker 1: discussed on the podcast before, but system one is a

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Speaker 1: suite of human brain functions that allow us to process

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Speaker 1: information in a fast, easy, automatic, and approximate way. It's intuition,

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Speaker 1: so you know, think rules of thumb, stereotypes, intuitive and

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Speaker 1: reactive thinking eyebawling it. Meanwhile, system two is described as

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Speaker 1: the slow, deliberate, effortful thinking, the kind of reasoning you

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Speaker 1: use when you make a list of pros and cons,

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Speaker 1: or you solve a math problem, or you carefully plan

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Speaker 1: a route of movement somewhere, or you effortfully trace out

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Speaker 1: the logic of an argument. We're all capable of both

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Speaker 1: kinds of thinking, but we also rely on systum tim

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Speaker 1: one most of the time for most things. And that's

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Speaker 1: because of efficiency. Like you can't process all information in

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Speaker 1: a slow, effortful way you don't have time. And system

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Speaker 1: one is not all bad in some tasks. I think

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Speaker 1: it might actually be superior. Like consider the way that

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Speaker 1: overthinking some athletic tasks like shooting a basketball actually makes

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Speaker 1: you worse at them, you know, like a lot of

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Speaker 1: players find that they sink more baskets if they just

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Speaker 1: try to relax and let the shot happen rather than

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Speaker 1: focusing on the distance and the angle and everything. And

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Speaker 1: I think this actually even comes through in some more

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Speaker 1: abstract tasks like sometimes I think people can be better

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Speaker 1: writers if they if they just kind of enter a

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Speaker 1: flow state and not sitting overthink over, deliberative lee about

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Speaker 1: the sentences they're constructing. This is very interesting and hopefully

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Speaker 1: this won't be too much of a tangent here, but um,

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Speaker 1: but it's also we have to be careful about thinking

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Speaker 1: about like system one a system to being just complete

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Speaker 1: like jackal and hides because actually the World's Science Festival,

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Speaker 1: another Penelty attendant had to do with risky behavior and

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Speaker 1: risk taking, especially in extreme sports UH such as say

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Speaker 1: free solo climbing UH and like free cloaks of solo

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Speaker 1: climbing is a great example because you have you have

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Speaker 1: certain individuals who definitely display more of a system to approach,

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Speaker 1: like they are methodical. They're they're not they're not climbing

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Speaker 1: at climbing that that that that that sheer cliff without

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Speaker 1: the aid of ropes, you know, without having practiced it

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Speaker 1: many times, without having climbed it many times with ropes

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Speaker 1: and thinking through several moves ahead, you see where you're

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Speaker 1: going to go. On the other hand, you have individuals

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Speaker 1: who do have more of an impulsive approach to risky behaviors,

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Speaker 1: risk taking activities. But like with the basketball, you can say,

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Speaker 1: well you can perhaps you're engaging in system too to

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Speaker 1: practice so that you can reach the point where you

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Speaker 1: can engage with the challenge with a system one mindset.

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Speaker 1: I think that's exactly right. So, yeah, system one is

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Speaker 1: not necessarily bad. It's not all bad. Sometimes it's bad.

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Speaker 1: And I think we're all aware of cases where we

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Speaker 1: have used fast, intuitive, reactive, likely inaccurate thinking when we

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Speaker 1: know that we probably could have and should have stopped

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Speaker 1: to think things out slowly and deliberately. Like you can

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Speaker 1: probably immediately think of examples where you really wish you

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Speaker 1: had switched over to system to thinking, but you made

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Speaker 1: a fast, intuitive decision and you came to regret it.

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Speaker 1: And I think it's this kind of frequently squandered mental

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Speaker 1: potential that becomes the nugget of truth in the ten

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Speaker 1: percent factoid. We use our whole brains, but we don't

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Speaker 1: always use our brains as effectively as we know we're

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Speaker 1: capable of in every scenario, because it's hard because we're

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Speaker 1: in a hurry. Does that make sense? Absolutely? Yeah, We we

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Speaker 1: we delive life. We have to engage both approaches to

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Speaker 1: our decision. Make yeah. All right, On that note, we're

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Speaker 1: going to take a quick break. But when we come back,

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Speaker 1: we're going to return to the idea of brain soup

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Speaker 1: and to the work of Susanna or Colna Ozel. Alright,

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Speaker 1: we're back. So we got sidetracked on the the idea

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Speaker 1: of whether or not people actually use more than ten

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Speaker 1: percent of their brains. We do in pretty much anyway

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Speaker 1: you interpret that. But that came up because it was

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Speaker 1: the basis of a journey of research that the neuroscientist

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Speaker 1: Susannah Orku Lana Zel went on. And so let's pick

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Speaker 1: back up with her story from the event you saw

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Speaker 1: in New York City. All right, So she was talking

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Speaker 1: about another number that she kept coming across that she

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Speaker 1: found curious, that she found suspicious, and that is the

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Speaker 1: number one billion. Oh. Now, this just came up a

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Speaker 1: minute ago when we were talking about an approximation. Now,

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Speaker 1: I don't want to indict the author that cited and

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Speaker 1: because it's a reasonable approximation. But well, yeah, and we'll

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Speaker 1: get into we'll get into that. But but yeah, she

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Speaker 1: kept running across this idea that the human brain contains

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Speaker 1: a hundred billion neurons and ten times as many glial cells.

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Speaker 1: But the thing is unlike that ten percent of the

428
00:22:56,680 --> 00:22:59,600
Speaker 1: brain thing. This was not merely the domain of pop

429
00:22:59,640 --> 00:23:02,800
Speaker 1: culture or in science fiction. No, this figure freak was

430
00:23:02,840 --> 00:23:06,840
Speaker 1: frequently cited by neuroscientists, by psychologists, and and sometimes applied

431
00:23:07,200 --> 00:23:09,800
Speaker 1: as a as a comparison to the number of stars

432
00:23:09,840 --> 00:23:12,159
Speaker 1: in the Milky Way. I imagine a number of you

433
00:23:12,160 --> 00:23:14,919
Speaker 1: have heard this one before. What a hundred billion stars

434
00:23:14,920 --> 00:23:17,280
Speaker 1: in the Milky Way. Yeah, So the idea of being like, hey,

435
00:23:17,320 --> 00:23:19,720
Speaker 1: you have a hundred a hundred billion eurons in your head.

436
00:23:19,760 --> 00:23:21,760
Speaker 1: That's as many stars as there are in the Milky Way,

437
00:23:21,800 --> 00:23:23,840
Speaker 1: which I mean, on one level, I think that's a

438
00:23:23,920 --> 00:23:27,720
Speaker 1: useful metaphor because you're basically making the statement that, um,

439
00:23:27,960 --> 00:23:31,320
Speaker 1: that that inner space is as complex and vast as

440
00:23:31,400 --> 00:23:34,359
Speaker 1: we take outer space to be. But on the other hand,

441
00:23:34,359 --> 00:23:37,600
Speaker 1: when you start looking at those actual numbers, uh, there's

442
00:23:37,640 --> 00:23:40,840
Speaker 1: some problems on both sides, because for starters, the hundred

443
00:23:40,840 --> 00:23:43,320
Speaker 1: billion star estimate in the Milky Way is not a

444
00:23:43,320 --> 00:23:46,520
Speaker 1: solid number. By some estimations, the Milky Way has the

445
00:23:46,800 --> 00:23:50,280
Speaker 1: mass of a hundred billion solar masses, but other estimates,

446
00:23:50,320 --> 00:23:53,320
Speaker 1: say four hundred billion or even seven hundred billion solar

447
00:23:53,359 --> 00:23:56,280
Speaker 1: masses is more accurate. Yeah, this is a fascinating question

448
00:23:56,320 --> 00:23:58,920
Speaker 1: on its own because so it's difficult to know the

449
00:23:59,000 --> 00:24:01,320
Speaker 1: number of stars in the mill Key Way galaxy because

450
00:24:01,359 --> 00:24:04,240
Speaker 1: obviously we can't just count them, as you can't look

451
00:24:04,280 --> 00:24:06,560
Speaker 1: up there and count them. The best we can do

452
00:24:06,680 --> 00:24:09,960
Speaker 1: is estimate on the basis of the mass and luminosity

453
00:24:10,000 --> 00:24:12,880
Speaker 1: of the galaxy as a whole. But this presents difficulties

454
00:24:12,880 --> 00:24:15,399
Speaker 1: too because there are plenty of things in the galaxy

455
00:24:15,520 --> 00:24:18,960
Speaker 1: other than stars. Right, the vast majority of the mass

456
00:24:19,000 --> 00:24:22,200
Speaker 1: of our galaxy is not even normal matter. It seems

457
00:24:22,280 --> 00:24:25,200
Speaker 1: to be dark matter, which is still an unsolved mystery

458
00:24:25,200 --> 00:24:29,000
Speaker 1: and astrophysics. And dark matter, of course, is this hypothetical

459
00:24:29,119 --> 00:24:32,760
Speaker 1: stuff that we detect by measuring its mass, in other words,

460
00:24:32,800 --> 00:24:36,159
Speaker 1: the gravitational effect it has on stuff around it, But

461
00:24:36,240 --> 00:24:39,640
Speaker 1: it doesn't appear to interact with electromagnetic radiation like light,

462
00:24:39,760 --> 00:24:43,040
Speaker 1: making it unlike any other ordinary matter that we know of.

