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Speaker 1: Welcome to Creature, future production of I Heart Radio. I'm

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Speaker 1: your host of Many Parasites, Katie Golden. I studied psychology

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Speaker 1: and evolutionary biology, and today on the show Math Okay,

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Speaker 1: don't run away. I promise that even those of you

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Speaker 1: have let's say, complicated relationship with math. The animals were

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Speaker 1: about to talk about will show how absolutely incredibly fascinating

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Speaker 1: math can be. From mathematical b sex to baby animals

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Speaker 1: who are cute little calculators, to the way that math

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Speaker 1: can produce some of the most beautiful living works of

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Speaker 1: art in the natural kingdom. These word problems are no

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Speaker 1: problem at all. Discover this and more as we answer

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Speaker 1: to the angel question should you let your chickens count

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Speaker 1: before or after they've patched? Joining me today is astrophysicist

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Speaker 1: and co host of the podcast Daniel and Jorge Explain

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Speaker 1: the Universe Daniel Whiteson. Welcome back, Daniel, Hello, thank you

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Speaker 1: very much for having me on. I am happy to

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Speaker 1: be described as an astro physicist because I like to

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Speaker 1: throw my mind out there into the cosmos of the universe,

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Speaker 1: and I'm also a deep lover of mathematics. When I

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Speaker 1: hear math, I don't run screaming, I go tell me more. Yeah,

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Speaker 1: I think that. You know, sometimes some of us, not

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Speaker 1: naming any names, not pointing any fingers, have a complicated

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Speaker 1: history with math. It's like, you know, it doesn't seem like, oh,

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Speaker 1: this is probably not all that interesting because it's a

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Speaker 1: bunch of numbers and a teacher getting mad at me

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Speaker 1: for not putting them together in the right way on

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Speaker 1: a piece of paper. But it can be really really interesting.

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Speaker 1: And even for people who don't have a big background

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Speaker 1: in math, like you don't. It's you know, obviously some

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Speaker 1: parts of math, especially like the more theoretical you get,

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Speaker 1: it's it's pretty sort of a dense labyrinth. But when

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Speaker 1: you're kind of looking at the big picture applications of

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Speaker 1: what it can describe and do, like that's accessible to

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Speaker 1: everyone and it can be really interesting. Yeah. And in

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Speaker 1: the end, it's the language of the universe, you know,

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Speaker 1: it's not just the way that we like write physics

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Speaker 1: down and do our theories. We think math is actually

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Speaker 1: like fundamental. We think it controls the way things happen.

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Speaker 1: So I love when we find things in physics or

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Speaker 1: in biology or in economics that like revealed the underlying

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Speaker 1: mathematics because it shows us like sort of the source

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Speaker 1: code of the universe, like how things are really working

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Speaker 1: deep down exactly. It puts the fun and fundamental it does.

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Speaker 1: And so that's why it's so frustrating to me when

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Speaker 1: I see my kids like developing an aversion to math,

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Speaker 1: you know, because I'm like, come on, math is fund

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Speaker 1: isn't it? And they're like, maybe math worksheets aren't fun.

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Speaker 1: You know, that's a good point, that's true. But maybe

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Speaker 1: you can just take them out to a field and

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Speaker 1: point out some bees and say like, look, they're doing math,

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Speaker 1: and you can too. So there is a big paradox

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Speaker 1: in evolutionary biology, which is that you have a moles

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Speaker 1: who seem to sometimes behave altruistically and even go as

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Speaker 1: far as to be you social, so you sociality is

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Speaker 1: a social structure that is found in bees, ants, termites,

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Speaker 1: naked mole rats, where and mostly non breeding. Colony works

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Speaker 1: for the good of the queen and for their fellow

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Speaker 1: members of the colony. So it's this very it's seemingly

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Speaker 1: extremely altruistic, right, you're all working for the good of

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Speaker 1: the queen, for the good of the colony without worrying

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Speaker 1: about reproducing yourself. But this is a big problem because

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Speaker 1: evolution seems like it should always reward genetic selfishness. So

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Speaker 1: if if you can pass on your genes through trickery, thieving,

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Speaker 1: selfishness or whatever, that should just count. Like mother Nature

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Speaker 1: is not going to judge you. If you get your

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Speaker 1: genes passed on, that's good enough for her. There's this

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Speaker 1: famous Gary Larson cartoon that shows like a bunch of

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Speaker 1: limbings going off a cliff, which is a myth, by

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Speaker 1: the way, they don't you that, but still in this cartoon,

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Speaker 1: one of the limmings has a little inner tube and

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Speaker 1: it's kind of like mugging for the camera, looking very

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Speaker 1: smug because it's the one limbing that's going to survive

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Speaker 1: this jump off the cliff. And actually this cartoon was

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Speaker 1: used in some of my evolutionary biology classes in college

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Speaker 1: to demonstrate like, well, if you have this one liming

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Speaker 1: doing this thing like cheating with this inner tube, all

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Speaker 1: of the limings should develop this trait soon enough, because

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Speaker 1: that's that's going to be the types of genes that survive.

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Speaker 1: Like the limmings who were the inner tube and survived

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Speaker 1: the jump off the cliff should all develop that trait

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Speaker 1: because again, like if you survive, those genes are the

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Speaker 1: ones that are going to win out. So I have

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Speaker 1: so many questions already. First of all, you social. Is

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Speaker 1: that like the letter you and social or the word

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Speaker 1: you or like you know, E W E social, it's

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Speaker 1: eu and then social. So it sounds a little bit European,

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Speaker 1: but I'm sure it has another explanation for it. But

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Speaker 1: my real question is that is selfishness really necessary? Like

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Speaker 1: do most species actually maximize selfishness? I mean, why can't

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Speaker 1: the species get forward by being all nice to each

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Speaker 1: other and just like enjoying life, Like why don't they

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Speaker 1: maximize the joy of sunshine and you know, a drop

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Speaker 1: of water on the leaf and something. Doesn't that make

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Speaker 1: people happy and and make for like happier and more

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Speaker 1: successful children. Why is it all about selfishness? Well, you know,

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Speaker 1: it's actually true that it turns out. I mean, we

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Speaker 1: can just look around and see that. No, it's not

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Speaker 1: true that all animals maximize selfishness. We can look at humans.

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Speaker 1: I mean, sure we like to say, oh, humans are selfish,

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Speaker 1: but when you really look at human society, we've only

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Speaker 1: gotten this far by working together. And you know, of course,

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Speaker 1: like there's all sorts of problems with society. I'm not

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Speaker 1: going to get into any of those because that's probably

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Speaker 1: different podcasts. But when it comes down to it, we

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Speaker 1: are a cooperative species. We do work together in order

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Speaker 1: to enhance our survival, and it's somewhat easy to see

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Speaker 1: how this works out in our favor. Like we are

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Speaker 1: not the toughest roughest animal. We could not win in

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Speaker 1: a fight one on one versus a bear, but when

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Speaker 1: we're in a group, we do pretty well. And we

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Speaker 1: all benefit from being in this group because if we

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Speaker 1: are within the group and then we survive, we get

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Speaker 1: to pass on our genes and our children who will

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Speaker 1: have that same sort of cooperative framework. They're born sort

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Speaker 1: of with the capability of having that cooperativeness and empathy,

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Speaker 1: and then they're raised in the group. So it's sort

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Speaker 1: of this interaction between genetic selection and also social selection,

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Speaker 1: right like when you when you raise your child in

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Speaker 1: a group, they learn the customs of the group and

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Speaker 1: they learn how to interact with people. So we can

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Speaker 1: see how through natural selection, kindness and cooperation can actually

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Speaker 1: be beneficial to a species. Um But there's this whole

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Speaker 1: other problem when it comes to bees, ants, and termites,

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Speaker 1: because they're not just a cooperative society like humans are,

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Speaker 1: or humans should be. Maybe I should say individual humans

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Speaker 1: have offspring. It's not just the king and queen of

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Speaker 1: human society that have offspring. We're all able to have offspring,

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Speaker 1: and it all benefits us to have a community that's

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Speaker 1: that's safe and bountiful for our offspring. So working to other,

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Speaker 1: working together helps us all kind of pass on our

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Speaker 1: genes collectively. Um, but with these with bees, ants, termites,

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Speaker 1: and these other you social animals, the individuals in the

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Speaker 1: colony do not reproduce. So you have your hive of bees,

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Speaker 1: your colony of ants. All of the workers are females,

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Speaker 1: The queen is female. The workers do not reproduce, generally speaking,

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Speaker 1: and so it seems like, well, that shouldn't work out

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Speaker 1: like that. It should only select for the ants that

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Speaker 1: are selfish enough to want to reproduce, or you know,

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Speaker 1: I'm saying selfish and sort of a more evolutionary biology terms,

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Speaker 1: not that there's like a little ant going like ha ha,

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Speaker 1: I'm going to reproduce, screw the colony, but it really

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Speaker 1: should like favor that kind of sneakery. And trickery, it

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Speaker 1: seems like, because it's like, well, how else would these

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Speaker 1: altruistic genes get passed on? Right, why do they still

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Speaker 1: exist if they're not getting propagated to the next generation

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Speaker 1: exactly exactly, So this is a seeming paradox in order

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Speaker 1: to get to the bottom of this. I mean, there

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Speaker 1: is a hot debate about this in the evolutionary biology community.

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Speaker 1: I mean, I think it's still going on to this day.

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Speaker 1: Like I just read this like scathing research paper about like, oh, well,

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Speaker 1: these guys are all wrong about this, and we can

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Speaker 1: prove it. It's one of the more controversial topics in

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Speaker 1: evolutionary biology because it is such a fundamental thing that

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Speaker 1: we have to figure out in order to fully understand

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Speaker 1: how natural selection works. Well, why can't evolutionary biologists cooperate

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Speaker 1: when their research about cooperation? Right, there's like selfishness in

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Speaker 1: the research about cultruism. That's ironic. They've got it. What

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Speaker 1: Jeff Goldblum in the Fly his problem was he didn't

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Speaker 1: like put a bee in the other end of the transporter,

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Speaker 1: so he turned half be because then he would actually

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Speaker 1: be really productive and really cooperatively working on papers together

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Speaker 1: in a hive of researchers. So there are a few

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Speaker 1: theories that I think you have to understand all of

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Speaker 1: them to actually get the full picture of how this works.

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Speaker 1: And we've talked about bees and ants on the show before,

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Speaker 1: and I've kind of discussed some of these things in

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Speaker 1: less detail in terms of like how the math behind

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Speaker 1: this works. But right now we're going to get really

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Speaker 1: deep into it. To get a full understanding about the

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Speaker 1: math of bees and answer termites and so on and

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Speaker 1: so forth. I've never been more ready. That is probably

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Speaker 1: not going to be the only b pun we do

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Speaker 1: to folks, so strap in. So first let's talk about

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Speaker 1: the kin selection theory. So this is one of the

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Speaker 1: few theories that helps explain how a bee colony could exist.

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Speaker 1: And this actually doesn't apply to the one group of

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Speaker 1: mammals who are used social naked mole rats, which we'll

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Speaker 1: get into a little later. We'll discuss in more detail

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Speaker 1: in just a little bit, but let's just focus on bees, ants, termites.

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Speaker 1: These groups of insects that have a colony of worker females.

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Speaker 1: None of them reproduce, just the queen reproduces, and somehow

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Speaker 1: this remains a stable system. So the so the workers

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Speaker 1: are all female, like the males are not doing any work. Yeah,

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Speaker 1: basically the males don't do much. They kind of sit

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Speaker 1: around eating until they can go off and mate. And

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Speaker 1: if they don't go off and mate, they actually get

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Speaker 1: kicked out of the colony for being you know, layabouts

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Speaker 1: too lazy to mate. Wow, that's quite I know, all right,

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Speaker 1: you think that just getting to eat food all your

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Speaker 1: life and then going off and mate would be a

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Speaker 1: pretty good life. But uh yeah, I mean the the

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Speaker 1: only function males really do. I think that sometimes bees

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Speaker 1: will cluster together and vibrate together to increase the temperature

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Speaker 1: of the colony and cold weather, and I think males

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Speaker 1: will actually engage in that as well. But other than that,

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Speaker 1: they just kind of hang it around and wait until

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Speaker 1: they can go and reproduce. Basically, any bee you see

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Speaker 1: going from flower to flower collecting nectar is like chance,

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Speaker 1: it's going to be a female. So you got like

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Speaker 1: women at the top because the queen is in charge,

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Speaker 1: You've got women doing all the work, and the males

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Speaker 1: are really just around to make more women. Right, it's

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Speaker 1: a very matriarchal society. Congratulations, men, you can finally feel

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Speaker 1: like you are you are being discriminated against in b society.

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Speaker 1: They're being held down by the honey ceiling. So, as

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Speaker 1: we've discussed before on the show, bees, ants and termites

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Speaker 1: are what's called haploid diploid. So haploid simply means one

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Speaker 1: set of chromosomes, whereas diploid means two sets of chromosomes.

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Speaker 1: So we humans and most other sex having animals are

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Speaker 1: actually diploid. So we get one full set of chromosomes

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Speaker 1: from our father and one full set of chromosomes from

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Speaker 1: our mother, and we take all that, we scrumble it up,

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Speaker 1: and that becomes us. So that's why we're mostly x

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Speaker 1: X or x Y for example, exactly exactly, and of course,

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Speaker 1: like there are exceptions to this, like there are times

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Speaker 1: when like there are children who will have xx y

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Speaker 1: or like these things, but that on average, most people

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Speaker 1: get one set of chromosomes from their father and one

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Speaker 1: set of chromosomes from their mother. Haploid diploid is a

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Speaker 1: combination of haploidy where you get only one set of

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Speaker 1: chromosomes and diploid e. So this is the way it

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Speaker 1: works in these insects that are haploid diploids. So female

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Speaker 1: offspring hatch from fertilized eggs and get a set of

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Speaker 1: chromosomes from their mother and from their father like a human,

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Speaker 1: but male offspring hatch from unfertilized eggs, and they will

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Speaker 1: only receive one set of chromosomes from their mother, the queen.

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Speaker 1: So this means that sisters share about seventy five percent

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Speaker 1: of their genes with each other when their father is

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Speaker 1: the same. And so this is kind of like a

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Speaker 1: little bit of a jump in mauth, Like, wait, how

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Speaker 1: does that work? Because in humans, Uh, it's kind of

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Speaker 1: varies how much we're related to our siblings. On average,

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Speaker 1: it's a statistical average of about fifty pc you're going

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Speaker 1: to be related to your sibling. Uh. And it's the

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Speaker 1: reason it varies, The reason we're not just like always

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Speaker 1: related to a sibling is that we are getting a

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Speaker 1: random grab bag of gene from each parent through the

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Speaker 1: process of biosis. It's somewhat randomized recombination of genes when

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Speaker 1: our parents create their gammets. So we're getting this kind

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Speaker 1: of like grab bag of stuff, and then we'll probably

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Speaker 1: share a lot of those genes with our siblings. Um,

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Speaker 1: but it's somewhat randomized. So Statistically speaking, we've got about

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Speaker 1: a fifty percent chance of sharing those genes from our

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Speaker 1: parents with our sibling. But there's a range there, right,

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Speaker 1: Like principle, it's possible to have no genes in common

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Speaker 1: with your brother if you just got like, exactly the

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Speaker 1: opposite version of every gene from each parent. Yes, it's

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Speaker 1: theoretically possible. Yes, it's theoretically possible for that to happen. Uh,

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Speaker 1: you can see like that's sometimes siblings look very similar,

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Speaker 1: sometimes they don't look related at all, So you can

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Speaker 1: and that actually, even looking similar or not related at

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Speaker 1: all doesn't necessarily mean that you're not genetically similar. You

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Speaker 1: could share more genes with someone who you don't look

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Speaker 1: at like, a sibling that you don't look like, then

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Speaker 1: you do with the sibling that you do look like.

