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Speaker 1: Hey, everybody, Welcome to Creature feature, the show where we

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Speaker 1: ask who are the real animals? Humans or animals? Join

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Speaker 1: us today as we look at some of the most

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Speaker 1: mind blowing, most important, and also weirdest scientific studies that

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Speaker 1: have been done on rats. Discovered this and more as

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Speaker 1: we answered the age old question is there eternal sunshine

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Speaker 1: in the spotless rat mine? So rats, they've been long

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Speaker 1: maligned by humans. They're synonymous with sneaky crooks and snitches.

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Speaker 1: They're considered disgusting, little harbingers of plagues. But on September

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Speaker 1: two thousand fifteen, something happened that caused a paradigm shift

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Speaker 1: in public opinion. Pizza rat, Pizza rapp was that in

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Speaker 1: trepid subway rat who dragged a huge slice of pizza

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Speaker 1: down the stairs. This little hero captured our hearts. He's

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Speaker 1: the classic American rags to riches or well rat to

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Speaker 1: pizza's story. But rats are deeper and more complex than

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Speaker 1: sewer nightmares or pizza entrepreneurs. They are wildly successful urban survivors,

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Speaker 1: highly social, intelligent, and have been the backbone of huge

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Speaker 1: wealth of scientific research. You've likely heard it in the news.

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Speaker 1: Such and such news study has been conducted on rats,

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Speaker 1: or some medication has been found to be effective in rats.

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Speaker 1: It's a grim truth. In order for medicine to advance,

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Speaker 1: rats are often on the front lines. It's a complex

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Speaker 1: moral issue that can't be boiled down to a brief statement.

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Speaker 1: But this is a podcast, so here here it goes.

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Speaker 1: I love rats and mines and rodents in general, but

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Speaker 1: I think they deserve respect. But I also super appreciate

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Speaker 1: having modern medicine, and I think researchers are doing extremely

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Speaker 1: important work. So today we're going to look at some

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Speaker 1: of this rats science, both to appreciate it from a

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Speaker 1: standpoint of immense scientific accomplishment, as well as taking the

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Speaker 1: point of view of what it would be like to

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Speaker 1: be a rat. Undergoing these studies with me today is

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Speaker 1: Polo v Ganalan biomedical engineer and an amazingly funny and

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Speaker 1: talented stand up comedian. Welcome, Thank you for having me.

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Speaker 1: So just briefly, do you deal in your research? I'll

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Speaker 1: kind of get into that more later on, but yeah, um,

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Speaker 1: I'm currently a PhD student at the University of Southern

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Speaker 1: California and Ted Burger's lab. He works on the hippocampus

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Speaker 1: and Hippocampbell Studies and our lab is in the process

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Speaker 1: of creating a computational model of the rat hippocampus UM,

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Speaker 1: and we My work within that is very specific. UM.

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Speaker 1: I'm trying to characterize the different neuron types within the

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Speaker 1: rat hippocampus because there are a bunch of different kinds.

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Speaker 1: And in order to do that, I'm working on using

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Speaker 1: a genetic algorithm, which I don't know. Have you heard

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Speaker 1: of genetic algorithms before? Not? Really, No, they're really interesting, UM.

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Speaker 1: Evolutionary algorithms UM and genetic algorithms are based on how

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Speaker 1: like populations work. So like in human populations, you have

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Speaker 1: a parent generation and then they breed, they make children.

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Speaker 1: Those breed they make children, you know, and as UM

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Speaker 1: you go down the line, certain things are shifted and

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Speaker 1: changed according to mutation and crossover with the genes. Right,

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Speaker 1: So they use individuals in the population as they're actually

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Speaker 1: computational models. So each individual is a computational model and

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Speaker 1: the inputs changed based on which individual they are in

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Speaker 1: the population. So you have a set of models at

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Speaker 1: the top, and then you determine how far off you

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Speaker 1: want their their inputs to be when they have children,

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Speaker 1: and then the models, the computational models breed and have kids,

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Speaker 1: so kind of like, uh, I mean probably this is

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Speaker 1: very oversimplified, but there was that the Game of Life,

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Speaker 1: kind of very early computer game where you would put

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Speaker 1: in a bunch of parameters and then you have these

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Speaker 1: these kind of competing pixel microbes out there and breeding

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Speaker 1: and competing with each other, and it would run the

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Speaker 1: program and you would see like the population density of

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Speaker 1: these different sort of like programmed in organisms. Uh yeah,

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Speaker 1: except so each UM, each program is actually like treated

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Speaker 1: like an individual in the population. Yeah, So it's like

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Speaker 1: it's just finding a way to explore different inputs and

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Speaker 1: whether the outputs match what we want UM with through

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Speaker 1: mimicking the diversity and in like a population of organisms. Right,

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Speaker 1: that's super cool. Yeah, it's really fun. UM, but it

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Speaker 1: is very uh you know deep. So with the with

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Speaker 1: the whole like with all the rat experiments and stuff,

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Speaker 1: I'm like very focused. Yeah, you're focused on this one

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Speaker 1: specific yea thing, which I mean, that's that's kind of

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Speaker 1: the reality of what you have to do when you're

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Speaker 1: when you're doing these um more like the more precise

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Speaker 1: as we'll see like in more historical route studies it's

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Speaker 1: very sort of on the surface, like you put a

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Speaker 1: rat in the maze and see what he does, which

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Speaker 1: I've also done before. Yeah, I as biomedical engineering encompasses

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Speaker 1: like any engineering applied to the healthcare field, which is

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Speaker 1: a problem in getting jobs because people don't know what

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Speaker 1: we do, what's that we have, because it can range

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Speaker 1: from work with animals, working with cells and my crosscoppy

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Speaker 1: to um, you know, more mechanical engineering, electrical engineering, to

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Speaker 1: computational stuff. And so I've actually been through that whole process.