463
00:24:43,080 --> 00:24:45,600
Speaker 1: So at this point we don't know what that stuff is,

464
00:24:45,640 --> 00:24:47,560
Speaker 1: and that's most of the mass out there. And then

465
00:24:47,600 --> 00:24:50,240
Speaker 1: past that, we know that a lot of the ordinary

466
00:24:50,240 --> 00:24:52,719
Speaker 1: matter in the galaxy is not just stars. Some of

467
00:24:52,760 --> 00:24:57,560
Speaker 1: it is uh like unincorporated debris, cosmic gas and dust,

468
00:24:57,680 --> 00:25:00,679
Speaker 1: just hydrogen clouds out there. Of course that's not the

469
00:25:00,720 --> 00:25:03,359
Speaker 1: majority of the luminous matter, but it's enough to complicate

470
00:25:03,400 --> 00:25:06,400
Speaker 1: the problem of estimating star counts. And then on top

471
00:25:06,440 --> 00:25:09,159
Speaker 1: of that, you've got ice and comets and planets and

472
00:25:09,320 --> 00:25:11,960
Speaker 1: black holes and a supermassive black hole at the center

473
00:25:11,960 --> 00:25:14,680
Speaker 1: of the galaxy. And on top of that also stars

474
00:25:14,720 --> 00:25:17,600
Speaker 1: are of dramatically different masses, so you have to figure

475
00:25:17,600 --> 00:25:21,680
Speaker 1: out the right average stellar mass to divide by. So

476
00:25:21,800 --> 00:25:24,800
Speaker 1: it's it's almost like this isn't a perfect metaphor, but

477
00:25:24,840 --> 00:25:26,600
Speaker 1: I was trying to think of one. It's almost like

478
00:25:26,680 --> 00:25:30,439
Speaker 1: catching an unknown species of fish and then weighing it

479
00:25:30,560 --> 00:25:33,480
Speaker 1: and then trying to guess how many bones it has.

480
00:25:33,880 --> 00:25:35,840
Speaker 1: You know, like you can't count them just from looking

481
00:25:36,080 --> 00:25:38,760
Speaker 1: from the outside, uh. And the bones aren't all the

482
00:25:38,800 --> 00:25:41,320
Speaker 1: same size and weight. Uh. And there's a lot of

483
00:25:41,400 --> 00:25:44,040
Speaker 1: other stuff in there too that's not bones. But so

484
00:25:44,200 --> 00:25:46,679
Speaker 1: you can sort of estimate based on a number of

485
00:25:46,720 --> 00:25:50,120
Speaker 1: likely assumptions, but you can't get an accurate count, which

486
00:25:50,200 --> 00:25:53,440
Speaker 1: leads us back to the human brain. And this idea

487
00:25:53,480 --> 00:25:56,720
Speaker 1: of this number that was thrown around again not non

488
00:25:56,720 --> 00:26:00,440
Speaker 1: inscience fiction films, but in pure viewed papers and in

489
00:26:00,440 --> 00:26:03,000
Speaker 1: in in textbooks as well. It was just out there

490
00:26:03,040 --> 00:26:06,399
Speaker 1: and sort of the scientific zeitgeis the idea that there

491
00:26:06,400 --> 00:26:09,439
Speaker 1: were a hundred billion neurons in the human brain. And

492
00:26:09,480 --> 00:26:11,879
Speaker 1: we didn't know where this number comes from, right, And

493
00:26:11,920 --> 00:26:16,359
Speaker 1: so Susannah Rocolano Hozel she was wondering too, and so

494
00:26:16,400 --> 00:26:18,640
Speaker 1: she she started looking into it, and she looked and looked,

495
00:26:18,640 --> 00:26:22,640
Speaker 1: and she found no pre existing count, no scientific basis

496
00:26:22,680 --> 00:26:24,600
Speaker 1: for the number at all. The number was just floating

497
00:26:24,640 --> 00:26:28,760
Speaker 1: around in the scientific world. So the obvious solution is, well,

498
00:26:28,800 --> 00:26:31,800
Speaker 1: somebody's got to to look it up. Somebody's got to

499
00:26:31,880 --> 00:26:35,199
Speaker 1: count these neurons. And so she set out to do

500
00:26:35,359 --> 00:26:39,159
Speaker 1: just that via a novel tool that she developed in

501
00:26:39,200 --> 00:26:42,919
Speaker 1: the lab in two thousand five, and that is turning

502
00:26:42,920 --> 00:26:46,600
Speaker 1: brains into soup. The cification of the of of of

503
00:26:46,680 --> 00:26:50,359
Speaker 1: the animal brain. Question, did she say, what the soup

504
00:26:50,400 --> 00:26:54,080
Speaker 1: tastes like? No? I believe she said that the soup

505
00:26:54,560 --> 00:26:58,720
Speaker 1: looked like unfiltered apple juice and apparently turned some of

506
00:26:58,720 --> 00:27:01,359
Speaker 1: the students off of apple is perhaps forever it looks.

507
00:27:01,359 --> 00:27:03,880
Speaker 1: I mean, I've seen it looks like murky swamp water. Yeah.

508
00:27:03,920 --> 00:27:06,720
Speaker 1: So I doubt that anybody tasted it, because this is

509
00:27:06,720 --> 00:27:11,399
Speaker 1: sort of like priceless research material. But uh I don't know.

510
00:27:11,440 --> 00:27:14,520
Speaker 1: Then again, you wonder if somebody got curious at some point.

511
00:27:14,720 --> 00:27:16,520
Speaker 1: I don't know. She's she's a she's a pretty good

512
00:27:17,240 --> 00:27:19,800
Speaker 1: she's a wonderful communicator, and she has a wonderful sense

513
00:27:19,800 --> 00:27:23,320
Speaker 1: of humor. I imagine if somebody had tasted it, even accidentally,

514
00:27:23,840 --> 00:27:25,720
Speaker 1: she would she would have mentioned it in one of

515
00:27:25,720 --> 00:27:28,040
Speaker 1: the talks or or write ups or interviews that we

516
00:27:28,119 --> 00:27:30,239
Speaker 1: looked at. You know what, I've got a guess as

517
00:27:30,280 --> 00:27:32,040
Speaker 1: to what it tastes like. I bet it tastes like

518
00:27:32,119 --> 00:27:36,480
Speaker 1: chicken stock probably probably a good good guess, and well

519
00:27:36,600 --> 00:27:38,520
Speaker 1: and perhaps from Mount hot but we'll get into that.

520
00:27:39,000 --> 00:27:42,560
Speaker 1: Uh So, Basically, yeah, so she she set out to

521
00:27:42,600 --> 00:27:46,000
Speaker 1: make brains into soup. Uh So, Basically she came across

522
00:27:46,119 --> 00:27:49,840
Speaker 1: previous lab efforts from the nineteen seventies to turn the

523
00:27:49,840 --> 00:27:54,000
Speaker 1: brain into soups to measure DNA concentrations um. And one

524
00:27:54,000 --> 00:27:57,080
Speaker 1: of the keys here is that soup is a homogeneous solution.