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Speaker 1: It just depends on whether you happen to share some

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Speaker 1: of the genes that code for like eye color or

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Speaker 1: hair color. People always tell me I look like Matt mconackey,

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Speaker 1: but we're not related at all. I'm pretty sure that

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Speaker 1: wasn't a joke, right, No, that was a serious laugh.

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Speaker 1: It's it's just how stunningly similar to Matthew McConaughey, you like,

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Speaker 1: all right, all right, all right, yeah, so right, so

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Speaker 1: with our sibling, your sister or your brother or your

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Speaker 1: whoever you're about related statistically, you could be less or

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Speaker 1: more depending on which sort of grab bag of jeans

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Speaker 1: you got from your parents when you were a little

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Speaker 1: when you're a little egg in a sperm, and then

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Speaker 1: those combined and then you then there you're you. Uh.

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Speaker 1: With bees, it's seventy that's way more. It's not always

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Speaker 1: exactly seventy five percent, but on average, that's how related

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Speaker 1: they are to their sisters, uh so much more than humans.

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Speaker 1: So hold onto your butts. This is where we're gonna

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Speaker 1: do some pretty simple I would say math, but it

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Speaker 1: can get a little confusing. I definitely failed some of

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Speaker 1: these quizzes in college because I like get sort of

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Speaker 1: confused with percentages. But it's easier to do when you're

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Speaker 1: looking at a diagram that somebody made using Pokemon bees.

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Speaker 1: So I will include that in the show notes. Someone

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Speaker 1: made this diagram. I think it's Comby and then like

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Speaker 1: Cambies evolutions. Um, I don't know what the names of

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Speaker 1: those are, but it's a great diagram. So basically the

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Speaker 1: queen gives of her jeans to her daughter, and the

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Speaker 1: father gives one of his genes to his daughter. How

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Speaker 1: does that work? Well, if you remember earlier what we

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Speaker 1: were talking about is that males results from unfertilized eggs.

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Speaker 1: They are haploid. They only get one set of chromosomes

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Speaker 1: from their mother, so they only have one set of

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Speaker 1: chromosomes they can get of to an offspring. So when

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Speaker 1: they are mating with the queen, while the queen has

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Speaker 1: two sets of chromosomes and she can give you of

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Speaker 1: whatever chromosomes she has, the male only has one set,

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Speaker 1: so he just gives you that that's all he's got

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Speaker 1: for you. So this means that at least half of

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Speaker 1: the daughter's genes are going to be shared with her

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Speaker 1: sister's genes at a minimum, because she gets they both

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Speaker 1: got like a of their genes from their father if

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Speaker 1: they share their father, So the other half of her

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Speaker 1: genes that she gets from her mother that's a little

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Speaker 1: more randomized, So she'll share about half of that amount

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Speaker 1: of genes with any given sister. So that means that

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Speaker 1: of like her total chromosomal amount, like she'll of her

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Speaker 1: mother's chromosomes, she has about a chance of sharing that

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Speaker 1: with another sister because it's half of a half. It's

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Speaker 1: so let's see if I get this math right. So

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Speaker 1: you're saying that all the female sisters they have the

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Speaker 1: same gene from their father, because the father only has one,

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Speaker 1: so they all have exactly the same one. They're like

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Speaker 1: identical twins. But the other one, the one they get

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Speaker 1: from their mom. They have one of their mom's genes,

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Speaker 1: so they might share it or they might not. Right,

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Speaker 1: basically similar ish, I mean, it's a little it's a

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Speaker 1: little more complicated because it's sets of chromosomes and when

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Speaker 1: when the mother is actually creating her egg it goes

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Speaker 1: through this process of miosis where there's some randomization, so

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Speaker 1: females can actually share more or less related nous through

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Speaker 1: their mother, whereas like they're getting I think basically of

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Speaker 1: their father's genes, because the father really doesn't have, uh

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Speaker 1: that the amount of genes to play around with that

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Speaker 1: the the mother does because he only gets like one

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Speaker 1: set of chromosomes and that's all he is. And when

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Speaker 1: she makes eggs, like eggs are either female or male, right,

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Speaker 1: even though they have only one of her genes, So

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Speaker 1: the male eggs do they always get the same gene

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Speaker 1: from her or do they get one or the other.

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Speaker 1: They get I think a randomized gene from her, so

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Speaker 1: they are unfertilized, so it's only getting its mother's genes.

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Speaker 1: But I think it's still goes through the process of myosis.

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Speaker 1: So the male offspring is still getting some randomized genes

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Speaker 1: from its mother, but it's all that's all it's getting.

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Speaker 1: It's getting nothing from the father because it's unfertilized. So

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Speaker 1: the eggs all have one gene and then the male

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Speaker 1: comes along and when he fertilizes, it gives it the

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Speaker 1: other one to make it female. But the male eggs

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Speaker 1: never get fertilized, so they just stay one gene and

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Speaker 1: stay male. I mean, I would just say, like instead

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Speaker 1: of saying one gene, one set of chromosomes, because like

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Speaker 1: there's many genes within a set of chromosomes. But yeah,

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Speaker 1: just like one set of chromosomes that it gets from

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Speaker 1: its mother, not add percent of its mother's chromosomes, because

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Speaker 1: its mother has more chromosomes than the male even needs.

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Speaker 1: So she that's going through that process of myosis, such

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Speaker 1: that the male offspring is getting, um, what is it?

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Speaker 1: I think the male is roughly related to its mother.

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Speaker 1: Let me just double check on that. Sorry. See, this

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Speaker 1: is why I fail these quizzes in college. It's complicated.

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Speaker 1: Oh my gosh, it really is. I mean it doesn't

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Speaker 1: seem like it should be right because you're dealing with

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Speaker 1: very simple percentages like a D. But then as you

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Speaker 1: get like a longer line of bees and you're trying

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Speaker 1: to figure out like how much of this be is

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Speaker 1: related to that be, it actually adds up to be

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Speaker 1: quite complicated. So the queen will share on average about

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Speaker 1: half of her jeans with her you know, she will

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Speaker 1: definitely share half of her jeans with her son. Weirdly,

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Speaker 1: the male be will share a hundred percent of his

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Speaker 1: jeans with his mother because he's like a hundred percent

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Speaker 1: of his genes is his mother's genes. But for her,

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Speaker 1: she's only half related to him because she's only giving

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Speaker 1: half of her jeans to him. Uh. It creates some

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Speaker 1: really really weird scenarios to where well, mothers and sons

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Speaker 1: always have complicated relationships. Well bees, male bees can only

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Speaker 1: have daughters, they can't have sons, but they can have

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Speaker 1: grandsons because again, like you have, uh, when the queen

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Speaker 1: is laying eggs and she mates. Let's let's call him

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Speaker 1: Jerry is her mate. You can't even really call him

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Speaker 1: the king because he just dies after mating. He has

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Speaker 1: no yeah, exactly, it's got he's got no like uh

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Speaker 1: power in the royal society. So like one time they

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Speaker 1: mate and explode. No wonder the wonder these men know what, mate?

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Speaker 1: I understand, I do what now? So so the queen

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Speaker 1: mates with Jerry, and Jerry dies, and the the only

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Speaker 1: eggs that the queen is going to use Jerry's DNA

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Speaker 1: for our female because the unfertilized eggs do not use

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Speaker 1: any of Jerry's DNA, and those are the Queen's sons.

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Speaker 1: But Jerry can still have a grandson because one of

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Speaker 1: his daughters could grow up to become a new queen.

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00:22:18,320 --> 00:22:21,400
Speaker 1: And if she mates, she can have a son, and

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Speaker 1: through the process of miosis, some of her father's genes

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Speaker 1: that she's scrumbling up to go into her son. Uh

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Speaker 1: could could like that then will make that son Jerry's grandson.

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Speaker 1: But he can't have any sons, so he has got

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Speaker 1: a skip a generation before he's got a got a grandson,

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Speaker 1: which doesn't really matter to him. Because he's dead anyway,

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Speaker 1: And so most of the queen's children to have no offspring. Right,

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Speaker 1: only if she springs to have a female child which

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Speaker 1: becomes a queen. Well, it's not even too randomized in

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Speaker 1: terms of female offspring becoming queens. That only happens if

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Speaker 1: they feed them royal jelly, right, And so yes, most

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Speaker 1: of the female offspring will not have uh any offspring.

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Speaker 1: And the way that this happens, one proposed way is

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Speaker 1: that because um, because they're more related to their sisters

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Speaker 1: than they are to like, potentially their own offspring even um,

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Speaker 1: then it doesn't make sense for them to have to

394
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Speaker 1: sneak around and try to have their own offspring when

395
00:23:24,720 --> 00:23:27,960
Speaker 1: they're they share so many genes with their sisters that

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Speaker 1: whatever altruistic gene they have, if they ensure the success

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Speaker 1: of their colony and one of their sisters ends up

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Speaker 1: being one of the new queens, it's like that increases

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Speaker 1: the chance of all of these genes that they share

400
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Speaker 1: of getting passed onto the next generation. So usually you

401
00:23:43,280 --> 00:23:45,760
Speaker 1: think about the next generation and like, I'm trying to

402
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Speaker 1: lift up the next generation and support the ones that

403
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Speaker 1: have my genes so they can probably go forward, sort

404
00:23:50,760 --> 00:23:54,720
Speaker 1: of down the evolutionary tree. But here these people will

405
00:23:54,800 --> 00:23:57,639
Speaker 1: never but here these beans will never have any offspring.

406
00:23:57,960 --> 00:24:00,119
Speaker 1: But you're saying that they're related to their mom them

407
00:24:00,119 --> 00:24:02,560
Speaker 1: into their siblings, and so they still have a sort

408
00:24:02,600 --> 00:24:05,760
Speaker 1: of genetic interest in their mother's success even if they're

409
00:24:05,760 --> 00:24:08,040
Speaker 1: not going to have any kids themselves. It's not really

410
00:24:08,080 --> 00:24:11,080
Speaker 1: that individually they have any interest in it. It's that

411
00:24:11,960 --> 00:24:15,800
Speaker 1: it's whether or not the genes that they have, uh,

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Speaker 1: are the same genes that get passed onto the new

413
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Speaker 1: generation and code for the same behavior. So it's like

414
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Speaker 1: they're like, they're these bees have no plans. They're not

415
00:24:24,840 --> 00:24:27,320
Speaker 1: thinking like, well, if I help out my sister, maybe

416
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Speaker 1: she'll have a she'll become queen and have a son.

417
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Speaker 1: There's not like neither the bee nor the genes inside

418
00:24:34,720 --> 00:24:37,119
Speaker 1: the bee can plan ahead like that or think like that.

419
00:24:37,200 --> 00:24:41,960
Speaker 1: It's really like, say you have it's about the statistical

420
00:24:42,040 --> 00:24:45,240
Speaker 1: probability that the genes that they have and they share

421
00:24:45,480 --> 00:24:48,760
Speaker 1: that codes for the same youth social behavior ends up

422
00:24:48,800 --> 00:24:53,000
Speaker 1: getting passed on to the next generation. So it's it's

423
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Speaker 1: it's counterintuitive because we really think about sort of intention

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Speaker 1: with things like like a kind of uh, you know,

425
00:25:00,600 --> 00:25:02,960
Speaker 1: I want to give my children a good life. I

426
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Speaker 1: want to you know, pass on sort of like good

427
00:25:05,680 --> 00:25:09,040
Speaker 1: qualities to my children, like kindness and happiness and things.

428
00:25:09,560 --> 00:25:13,359
Speaker 1: But you know, in terms of natural selection, especially when

429
00:25:13,400 --> 00:25:15,439
Speaker 1: you're looking at something like a B, it's not a

430
00:25:15,480 --> 00:25:18,919
Speaker 1: conscious decision of I'm trying to pass this on. It's

431
00:25:18,920 --> 00:25:21,480
Speaker 1: it's like, if those genes get passed on, you did it,

432
00:25:21,560 --> 00:25:24,120
Speaker 1: You won, And those behaviors that got passed on through

433
00:25:24,160 --> 00:25:28,120
Speaker 1: those genes are going to be replicated over and over

434
00:25:28,160 --> 00:25:31,280
Speaker 1: throughout the generations. Yeah, and it's even deeper than that, right,

435
00:25:31,520 --> 00:25:34,399
Speaker 1: because you're saying that that behavior caring for your kids,

436
00:25:34,520 --> 00:25:38,160
Speaker 1: loving your kids is really just a product of evolutionary

437
00:25:38,200 --> 00:25:41,480
Speaker 1: pressure that folks that ended up feeling things for their

438
00:25:41,560 --> 00:25:43,199
Speaker 1: kids are the ones that took care of them and

439
00:25:43,240 --> 00:25:46,600
Speaker 1: then propagating their own genes. It is. It is, And

440
00:25:46,760 --> 00:25:49,040
Speaker 1: I know that for some people that may like feel like,

441
00:25:49,080 --> 00:25:51,439
Speaker 1: oh man, well then we don't have free will, and

442
00:25:51,480 --> 00:25:55,119
Speaker 1: it's it's meaningless, and it's like everything in human society

443
00:25:55,320 --> 00:25:59,120
Speaker 1: is basically a product of evolution, but we have this

444
00:25:59,400 --> 00:26:03,720
Speaker 1: kind of unique ability to have some fun with it

445
00:26:03,840 --> 00:26:06,959
Speaker 1: and to kind of have some decision, like if you know,

446
00:26:07,960 --> 00:26:10,879
Speaker 1: as evidenced by the fact that people adopt children and

447
00:26:10,960 --> 00:26:14,439
Speaker 1: love them as as you know their children because they

448
00:26:14,480 --> 00:26:17,480
Speaker 1: are their children, and that has no like, you know,

449
00:26:17,720 --> 00:26:22,920
Speaker 1: genetic motivation. It's just because we have developed this empathy

450
00:26:23,000 --> 00:26:26,160
Speaker 1: and this kindness towards other humans where we realize, like, hey,

451
00:26:26,200 --> 00:26:28,160
Speaker 1: you know, a family can be anything that we want

452
00:26:28,200 --> 00:26:30,280
Speaker 1: it to be. So, if anything, we're kind of like

453
00:26:30,359 --> 00:26:32,480
Speaker 1: giving the middle finger to Mother Nature and saying like, hey,

454
00:26:32,520 --> 00:26:36,920
Speaker 1: we're gonna be nice whether you want it or not. Right,

455
00:26:36,960 --> 00:26:40,640
Speaker 1: and not every behavior is perfectly sculpted by evolution. Right,

456
00:26:40,960 --> 00:26:43,440
Speaker 1: It's like, if something develops and it's useful for propping

457
00:26:43,480 --> 00:26:46,040
Speaker 1: any of your genes, it might also have other effects

458
00:26:46,160 --> 00:26:49,359
Speaker 1: which aren't harmful, like adopting children and also loving them

459
00:26:49,400 --> 00:26:53,320
Speaker 1: exactly because we live in a society, as the Joker

460
00:26:53,359 --> 00:26:55,119
Speaker 1: likes to say, but I also like to say it,

461
00:26:55,560 --> 00:26:59,800
Speaker 1: because through our society we can actually achieve much more

462
00:26:59,840 --> 00:27:02,600
Speaker 1: in terms of like propping each other up and helping

463
00:27:02,600 --> 00:27:05,960
Speaker 1: each other survive in a way that takes off a

464
00:27:05,960 --> 00:27:08,879
Speaker 1: lot of the selective pressures from us and allows us

465
00:27:08,880 --> 00:27:12,400
Speaker 1: to be kinder and and have more freedom in terms

466
00:27:12,480 --> 00:27:14,320
Speaker 1: of what we choose to do with our behaviors, which

467
00:27:14,359 --> 00:27:17,639
Speaker 1: I think is really lovely. Unfortunately not the case for

468
00:27:17,920 --> 00:27:21,680
Speaker 1: bees and and ants and and uh also naked molerats.