525
00:27:57,480 --> 00:28:01,240
Speaker 1: So if you reduce something like like the brain that

526
00:28:01,640 --> 00:28:04,399
Speaker 1: to soup, then you have a better ability to like

527
00:28:04,480 --> 00:28:07,960
Speaker 1: get a sample of that soup, do account within that sample,

528
00:28:08,280 --> 00:28:12,440
Speaker 1: and then apply that, you know, to the full volume

529
00:28:12,480 --> 00:28:14,879
Speaker 1: of the soup. So anyway, she said, well, well I

530
00:28:14,880 --> 00:28:18,360
Speaker 1: could use this method then, uh, to figure out how

531
00:28:18,359 --> 00:28:21,119
Speaker 1: many neurons during the brain. You know, liquefy cell membranes

532
00:28:21,359 --> 00:28:25,000
Speaker 1: leave the nuclei intact, allowing them, allowing everyone to you know,

533
00:28:25,040 --> 00:28:27,800
Speaker 1: to count the remaining nuclei in a small sample and

534
00:28:27,840 --> 00:28:30,440
Speaker 1: then multiply the number by the overall volume to get

535
00:28:30,480 --> 00:28:32,639
Speaker 1: the whole brain total of neurons. This is great. You

536
00:28:32,680 --> 00:28:35,320
Speaker 1: can't do this with a galaxy, can you, Like, you

537
00:28:35,359 --> 00:28:37,960
Speaker 1: can't just look at one section of a galaxy and say, okay,

538
00:28:38,000 --> 00:28:40,520
Speaker 1: now multiply that by the total area of the galaxy.

539
00:28:40,600 --> 00:28:44,000
Speaker 1: Because galaxies are not homogeneous. You would have to turn

540
00:28:44,040 --> 00:28:46,920
Speaker 1: it to soup first, which you cannot do or I

541
00:28:46,960 --> 00:28:48,959
Speaker 1: mean not on our scale. Anyway, you would have to

542
00:28:49,000 --> 00:28:51,840
Speaker 1: have godlike powers, and then you would destroy the universe.

543
00:28:52,000 --> 00:28:55,080
Speaker 1: Maybe that's how the world ends. That's their shrewd Ragnarock scenario.

544
00:28:55,640 --> 00:28:59,719
Speaker 1: As the god or god's asked the question, how many planets,

545
00:28:59,720 --> 00:29:02,560
Speaker 1: how many stars are kicking around this thing? Well, let's

546
00:29:02,560 --> 00:29:04,800
Speaker 1: turn it to soup and find out exactly, Loki says,

547
00:29:05,040 --> 00:29:07,200
Speaker 1: or you can't count the number of stars in the sky,

548
00:29:07,360 --> 00:29:10,400
Speaker 1: and he's like, watch me, Well this sounds this is

549
00:29:10,440 --> 00:29:13,040
Speaker 1: exactly the kind of gamble that that ancient gods would

550
00:29:13,080 --> 00:29:15,760
Speaker 1: get into. You know, um, just a mere bet and

551
00:29:15,880 --> 00:29:20,000
Speaker 1: everything for mortals is at stake, right, But anyway, or

552
00:29:20,080 --> 00:29:23,400
Speaker 1: Colono was not interested in that. She's interested in the brain.

553
00:29:23,480 --> 00:29:27,120
Speaker 1: So the main challenge with souping the brain was souping

554
00:29:27,160 --> 00:29:29,920
Speaker 1: it just enough to retain the cell nuclei, but without

555
00:29:29,920 --> 00:29:34,160
Speaker 1: breaking any of that down. Initial souping experiments via essentially

556
00:29:34,200 --> 00:29:37,600
Speaker 1: a detergent went too far and attempts to flash freeze

557
00:29:37,600 --> 00:29:40,480
Speaker 1: them with wickid N liquid nitrogen and then blend them. Well,

558
00:29:40,480 --> 00:29:42,240
Speaker 1: that just caused a cracked mess. And she says that

559
00:29:42,280 --> 00:29:44,720
Speaker 1: there were like frozen pieces of brain all over the place.

560
00:29:45,160 --> 00:29:47,640
Speaker 1: I think if anyone was going to taste her brain soup,

561
00:29:47,680 --> 00:29:49,720
Speaker 1: that was probably gonna be when it would have occurred.

562
00:29:50,680 --> 00:29:53,600
Speaker 1: But Anyway, then she found a solution fixing the brain

563
00:29:53,640 --> 00:29:56,520
Speaker 1: tissue with from all the eyed before the dissolving it.

564
00:29:56,560 --> 00:29:59,960
Speaker 1: And the result, she says again, looks like unfiltered apple

565
00:30:00,080 --> 00:30:02,720
Speaker 1: juice and allows this kind of count to take place. Right, So,

566
00:30:02,800 --> 00:30:05,520
Speaker 1: you can pull out a small sample, you know what

567
00:30:05,600 --> 00:30:08,400
Speaker 1: the volume of that is compared to the entire volume

568
00:30:08,400 --> 00:30:10,600
Speaker 1: of the sample, and then you count the number of

569
00:30:11,040 --> 00:30:14,160
Speaker 1: neuron nuclei in the small sample and then multiply that

570
00:30:14,200 --> 00:30:17,600
Speaker 1: by the total volume exactly. Okay, So I think I

571
00:30:17,640 --> 00:30:20,680
Speaker 1: mentioned earlier there's a TED talk she did in where

572
00:30:20,680 --> 00:30:23,720
Speaker 1: she shows off anyway. In the middle of this this

573
00:30:23,760 --> 00:30:26,200
Speaker 1: TED talk, she shows off a vial of this brain

574
00:30:26,240 --> 00:30:28,560
Speaker 1: soup on the stage. It's a little glass jar of

575
00:30:28,680 --> 00:30:30,720
Speaker 1: mouse brain souper. It might be plastic, I don't know.

576
00:30:30,880 --> 00:30:32,880
Speaker 1: It's a jar of mouse brain soup. It looks like

577
00:30:32,960 --> 00:30:35,400
Speaker 1: murky swamp water. It's kind of sort of like an

578
00:30:35,520 --> 00:30:38,400
Speaker 1: off beige kind of color. One of the best things

579
00:30:38,440 --> 00:30:41,320
Speaker 1: about this particular talk, though, is that when she shows

580
00:30:41,360 --> 00:30:43,560
Speaker 1: off the jar, she doesn't go and pick it up

581
00:30:43,560 --> 00:30:47,440
Speaker 1: from a table or demonstration stand or something. She's just

582
00:30:47,480 --> 00:30:49,600
Speaker 1: got it tucked into the back of her belt like

583
00:30:49,640 --> 00:30:53,720
Speaker 1: a gun just pulls it out, and then when she's

584
00:30:53,760 --> 00:30:56,400
Speaker 1: done showing it off, she just slips it right back

585
00:30:56,400 --> 00:30:58,480
Speaker 1: in under the belt. I mean, I guess it must

586
00:30:58,480 --> 00:31:00,840
Speaker 1: have been there all day. Well. You know, one of

587
00:31:00,880 --> 00:31:02,720
Speaker 1: the things that she pointed out in the talk is

588
00:31:02,760 --> 00:31:05,320
Speaker 1: that some when she started doing these experiments, some uh

589
00:31:05,520 --> 00:31:07,680
Speaker 1: people objected. They were like, what are you doing through

590
00:31:07,680 --> 00:31:11,040
Speaker 1: these precious brains, as if she were wasting brains, as

591
00:31:11,040 --> 00:31:14,080
Speaker 1: if she were essentially the dan ackroid character from it

592
00:31:14,120 --> 00:31:17,840
Speaker 1: came from Hollywood just you know, massacuring brain material. But

593
00:31:17,920 --> 00:31:19,920
Speaker 1: she pointed out that, you know, the aside from it

594
00:31:19,960 --> 00:31:23,280
Speaker 1: being highly useful, as they will explore the rest of

595
00:31:23,280 --> 00:31:26,680
Speaker 1: this episode, also they freeze it and so they can

596
00:31:26,720 --> 00:31:29,240
Speaker 1: save it for later. She says, she has a whole database.

597
00:31:29,400 --> 00:31:32,280
Speaker 1: The freezer is full of brain soup. I'm sure, all

598
00:31:32,280 --> 00:31:36,320
Speaker 1: properly labeled and dated. Yes, So what they did is, yeah,

599
00:31:36,360 --> 00:31:39,960
Speaker 1: they apply to fluorescent stain to differentiate the neurons from

600
00:31:39,960 --> 00:31:43,440
Speaker 1: other cells. And she started with rats and other rodents,

601
00:31:43,760 --> 00:31:47,440
Speaker 1: eventually working up to human brains, and eventually she had

602
00:31:47,480 --> 00:31:52,640
Speaker 1: a verified count not one billion neurons, but eighty six

603
00:31:52,680 --> 00:31:55,920
Speaker 1: billion neurons. Though apparently this can be higher as high

604
00:31:55,920 --> 00:31:58,160
Speaker 1: as say ninety one billion, for instance, but eighty six

605
00:31:58,200 --> 00:32:01,600
Speaker 1: billion is the like the ballpark account. That's uh, well,

606
00:32:01,640 --> 00:32:03,680
Speaker 1: that that doesn't seem too far off. I mean, that

607
00:32:03,720 --> 00:32:07,840
Speaker 1: seems within a rough order of magnitude level of reasonableness.