469
00:27:21,720 --> 00:27:25,200
Speaker 1: And so this is where I get into part two

470
00:27:25,480 --> 00:27:30,760
Speaker 1: of the explanation of what is going on. So unfortunately,

471
00:27:31,119 --> 00:27:35,359
Speaker 1: kin selection is not the sort of tidy explanation that

472
00:27:35,400 --> 00:27:37,320
Speaker 1: we would hope for where it's like, okay, so just

473
00:27:37,400 --> 00:27:40,240
Speaker 1: like statistically speaking, like you know, they share their genes

474
00:27:40,240 --> 00:27:42,800
Speaker 1: with their sisters, and that would would be able to

475
00:27:42,840 --> 00:27:45,920
Speaker 1: allow them to like, you know, those genes to survive.

476
00:27:46,520 --> 00:27:50,159
Speaker 1: It's it's not enough of an explanation potentially. And we

477
00:27:50,280 --> 00:27:53,639
Speaker 1: see this because of the pesky naked molerat, which is

478
00:27:53,640 --> 00:27:57,680
Speaker 1: a mammal uh, a little cutie that looks um sort

479
00:27:57,720 --> 00:28:00,720
Speaker 1: of like you took a hamster and shaved it, uh

480
00:28:00,760 --> 00:28:04,640
Speaker 1: and uh put it underground. Don't do that anybody from

481
00:28:07,160 --> 00:28:11,960
Speaker 1: They're wrinkly, they're flesh colored, you know, they look slightly phallic.

482
00:28:12,560 --> 00:28:17,719
Speaker 1: But they're really interesting animals. So uh. They are diploid

483
00:28:18,080 --> 00:28:20,960
Speaker 1: like humans, like most other sex having animals, like all

484
00:28:21,000 --> 00:28:24,600
Speaker 1: other mammals. Uh, they you know, get a pair of

485
00:28:24,880 --> 00:28:27,200
Speaker 1: a set of genes from their mother and from their father.

486
00:28:27,359 --> 00:28:30,120
Speaker 1: All of them there's none of this like weird genetic

487
00:28:30,119 --> 00:28:33,160
Speaker 1: shenanigans going on for these naked mole rats like there

488
00:28:33,280 --> 00:28:36,960
Speaker 1: is for bees and ants and so on. But naked

489
00:28:37,000 --> 00:28:41,280
Speaker 1: mole rats have a youth social society, which shouldn't make sense,

490
00:28:41,680 --> 00:28:45,040
Speaker 1: but somehow it works out. So they have one reproducing

491
00:28:45,080 --> 00:28:49,400
Speaker 1: dominant queen and subservient worker females who do not reproduce,

492
00:28:49,680 --> 00:28:53,200
Speaker 1: just like bees and ant colonies, and and they kind

493
00:28:53,240 --> 00:28:56,640
Speaker 1: of act in a way that's very similar to bee colonies,

494
00:28:56,640 --> 00:28:59,680
Speaker 1: where you have the queen kind of passing on orders

495
00:29:00,040 --> 00:29:04,400
Speaker 1: and bullying her underlings. So this brings us to another

496
00:29:04,480 --> 00:29:08,720
Speaker 1: force that may help create youth social societies, and that's

497
00:29:08,800 --> 00:29:15,640
Speaker 1: brutal tyranny, so, or, perhaps more gently put the manipulation

498
00:29:15,680 --> 00:29:18,880
Speaker 1: on the part of the queens. So both use social

499
00:29:18,920 --> 00:29:23,240
Speaker 1: insects and naked molerat queens use both chemical and behavioral

500
00:29:23,240 --> 00:29:28,440
Speaker 1: manipulation to force their colony into compliance. So you social

501
00:29:28,520 --> 00:29:33,800
Speaker 1: insects use pheromones often that will suppress other females ability

502
00:29:33,880 --> 00:29:36,880
Speaker 1: to reproduce. So they have these pheromone signals that will

503
00:29:36,920 --> 00:29:42,320
Speaker 1: actively suppress female reproduction in their offspring. But we're talking

504
00:29:42,320 --> 00:29:45,520
Speaker 1: about bees now bees using SMA because I thought that

505
00:29:45,520 --> 00:29:48,280
Speaker 1: the female bees couldn't reproduce because they're not queens and

506
00:29:48,320 --> 00:29:50,680
Speaker 1: they needed the royal jelly to be a queen. But

507
00:29:50,760 --> 00:29:52,920
Speaker 1: you're saying that even without the royal jelly they could

508
00:29:52,960 --> 00:29:57,600
Speaker 1: somehow reproduce. Sometimes they can sneakily reproduce. While the pheromones

509
00:29:57,760 --> 00:30:01,800
Speaker 1: do suppress their reproduct of hormones. If you've got to

510
00:30:01,840 --> 00:30:04,080
Speaker 1: be that maybe is not getting as exposed to that.

511
00:30:04,440 --> 00:30:08,880
Speaker 1: Sometimes they do sneakily reproduce even if they're not queens,

512
00:30:09,120 --> 00:30:11,920
Speaker 1: and that is met with the punishment of having their

513
00:30:11,920 --> 00:30:14,120
Speaker 1: eggs eaten by the queen. So the queen will go

514
00:30:14,200 --> 00:30:17,040
Speaker 1: around eating eggs that she finds that are not her own.

515
00:30:17,440 --> 00:30:20,320
Speaker 1: So it is an extra safety measure to make sure

516
00:30:20,360 --> 00:30:24,200
Speaker 1: nobody's reproducing but the queen. Uh. And even in terms

517
00:30:24,200 --> 00:30:27,000
Speaker 1: of like once a queen hatches, right, and if she

518
00:30:27,120 --> 00:30:31,040
Speaker 1: has other sisters that are being developed into queens by

519
00:30:31,040 --> 00:30:33,960
Speaker 1: being fed this royal jelly, she will kill them to

520
00:30:34,080 --> 00:30:36,680
Speaker 1: make sure that she's the only queen. Uh. There are

521
00:30:36,920 --> 00:30:40,160
Speaker 1: very few instances where a colony can have two queens

522
00:30:40,200 --> 00:30:43,520
Speaker 1: at once. That doesn't typically happen. Um. So like once

523
00:30:43,600 --> 00:30:47,800
Speaker 1: the old queen dies and her pheromones are gone. UH.

524
00:30:47,840 --> 00:30:51,560
Speaker 1: This signals like the bees to start creating, just feeding

525
00:30:51,680 --> 00:30:54,840
Speaker 1: royal jelly hither and thither, to like create new queens

526
00:30:55,000 --> 00:30:57,959
Speaker 1: and to start reproducing until a new queen will emerge

527
00:30:57,960 --> 00:31:00,880
Speaker 1: and reproduce for the rest of the colony. But naked

528
00:31:00,920 --> 00:31:05,960
Speaker 1: mole rats now, there was some research done to see

529
00:31:05,960 --> 00:31:09,320
Speaker 1: whether they use pheromones to UH for the queen to

530
00:31:09,520 --> 00:31:13,520
Speaker 1: enforce her monarchy. That research a little it's a little shaky.

531
00:31:13,680 --> 00:31:16,240
Speaker 1: They're not really sure whether or not. There there was

532
00:31:16,320 --> 00:31:19,680
Speaker 1: like some evidence that pointed towards maybe some pheromones excreted

533
00:31:19,920 --> 00:31:23,840
Speaker 1: in the queen's urine that may have some impact, but

534
00:31:24,320 --> 00:31:26,720
Speaker 1: there have been other studies that were like showing that

535
00:31:26,840 --> 00:31:29,280
Speaker 1: maybe it doesn't have as much as an effect. What

536
00:31:29,320 --> 00:31:32,680
Speaker 1: we do know is that she will physically bully them,

537
00:31:34,040 --> 00:31:38,040
Speaker 1: like any female that is disobedient or you know, is

538
00:31:38,600 --> 00:31:41,320
Speaker 1: trying to shirk her work or or go off to

539
00:31:41,400 --> 00:31:44,040
Speaker 1: mate somewhere, Like we'll get bullied and shoved around and

540
00:31:44,080 --> 00:31:47,880
Speaker 1: sat on by the queen UH to Like basically this

541
00:31:48,240 --> 00:31:53,680
Speaker 1: and this physical dominance displays UH seems to inhibit the

542
00:31:53,720 --> 00:31:58,960
Speaker 1: females reproductive hormones by basically constant bullying makes them less

543
00:31:59,000 --> 00:32:04,440
Speaker 1: likely to reproduce actually inhibit yes, inhibits the reproductive hormones,

544
00:32:04,680 --> 00:32:08,280
Speaker 1: um so, and can somebody else take over? And maybe

545
00:32:08,280 --> 00:32:10,720
Speaker 1: it's just like physical dominance. Can you get like a

546
00:32:10,760 --> 00:32:13,080
Speaker 1: really tough muscley one of these things that's born and

547
00:32:13,120 --> 00:32:17,080
Speaker 1: eventually take over and be the new queen? Yeah, but generally, interestingly,

548
00:32:17,080 --> 00:32:21,000
Speaker 1: it seems like that is often passed, like the daughters

549
00:32:21,040 --> 00:32:26,200
Speaker 1: of the queen, Like, uh, it's usually I think it

550
00:32:26,280 --> 00:32:28,640
Speaker 1: happens like when the queen is older and like if

551
00:32:28,680 --> 00:32:32,280
Speaker 1: she's like kind of losing losing her head or her edge,

552
00:32:32,480 --> 00:32:34,800
Speaker 1: like one, yeah, one of these daughters will take over

553
00:32:36,120 --> 00:32:40,000
Speaker 1: the colony. And sometimes, and this is what's interesting and

554
00:32:40,040 --> 00:32:43,760
Speaker 1: this seems to happen more often in these colonies than

555
00:32:43,840 --> 00:32:47,520
Speaker 1: with the colonies, is that like the female members, the

556
00:32:47,760 --> 00:32:50,480
Speaker 1: ones that are sort of being bullied, these workers will

557
00:32:50,600 --> 00:32:53,280
Speaker 1: try to sneak off and start there like reproduce and

558
00:32:53,320 --> 00:32:57,160
Speaker 1: start their own colony essentially, and they can manage to

559
00:32:57,200 --> 00:32:59,480
Speaker 1: do that. So it's sort of this like release valve

560
00:32:59,600 --> 00:33:04,080
Speaker 1: of like of these workers sneaking off starting their new colonies,

561
00:33:04,280 --> 00:33:06,440
Speaker 1: Which makes a lot of sense because in this in

562
00:33:06,480 --> 00:33:10,560
Speaker 1: this kind of you social situation. It really is sort

563
00:33:10,560 --> 00:33:14,800
Speaker 1: of just however you can manage, uh, manage it. So

564
00:33:14,880 --> 00:33:16,920
Speaker 1: like the queen is using kind of brute force to

565
00:33:17,000 --> 00:33:19,200
Speaker 1: make sure that she's got a lot of support for

566
00:33:19,240 --> 00:33:23,040
Speaker 1: her offspring. But other females, if they can get away

567
00:33:23,400 --> 00:33:27,640
Speaker 1: with going off in and reproducing, they will, and bees

568
00:33:27,720 --> 00:33:30,320
Speaker 1: in theory probably would too. It's just like in the

569
00:33:30,400 --> 00:33:33,840
Speaker 1: b society, it's probably a lot more iron clad than

570
00:33:33,880 --> 00:33:37,800
Speaker 1: these naked molerat societies in terms of preventing that from happening.

571
00:33:38,400 --> 00:33:40,920
Speaker 1: And what are the men doing in these naked molerat societies?

572
00:33:40,920 --> 00:33:43,680
Speaker 1: So they also like not doing any work. I think

573
00:33:44,200 --> 00:33:46,840
Speaker 1: that's a good question. I think they might do more

574
00:33:46,880 --> 00:33:50,360
Speaker 1: work than than the than in bees. So like there

575
00:33:50,440 --> 00:33:53,040
Speaker 1: is like brute force being used both in the naked

576
00:33:53,120 --> 00:33:56,280
Speaker 1: molerat society and in bees and ants and these you

577
00:33:56,520 --> 00:34:00,920
Speaker 1: social insects. Uh So, so that's another part of it.

578
00:34:00,960 --> 00:34:03,320
Speaker 1: I think that adds onto like the kin selection. So

579
00:34:03,360 --> 00:34:06,040
Speaker 1: if the kin selection is not enough, like you also

580
00:34:06,080 --> 00:34:11,280
Speaker 1: have this behavior where you're basically having a tyranny which

581
00:34:11,480 --> 00:34:14,840
Speaker 1: can propagate. We know in human society tyranny's can continue

582
00:34:14,880 --> 00:34:17,680
Speaker 1: on for generations even if it's not to the benefit

583
00:34:17,719 --> 00:34:21,480
Speaker 1: of individuals. But um, but in this case, it's like

584
00:34:21,960 --> 00:34:25,400
Speaker 1: it's helped even more out by the fact that genetically speaking,

585
00:34:25,840 --> 00:34:27,840
Speaker 1: UH they do like they do share a lot of genes,

586
00:34:27,880 --> 00:34:30,719
Speaker 1: even even the naked moleras, even though it's not the

587
00:34:30,800 --> 00:34:35,799
Speaker 1: same as with U. With the uh bees, like they

588
00:34:35,800 --> 00:34:38,839
Speaker 1: are often sisters and related to their mother in some way.

589
00:34:38,880 --> 00:34:42,040
Speaker 1: But then the next theory, the last theory we're going

590
00:34:42,080 --> 00:34:46,239
Speaker 1: to talk about, is the multi level selection theory, which

591
00:34:46,280 --> 00:34:50,760
Speaker 1: is basically that evolutionary selective pressures acts on all levels

592
00:34:50,800 --> 00:34:55,680
Speaker 1: of life, from genetic to cellular to an individual organism

593
00:34:55,680 --> 00:34:59,640
Speaker 1: to a group of organisms. So the analogy used as

594
00:34:59,680 --> 00:35:03,120
Speaker 1: like a Russian nesting doll, where it's like inside like

595
00:35:03,200 --> 00:35:05,719
Speaker 1: that little the tiniest Russian nesting doll, Like that's on

596
00:35:05,760 --> 00:35:09,920
Speaker 1: the genetic level, your genes uh will will often compete

597
00:35:09,960 --> 00:35:13,040
Speaker 1: and will uh you know, like there're selective pressures happening

598
00:35:13,040 --> 00:35:16,160
Speaker 1: directly on the genes at the genetic level. And then

599
00:35:16,200 --> 00:35:20,759
Speaker 1: you have evolutionary selection pressures acting on your cells individual

600
00:35:20,800 --> 00:35:23,560
Speaker 1: like cells can compete and interact in interesting ways, which

601
00:35:23,560 --> 00:35:25,680
Speaker 1: are actually going to talk about a little bit more later.

602
00:35:26,760 --> 00:35:29,960
Speaker 1: And then you have the individual organism the organism made

603
00:35:30,000 --> 00:35:32,560
Speaker 1: up of all these cells, made up of all these genes,

604
00:35:32,560 --> 00:35:35,400
Speaker 1: and then you have pressures acting on the organism itself,

605
00:35:35,440 --> 00:35:38,240
Speaker 1: like cheetah has to go fast to catch its prey.

606
00:35:38,320 --> 00:35:41,759
Speaker 1: You know, a bird flies so that it can like

607
00:35:41,800 --> 00:35:45,240
Speaker 1: eat insects and escape predators. So you have that the

608
00:35:45,719 --> 00:35:49,200
Speaker 1: organism itself, like this collective of genes and of cells,

609
00:35:49,520 --> 00:35:53,160
Speaker 1: has evolutionary pressures acting on it. And then the bigger

610
00:35:53,239 --> 00:35:57,320
Speaker 1: step is that a group of organisms has selective pressures

611
00:35:57,360 --> 00:36:01,880
Speaker 1: acting on it, Like you have, say, um, a group

612
00:36:02,120 --> 00:36:06,200
Speaker 1: of like lemurs fighting with another group of lemurs over resources,

613
00:36:06,200 --> 00:36:09,800
Speaker 1: Like you'll have selective pressures fighting at the group level

614
00:36:10,280 --> 00:36:12,759
Speaker 1: and as well as on the individual level, as well

615
00:36:12,800 --> 00:36:14,680
Speaker 1: as on the cellular level, as well as on the

616
00:36:14,719 --> 00:36:17,319
Speaker 1: genetic levels. So that's why it's that Russian nesting doll

617
00:36:18,080 --> 00:36:23,160
Speaker 1: um and so kind of like a over probably oversimplified

618
00:36:23,239 --> 00:36:26,040
Speaker 1: way to look at it. Like in practices, like you know,

619
00:36:26,360 --> 00:36:29,080
Speaker 1: sperm will compete to reach the egg, right, so like this,

620
00:36:29,320 --> 00:36:32,680
Speaker 1: these cells, the fastest ones to reach the egg will

621
00:36:32,840 --> 00:36:36,480
Speaker 1: end up being the ones that survive. Individuals will compete

622
00:36:36,520 --> 00:36:39,400
Speaker 1: with other individuals or with their environment in order to

623
00:36:39,440 --> 00:36:43,080
Speaker 1: reach food. And then groups will compete with each other

624
00:36:43,160 --> 00:36:47,000
Speaker 1: in order to secure resources for their own groups. So

625
00:36:47,080 --> 00:36:50,760
Speaker 1: the way that EO. Wilson, who's the famous ant man

626
00:36:50,880 --> 00:36:54,200
Speaker 1: or maybe I should say entomologists, kind of uses this

627
00:36:54,280 --> 00:36:57,600
Speaker 1: as a way to explain how altruism and these other

628
00:36:57,800 --> 00:37:01,240
Speaker 1: these other seemingly paradoxical behavior just conform. So he says,

629
00:37:01,560 --> 00:37:06,960
Speaker 1: quote in a group, selfish individuals beat altruistic individuals, but

630
00:37:07,200 --> 00:37:12,520
Speaker 1: groups of altruistic individuals beat groups of selfish individuals. So

631
00:37:12,960 --> 00:37:15,200
Speaker 1: you know, this can make sense for humans. Like if

632
00:37:15,239 --> 00:37:18,680
Speaker 1: we have a group of altruistic individuals, uh, you know

633
00:37:18,760 --> 00:37:21,600
Speaker 1: that are all working together, and then we have like Jerry.

634
00:37:21,640 --> 00:37:23,600
Speaker 1: I don't know why I'm piling on Jerry today, but

635
00:37:23,640 --> 00:37:26,319
Speaker 1: you know Jerry over there, who's like being selfish and

636
00:37:26,320 --> 00:37:29,439
Speaker 1: not cooperative, Like the group of people who are all

637
00:37:29,600 --> 00:37:33,640
Speaker 1: being cooperative and kind can end up beating you know, Jerry,

638
00:37:33,680 --> 00:37:37,279
Speaker 1: Like who's over there just like you know, you know,

639
00:37:37,560 --> 00:37:40,759
Speaker 1: pounding a stone against a rock and screaming angrily and

640
00:37:41,200 --> 00:37:44,320
Speaker 1: you know, not picking up his trash. A whole hunting

641
00:37:44,360 --> 00:37:46,239
Speaker 1: party can take down a mammoth, and that can feed

642
00:37:46,320 --> 00:37:48,759
Speaker 1: him through the winter because Jerry's not going to make

643
00:37:48,800 --> 00:37:50,719
Speaker 1: that kind of kill on his own right, right, if

644
00:37:50,800 --> 00:37:53,720
Speaker 1: Jerry is just spending all his time like trolling people

645
00:37:53,719 --> 00:37:56,960
Speaker 1: on Twitter, like he's not gonna get a mammoth. But

646
00:37:57,000 --> 00:37:59,120
Speaker 1: there must be multi levels within the levels, Like if

647
00:37:59,120 --> 00:38:01,719
Speaker 1: you're an altruistic group and you're all helping each other,

648
00:38:02,040 --> 00:38:04,840
Speaker 1: still you prefer to support the folks who like have

649
00:38:04,960 --> 00:38:07,959
Speaker 1: more genes um connected with you, right, Like you're gonna

650
00:38:08,000 --> 00:38:09,960
Speaker 1: help your kids more than other people's kids. And there

651
00:38:10,000 --> 00:38:13,200
Speaker 1: must be like nuances there also, yes, yeah, exactly, So

652
00:38:13,600 --> 00:38:16,920
Speaker 1: you know, these kinds of like each of these theories.

653
00:38:16,960 --> 00:38:20,200
Speaker 1: In my opinion, while there's a lot of fighting going on,

654
00:38:20,280 --> 00:38:23,399
Speaker 1: you know, in terms of like really figuring out, and

655
00:38:23,400 --> 00:38:26,000
Speaker 1: when I say fighting, it's often like good natured like

656
00:38:26,080 --> 00:38:28,399
Speaker 1: actually trying to get to the truth of things. It's

657
00:38:28,400 --> 00:38:31,440
Speaker 1: not just like a biologists being petty, although there's probably

658
00:38:31,440 --> 00:38:34,759
Speaker 1: some of that, uh you know, it's I think that

659
00:38:34,840 --> 00:38:37,440
Speaker 1: all of these theories kind of interlocked in a lot

660
00:38:37,480 --> 00:38:41,200
Speaker 1: of ways. So like you have you can say, have

661
00:38:41,400 --> 00:38:44,200
Speaker 1: like a flock of birds, right that like works together

662
00:38:44,239 --> 00:38:48,200
Speaker 1: as a flock, but individual birds really do want to reproduce,

663
00:38:48,280 --> 00:38:52,520
Speaker 1: and within that like often like offspring of birds will

664
00:38:52,560 --> 00:38:55,759
Speaker 1: stay around with their parents and help raise uh, their

665
00:38:55,880 --> 00:38:59,839
Speaker 1: younger siblings, not only because it like is beneficial because

666
00:38:59,880 --> 00:39:02,280
Speaker 1: the share genes with their siblings, but it also helps

667
00:39:02,320 --> 00:39:05,799
Speaker 1: them trained to become parents. So in that example, you

668
00:39:05,920 --> 00:39:09,160
Speaker 1: have all of these layers of sort of like selection

669
00:39:09,200 --> 00:39:12,719
Speaker 1: where you have the flock doing better all you know,

670
00:39:12,800 --> 00:39:17,400
Speaker 1: sticking together, and then you have the offspring helping raise

671
00:39:17,440 --> 00:39:21,080
Speaker 1: the new generation of offspring that they are related to. UM.

672
00:39:21,160 --> 00:39:24,520
Speaker 1: But they are also selfishly getting a benefit from helping

673
00:39:24,520 --> 00:39:27,359
Speaker 1: to raise their younger siblings because it trains them how

674
00:39:27,360 --> 00:39:29,880
Speaker 1: to better raise their own offspring. So that's you know,

675
00:39:29,960 --> 00:39:33,879
Speaker 1: the individual need to reproduce as well being shown there.

676
00:39:33,920 --> 00:39:36,960
Speaker 1: So it's really interesting and I think that like when

677
00:39:36,960 --> 00:39:40,200
Speaker 1: you simplify things to like it's just kin selection or something,

678
00:39:40,239 --> 00:39:43,799
Speaker 1: you do get uh, some complications that really can't be

679
00:39:43,880 --> 00:39:46,400
Speaker 1: explained unless you are using all of these other different

680
00:39:46,440 --> 00:39:49,360
Speaker 1: theories and and nuance like you said, to try to

681
00:39:49,400 --> 00:39:54,239
Speaker 1: explain it. Well, it's complicated. I can see why biologists

682
00:39:54,239 --> 00:39:56,279
Speaker 1: gets sort of heated about it. Are you saying that

683
00:39:56,320 --> 00:39:59,680
Speaker 1: they're all playing nicely? There's no really like scathing papers

684
00:39:59,719 --> 00:40:01,760
Speaker 1: being written back and forth, But oh no, there are

685
00:40:01,800 --> 00:40:05,279
Speaker 1: there are There are some like in a way. It's

686
00:40:05,280 --> 00:40:07,399
Speaker 1: like I'm almost scared to talk about it because it's

687
00:40:07,440 --> 00:40:09,279
Speaker 1: like I don't want to like come down on the

688
00:40:09,400 --> 00:40:11,960
Speaker 1: incorrect side or something, because like I'm not I'm not

689
00:40:12,000 --> 00:40:15,799
Speaker 1: necessarily an expert on on things like kin selection and

690
00:40:15,800 --> 00:40:19,400
Speaker 1: and multi layer multi level selection. So I don't want

691
00:40:19,400 --> 00:40:22,200
Speaker 1: to get something wrong and like start getting like, you know,

692
00:40:22,560 --> 00:40:24,279
Speaker 1: knocks on my door in the middle of the night

693
00:40:24,320 --> 00:40:28,640
Speaker 1: from angry biologists. Well, you know, there's another level there also,

694
00:40:29,080 --> 00:40:32,799
Speaker 1: because there must be like academic structures where people tend

695
00:40:32,840 --> 00:40:35,400
Speaker 1: to support their students who believe in their theory and

696
00:40:35,440 --> 00:40:39,680
Speaker 1: probably their theory of evolution right against the other academic

697
00:40:39,719 --> 00:40:43,239
Speaker 1: biologists who are promoting their students to become professors, um,

698
00:40:43,280 --> 00:40:45,720
Speaker 1: and so there must be the competition between those groups.

699
00:40:46,880 --> 00:40:51,160
Speaker 1: You have, you have kin selection of your your research assistance.

700
00:40:51,280 --> 00:40:56,719
Speaker 1: I get it. Be society may be based at least

701
00:40:56,719 --> 00:41:00,239
Speaker 1: partially on math. But can these themselves do math? A

702
00:41:00,280 --> 00:41:03,239
Speaker 1: recent study done at r M i T in Australia

703
00:41:03,360 --> 00:41:06,080
Speaker 1: suggests that you can train a bee to do basic

704
00:41:06,120 --> 00:41:09,800
Speaker 1: addition and subtraction. So say you're a bee in this study,

705
00:41:10,200 --> 00:41:12,520
Speaker 1: you would enter into a maze where you're shown a

706
00:41:12,520 --> 00:41:16,560
Speaker 1: group of blue shapes, let's say three blue squares. Then

707
00:41:16,600 --> 00:41:19,240
Speaker 1: you'll enter a second room in the maze with two

708
00:41:19,239 --> 00:41:23,120
Speaker 1: holes above whole A is four blue squares above whole

709
00:41:23,160 --> 00:41:27,560
Speaker 1: B is two blue squares. The correct answer is A.

710
00:41:28,000 --> 00:41:31,040
Speaker 1: You were supposed to add one blue square to the

711
00:41:31,080 --> 00:41:34,400
Speaker 1: total for the correct solution. Of course, you don't know

712
00:41:34,480 --> 00:41:37,799
Speaker 1: this initially because the researchers don't speak B, so you

713
00:41:37,880 --> 00:41:41,000
Speaker 1: have to do trial and error, either getting a reward

714
00:41:41,120 --> 00:41:44,760
Speaker 1: of tasty sugar or nasty quinine to help you figure

715
00:41:44,760 --> 00:41:47,960
Speaker 1: out the correct solution. But then you're presented with a

716
00:41:48,000 --> 00:41:51,400
Speaker 1: maze in which there are yellow squares instead of blue.

717
00:41:51,760 --> 00:41:55,600
Speaker 1: This time you have to subtract to get the correct solution.

718
00:41:56,080 --> 00:41:59,600
Speaker 1: In the first room, you see three yellow squares, and

719
00:41:59,680 --> 00:42:02,280
Speaker 1: you can go to the food behind the door labeled

720
00:42:02,440 --> 00:42:05,640
Speaker 1: with either two yellow squares or the door labeled with

721
00:42:05,719 --> 00:42:09,640
Speaker 1: four yellow squares. The correct answer this time is two

722
00:42:09,719 --> 00:42:13,520
Speaker 1: yellow squares, and again you're either rewarded with good food

723
00:42:13,600 --> 00:42:19,160
Speaker 1: or punished with yucky food for the correct incorrect answers. Well, congratulations,

724
00:42:19,200 --> 00:42:22,319
Speaker 1: because if you're a B, you'll figure it out kind of.