608
00:32:08,040 --> 00:32:10,720
Speaker 1: Well it does, and it doesn't like you know, she

609
00:32:10,800 --> 00:32:12,760
Speaker 1: points out that this may not may not sound like

610
00:32:12,880 --> 00:32:14,640
Speaker 1: much of a difference to a lot of us. And

611
00:32:14,640 --> 00:32:16,240
Speaker 1: I have to admit when I first heard it it,

612
00:32:16,320 --> 00:32:18,720
Speaker 1: I didn't really register that it was that much different

613
00:32:18,760 --> 00:32:21,959
Speaker 1: thundered billion verses six. It sounds like, all right, well,

614
00:32:22,160 --> 00:32:25,640
Speaker 1: we almost got it right. And uh, the thing is,

615
00:32:25,720 --> 00:32:29,640
Speaker 1: you know, she says that ten billion neurons is really

616
00:32:29,720 --> 00:32:32,840
Speaker 1: is not a small sum when it comes to neural change,

617
00:32:32,920 --> 00:32:36,360
Speaker 1: and when we're talking about the evolution of of of brains,

618
00:32:36,640 --> 00:32:39,760
Speaker 1: she says, the difference there is an entire baboon brain

619
00:32:39,880 --> 00:32:43,080
Speaker 1: and chain. So so yeah, that's a lot of neural

620
00:32:43,080 --> 00:32:46,440
Speaker 1: power we're talking about there, and to be off on

621
00:32:46,640 --> 00:32:50,720
Speaker 1: that can you know, can have consequences as well, explore

622
00:32:50,760 --> 00:32:53,280
Speaker 1: when we're talking about like what makes the human brain

623
00:32:53,640 --> 00:32:56,280
Speaker 1: seemingly special. Yeah, that that's interesting, I mean, and that's

624
00:32:56,320 --> 00:32:58,720
Speaker 1: where it ties into her larger theory about what it

625
00:32:58,920 --> 00:33:03,000
Speaker 1: is that special out human brains. So obviously, neuro anatomists

626
00:33:03,040 --> 00:33:05,840
Speaker 1: and other scientists have long debated this question, what makes

627
00:33:05,920 --> 00:33:09,080
Speaker 1: human brains unique? Why? Why are we the only ones

628
00:33:09,200 --> 00:33:13,360
Speaker 1: with computers? You know? Why don't rabbits have computers and

629
00:33:13,400 --> 00:33:16,760
Speaker 1: all that? Uh? And so what is the physical property

630
00:33:16,800 --> 00:33:19,560
Speaker 1: that gives the human brain its power? Is its size?

631
00:33:19,800 --> 00:33:22,560
Speaker 1: I mean that clearly doesn't make any sense because sperm

632
00:33:22,600 --> 00:33:25,640
Speaker 1: whales have brains there's something like, I think at least

633
00:33:25,640 --> 00:33:29,040
Speaker 1: six or seven times larger than human brains, and yet

634
00:33:29,080 --> 00:33:32,040
Speaker 1: they don't seem to be smarter than us. Likewise, the

635
00:33:32,080 --> 00:33:34,440
Speaker 1: elephant brain is much bigger and exactly and we're not

636
00:33:34,480 --> 00:33:38,120
Speaker 1: discounting the intelligence of whales and elephants, but but but

637
00:33:38,400 --> 00:33:42,080
Speaker 1: clearly their their neural power is not on the same

638
00:33:42,120 --> 00:33:44,880
Speaker 1: scale as as human power. And we have to ask

639
00:33:44,880 --> 00:33:47,600
Speaker 1: the question why, right, so we're able to do things

640
00:33:47,640 --> 00:33:50,440
Speaker 1: that they're not able to do. And is that difference

641
00:33:50,520 --> 00:33:53,760
Speaker 1: related to sheer size of the brain? Clearly not? Could

642
00:33:53,800 --> 00:33:56,160
Speaker 1: it be? Another thing that's often put forward is the

643
00:33:56,360 --> 00:34:00,120
Speaker 1: ratio of brain size to body size. Right? Uh, the

644
00:34:00,120 --> 00:34:03,800
Speaker 1: the encephialization quotation that actually doesn't seem to quite cover

645
00:34:03,880 --> 00:34:06,160
Speaker 1: it either. Right. So one of the things you pointed

646
00:34:06,200 --> 00:34:09,040
Speaker 1: out here is that we'll just consider a couple of

647
00:34:09,040 --> 00:34:13,520
Speaker 1: these neuron counts again, humans eighty six billion neurons, uh.

648
00:34:13,560 --> 00:34:16,040
Speaker 1: And then we have something like like a rat two

649
00:34:16,080 --> 00:34:20,000
Speaker 1: hundred million neurons uh. On a goody, which is a

650
00:34:20,000 --> 00:34:23,480
Speaker 1: South American like rather plump kind of critter, but it

651
00:34:23,600 --> 00:34:27,200
Speaker 1: is a rodent eight hundred fifty seven million neurons. Al

652
00:34:27,280 --> 00:34:31,120
Speaker 1: monkeys have a one thousand, four hundred sixty eight million neurons,

653
00:34:31,120 --> 00:34:34,279
Speaker 1: while a cappy bara again a rodent one thousand, six

654
00:34:34,360 --> 00:34:37,640
Speaker 1: hundred million neurons. Okay, so they're up into the billion range,

655
00:34:37,680 --> 00:34:42,080
Speaker 1: but the difference here is still orders of magnitude of difference, right.

656
00:34:42,160 --> 00:34:44,920
Speaker 1: And in her argument is like when you compare like

657
00:34:44,920 --> 00:34:49,840
Speaker 1: like sized rodents and primates the primate brains, uh, you know,

658
00:34:49,920 --> 00:34:52,640
Speaker 1: maybe the same size, but there are more neurons in

659
00:34:52,680 --> 00:34:55,640
Speaker 1: the more advanced primate brains. Right. So what seems to

660
00:34:55,640 --> 00:34:57,839
Speaker 1: be going on here is that it's not so much

661
00:34:57,880 --> 00:35:01,040
Speaker 1: that there's something really special about human brains, but there's

662
00:35:01,040 --> 00:35:05,839
Speaker 1: something special about primate brains. Primate the brains of monkeys

663
00:35:05,920 --> 00:35:09,239
Speaker 1: and apes, you know, the primate creatures like us, that

664
00:35:09,360 --> 00:35:14,399
Speaker 1: they have these densely packed neuron heavy cortex cortices. Yeah,

665
00:35:14,440 --> 00:35:16,400
Speaker 1: and then it comes down to concentration of urons. And

666
00:35:16,440 --> 00:35:19,200
Speaker 1: by the way, she also has found that birds have

667
00:35:19,360 --> 00:35:22,759
Speaker 1: primate like concentrations in the forebrain, which lines up with

668
00:35:22,800 --> 00:35:26,480
Speaker 1: the with their intelligence despite much smaller brains than other

669
00:35:26,520 --> 00:35:29,680
Speaker 1: intelligent animals. Yeah, we mentioned this before, but this really

670
00:35:29,680 --> 00:35:34,120
Speaker 1: comes through in the sometimes startling intelligence of birds like

671
00:35:34,160 --> 00:35:36,759
Speaker 1: Corvid's and parrots. Yeah. You look at a parrot, like,

672
00:35:36,760 --> 00:35:39,080
Speaker 1: how big is it's brain? Right, It's like, you know,

673
00:35:38,880 --> 00:35:42,160
Speaker 1: you you snack on nuts larger than this creature's brain,

674
00:35:42,239 --> 00:35:46,320
Speaker 1: and yet it has this startling intelligence about the efficiently

675
00:35:46,400 --> 00:35:51,240
Speaker 1: packed to the neuron crammed forebrain sort of like a primate. Yeah. Yeah. Meanwhile,

676
00:35:51,320 --> 00:35:54,040
Speaker 1: whale brain, you could climb inside of it and it's

677
00:35:54,719 --> 00:35:57,400
Speaker 1: and it's it's it's not quite there. All right. On

678
00:35:57,440 --> 00:35:58,960
Speaker 1: that note, we're gonna take a quick break, and when

679
00:35:58,960 --> 00:36:01,800
Speaker 1: we come back, we're gonna or more of Susanna or

680
00:36:01,840 --> 00:36:06,000
Speaker 1: colono Ozell's ideas about the human brain, how it is

681
00:36:06,080 --> 00:36:11,880
Speaker 1: evolved and whine has so many neurons than all right,

682
00:36:11,920 --> 00:36:15,919
Speaker 1: we're back, so more about brains, all right, So One

683
00:36:15,960 --> 00:36:20,680
Speaker 1: of the big take aways from Susanna or Colono Ozell's

684
00:36:20,760 --> 00:36:23,640
Speaker 1: work is that, of course a big brain doesn't necessarily

685
00:36:23,680 --> 00:36:27,120
Speaker 1: mean high levels of cognition. Again, it's more about the

686
00:36:27,200 --> 00:36:29,879
Speaker 1: neuron concentration. Yeah, this is what we were just talking

687
00:36:29,920 --> 00:36:32,480
Speaker 1: about with the fact that obviously, you know, like whales, elephants,

688
00:36:32,480 --> 00:36:34,960
Speaker 1: all these have much bigger brains than us. But there's

689
00:36:34,960 --> 00:36:37,200
Speaker 1: a lot that our brains seem able to do that

690
00:36:37,280 --> 00:36:40,560
Speaker 1: those brains can't. So it's not just size. There does

691
00:36:40,560 --> 00:36:44,520
Speaker 1: seem to be something special about the way that primate

692
00:36:44,760 --> 00:36:49,200
Speaker 1: brains are organized. And that's the thing, primate brains not

693
00:36:49,200 --> 00:36:53,480
Speaker 1: not just human brains, Because that's something that Orcolano Ozel

694
00:36:53,840 --> 00:36:57,560
Speaker 1: really drives home, is that the human brain is just

695
00:36:57,719 --> 00:37:01,200
Speaker 1: a scaled up primate brain. There's nothing special about the

696
00:37:01,280 --> 00:37:05,759
Speaker 1: human brain beyond that, nothing God touched or anything of

697
00:37:05,800 --> 00:37:08,080
Speaker 1: the sort. She says that this is very much in

698
00:37:08,160 --> 00:37:11,560
Speaker 1: line with what Darwin thought and was criticized for. Darwin

699
00:37:11,640 --> 00:37:15,200
Speaker 1: thought the human brain was just another primate brain, but

700
00:37:15,239 --> 00:37:17,240
Speaker 1: those who came after him, they often took their evolution

701
00:37:17,239 --> 00:37:21,040
Speaker 1: with a hefty dose of human exceptionalism. Wanting to think

702
00:37:21,080 --> 00:37:23,759
Speaker 1: about us is a special case that the rules of

703
00:37:23,800 --> 00:37:26,560
Speaker 1: life don't apply to like they apply to every other

704
00:37:26,600 --> 00:37:31,000
Speaker 1: animal on earth, right And likewise, Umsel says that she

705
00:37:31,120 --> 00:37:33,239
Speaker 1: faced resistance to a results due to the fact that

706
00:37:33,280 --> 00:37:36,480
Speaker 1: they didn't support this view of human brain exceptionalism versus

707
00:37:36,520 --> 00:37:39,720
Speaker 1: the brains of other primates. Again, it's just a scaled

708
00:37:39,800 --> 00:37:42,800
Speaker 1: up a scaled up primate brain, right. So her idea,

709
00:37:43,000 --> 00:37:46,480
Speaker 1: what she argues, is that what the human brain is,

710
00:37:46,960 --> 00:37:49,560
Speaker 1: you start with the primate brain, which primate brains, like

711
00:37:49,640 --> 00:37:53,360
Speaker 1: brains of monkeys and apes in general, are more crammed

712
00:37:53,360 --> 00:37:55,600
Speaker 1: with neurons than other types of brains in the animal

713
00:37:55,680 --> 00:37:58,160
Speaker 1: kingdom usually. And then you look at all the primates,

714
00:37:58,200 --> 00:38:01,680
Speaker 1: and humans have by far the guest primate brain. And

715
00:38:01,719 --> 00:38:04,920
Speaker 1: then because it's the biggest version of this densely packed

716
00:38:05,440 --> 00:38:10,200
Speaker 1: primate neuron housing center, we are the smartest. That's essentially

717
00:38:10,200 --> 00:38:12,760
Speaker 1: what makes us the smartest. And we'll get into why

718
00:38:12,800 --> 00:38:15,640
Speaker 1: this seems to be the case and why um the

719
00:38:15,640 --> 00:38:19,600
Speaker 1: the argument that Susanna rocol Hosel makes for it as well. Uh,

720
00:38:19,640 --> 00:38:21,120
Speaker 1: But before we do that, I want to I want

721
00:38:21,160 --> 00:38:23,120
Speaker 1: to touch base in another area that she lines up

722
00:38:23,120 --> 00:38:25,520
Speaker 1: with all of this, and that is the subject of

723
00:38:25,600 --> 00:38:30,840
Speaker 1: longevity in warm blooded vertebrates anyway, um, because it seems

724
00:38:31,040 --> 00:38:33,759
Speaker 1: to be the case that neurons are more important than

725
00:38:33,800 --> 00:38:37,560
Speaker 1: body size here as well. So in two thousand eighteen,

726
00:38:37,600 --> 00:38:41,160
Speaker 1: research that was published in the Journal of Comparative Neurology

727
00:38:41,800 --> 00:38:46,160
Speaker 1: UM Rocolno Mozel and co authors found that in primates, birds,

728
00:38:46,160 --> 00:38:48,960
Speaker 1: and other warm blooded creatures, the number of neurons that

729
00:38:49,040 --> 00:38:51,400
Speaker 1: you have in the core in the cortex of a

730
00:38:51,480 --> 00:38:56,000
Speaker 1: species predicts about seventy five of all the variation in

731
00:38:56,040 --> 00:39:00,520
Speaker 1: the longevity across species. Body size and metabolism the usual

732
00:39:00,520 --> 00:39:02,319
Speaker 1: standard because usually that's what we think of, right. An

733
00:39:02,320 --> 00:39:05,680
Speaker 1: elephant lives a while it's big, whales oft a while big. Right,

734
00:39:06,440 --> 00:39:11,399
Speaker 1: She says that this only predicts around tw of of

735
00:39:11,400 --> 00:39:15,120
Speaker 1: of the cases of longevity. And here's another slice of

736
00:39:15,320 --> 00:39:18,720
Speaker 1: enough human non exceptionalism. If you do the math, she says,

737
00:39:18,760 --> 00:39:21,160
Speaker 1: this means that humans live about as long as you

738
00:39:21,680 --> 00:39:26,000
Speaker 1: expect based on their neuron load. In this particular brain

739
00:39:26,040 --> 00:39:29,880
Speaker 1: soup study examined more than seven hundred warm blooded animal species.

740
00:39:30,120 --> 00:39:33,520
Speaker 1: I want to see a list of those soups flavors. Yes,

741
00:39:33,560 --> 00:39:38,080
Speaker 1: the sou menu is it's quite extensive. At her lap now,

742
00:39:38,160 --> 00:39:40,720
Speaker 1: why is there a connection between neurons and the cerebral

743
00:39:40,800 --> 00:39:43,720
Speaker 1: cortex and longevity. Well, she says more work is required

744
00:39:43,719 --> 00:39:45,520
Speaker 1: to figure this out, but she does have some ideas,

745
00:39:45,560 --> 00:39:47,279
Speaker 1: and here's what she said in a press release on

746
00:39:47,320 --> 00:39:51,880
Speaker 1: the paper from quote. The data suggests the warm blooded

747
00:39:51,920 --> 00:39:55,200
Speaker 1: species accumulate damages at the same rate as they age,

748
00:39:55,280 --> 00:39:58,400
Speaker 1: But what curtails life are damages to the cerebral cortex,

749
00:39:58,640 --> 00:40:01,360
Speaker 1: not the rest of the body. The more cortical neurons

750
00:40:01,360 --> 00:40:03,040
Speaker 1: you have, the longer you will still have enough to

751
00:40:03,120 --> 00:40:05,799
Speaker 1: keep your body functional. The cortex is the part of

752
00:40:05,840 --> 00:40:08,759
Speaker 1: your brain that is capable of making our behavior complex