725
00:42:22,480 --> 00:42:25,480
Speaker 1: After a few hours of training, these were able to

726
00:42:25,520 --> 00:42:30,440
Speaker 1: pick the correct solutions, doing basic edition and subtractions well

727
00:42:30,760 --> 00:42:33,919
Speaker 1: at least significantly better than chance. They probably only got

728
00:42:33,960 --> 00:42:37,360
Speaker 1: like a B minus on their report cards. When we return,

729
00:42:37,440 --> 00:42:40,840
Speaker 1: we're going to talk about more animals who can do math.

730
00:42:43,440 --> 00:42:46,680
Speaker 1: Clever Hans was a famous German horse in the early

731
00:42:46,800 --> 00:42:51,120
Speaker 1: nineteen hundreds who purportedly could count and do math. He

732
00:42:51,160 --> 00:42:54,120
Speaker 1: could stomp his foot the correct number of times when

733
00:42:54,239 --> 00:42:58,319
Speaker 1: faced with a math problem. Unfortunately, it turns out that

734
00:42:58,440 --> 00:43:02,360
Speaker 1: Hans was no math is, but rather a body language expert.

735
00:43:02,560 --> 00:43:05,640
Speaker 1: He could only get the math problem right if his

736
00:43:05,760 --> 00:43:08,640
Speaker 1: owner knew the answer, and if he could see his

737
00:43:08,760 --> 00:43:13,320
Speaker 1: owner indicating there was some subtle facial expression or posture.

738
00:43:13,400 --> 00:43:17,840
Speaker 1: His owner would probably unconsciously exhibit when Hans was on

739
00:43:17,920 --> 00:43:21,279
Speaker 1: the right number of hoof stumps. I'm pretty sure this

740
00:43:21,400 --> 00:43:25,120
Speaker 1: still means Hans deserves that title of clever. But can

741
00:43:25,200 --> 00:43:29,920
Speaker 1: other animals really do complex math problems? So what do

742
00:43:29,960 --> 00:43:33,040
Speaker 1: you think? You think animals can do a math? I

743
00:43:33,040 --> 00:43:37,319
Speaker 1: think humans struggle with maths. But yeah, I think if

744
00:43:37,400 --> 00:43:41,080
Speaker 1: we think mathematically, and you know, human brains are similar

745
00:43:41,120 --> 00:43:44,680
Speaker 1: to human brains are similar to animal brains, I imagine

746
00:43:44,880 --> 00:43:46,880
Speaker 1: there must be some level of math that they can do.

747
00:43:46,920 --> 00:43:50,000
Speaker 1: It must be continuum of ability rather than it's solely

748
00:43:50,040 --> 00:43:53,640
Speaker 1: a human facility. Yeah, I mean, like we don't have

749
00:43:54,040 --> 00:43:56,879
Speaker 1: we don't have like an orangutan with a little like

750
00:43:57,120 --> 00:44:00,840
Speaker 1: a motorboard hat and like smoke a pipe with a

751
00:44:00,920 --> 00:44:06,360
Speaker 1: chalkboard doing like mathematics with like banana symbols. But pretty close,

752
00:44:06,440 --> 00:44:11,080
Speaker 1: actually pretty close maybe minus the pipe. Uh. In two

753
00:44:11,160 --> 00:44:16,239
Speaker 1: thousand and seven, Duke University made macaque monkeys and college

754
00:44:16,239 --> 00:44:20,080
Speaker 1: students take a math test, which I think if I

755
00:44:20,160 --> 00:44:22,799
Speaker 1: was a college student, I would be very nervous to

756
00:44:22,880 --> 00:44:25,799
Speaker 1: be outperformed by a monkey. I would be is. I

757
00:44:25,840 --> 00:44:29,480
Speaker 1: would just be like beside myself hoping that I performed better,

758
00:44:29,760 --> 00:44:33,840
Speaker 1: and I probably wouldn't. So they were shown dots on

759
00:44:33,880 --> 00:44:36,600
Speaker 1: the screen instead of numbers, because it's kind of unfair

760
00:44:36,680 --> 00:44:39,719
Speaker 1: to expect a maccaque monkey to know what a number is,

761
00:44:39,800 --> 00:44:44,200
Speaker 1: like they don't know. Um. The humans were told that

762
00:44:44,239 --> 00:44:47,320
Speaker 1: they would be shown one set of dots and then another,

763
00:44:47,600 --> 00:44:50,480
Speaker 1: and then they could select from a range of choices,

764
00:44:51,040 --> 00:44:53,439
Speaker 1: and the correct choice would be adding the first set

765
00:44:53,440 --> 00:44:56,000
Speaker 1: of dots to the second set of dots. So like

766
00:44:56,040 --> 00:44:59,239
Speaker 1: say you're shown two dots and then you're shown one dot,

767
00:44:59,320 --> 00:45:02,200
Speaker 1: and then you have like you're on the final screen,

768
00:45:02,200 --> 00:45:05,239
Speaker 1: you're shown like three dots, seven dots, four dots. The

769
00:45:05,280 --> 00:45:08,080
Speaker 1: correct answer is three dots because you've added two to

770
00:45:08,120 --> 00:45:11,360
Speaker 1: one dots. We can't talk to monkeys and explain the

771
00:45:11,440 --> 00:45:14,840
Speaker 1: rules to them, not yet anyways. We can't also brible

772
00:45:14,880 --> 00:45:17,640
Speaker 1: them with class credits. So they had to figure out

773
00:45:17,680 --> 00:45:20,480
Speaker 1: another way to teach these monkeys to try to make

774
00:45:20,719 --> 00:45:24,400
Speaker 1: do this math. So they would get treats in exchange

775
00:45:24,440 --> 00:45:27,320
Speaker 1: for selecting the right answers. And it's interesting to me

776
00:45:27,440 --> 00:45:30,200
Speaker 1: what they're measuring, Like, they must be measuring the ability

777
00:45:30,200 --> 00:45:33,000
Speaker 1: of the monkeys to generalize. So they show them a

778
00:45:33,000 --> 00:45:35,480
Speaker 1: bunch of patterns, reward them when they're correct, and then

779
00:45:35,560 --> 00:45:37,399
Speaker 1: do they show them a math problem they haven't seen

780
00:45:37,480 --> 00:45:41,920
Speaker 1: before and seeing if they do it better than random chance? Yes, exactly.

781
00:45:41,960 --> 00:45:43,960
Speaker 1: So they don't want to just train them to do

782
00:45:44,120 --> 00:45:46,560
Speaker 1: the same types of math problems over and over again,

783
00:45:46,640 --> 00:45:51,279
Speaker 1: so they are introducing novel math problems and training the

784
00:45:51,320 --> 00:45:53,560
Speaker 1: macaques that they do want to get the math correct.

785
00:45:53,760 --> 00:45:57,200
Speaker 1: So they found that not only were the macaques good

786
00:45:57,239 --> 00:46:00,239
Speaker 1: at doing the math, they did almost as well as

787
00:46:00,320 --> 00:46:03,920
Speaker 1: the college students. So I think that we should be

788
00:46:03,960 --> 00:46:07,920
Speaker 1: giving the maccas a degree and asking what the college

789
00:46:07,920 --> 00:46:10,799
Speaker 1: students they've been up to clearly not studying. No, But

790
00:46:10,880 --> 00:46:13,400
Speaker 1: I mean it makes sense because as humans we don't

791
00:46:13,400 --> 00:46:17,560
Speaker 1: necessarily do We're not at least taught to do math

792
00:46:17,600 --> 00:46:20,480
Speaker 1: in terms of looking at groups of dots and then

793
00:46:20,480 --> 00:46:23,160
Speaker 1: trying to add them and just visually doing the math

794
00:46:23,239 --> 00:46:28,000
Speaker 1: really quickly. We have this symbolic system which really works

795
00:46:28,000 --> 00:46:32,120
Speaker 1: out well. We have incredible brains that are so good

796
00:46:32,160 --> 00:46:35,120
Speaker 1: at like nesting concepts that it really cuts out a

797
00:46:35,120 --> 00:46:38,640
Speaker 1: lot of this like uncertainty and having to make this

798
00:46:38,719 --> 00:46:41,160
Speaker 1: really quick calculation of like how many dots are there,

799
00:46:41,160 --> 00:46:43,960
Speaker 1: because we can just we have the number five, and

800
00:46:44,040 --> 00:46:47,400
Speaker 1: we can store all that information within this concept of

801
00:46:47,440 --> 00:46:50,919
Speaker 1: five and then learn, you know, uh, five minus two

802
00:46:51,000 --> 00:46:54,680
Speaker 1: is three I'm pretty sure, and so like. But with

803
00:46:54,719 --> 00:46:59,000
Speaker 1: the macaques, they don't have that symbolic uh representation that

804
00:46:59,040 --> 00:47:01,759
Speaker 1: they can fall back. So it's actually not too surprising

805
00:47:01,800 --> 00:47:04,560
Speaker 1: to me that they're good at just seeing a number

806
00:47:04,560 --> 00:47:07,759
Speaker 1: of dots and kind of getting like what that quantity is.

807
00:47:08,000 --> 00:47:11,200
Speaker 1: If anything, I would say that it I would think

808
00:47:11,239 --> 00:47:13,799
Speaker 1: that sometimes animals might even be better at that than

809
00:47:13,920 --> 00:47:16,319
Speaker 1: humans because they have to rely on that much more,

810
00:47:16,640 --> 00:47:20,279
Speaker 1: whereas humans we have symbolic thinking that we can use

811
00:47:20,360 --> 00:47:23,960
Speaker 1: that animals don't always seem to be able to demonstrate.

812
00:47:24,719 --> 00:47:28,520
Speaker 1: It's fascinating to imagine what's going on inside the monkey's brain,

813
00:47:28,600 --> 00:47:31,400
Speaker 1: you know, has it developed some basic mathematics as it

814
00:47:31,480 --> 00:47:34,160
Speaker 1: found some like tricky work around to find the solution

815
00:47:34,160 --> 00:47:37,360
Speaker 1: of these problems? Or is any workaround? Is any solution

816
00:47:37,360 --> 00:47:41,280
Speaker 1: of these problems then defined as mathematics. I can imagine

817
00:47:41,719 --> 00:47:43,960
Speaker 1: the monkey sees the first group and then the second group,

818
00:47:44,160 --> 00:47:46,279
Speaker 1: and it solves the problem by saying, oh, I want

819
00:47:46,280 --> 00:47:49,160
Speaker 1: the third group to be both of them. It's not

820
00:47:49,200 --> 00:47:51,960
Speaker 1: like actually coming up with the concept of the larger numbers,

821
00:47:52,000 --> 00:47:55,560
Speaker 1: just like recognizing that these two things emerged. I'll argue,

822
00:47:56,920 --> 00:48:00,319
Speaker 1: right exactly, and it's it maybe is partially just like

823
00:48:00,400 --> 00:48:04,360
Speaker 1: combining these two visuals in their head and thinking about

824
00:48:04,400 --> 00:48:07,839
Speaker 1: what that combined visual looks like. And some some more

825
00:48:07,880 --> 00:48:10,799
Speaker 1: evidence that this is sort of a math sense rather

826
00:48:10,920 --> 00:48:15,239
Speaker 1: than actual like human type math is that well, what's

827
00:48:15,280 --> 00:48:20,520
Speaker 1: interesting is both students and macaques struggled when the ratio

828
00:48:21,360 --> 00:48:25,040
Speaker 1: of like the the math was like really close. So basically,

829
00:48:25,080 --> 00:48:29,880
Speaker 1: if you have a similar of dots relative to the total.

830
00:48:30,000 --> 00:48:32,800
Speaker 1: So like when they had to choose between like eleven

831
00:48:32,840 --> 00:48:34,880
Speaker 1: and twelve dots, because you have a lot of dots

832
00:48:34,960 --> 00:48:37,759
Speaker 1: and then they're only off by one versus having to

833
00:48:37,840 --> 00:48:42,360
Speaker 1: choose between five and seven dots, or even like, uh,

834
00:48:42,400 --> 00:48:45,880
Speaker 1: five and six is easier to differentiate between than like

835
00:48:46,120 --> 00:48:48,920
Speaker 1: twelve and thirteen, because like, once you get more dots,

836
00:48:49,000 --> 00:48:51,759
Speaker 1: it's like, even though they're both, both of those sets

837
00:48:51,800 --> 00:48:54,440
Speaker 1: of solutions are only off by one. It's harder to

838
00:48:54,520 --> 00:48:57,520
Speaker 1: see that with the larger proportion of dots than like

839
00:48:57,760 --> 00:49:00,040
Speaker 1: you see five, you see six, it's a little a

840
00:49:00,160 --> 00:49:03,440
Speaker 1: bit easier to see the difference there. Absolutely, I know

841
00:49:03,480 --> 00:49:05,920
Speaker 1: that just from like writing exams that you've got to

842
00:49:05,960 --> 00:49:09,200
Speaker 1: put potential answers that are pretty similar to really figure

843
00:49:09,200 --> 00:49:11,440
Speaker 1: out if the students have any idea how to do

844
00:49:11,480 --> 00:49:15,000
Speaker 1: it or they're just guessing exactly. Yeah. I wonder how

845
00:49:15,000 --> 00:49:16,840
Speaker 1: the monkeys feel when they see like none of the

846
00:49:16,880 --> 00:49:19,839
Speaker 1: above as an option. They're like, oh, man, that's that's fair.

847
00:49:20,040 --> 00:49:23,120
Speaker 1: That's when they start throwing poop. The students too, to

848
00:49:23,160 --> 00:49:25,879
Speaker 1: be fair, and so can the monkeys do more than

849
00:49:25,960 --> 00:49:28,480
Speaker 1: just add to the test. Other math concepts like subtraction

850
00:49:28,560 --> 00:49:31,840
Speaker 1: or multiplication? What can they do? Can they do integration? No?