753
00:40:08,760 --> 00:40:11,960
Speaker 1: and flexible. Yes, but that extends well beyond cognition and

754
00:40:12,040 --> 00:40:15,799
Speaker 1: doing mental math and logic reasoning. The cerebrial cortex also

755
00:40:15,880 --> 00:40:19,319
Speaker 1: gives your body adaptability as it adjusts and learns how

756
00:40:19,360 --> 00:40:22,440
Speaker 1: to react to stresses and predicts them. That includes keeping

757
00:40:22,520 --> 00:40:26,000
Speaker 1: your physiological functions running smoothly and making sure your heart rate,

758
00:40:26,080 --> 00:40:29,359
Speaker 1: your respiratory rate, and your metabolism are on track with

759
00:40:29,400 --> 00:40:32,799
Speaker 1: what you're doing and how you feel uh and with

760
00:40:32,880 --> 00:40:35,799
Speaker 1: what you expect to happen next. And that apparently is

761
00:40:35,840 --> 00:40:39,880
Speaker 1: a key factor that impacts longevity. Fascinating. Yeah, so I

762
00:40:39,920 --> 00:40:43,200
Speaker 1: love how already just this this concept of of brain,

763
00:40:43,480 --> 00:40:45,839
Speaker 1: not this concept of brain soup, but this tool of

764
00:40:45,920 --> 00:40:49,600
Speaker 1: making brain soup. It already you know, is turning certain

765
00:40:49,640 --> 00:40:52,640
Speaker 1: things that uh we used to think we knew about

766
00:40:53,040 --> 00:40:55,239
Speaker 1: brains and the human brain and how it's different from

767
00:40:55,239 --> 00:40:58,600
Speaker 1: other animals and indeed how how other animals think, turning

768
00:40:58,600 --> 00:41:00,640
Speaker 1: all of that on its head and us another way

769
00:41:00,640 --> 00:41:04,200
Speaker 1: of thinking about it. Yeah. So one thing that this would, obviously,

770
00:41:04,320 --> 00:41:07,960
Speaker 1: I think, have somebody wondering about, is, Okay, if if

771
00:41:08,040 --> 00:41:12,040
Speaker 1: humans are the upper end of the primate brain, you know,

772
00:41:12,080 --> 00:41:14,640
Speaker 1: with your neuronal loads, you've got a lot of neurons

773
00:41:14,640 --> 00:41:16,959
Speaker 1: in the human human brain, how did we get that way?

774
00:41:17,600 --> 00:41:19,680
Speaker 1: Does it happened? Because again, we can't go with any

775
00:41:19,760 --> 00:41:24,320
Speaker 1: mythic interpretation here, whether there's no ancient alien or gods

776
00:41:24,320 --> 00:41:28,480
Speaker 1: scenario where where the this particular primate was was touched

777
00:41:28,560 --> 00:41:30,680
Speaker 1: and made special. No, we we need to look to

778
00:41:30,800 --> 00:41:35,319
Speaker 1: something in the natural world, something that that that explains

779
00:41:35,320 --> 00:41:38,640
Speaker 1: this or like rapid growth of the brain, and uh

780
00:41:38,800 --> 00:41:43,880
Speaker 1: Erculano Uzel's hypothesis has to do with straightforward energy economy,

781
00:41:43,960 --> 00:41:47,160
Speaker 1: the energy that different species are able to take into

782
00:41:47,200 --> 00:41:49,239
Speaker 1: their bodies. Right. She says that she thinks it all

783
00:41:49,280 --> 00:41:52,640
Speaker 1: comes down to the calories and mass via a very

784
00:41:52,719 --> 00:41:57,879
Speaker 1: early technological development that our ancestors made, that being cooking. Now,

785
00:41:57,920 --> 00:42:00,920
Speaker 1: this is an interesting hypothesis. I like this. Yeah, we know,

786
00:42:01,000 --> 00:42:04,200
Speaker 1: we've discussed the essential role that cooking played in human

787
00:42:04,239 --> 00:42:09,719
Speaker 1: advancement before. How it externalized. Digestion allows you to uh,

788
00:42:09,880 --> 00:42:12,040
Speaker 1: sort of to you know, heat up this pod or

789
00:42:12,200 --> 00:42:14,239
Speaker 1: not even necessarily a pod, just you know, a pit

790
00:42:14,400 --> 00:42:21,080
Speaker 1: with fire even and partially digest various organic matter before

791
00:42:21,400 --> 00:42:24,880
Speaker 1: you give your own digestions, a digestive system a shot

792
00:42:24,920 --> 00:42:27,799
Speaker 1: at it. This makes it easier to digest you know

793
00:42:28,080 --> 00:42:31,920
Speaker 1: a lot of foods that would be impossible or difficult

794
00:42:31,920 --> 00:42:36,120
Speaker 1: to consume otherwise, either because of their chemical components, or

795
00:42:36,160 --> 00:42:38,960
Speaker 1: maybe they're just tough they're hard to chew, or or

796
00:42:39,400 --> 00:42:41,680
Speaker 1: or they can't be chewed and cooking can make them softer,

797
00:42:41,800 --> 00:42:45,160
Speaker 1: and of course just speed up overall digestion. Yeah, it

798
00:42:45,239 --> 00:42:47,960
Speaker 1: just gives your digestive system much more access to the

799
00:42:48,040 --> 00:42:52,080
Speaker 1: nutrients inside through a variety of means. Yeah. Because and

800
00:42:52,120 --> 00:42:55,640
Speaker 1: this ties into neurons, because neurons require energy, a lot

801
00:42:55,680 --> 00:42:58,239
Speaker 1: of energy actually, Yeah, and the more neurons you have

802
00:42:58,320 --> 00:43:02,359
Speaker 1: and the more energy you require fire to charge them up. Like,

803
00:43:02,440 --> 00:43:05,719
Speaker 1: your brain consumes energy at a rate that is not

804
00:43:05,880 --> 00:43:09,160
Speaker 1: proportional to its size relative to the rest of your body.

805
00:43:09,200 --> 00:43:12,719
Speaker 1: It is the the great energy hog of your body. Yeah,

806
00:43:12,719 --> 00:43:17,200
Speaker 1: we're talking. Neurons require six kilo calories per billion neurons

807
00:43:17,200 --> 00:43:21,800
Speaker 1: per day, so um urkileno. Ozell points out that the

808
00:43:21,880 --> 00:43:27,000
Speaker 1: human brain costs on average five kilo calories per day, so,

809
00:43:27,200 --> 00:43:31,319
Speaker 1: in her words, not quite a whole hamburger. But but

810
00:43:31,360 --> 00:43:33,720
Speaker 1: I mean, when you think about that in terms of food,

811
00:43:33,840 --> 00:43:36,240
Speaker 1: the types of calories you can acquire in the wild,

812
00:43:36,680 --> 00:43:40,359
Speaker 1: without grains, without access to easy meat and all that,

813
00:43:40,360 --> 00:43:43,600
Speaker 1: that that's that's a tough requirement. Yeah, let's take a

814
00:43:43,600 --> 00:43:47,920
Speaker 1: moment to just realize how how, how how amazing the

815
00:43:47,960 --> 00:43:50,440
Speaker 1: hamburger is. I mean, and just in terms of like

816
00:43:50,480 --> 00:43:55,160
Speaker 1: how much uh, you know, protein and potential nutrition is

817
00:43:55,200 --> 00:43:57,440
Speaker 1: just jammed into that thing. There's a lot of energy

818
00:43:57,480 --> 00:44:00,279
Speaker 1: in the hamburger, like it or not. And we you know,

819
00:44:00,480 --> 00:44:02,879
Speaker 1: we take for granted, you know what a robust chunk

820
00:44:02,920 --> 00:44:05,080
Speaker 1: of energy that is. And if we had and if

821
00:44:05,120 --> 00:44:07,000
Speaker 1: we had we didn't have the hamburger, and of course

822
00:44:07,040 --> 00:44:09,040
Speaker 1: all the things that are comparable to the hamburger and

823
00:44:09,400 --> 00:44:12,880
Speaker 1: modern culinary tradition. And if we didn't have that to

824
00:44:12,880 --> 00:44:15,000
Speaker 1: to eat, if we had to eat like our primate

825
00:44:15,040 --> 00:44:20,120
Speaker 1: brethren did and still do without cooking and modern food. Uh.