851
00:49:31,920 --> 00:49:34,839
Speaker 1: I don't think they can do integration. I haven't seen

852
00:49:34,880 --> 00:49:37,520
Speaker 1: anything that suggests they can do multiplication. I think the

853
00:49:37,640 --> 00:49:43,600
Speaker 1: most is it seems like simple addition, subtraction, and counting

854
00:49:44,000 --> 00:49:47,360
Speaker 1: are sort of the things that uh primates seem to

855
00:49:47,360 --> 00:49:51,480
Speaker 1: be able to do. Um. But you know, you might think, like, okay,

856
00:49:51,520 --> 00:49:54,600
Speaker 1: that makes sense, right, they're really close to us evolutionarily,

857
00:49:54,760 --> 00:49:57,880
Speaker 1: like it would make sense that's sort like our basic

858
00:49:57,920 --> 00:50:01,600
Speaker 1: animal cousins can do math. Um. But surely, like really

859
00:50:01,800 --> 00:50:04,600
Speaker 1: a stupid animal couldn't do math, like a chicken, Like

860
00:50:04,880 --> 00:50:09,600
Speaker 1: they're not very bright. But actually there's evidence that chickens

861
00:50:09,640 --> 00:50:12,560
Speaker 1: can count. And not only can they count, they can

862
00:50:12,600 --> 00:50:16,240
Speaker 1: do it at a very young age, so they can

863
00:50:16,239 --> 00:50:21,640
Speaker 1: count themselves before they've hatched. But ump, bump, that's my

864
00:50:21,760 --> 00:50:24,840
Speaker 1: that's my chicken joke of the day. I've fulfilled my

865
00:50:24,920 --> 00:50:29,840
Speaker 1: chicken joke of the day. Quota, So right after they've hatched,

866
00:50:30,600 --> 00:50:34,280
Speaker 1: about three day old chickens are able to understand basic

867
00:50:34,320 --> 00:50:40,520
Speaker 1: amounts and basic summation. Uh So these baby chickens are

868
00:50:40,960 --> 00:50:43,279
Speaker 1: presented with a game of Peek a boo or I

869
00:50:43,280 --> 00:50:46,000
Speaker 1: guess pekaboo. Okay, that was my second chicken joke. I

870
00:50:46,040 --> 00:50:49,960
Speaker 1: promise I'm done with chicken jokes. Um. The the way

871
00:50:50,000 --> 00:50:52,200
Speaker 1: that the study was done is based on the fact

872
00:50:52,200 --> 00:50:56,640
Speaker 1: that baby chickens seem to like more stuff than less stuff,

873
00:50:56,880 --> 00:50:59,440
Speaker 1: which is weird. They don't actually know why. Like you

874
00:50:59,520 --> 00:51:02,600
Speaker 1: have some objects, like a group of objects, they will

875
00:51:02,600 --> 00:51:05,359
Speaker 1: walk over to the bigger pile of objects, and they

876
00:51:05,360 --> 00:51:08,279
Speaker 1: will to the smaller pile of objects. And it's not

877
00:51:08,960 --> 00:51:12,200
Speaker 1: really clear why they prefer more stuff. I guess they

878
00:51:12,280 --> 00:51:18,960
Speaker 1: just like to live extravagantly. They're greedy little chickens. They

879
00:51:18,960 --> 00:51:23,279
Speaker 1: don't believe in tidying up, right. They're not these clean freaks. No, no, no,

880
00:51:23,560 --> 00:51:26,800
Speaker 1: they they like yeah, like they are like more stuff,

881
00:51:26,880 --> 00:51:28,960
Speaker 1: give me more stuff. It's a little scary if you

882
00:51:28,960 --> 00:51:31,320
Speaker 1: think about it. There are so many chickens in the world.

883
00:51:31,360 --> 00:51:34,560
Speaker 1: So if they decide that they can demand more and

884
00:51:34,640 --> 00:51:38,160
Speaker 1: get more stuff, and they rise up against us. Um.

885
00:51:38,800 --> 00:51:42,680
Speaker 1: So they these baby chickens are shown these two opaque

886
00:51:42,680 --> 00:51:46,840
Speaker 1: screens where the researchers drop these little balls behind uh

887
00:51:46,840 --> 00:51:49,319
Speaker 1: and so like when they're dropping the ball, they can

888
00:51:49,360 --> 00:51:51,400
Speaker 1: see the ball, but once it falls behind the screen,

889
00:51:51,400 --> 00:51:55,920
Speaker 1: they can't see anymore. Um, so when like they dropped

890
00:51:56,040 --> 00:51:59,520
Speaker 1: one ball behind screen A and three balls behind screen

891
00:51:59,600 --> 00:52:02,799
Speaker 1: B and release the baby chicken, the baby chicken goes

892
00:52:02,880 --> 00:52:05,520
Speaker 1: towards screen BE because it's like, ah, yeah, I'm gonna

893
00:52:05,560 --> 00:52:09,239
Speaker 1: get more balls, gonna get some balls. Us just really

894
00:52:09,239 --> 00:52:13,440
Speaker 1: excited about collecting little toys. It can both conceptualize and

895
00:52:13,520 --> 00:52:17,800
Speaker 1: remember basic amounts. That is really exciting in event itself.

896
00:52:18,160 --> 00:52:20,920
Speaker 1: But then the researchers got more tricky with it. So

897
00:52:20,960 --> 00:52:23,759
Speaker 1: they dropped like some balls behind screen A and then

898
00:52:23,800 --> 00:52:26,680
Speaker 1: drop some balls behind screen B. But then they moved

899
00:52:26,719 --> 00:52:30,600
Speaker 1: some of the balls from screen B back to screen A,

900
00:52:31,000 --> 00:52:34,160
Speaker 1: so that now there are actually more behind screen A

901
00:52:34,360 --> 00:52:37,839
Speaker 1: than screen B, whereas originally when they had first dropped it,

902
00:52:37,880 --> 00:52:42,919
Speaker 1: there was a higher amount behind screen B. So yeah,

903
00:52:42,960 --> 00:52:46,600
Speaker 1: so this chickens. The little chicks can't see behind the screen,

904
00:52:46,640 --> 00:52:49,839
Speaker 1: but they can see the researchers hands moving things. So

905
00:52:49,920 --> 00:52:55,000
Speaker 1: like so say like they drop um, like one ball

906
00:52:55,080 --> 00:52:58,759
Speaker 1: behind screen A and then dropped four balls behind screen B.

907
00:52:59,480 --> 00:53:02,279
Speaker 1: Then they pick up one ball from behind screen BE

908
00:53:02,520 --> 00:53:04,839
Speaker 1: lifted up so the chicken see that ball, and then

909
00:53:04,840 --> 00:53:08,080
Speaker 1: move it behind screen A, pick up another ball, move

910
00:53:08,120 --> 00:53:11,200
Speaker 1: it behind screen A. Now, so the chicks have seen

911
00:53:11,400 --> 00:53:15,360
Speaker 1: that they've just moved two balls behind screen B over

912
00:53:15,400 --> 00:53:19,200
Speaker 1: to screen A. So now they should prefer screen A

913
00:53:19,400 --> 00:53:22,360
Speaker 1: because now it's actually had more balls than screen B.

914
00:53:22,520 --> 00:53:27,719
Speaker 1: But that would require them to understand that they're removing

915
00:53:27,800 --> 00:53:30,920
Speaker 1: quantities from screen B adding it to screen A, and

916
00:53:30,960 --> 00:53:34,919
Speaker 1: remember that screen A now has more balls. And they

917
00:53:34,960 --> 00:53:38,880
Speaker 1: do They figure that out like about eight of the time,

918
00:53:39,719 --> 00:53:43,879
Speaker 1: which is a really high success rate. Uh So, yeah,

919
00:53:43,960 --> 00:53:47,680
Speaker 1: so a little baby chicks can do very simple math.

920
00:53:48,200 --> 00:53:52,439
Speaker 1: Uh so, yeah, sign them up for Harvard. Little baby

921
00:53:52,480 --> 00:53:56,840
Speaker 1: chicks are definitely better at math than little baby humans. Well,

922
00:53:56,880 --> 00:54:00,560
Speaker 1: there have been research studies done on human infants as well.

923
00:54:01,280 --> 00:54:05,480
Speaker 1: Uh And one of the problems actually with testing infants

924
00:54:05,560 --> 00:54:08,520
Speaker 1: is they don't seem to have this weird preference for

925
00:54:08,680 --> 00:54:12,200
Speaker 1: more objects that baby chicks have. So, like you put

926
00:54:12,200 --> 00:54:15,359
Speaker 1: a baby like in front of some objects, uh like

927
00:54:15,400 --> 00:54:18,160
Speaker 1: a young enough baby like even something with like candy,

928
00:54:18,200 --> 00:54:20,680
Speaker 1: they're not necessarily going to just like crawl towards a

929
00:54:20,680 --> 00:54:23,439
Speaker 1: pile with more candy or something when they're super young,

930
00:54:23,640 --> 00:54:25,840
Speaker 1: because they don't have a concept of like, you know,

931
00:54:26,120 --> 00:54:29,080
Speaker 1: I need to get that more candy that's only for

932
00:54:29,160 --> 00:54:32,719
Speaker 1: older children. So for really young children, our best way

933
00:54:32,800 --> 00:54:37,120
Speaker 1: of measuring their interest is something is I gaze, because

934
00:54:37,160 --> 00:54:40,360
Speaker 1: the longer they stare at something, the more interested or

935
00:54:40,400 --> 00:54:44,200
Speaker 1: surprised they are by something. They seem to like novelty.

936
00:54:44,360 --> 00:54:47,239
Speaker 1: So that's one thing we've established, is they'll stare at

937
00:54:47,239 --> 00:54:51,319
Speaker 1: something that is new or surprising to them. And so like,

938
00:54:52,440 --> 00:54:55,839
Speaker 1: basically when researchers show them the same kind of test

939
00:54:55,920 --> 00:54:58,440
Speaker 1: that they do with the chicks, except that they will

940
00:54:58,520 --> 00:55:01,319
Speaker 1: do like impossible math problems. So they'll put like one

941
00:55:01,320 --> 00:55:05,120
Speaker 1: ball behind a screen, two balls behind another screen, move

942
00:55:05,239 --> 00:55:07,759
Speaker 1: one of the balls back to screen one. So they

943
00:55:07,760 --> 00:55:10,239
Speaker 1: would expect when you lift up the screens, screen A

944
00:55:10,400 --> 00:55:13,160
Speaker 1: has two balls, screen B has one ball. But then

945
00:55:13,200 --> 00:55:15,319
Speaker 1: if you lift the screens and like screen A still

946
00:55:15,360 --> 00:55:18,200
Speaker 1: has like one ball and screen B has like three balls,

947
00:55:18,640 --> 00:55:22,399
Speaker 1: they stare and they're confused. They don't understand what's going on.

948
00:55:22,640 --> 00:55:26,640
Speaker 1: It seems to be like, like that's our best measure

949
00:55:26,680 --> 00:55:28,960
Speaker 1: that we can get that they can understand this math

950
00:55:29,080 --> 00:55:32,959
Speaker 1: because they're like staring at it longer. It's pretty tough,

951
00:55:32,960 --> 00:55:35,040
Speaker 1: I guess to do experiments with infants and so like,

952
00:55:35,120 --> 00:55:37,920
Speaker 1: kudos to anybody who figures out how to do these experiments.

953
00:55:38,120 --> 00:55:40,680
Speaker 1: But I'm always skeptical that we're like really understanding what's

954
00:55:40,719 --> 00:55:43,239
Speaker 1: going on inside these infants based on like, you know,

955
00:55:43,280 --> 00:55:45,840
Speaker 1: where their eyes are pointing. I get that it's the

956
00:55:45,880 --> 00:55:48,440
Speaker 1: best we can do. That doesn't necessarily mean that it's

957
00:55:48,520 --> 00:55:51,200
Speaker 1: actually telling us what the infants are doing, right, right,

958
00:55:51,680 --> 00:55:55,399
Speaker 1: that something totally different is going on. It is it's

959
00:55:55,440 --> 00:55:59,399
Speaker 1: a significant problem, right, Like for the baby chick experiment, right,

960
00:55:59,440 --> 00:56:02,120
Speaker 1: Like we have of this weird innate thing that the

961
00:56:02,200 --> 00:56:04,719
Speaker 1: chicks do, which is we want more stuff, give us

962
00:56:04,760 --> 00:56:08,440
Speaker 1: more things. We're going to Vegas. Like these chicks, uh

963
00:56:08,760 --> 00:56:13,040
Speaker 1: are ambitious little money makers. So like we can easily

964
00:56:13,440 --> 00:56:16,600
Speaker 1: see that what preference the chick has for the most

965
00:56:16,800 --> 00:56:19,880
Speaker 1: the most stuff. Babies don't act that way. We can't

966
00:56:19,920 --> 00:56:22,400
Speaker 1: like get a baby to do what we want necessarily

967
00:56:22,400 --> 00:56:24,719
Speaker 1: in an experiment. So yeah, the the eye gaze is

968
00:56:24,800 --> 00:56:29,880
Speaker 1: the best metric that we often have for really young infants.

969
00:56:30,840 --> 00:56:34,959
Speaker 1: But it's certainly something that it's like definitely up to interpretation,

970
00:56:35,040 --> 00:56:37,440
Speaker 1: like what that means, Like does that really mean they

971
00:56:37,440 --> 00:56:41,360
Speaker 1: think it's a wrong answer? Um, you know, like it's

972
00:56:41,520 --> 00:56:45,440
Speaker 1: it at least into like the I think most we

973
00:56:45,520 --> 00:56:50,360
Speaker 1: can say is that it indicates when we compare eye gaze, right, like,

974
00:56:50,400 --> 00:56:53,080
Speaker 1: if they spend less time gazing at something than something else,

975
00:56:53,120 --> 00:56:57,239
Speaker 1: it indicates they see the difference between the two things, right,

976
00:56:57,280 --> 00:57:00,719
Speaker 1: that there's a difference in there, like something different is

977
00:57:00,760 --> 00:57:02,520
Speaker 1: happening in their brain when they look at the thing

978
00:57:02,560 --> 00:57:04,640
Speaker 1: that they're staring at longer versus the thing they're not

979
00:57:04,719 --> 00:57:07,919
Speaker 1: staring at as much. Whether that means they like get

980
00:57:08,040 --> 00:57:11,080
Speaker 1: that it's a wrong answer, It's hard to say whether

981
00:57:11,160 --> 00:57:13,600
Speaker 1: or not that's true, but it seems to indicate that

982
00:57:14,360 --> 00:57:16,760
Speaker 1: it's like the best indication that we have that they're

983
00:57:16,800 --> 00:57:20,880
Speaker 1: like not expecting that, which is interesting because we also

984
00:57:20,960 --> 00:57:23,840
Speaker 1: see that with physics problems, like they see they seem

985
00:57:23,880 --> 00:57:28,040
Speaker 1: to have sort of this innate understanding of conservation of momentum.

986
00:57:28,040 --> 00:57:30,400
Speaker 1: Like you have a tiny ball hit a big ball,

987
00:57:30,880 --> 00:57:33,280
Speaker 1: and the big ball goes flying off. They stare at

988
00:57:33,320 --> 00:57:36,920
Speaker 1: that longer than an appropriate response from the big ball,

989
00:57:37,320 --> 00:57:41,840
Speaker 1: which seems to indicate maybe it's unexpected to them. Babies

990
00:57:41,880 --> 00:57:45,960
Speaker 1: can do physics. You're saying, exactly, see it, Daniel, Even

991
00:57:46,000 --> 00:57:51,120
Speaker 1: a baby could do your job. Sometimes I feel like

992
00:57:51,120 --> 00:57:53,520
Speaker 1: an infant when I gaze at the wonder of the universe.