826
00:44:20,280 --> 00:44:22,520
Speaker 1: She points out that we'd have to spend nine point

827
00:44:22,600 --> 00:44:25,640
Speaker 1: five hours every day eating so instead of so you're

828
00:44:25,640 --> 00:44:28,360
Speaker 1: just eating like raw vegetable matter most all that you

829
00:44:28,880 --> 00:44:31,000
Speaker 1: like a lot of animals do in the world. If you,

830
00:44:31,200 --> 00:44:33,719
Speaker 1: if you like, like we do, watch a lot of documentaries,

831
00:44:33,920 --> 00:44:37,680
Speaker 1: you'll frequently encounter, uh, you know, some an animal or

832
00:44:37,680 --> 00:44:40,439
Speaker 1: another of panda, and you're like, oh, all it does

833
00:44:40,520 --> 00:44:42,799
Speaker 1: is eat because it has to. It has to eat

834
00:44:42,880 --> 00:44:46,560
Speaker 1: all the time, you know, to maintain um uh, this

835
00:44:46,560 --> 00:44:48,960
Speaker 1: this body and ultimately it's brain as well. But of

836
00:44:49,000 --> 00:44:53,120
Speaker 1: course our brain again has tremendous energy requirements. It only

837
00:44:53,160 --> 00:44:55,360
Speaker 1: makes up two percent of our body, but it requires

838
00:44:55,400 --> 00:44:58,880
Speaker 1: twenty five of the body's energy. So that five calorie

839
00:44:58,880 --> 00:45:01,080
Speaker 1: burger is just part of a two thousand calorie per

840
00:45:01,160 --> 00:45:06,720
Speaker 1: day diet. So kolona Ozel contends that without cooking, we'd

841
00:45:06,719 --> 00:45:09,720
Speaker 1: be in the same state we'd evolved to one point

842
00:45:09,719 --> 00:45:13,520
Speaker 1: five million years ago, we'd be small primates with essentially

843
00:45:13,560 --> 00:45:16,200
Speaker 1: the brain power of a modern guerrilla, but with some

844
00:45:16,280 --> 00:45:19,879
Speaker 1: stone tool making abilities. This is kind of encouraging as

845
00:45:19,920 --> 00:45:23,160
Speaker 1: a as a hypothesis for people who love cooking kitchen

846
00:45:23,360 --> 00:45:27,840
Speaker 1: culinary enthusiasts. You you may well be taking part in

847
00:45:28,080 --> 00:45:31,759
Speaker 1: the most crucially human of all activities. Yeah, and I

848
00:45:31,800 --> 00:45:34,760
Speaker 1: believe that Michael Pollen has made a very very similar

849
00:45:34,800 --> 00:45:37,239
Speaker 1: argument before in terms of like why we we we

850
00:45:37,320 --> 00:45:39,560
Speaker 1: love cooking, and why even if we don't cook, we're

851
00:45:39,640 --> 00:45:42,799
Speaker 1: drawn to the myriad cooking shows that are out there.

852
00:45:43,200 --> 00:45:44,960
Speaker 1: You know, we want to watch somebody cook, We want

853
00:45:44,960 --> 00:45:48,520
Speaker 1: to learn about about different culinaries, traditions, and even for

854
00:45:48,600 --> 00:45:50,360
Speaker 1: my own part, I've never been much of a chef.

855
00:45:51,000 --> 00:45:54,319
Speaker 1: I am not much of a chef either, but I've

856
00:45:54,320 --> 00:45:56,799
Speaker 1: been enjoying some of these various like meal box things

857
00:45:56,840 --> 00:46:00,600
Speaker 1: recently and it's teaching me some of like the basics

858
00:46:00,719 --> 00:46:04,239
Speaker 1: of cooking. And you know, I'll still curse at a tomato,

859
00:46:04,560 --> 00:46:08,360
Speaker 1: but there's something fulfilling about going through all the instructions

860
00:46:08,360 --> 00:46:13,080
Speaker 1: and in turning this sack of raw materials into a

861
00:46:13,120 --> 00:46:17,319
Speaker 1: delicious meal. Well, Uh, tomatoes are tricky devils. That can

862
00:46:17,400 --> 00:46:22,080
Speaker 1: be seriously, I there, they're at both ends of my

863
00:46:22,280 --> 00:46:25,240
Speaker 1: you know, food love and hate spectrum. Basically, my favorite

864
00:46:25,239 --> 00:46:28,880
Speaker 1: food in the entire world is a really good ripe

865
00:46:29,000 --> 00:46:32,919
Speaker 1: summer tomato, and my least favorite food in the entire

866
00:46:33,000 --> 00:46:36,000
Speaker 1: world is like a mealy white winter tomato. It's the

867
00:46:36,040 --> 00:46:38,759
Speaker 1: worst thing on earth. Yeah, it's there's It's kind of

868
00:46:38,760 --> 00:46:41,000
Speaker 1: I feel a similar way with cantalope, Like, there are

869
00:46:41,040 --> 00:46:43,600
Speaker 1: a lot of mediocre cantle loopes out there, and if

870
00:46:43,640 --> 00:46:45,799
Speaker 1: you have one, you could easily turn your back on

871
00:46:45,880 --> 00:46:48,400
Speaker 1: cantal loops forever. But when you have like a really

872
00:46:48,400 --> 00:46:51,680
Speaker 1: good cantlelope, there's nothing else that can beat it. Well,

873
00:46:51,680 --> 00:46:54,600
Speaker 1: it's funny we talked about these extremes that agriculturally produced

874
00:46:55,120 --> 00:46:59,000
Speaker 1: fruits and vegetables. Arcleanoz l points out in somewhere I

875
00:46:59,080 --> 00:47:01,560
Speaker 1: was reading or one of her talks, I think where

876
00:47:01,600 --> 00:47:03,200
Speaker 1: she says, you know, it's kind of funny that the

877
00:47:03,239 --> 00:47:06,319
Speaker 1: inherent logic of of what she's showing here is even

878
00:47:06,360 --> 00:47:10,200
Speaker 1: there in some of the diet trends where like what

879
00:47:10,239 --> 00:47:12,480
Speaker 1: do people do when they want to lose weight? Well,

880
00:47:12,520 --> 00:47:15,240
Speaker 1: one popular thing is the raw food diet, because suddenly

881
00:47:15,239 --> 00:47:18,759
Speaker 1: you're condemning yourself to a forging type existence without you know,

882
00:47:18,840 --> 00:47:24,200
Speaker 1: without the calorie benefits of cooked food. Yeah, exactly, but

883
00:47:24,200 --> 00:47:27,600
Speaker 1: but cook But cooking food is something we did develop again,

884
00:47:27,600 --> 00:47:30,399
Speaker 1: and that was roughly one point five million years ago. Um.

885
00:47:30,480 --> 00:47:33,440
Speaker 1: And this is the key technology, she says. It changed

886
00:47:34,000 --> 00:47:36,799
Speaker 1: what was possible energy wise for the evolving brain. Our

887
00:47:36,840 --> 00:47:39,839
Speaker 1: brains got big in a hurry after this, and our

888
00:47:39,880 --> 00:47:43,000
Speaker 1: food technology, of course, continued to evolve, most notably via

889
00:47:43,040 --> 00:47:47,720
Speaker 1: the agricultural revolution. Definitely. I mean that the history of

890
00:47:47,719 --> 00:47:51,759
Speaker 1: of of human civilization is the history of our manipulation

891
00:47:51,800 --> 00:47:54,600
Speaker 1: of food really and our stockpiling of food, and then

892
00:47:54,640 --> 00:47:57,160
Speaker 1: our trade of food and our wars for food. So

893
00:47:57,440 --> 00:48:02,200
Speaker 1: we are using research that makes use of the brain

894
00:48:02,320 --> 00:48:08,160
Speaker 1: soup technique to discover the possibility to discover how important

895
00:48:08,239 --> 00:48:11,920
Speaker 1: the invention of literal soup might have been. Yeah. I

896
00:48:11,960 --> 00:48:15,680
Speaker 1: love the cyclical nature of this particular episode. We started

897
00:48:15,680 --> 00:48:18,279
Speaker 1: with with the brain soup and we came back to

898
00:48:18,320 --> 00:48:23,720
Speaker 1: brain soup. It's glorious, Okay. Introducing a new show segment,

899
00:48:23,920 --> 00:48:28,479
Speaker 1: Soup Facts, Soup Facts Facts with Joe McCornick. Here's something

900
00:48:28,560 --> 00:48:30,680
Speaker 1: if you're ever trying to figure out how much to

901
00:48:30,840 --> 00:48:33,000
Speaker 1: season your soup. You know, you don't want it to

902
00:48:33,000 --> 00:48:35,480
Speaker 1: be too salty, of course, but you also don't want

903
00:48:35,480 --> 00:48:37,720
Speaker 1: to underseason. And how much salt should go in a soup.