993
00:57:56,720 --> 00:57:59,400
Speaker 1: I know lions wouldn't strike you as such, but they're

994
00:57:59,480 --> 00:58:03,840
Speaker 1: secretly nerds. Lionesses can tell the size of a competing

995
00:58:03,920 --> 00:58:08,360
Speaker 1: pride based on the number of simultaneous roars. In a study,

996
00:58:08,720 --> 00:58:12,600
Speaker 1: researchers found that with a great degree of accuracy, lionesses

997
00:58:12,640 --> 00:58:16,640
Speaker 1: could figure out whether a recording of lions featured fewer

998
00:58:16,840 --> 00:58:20,480
Speaker 1: or more potential rivals than their own group, and they

999
00:58:20,560 --> 00:58:24,200
Speaker 1: chose to either run towards the speakers for a fight

1000
00:58:24,560 --> 00:58:27,400
Speaker 1: or to retreat based on whether or not they since

1001
00:58:27,440 --> 00:58:32,080
Speaker 1: there were more lions or fewer. Wolves too, are able

1002
00:58:32,120 --> 00:58:36,080
Speaker 1: to distinguish between larger numbers of treats versus smaller numbers

1003
00:58:36,080 --> 00:58:40,560
Speaker 1: of treats and choose a can of treats accordingly. Unfortunately,

1004
00:58:40,760 --> 00:58:44,240
Speaker 1: in a research setting, it seems like dogs failed this test,

1005
00:58:44,600 --> 00:58:47,960
Speaker 1: meaning that in our domestication of dogs we may have

1006
00:58:48,160 --> 00:58:52,640
Speaker 1: bred the ability to do math out of them. So sorry, spot,

1007
00:58:53,080 --> 00:58:55,760
Speaker 1: but speaking of spots, when we return, we're going to

1008
00:58:55,880 --> 00:59:06,920
Speaker 1: find out the math behind animal spots. While spots and stripes,

1009
00:59:07,000 --> 00:59:09,480
Speaker 1: like the code of a leopard or tiger, may appear

1010
00:59:09,640 --> 00:59:13,240
Speaker 1: flashy and attention grabbing to us, out of context in

1011
00:59:13,320 --> 00:59:18,080
Speaker 1: their natural environment. These clever markings help them blend into

1012
00:59:18,400 --> 00:59:21,320
Speaker 1: their surroundings, and it depends on the time of day

1013
00:59:21,400 --> 00:59:26,320
Speaker 1: to Leopards hunt at night in dark, tree dense areas,

1014
00:59:26,360 --> 00:59:29,840
Speaker 1: so those spots help them blend into the shadowy modeled

1015
00:59:29,920 --> 00:59:34,000
Speaker 1: moonlit forests, and a tiger stripes helps it hunt in

1016
00:59:34,080 --> 00:59:37,280
Speaker 1: tall grass where the shadows and patterns of the grass

1017
00:59:37,320 --> 00:59:41,400
Speaker 1: help it blend effortlessly into its environment. And while being

1018
00:59:41,480 --> 00:59:45,320
Speaker 1: orange looks flashy to us, to its main prey who

1019
00:59:45,360 --> 00:59:50,080
Speaker 1: have more limited color vision, the tigers can become practically invisible.

1020
00:59:51,560 --> 00:59:55,240
Speaker 1: So Daniel, I think it's relatively well known, like hey,

1021
00:59:55,280 --> 00:59:59,880
Speaker 1: you know, like spots and stripes serve this uh evil

1022
01:00:00,040 --> 01:00:04,960
Speaker 1: utionary function right for like camouflage mimicry. Uh. It can

1023
01:00:05,040 --> 01:00:07,960
Speaker 1: even be more complex things like in zebras sort of

1024
01:00:08,000 --> 01:00:10,920
Speaker 1: this illusion this barber Pole effect where when you get

1025
01:00:10,920 --> 01:00:13,120
Speaker 1: a group of zebras, it's hard to tell which way

1026
01:00:13,160 --> 01:00:16,280
Speaker 1: the stripes are going, which makes it hard for predators

1027
01:00:16,280 --> 01:00:18,680
Speaker 1: to figure out the direction that the hurt is moving.

1028
01:00:19,520 --> 01:00:22,960
Speaker 1: But the question is not like why they have these

1029
01:00:22,960 --> 01:00:25,760
Speaker 1: stripes in terms of like why there are evolutionary pressures,

1030
01:00:25,760 --> 01:00:29,000
Speaker 1: but like how their bodies can even form stripes, Like,

1031
01:00:29,040 --> 01:00:34,560
Speaker 1: how could that even happen? Like why would there um

1032
01:00:34,760 --> 01:00:38,880
Speaker 1: pegman or melanin producing cells ever arrange themselves in that

1033
01:00:38,960 --> 01:00:42,520
Speaker 1: way even by chance? Right, because you have to create

1034
01:00:42,560 --> 01:00:45,160
Speaker 1: the organism out of the cells, and so the cells

1035
01:00:45,160 --> 01:00:47,720
Speaker 1: have to like organize themselves in such a way to

1036
01:00:47,840 --> 01:00:51,520
Speaker 1: make these macroscopic effects from the microscopic cells. That's pretty

1037
01:00:51,520 --> 01:00:56,000
Speaker 1: fascinating exactly right. As you probably know through physics, like

1038
01:00:56,040 --> 01:01:00,320
Speaker 1: when you have sort of a great number of things, right,

1039
01:01:00,360 --> 01:01:03,400
Speaker 1: like you have a randomization of things over great numbers,

1040
01:01:03,440 --> 01:01:07,040
Speaker 1: it has this diffusion effect of kind of smoothing things out,

1041
01:01:08,080 --> 01:01:11,400
Speaker 1: so like for for spots and stripes, it's a little

1042
01:01:11,400 --> 01:01:13,760
Speaker 1: bit surprising because you would think that, like if you

1043
01:01:13,840 --> 01:01:18,640
Speaker 1: have random movement of like pigment cells, you wouldn't get

1044
01:01:18,960 --> 01:01:22,000
Speaker 1: organized patterns out of that. You would get some kind

1045
01:01:22,000 --> 01:01:25,680
Speaker 1: of diffuse pattern. And this has been a question for

1046
01:01:25,760 --> 01:01:29,320
Speaker 1: quite a while. In fact, Alan Turing was on the

1047
01:01:29,360 --> 01:01:33,400
Speaker 1: case way back in nineteen fifty two. Of the many

1048
01:01:33,560 --> 01:01:35,880
Speaker 1: things that he did, this was one of the problems

1049
01:01:35,960 --> 01:01:38,320
Speaker 1: he was interested in, which is fascinating. I never knew

1050
01:01:38,360 --> 01:01:42,480
Speaker 1: this about Turing. He accomplished so many things, so his

1051
01:01:42,600 --> 01:01:48,680
Speaker 1: paper The Chemical Basis of Morphogenesis looked at basically these

1052
01:01:48,720 --> 01:01:52,439
Speaker 1: sort of mathematical models of how stripes and spots could

1053
01:01:52,560 --> 01:01:56,560
Speaker 1: occur in animal skin for scales doesn't matter, just he

1054
01:01:56,640 --> 01:02:00,120
Speaker 1: was looking at whether the random movement and into her

1055
01:02:00,120 --> 01:02:03,760
Speaker 1: action of proteins, uh that my code from element or

1056
01:02:03,840 --> 01:02:09,400
Speaker 1: pegment could produce these patterns. And basically he was able

1057
01:02:09,440 --> 01:02:14,200
Speaker 1: to show mathematically that you could have situations in which

1058
01:02:14,440 --> 01:02:19,080
Speaker 1: random movements of protein through tissue could actually result in

1059
01:02:19,120 --> 01:02:24,080
Speaker 1: an organized pattern. And it was this extremely advanced bit

1060
01:02:24,240 --> 01:02:27,080
Speaker 1: of math and evolutionary theory for the time. It's kind

1061
01:02:27,120 --> 01:02:30,160
Speaker 1: of incredible he was able to come up with this.

1062
01:02:30,960 --> 01:02:35,360
Speaker 1: Of course, the issue though, which turn didn't necessarily factor

1063
01:02:35,400 --> 01:02:38,280
Speaker 1: into his paper, which to be fair, I think the

1064
01:02:38,280 --> 01:02:41,480
Speaker 1: paper was more of a mathematical exploration. It wasn't literally

1065
01:02:41,520 --> 01:02:44,919
Speaker 1: trying to figure out, say, like how a zebra gets

1066
01:02:44,960 --> 01:02:47,720
Speaker 1: it stripes. It doesn't take into account the fact that

1067
01:02:47,840 --> 01:02:51,640
Speaker 1: cells don't move in an entirely random way. They are

1068
01:02:51,680 --> 01:02:54,080
Speaker 1: actually like I know that we don't think of cells

1069
01:02:54,120 --> 01:02:56,919
Speaker 1: as having autonomy, but they do, like they actually move

1070
01:02:57,400 --> 01:03:04,080
Speaker 1: in um like purposeful ways. They do in a way

1071
01:03:04,320 --> 01:03:07,280
Speaker 1: and although they don't necessarily they don't have brains. They

1072
01:03:07,280 --> 01:03:10,400
Speaker 1: don't have nerves because of course, like our nerves and

1073
01:03:10,440 --> 01:03:13,200
Speaker 1: our brains are made out of cells, and even a

1074
01:03:13,400 --> 01:03:18,000
Speaker 1: single neuron is itself a cell, but they do have

1075
01:03:18,240 --> 01:03:21,880
Speaker 1: chemical reactions that happen throughout the cell that will cause

1076
01:03:21,920 --> 01:03:24,479
Speaker 1: it to move in certain ways and act in certain ways.

1077
01:03:24,480 --> 01:03:27,280
Speaker 1: So when you trigger chemical reaction, I mean, that's how

1078
01:03:27,320 --> 01:03:30,560
Speaker 1: our brains work, you know, we have chemical reaction that

1079
01:03:30,640 --> 01:03:34,720
Speaker 1: creates the synaptic firing um. So that so basically you

1080
01:03:34,720 --> 01:03:37,200
Speaker 1: can have a cell move in what seems like a

1081
01:03:37,320 --> 01:03:41,680
Speaker 1: very purposeful and almost cognizant way, but it's simply through

1082
01:03:41,920 --> 01:03:45,360
Speaker 1: chemical reactions happening throughout the cell. And of course you

1083
01:03:45,440 --> 01:03:47,880
Speaker 1: scale that up to a whole organism, then the organism

1084
01:03:47,920 --> 01:03:50,800
Speaker 1: really does have this kind of like purposeful thinking and movement,

1085
01:03:51,440 --> 01:03:53,560
Speaker 1: which kind of ties back into what we're talking about,

1086
01:03:53,680 --> 01:03:55,600
Speaker 1: you know, earlier on in the episode, where you have

1087
01:03:55,720 --> 01:03:59,200
Speaker 1: like cells on the cellular level acting in a certain way,

1088
01:03:59,240 --> 01:04:02,280
Speaker 1: having evolution pressures on them individually, and then you scale

1089
01:04:02,320 --> 01:04:04,720
Speaker 1: that up and the organism made out of cells also

1090
01:04:04,760 --> 01:04:07,680
Speaker 1: has evolutionary pressures on it, and you actually see this

1091
01:04:07,760 --> 01:04:12,520
Speaker 1: happening rather beautifully in these studies of zebra fish. So,

1092
01:04:13,320 --> 01:04:15,960
Speaker 1: zebra fish are these cute little fish. If you've ever

1093
01:04:16,000 --> 01:04:18,320
Speaker 1: owned an aquarium or been to a pet store, you've

1094
01:04:18,360 --> 01:04:22,240
Speaker 1: probably seen them. They have these blue and white horizontal stripes.

1095
01:04:22,320 --> 01:04:24,920
Speaker 1: They're very pretty, really cute little fish. I've had a

1096
01:04:24,960 --> 01:04:29,880
Speaker 1: few throughout my aquarium owning days. But the research done

1097
01:04:29,880 --> 01:04:32,920
Speaker 1: on them is amazing, Like the amount of information we're

1098
01:04:32,960 --> 01:04:35,480
Speaker 1: getting out of these tiny fish is mind blowing. So

1099
01:04:36,120 --> 01:04:42,280
Speaker 1: Professor Shigero Kondo's lab of Osaka University found something incredibly

1100
01:04:42,360 --> 01:04:46,320
Speaker 1: cool when it comes to zebra fish is color creating cells.

1101
01:04:46,360 --> 01:04:50,880
Speaker 1: So zebra fish have light color cells called xanthophores and

1102
01:04:51,040 --> 01:04:56,400
Speaker 1: dark color cells called melanophores, and these cells interact in

1103
01:04:56,440 --> 01:04:59,880
Speaker 1: a beautiful and incredible way, almost like a ballet. So

1104
01:05:00,240 --> 01:05:03,720
Speaker 1: the light cells have tendrils that they used to navigate

1105
01:05:03,760 --> 01:05:07,800
Speaker 1: and since their environment. When these tendrils touch a dark cell,

1106
01:05:08,400 --> 01:05:11,840
Speaker 1: the dark cell will run away from the light cell

1107
01:05:12,280 --> 01:05:15,040
Speaker 1: because there's a chemical reaction that occurs in the dark

1108
01:05:15,080 --> 01:05:19,200
Speaker 1: cell that is triggered by the light cells tendril that

1109
01:05:19,280 --> 01:05:23,800
Speaker 1: causes the cell to move away from that direction. But

1110
01:05:24,160 --> 01:05:27,720
Speaker 1: the light cell meanwhile gets the chemical signal to actually

1111
01:05:27,720 --> 01:05:30,160
Speaker 1: move in the direction of the dark cell that is

1112
01:05:30,200 --> 01:05:33,600
Speaker 1: now moving away from the light cell. And so this

1113
01:05:33,800 --> 01:05:37,240
Speaker 1: reaction it's almost like magnets, right, like when you have

1114
01:05:37,360 --> 01:05:42,000
Speaker 1: a opposite polarity magnets or same polarity magnets, sorry, like

1115
01:05:42,160 --> 01:05:44,800
Speaker 1: and you push the same polarity against the same polarity,

1116
01:05:44,800 --> 01:05:47,520
Speaker 1: it will push it away. Uh you know, it's it's

1117
01:05:47,560 --> 01:05:50,919
Speaker 1: like that, but with these cells. Uh, and like where

1118
01:05:50,960 --> 01:05:55,200
Speaker 1: one cell is chase literally chasing the other through this

1119
01:05:55,200 --> 01:05:59,200
Speaker 1: this chemical reaction. And so what you get is like

1120
01:05:59,240 --> 01:06:02,600
Speaker 1: this battel a of these dark and light cells and

1121
01:06:02,680 --> 01:06:07,600
Speaker 1: as they're chasing each other. Uh. In the zebra fishes development,

1122
01:06:07,960 --> 01:06:12,080
Speaker 1: it will create uh, this kind of straight line. It's

1123
01:06:12,080 --> 01:06:16,480
Speaker 1: like these these bluish lines that run horizontal across its body.

1124
01:06:16,520 --> 01:06:20,000
Speaker 1: And so it'll create this these stacked lines of stripes.

1125
01:06:21,040 --> 01:06:24,480
Speaker 1: So you see this like incredible way in which like

1126
01:06:24,520 --> 01:06:27,200
Speaker 1: the cell's autonomy, like the way that the cells actually

1127
01:06:27,280 --> 01:06:31,280
Speaker 1: move has this huge impact on animal patterns. So the

1128
01:06:31,400 --> 01:06:34,440
Speaker 1: cells themselves, they're not just like autonomous making up their

1129
01:06:34,440 --> 01:06:36,680
Speaker 1: own minds, and they're not just random you're saying, they're

1130
01:06:36,680 --> 01:06:39,440
Speaker 1: sort of sensitive to their environment and they queue off

1131
01:06:39,480 --> 01:06:42,240
Speaker 1: of effects and that can create these larger scale patterns

1132
01:06:42,520 --> 01:06:46,040
Speaker 1: exactly how a kid goes to sort of like how

1133
01:06:46,080 --> 01:06:48,800
Speaker 1: a kid goes to lunch and elementary school and ends

1134
01:06:48,840 --> 01:06:50,880
Speaker 1: up sitting like at a table of other girls or

1135
01:06:50,880 --> 01:06:52,680
Speaker 1: at a table of other boys, and then you end

1136
01:06:52,760 --> 01:06:54,640
Speaker 1: up with like a girl table and a boy table

1137
01:06:54,800 --> 01:06:58,840
Speaker 1: because they're queuing off what everybody else right, right, yes, exactly,

1138
01:06:59,320 --> 01:07:02,000
Speaker 1: or in this case, like chasing each other around, or

1139
01:07:02,040 --> 01:07:03,880
Speaker 1: like you know in Star Wars you have like the

1140
01:07:03,960 --> 01:07:06,600
Speaker 1: light side chasing the dark side all the time or something.

1141
01:07:06,720 --> 01:07:10,520
Speaker 1: Maybe that's not a good Star Wars reference. I'm not sure. Uh,

1142
01:07:10,520 --> 01:07:14,560
Speaker 1: but you know this, you know you would think then, okay,

1143
01:07:14,560 --> 01:07:17,479
Speaker 1: so Alan Turing got it all wrong. It's actually through

1144
01:07:17,640 --> 01:07:20,920
Speaker 1: these like chemical reactions and these cells that actually make

1145
01:07:20,960 --> 01:07:24,120
Speaker 1: them behave in these specific ways. But that research was

1146
01:07:24,200 --> 01:07:29,600
Speaker 1: not for nothing. In fact, mathematical biologist Dr Thomas Woolley

1147
01:07:29,640 --> 01:07:34,320
Speaker 1: is combining Turing's theory and the modern research on zebra

1148
01:07:34,400 --> 01:07:38,600
Speaker 1: fish into a new mathematical framework that aims to describe

1149
01:07:38,640 --> 01:07:42,760
Speaker 1: all sorts of natural patterns, from stripes to spots, even

1150
01:07:42,760 --> 01:07:46,560
Speaker 1: two tiny hexagons that these cells form that can create

1151
01:07:46,600 --> 01:07:51,160
Speaker 1: these very complicated patterns. Because if you look at animals

1152
01:07:51,200 --> 01:07:56,200
Speaker 1: like from fish too, octopuses to uh two tigers, and

1153
01:07:56,280 --> 01:07:59,760
Speaker 1: to to like like the the awful lot. You have

1154
01:08:00,200 --> 01:08:03,760
Speaker 1: so many different interesting patterns. It's not just stripes and spots.

1155
01:08:03,800 --> 01:08:07,200
Speaker 1: It's like these rosebud patterns and leopards and like all

1156
01:08:07,320 --> 01:08:11,840
Speaker 1: sorts of beautiful interesting patterns and fish. It's it's really

1157
01:08:11,960 --> 01:08:15,959
Speaker 1: interesting to me that we're slowly starting to be able

1158
01:08:16,000 --> 01:08:21,160
Speaker 1: to find mathematical models that interact with our understanding of

1159
01:08:21,200 --> 01:08:24,360
Speaker 1: how the cells work, and combining those to be able

1160
01:08:24,400 --> 01:08:27,439
Speaker 1: to describe like how you know, say, like a puffer

1161
01:08:27,479 --> 01:08:32,360
Speaker 1: fish's skin ends up being this beautiful canvas of patterns.

1162
01:08:33,240 --> 01:08:35,640
Speaker 1: This is something which I think is really beautiful that

1163
01:08:35,680 --> 01:08:39,360
Speaker 1: you see all over science. Actually, this emergent phenomena. We

1164
01:08:39,479 --> 01:08:41,599
Speaker 1: have rules, it's sort of the lower level to govern

1165
01:08:41,640 --> 01:08:44,519
Speaker 1: how really small things interact, and then you get these

1166
01:08:44,560 --> 01:08:49,639
Speaker 1: incredibly complex, sometimes beautiful effects at a larger scale which

1167
01:08:49,640 --> 01:08:51,599
Speaker 1: can be sort of simple to describe in their own

1168
01:08:51,760 --> 01:08:54,680
Speaker 1: level of mathematics. You know, in physics we have like

1169
01:08:54,840 --> 01:08:57,280
Speaker 1: tiny particles which come together to make atoms, which make

1170
01:08:57,320 --> 01:08:59,400
Speaker 1: all sorts of different kinds of materials which can come

1171
01:08:59,439 --> 01:09:02,040
Speaker 1: together to make chemistry and life and all sorts of

1172
01:09:02,040 --> 01:09:04,920
Speaker 1: amazing stuff. So I love seeing sort of signs done

1173
01:09:04,920 --> 01:09:07,840
Speaker 1: at these different levels and seeing the connections between them.

1174
01:09:07,880 --> 01:09:11,760
Speaker 1: That's really fascinating. Yeah, it's I also just love the

1175
01:09:11,800 --> 01:09:14,760
Speaker 1: fact that you have this little tiny fish that my

1176
01:09:14,880 --> 01:09:18,480
Speaker 1: memory of the zebra fish is like my first experience

1177
01:09:18,560 --> 01:09:21,200
Speaker 1: owning fish, Like it came in this little tiny fish

1178
01:09:21,240 --> 01:09:24,880
Speaker 1: bowl and like with this neon colored pebbles and it's like,

1179
01:09:25,080 --> 01:09:27,160
Speaker 1: here's your little bag of fish food and you feed

1180
01:09:27,200 --> 01:09:30,000
Speaker 1: it that, and then I like did some online research

1181
01:09:30,040 --> 01:09:31,920
Speaker 1: and it's like, you shouldn't have this fish in this

1182
01:09:32,040 --> 01:09:34,520
Speaker 1: tiny little thing, So then I got a bigger aquarium

1183
01:09:34,560 --> 01:09:38,080
Speaker 1: to put it in, this like tiny cute fish. But

1184
01:09:38,280 --> 01:09:41,840
Speaker 1: even in this single fish who just loved munching on

1185
01:09:41,880 --> 01:09:45,800
Speaker 1: the little little flakes that I gave it, Uh, there

1186
01:09:45,920 --> 01:09:48,519
Speaker 1: is so much to learn about the way that it

1187
01:09:48,680 --> 01:09:52,120
Speaker 1: sells are interacting and how the math of how these

1188
01:09:52,120 --> 01:09:56,360
Speaker 1: stripes form that um it maybe decades before we even

1189
01:09:56,400 --> 01:09:59,840
Speaker 1: fully understand how the zebra fishes stripes form, so like,

1190
01:10:00,280 --> 01:10:04,200
Speaker 1: there are so many biological and mathematical mysteries just in

1191
01:10:04,240 --> 01:10:08,400
Speaker 1: this single teeny tiny fish that it's like when you

1192
01:10:09,240 --> 01:10:12,080
Speaker 1: think about all of the life on Earth that would

1193
01:10:12,120 --> 01:10:14,600
Speaker 1: have this apply to them. It's kind of gives me

1194
01:10:14,640 --> 01:10:17,000
Speaker 1: a little bit of a headache, but in a good way.

1195
01:10:17,200 --> 01:10:19,559
Speaker 1: We might have to recruit an army of maccaque monkeys

1196
01:10:19,600 --> 01:10:22,080
Speaker 1: and clare chickens to help us with his research. Al Right,

1197
01:10:22,160 --> 01:10:24,879
Speaker 1: baby chickens and maccaques, we got, we got some serious

1198
01:10:24,960 --> 01:10:28,280
Speaker 1: numbers to crunch. Stop stop throwing that poo, and you

1199
01:10:28,320 --> 01:10:31,080
Speaker 1: stop packing on the floor. Well, it's funny you talk

1200
01:10:31,120 --> 01:10:34,040
Speaker 1: about zebra fish because I had friends in grad school

1201
01:10:34,040 --> 01:10:36,479
Speaker 1: who are biologists, and they always had to go into

1202
01:10:36,479 --> 01:10:39,680
Speaker 1: the lab at four am to feed the zebrafish. And

1203
01:10:39,760 --> 01:10:42,360
Speaker 1: so I know that zebrafish are like a common thing

1204
01:10:42,400 --> 01:10:44,200
Speaker 1: in research. But when I was a kid, we had

1205
01:10:44,240 --> 01:10:46,439
Speaker 1: little fish with neon stripes on them, but we called

1206
01:10:46,479 --> 01:10:49,080
Speaker 1: them neon fish. Is that the same thing? No, there

1207
01:10:49,080 --> 01:10:52,439
Speaker 1: are there's the neon tetra. I actually have some of

1208
01:10:52,479 --> 01:10:54,720
Speaker 1: those right now in my fish tank. Those are a

1209
01:10:54,760 --> 01:10:56,880
Speaker 1: different fish. I wonder if they get along or if

1210
01:10:56,880 --> 01:10:59,639
Speaker 1: they argue about whose bright Well that's I have actually

1211
01:10:59,760 --> 01:11:02,080
Speaker 1: had to both of them at the same time, and

1212
01:11:02,320 --> 01:11:06,000
Speaker 1: mine there was a little bit of chasing, like generally speaking,

1213
01:11:06,040 --> 01:11:08,519
Speaker 1: like fish can do a little bit of tail chasing

1214
01:11:08,520 --> 01:11:11,519
Speaker 1: and nipping um that sort of normal behaviors between like

1215
01:11:11,600 --> 01:11:14,040
Speaker 1: even within the same species. Like if you get guppies,

1216
01:11:14,040 --> 01:11:16,559
Speaker 1: they're going to be constantly chasing each other. That's how

1217
01:11:16,560 --> 01:11:18,720
Speaker 1: they bully each other. So we have fish bullying and

1218
01:11:18,840 --> 01:11:22,160
Speaker 1: naked mule red bully bullying, naked mule rat bulling. Yeah,

1219
01:11:22,439 --> 01:11:25,360
Speaker 1: animals are all bullies. No, that's not the message of

1220
01:11:25,400 --> 01:11:30,360
Speaker 1: this this episode. The message is that it's altruism. Matth

1221
01:11:30,560 --> 01:11:35,080
Speaker 1: is cool and math explains kindness exactly. Matthew is love, right,

1222
01:11:35,280 --> 01:11:39,360
Speaker 1: matthe is love exactly. Well, on that note, thank you

1223
01:11:39,600 --> 01:11:44,519
Speaker 1: so much for joining me today. Daniel, Um, how about

1224
01:11:44,520 --> 01:11:46,960
Speaker 1: you tell the people where they can find you, because

1225
01:11:47,040 --> 01:11:49,479
Speaker 1: I have a feeling they would really be interested in

1226
01:11:49,520 --> 01:11:52,080
Speaker 1: the stuff that you do. Well, thanks very much for

1227
01:11:52,120 --> 01:11:55,120
Speaker 1: having me on. Always super fascinated to learn about any

1228
01:11:55,200 --> 01:11:58,720
Speaker 1: aspect of science and unraveled mysteries of any part of

1229
01:11:58,760 --> 01:12:02,919
Speaker 1: the universe. But normally I'm a particle physicist at UC Irvine,

1230
01:12:02,960 --> 01:12:05,400
Speaker 1: but I'm also the co host of a different podcast

1231
01:12:05,479 --> 01:12:08,840
Speaker 1: called Daniel and Jorge Explain the Universe, where we talk

1232
01:12:08,880 --> 01:12:12,479
Speaker 1: about black holes and neutron stars and tiny particles. In

1233
01:12:12,520 --> 01:12:14,600
Speaker 1: the beginning of the universe and the end of the

1234
01:12:14,680 --> 01:12:19,000
Speaker 1: universe and all the zebra fish in between. I like

1235
01:12:19,120 --> 01:12:21,559
Speaker 1: to think that some star systems are just a bunch

1236
01:12:21,560 --> 01:12:25,679
Speaker 1: of zebra fish swimming out in space in an infinite universe.

1237
01:12:25,760 --> 01:12:28,880
Speaker 1: Everything happened, so there is a fish star somewhere out there.

1238
01:12:28,920 --> 01:12:31,480
Speaker 1: Of course, there's got to be a fish shaped galaxy

1239
01:12:31,560 --> 01:12:34,760
Speaker 1: somewhere out there. I wonder if stars bully each other. Oh,

1240
01:12:34,800 --> 01:12:37,360
Speaker 1: I mean, they do seem to chase each other right. Sometimes.

1241
01:12:39,280 --> 01:12:41,280
Speaker 1: You can find the show on the internet at Creature

1242
01:12:41,320 --> 01:12:44,519
Speaker 1: feature Pot on Instagram, at Creature feet Pot on Twitter.

1243
01:12:44,600 --> 01:12:47,960
Speaker 1: That's f F E T. That is something very different.

1244
01:12:48,439 --> 01:12:52,320
Speaker 1: You can also send me an email with your questions, comments,

1245
01:12:52,400 --> 01:12:55,800
Speaker 1: pictures of your cute animals, pictures of your zebra fish

1246
01:12:55,880 --> 01:12:59,439
Speaker 1: at Creature feature Pot at gmail dot com. And thank

1247
01:12:59,479 --> 01:13:02,280
Speaker 1: you so much for listening. If you leave or rating

1248
01:13:02,320 --> 01:13:05,080
Speaker 1: in review, I read all of the reviews and they

1249
01:13:05,080 --> 01:13:07,599
Speaker 1: make me happy and they really help out the podcast,

1250
01:13:07,680 --> 01:13:10,280
Speaker 1: so I do appreciate that. Uh and thanks to the

1251
01:13:10,280 --> 01:13:13,559
Speaker 1: Space Classics for their super awesome song x Alumina. Creature

1252
01:13:13,560 --> 01:13:16,760
Speaker 1: features a production of I Heart Radio. For more podcasts

1253
01:13:16,760 --> 01:13:18,920
Speaker 1: like the one you just heard, visit the I Heart

1254
01:13:19,000 --> 01:13:21,360
Speaker 1: Radio app Apple podcast or Hey, guess what. I don't

1255
01:13:21,360 --> 01:13:23,160
Speaker 1: know more if you listen to your podcasts, even if

1256
01:13:23,160 --> 01:13:25,519
Speaker 1: you're like listening to a C. Shehell right now and

1257
01:13:25,680 --> 01:13:29,040
Speaker 1: somehow my podcast got in there. I won't judge you.

1258
01:13:29,560 --> 01:13:31,519
Speaker 1: See you next Wednesday.