904
00:48:38,000 --> 00:48:40,680
Speaker 1: The best way to figure that out is to get

905
00:48:40,719 --> 00:48:43,879
Speaker 1: the soup to the temperature that you plan to serve

906
00:48:43,920 --> 00:48:46,680
Speaker 1: it at, and then taste and see how much salt

907
00:48:46,680 --> 00:48:48,799
Speaker 1: it needs there. Because the amount of salt that we

908
00:48:48,840 --> 00:48:52,480
Speaker 1: can taste when we taste something for seasoning varies drastically

909
00:48:52,560 --> 00:48:55,640
Speaker 1: depending on what temperature the food is at. So you

910
00:48:55,760 --> 00:48:58,319
Speaker 1: might you might taste it and it tastes like it

911
00:48:58,360 --> 00:49:01,399
Speaker 1: needs more salt, or it already has enough salt at

912
00:49:01,400 --> 00:49:05,520
Speaker 1: one temperature, but in a different temperature might taste totally different. Interesting,

913
00:49:05,719 --> 00:49:07,600
Speaker 1: I hadn't thought about that. I will use that the

914
00:49:07,680 --> 00:49:10,800
Speaker 1: next time. I'm I'm, I'm following instructions from a box

915
00:49:10,840 --> 00:49:14,400
Speaker 1: and have the mix soup all right. Another place I

916
00:49:14,440 --> 00:49:17,399
Speaker 1: always mess up with salt is with with pasta. If

917
00:49:17,440 --> 00:49:21,080
Speaker 1: I'm boiling pasta like I keep putting into little soup,

918
00:49:21,120 --> 00:49:24,600
Speaker 1: like I'm so afraid of oversalting something. Well, then i'm i'm,

919
00:49:25,080 --> 00:49:26,759
Speaker 1: and then my my wife will come in and say, oh,

920
00:49:26,800 --> 00:49:28,520
Speaker 1: then you're supposed to be throwing like a whole fistful

921
00:49:28,520 --> 00:49:31,120
Speaker 1: assault in there. Basically, it's a reasonable concern. I mean,

922
00:49:31,600 --> 00:49:34,080
Speaker 1: you can if you under salt something, you can always

923
00:49:34,120 --> 00:49:36,920
Speaker 1: add more. But if you over salt, you can't take

924
00:49:36,920 --> 00:49:38,920
Speaker 1: it out right. Well, you know, I think back to

925
00:49:39,040 --> 00:49:41,120
Speaker 1: the cocktail scenario, like if I mix up, if I

926
00:49:41,120 --> 00:49:43,160
Speaker 1: mess up a cocktail to the point where I can't

927
00:49:43,200 --> 00:49:47,360
Speaker 1: fix it, um, then I've wasted like two drinks, you know.

928
00:49:47,719 --> 00:49:50,200
Speaker 1: But if I do that with a super stew and

929
00:49:50,239 --> 00:49:52,600
Speaker 1: I've ruined dinner, like I have to then go and

930
00:49:52,640 --> 00:49:54,600
Speaker 1: get a pizza or something, and I've wasted all of

931
00:49:54,640 --> 00:49:57,279
Speaker 1: these resources. And you know, the most heartbreaking thing you

932
00:49:57,280 --> 00:50:00,080
Speaker 1: can do is have to potentially throw food away. And

933
00:50:00,360 --> 00:50:02,839
Speaker 1: I have, I have got I have over salted things

934
00:50:02,920 --> 00:50:05,359
Speaker 1: in the past to that point, you know where it's

935
00:50:05,400 --> 00:50:08,399
Speaker 1: just basically inedible. There was some recipe where I had

936
00:50:08,400 --> 00:50:10,520
Speaker 1: to and this was like a box meal years ago

937
00:50:10,600 --> 00:50:12,879
Speaker 1: when I was first figuring it out, and I had

938
00:50:12,920 --> 00:50:14,400
Speaker 1: like a thing of salt and a thing of sugar,

939
00:50:14,440 --> 00:50:16,719
Speaker 1: and the sugar went into one component salting the other

940
00:50:17,520 --> 00:50:20,960
Speaker 1: flipped them which made like a pretty sweet cole slab

941
00:50:21,000 --> 00:50:23,120
Speaker 1: that also just did whatever the other thing was was

942
00:50:23,400 --> 00:50:28,040
Speaker 1: just inedible. No, that's a sad story, all right, So

943
00:50:28,080 --> 00:50:31,880
Speaker 1: I guess we're gonna leave it there. Uh again Susannah

944
00:50:31,960 --> 00:50:36,440
Speaker 1: Roclinol wonderful science communicator. Uh look her up. She's all

945
00:50:36,480 --> 00:50:39,600
Speaker 1: over the internet. You can find her ted talk readily easily,

946
00:50:40,000 --> 00:50:42,319
Speaker 1: and they're also various interviews with her. And then of

947
00:50:42,360 --> 00:50:45,359
Speaker 1: course the scientific papers are out there to look at

948
00:50:45,440 --> 00:50:49,160
Speaker 1: as well. Uh. So also thanks to the World Science

949
00:50:49,200 --> 00:50:52,239
Speaker 1: Festival um for letting me attend and uh and you know,

950
00:50:52,320 --> 00:50:54,480
Speaker 1: taking in all of this data. Look at the World

951
00:50:54,560 --> 00:50:58,240
Speaker 1: Science Festival as well. Uh. They have a wonderful YouTube page.

952
00:50:58,400 --> 00:51:00,839
Speaker 1: They'll put a lot of these talks up um, you know,

953
00:51:01,120 --> 00:51:04,760
Speaker 1: over the course of the weeks and months ahead moving

954
00:51:04,760 --> 00:51:06,799
Speaker 1: into next year. And in the meantime, if you want

955
00:51:06,800 --> 00:51:08,799
Speaker 1: to check out more episodes of Stuff to Blow your Mind,

956
00:51:08,800 --> 00:51:10,399
Speaker 1: head on over to Stuff to Blow your Mind dot com.

957
00:51:10,400 --> 00:51:12,040
Speaker 1: That's the mother ship. That's where you'll find them all.

958
00:51:12,280 --> 00:51:14,960
Speaker 1: And if you want to support our show, we commend

959
00:51:15,000 --> 00:51:17,279
Speaker 1: you for wanting to do so. The best thing you

960
00:51:17,320 --> 00:51:19,560
Speaker 1: can do is to just rate and review us wherever

961
00:51:19,560 --> 00:51:21,160
Speaker 1: you have the power to do so, wherever you give it,

962
00:51:21,160 --> 00:51:23,640
Speaker 1: wherever you get this podcast, leave some stars and a

963
00:51:23,719 --> 00:51:25,839
Speaker 1: nice review, and of course just tell people about it,

964
00:51:26,080 --> 00:51:29,279
Speaker 1: about us, uh, tell your friends, tell your family, and

965
00:51:29,280 --> 00:51:32,040
Speaker 1: then tell them how to find this show. Huge thanks

966
00:51:32,080 --> 00:51:35,879
Speaker 1: as always to our excellent audio producer, Tari Harrison. If

967
00:51:35,880 --> 00:51:37,360
Speaker 1: you would like to get in touch with us with

968
00:51:37,480 --> 00:51:40,000
Speaker 1: feedback on this episode or any other, to suggest a

969
00:51:40,080 --> 00:51:42,120
Speaker 1: topic or a guest for the future of the show,

970
00:51:42,480 --> 00:51:44,799
Speaker 1: or just to say hello, you can email us at

971
00:51:45,120 --> 00:51:57,719
Speaker 1: contact at stuff to Blow Your Mind dot com Stuff

972
00:51:57,760 --> 00:51:59,319
Speaker 1: to Blow Your Mind. It's a production of i Heeart

973
00:51:59,360 --> 00:52:02,040
Speaker 1: Radio's hows to Works. For more podcasts from my Heart Radio,

974
00:52:02,120 --> 00:52:04,760
Speaker 1: visit the i Heart Radio app, Apple Podcasts, or wherever

975
00:52:04,800 --> 00:52:17,279
Speaker 1: you listen to your favorite shows.