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Speaker 1: So I've gone from you know, working with animals and cells,

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Speaker 1: and then I worked on neural probes for a while,

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Speaker 1: which was more mechanical electrical engineering, and now I'm doing

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Speaker 1: computational UM. And part of the reason that I wanted

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Speaker 1: to go to computational was because I was tired of

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Speaker 1: being in a wet lab basically um, because I started

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Speaker 1: in like middle school working in labs, so I was

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Speaker 1: kind of over it. Yeah. Is it is it the

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Speaker 1: smell or just the general environment, because I have worked

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Speaker 1: in a monkey lab before, and for me, it was

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Speaker 1: the smell that was like the worst. It was, um,

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Speaker 1: you know, it's weird. Is I like when of a

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Speaker 1: lab smells like mice really, yeah, because I'm used to

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Speaker 1: and I'm like, ah, this smells like science. But no,

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Speaker 1: it has to do with, um, just the type of work,

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Speaker 1: because the behavioral experiments are take a long time. They're

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Speaker 1: very precise. Same with you know, cells, it can take

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Speaker 1: a while to get the results that you want. And

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Speaker 1: I just felt, you know, I learned a lot in

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Speaker 1: my time, and I felt like I wanted something more

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Speaker 1: computationally challenging. I wanted to acquire those skills um. And

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Speaker 1: before I, right before my PhD, I was working in

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Speaker 1: a completely different field in infectious diseases, and I was

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Speaker 1: like carrying around vials of HIV and like nine leaders

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Speaker 1: of human plasma. And I was tired of the manual

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Speaker 1: labor associated with it because I imagine that's a very

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Speaker 1: like a lot of protocol must be involved. It was

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Speaker 1: a lot of protocol. I was very happy to be

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Speaker 1: in the biotech industry to learn all the protocol and

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Speaker 1: like how sales works and stuff like that, because in

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Speaker 1: academia you're just kind of you're in a lab, you're

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Speaker 1: just you're very focused on yeah, UM, and so you

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Speaker 1: don't really understand that like practical application of how this

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Speaker 1: like impacts people and how it reaches the general public,

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Speaker 1: the solutions that you find in lab um. So I'm

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Speaker 1: glad I got that kind of gen all information. But yeah,

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Speaker 1: I was done, Carrie, Like having to wash my clothes

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Speaker 1: irrationally to feel clean at night. It was necessistant a

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Speaker 1: labor um. This is nothing compared to having to carry

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Speaker 1: infectious diseases. But it was a psychology study where we

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Speaker 1: had to trick them into giving us like saliva samples,

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Speaker 1: or not trick them into giving us saliva samples because

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Speaker 1: that would be illegal. But we we took saliva samples

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Speaker 1: for no reason because we wanted them to think we

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Speaker 1: were studying their saliva. It's like, yeah, yeah, you lie

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Speaker 1: a lot in like these behavioral studies. But like so

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Speaker 1: I'd have to collect the saliva from them and immediately

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Speaker 1: like throw it out, like wash it out, like we

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Speaker 1: didn't need it. But and it just felt so it

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Speaker 1: was so disheartening to be like, all right, here's this

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Speaker 1: spin and it's like people, it's hard to fill a

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Speaker 1: little wiless but you know, you think that it would

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Speaker 1: be easy, but it's not, because then they had to

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Speaker 1: fill it all the way up and then like you know,

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Speaker 1: got the like spitty tube that it's like I don't

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Speaker 1: need this, Yeah I don't. It's it's going directly in

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Speaker 1: the trash. But you can tell they've worked very hard

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Speaker 1: on giving you the a nice saliva sample and there.

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Speaker 1: You know, it does feel like a lot of um

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Speaker 1: that those physical like wet labbed experiments require a lot

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Speaker 1: of things that are very like tedious and menial and

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Speaker 1: like disheartening, um be, But it is necessary for the

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Speaker 1: work to be done. Um so somebody has to do it,

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Speaker 1: and a lot of times it's like grad students. But

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Speaker 1: for Yeah, that's why with the computational stuff, I'm like, Okay,

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Speaker 1: I'm like I have a goal and I'm like trying

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Speaker 1: all these things and it is really hard and it

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Speaker 1: takes a lot longer than people think because computational stuff

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Speaker 1: generally is faster because computers are faster than physical system.

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Speaker 1: Takes time to run a program and then and then

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Speaker 1: there's so many there's so much like debugging and troubleshooting

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Speaker 1: and none of that matter. I can run it for

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Speaker 1: hours and hours and hours and come back and it's

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Speaker 1: like nope, yeah, which is what's happening right now. I'm sorry,

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Speaker 1: but yeah, when you think rat studies, you probably picture

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Speaker 1: a rat in a maze. Obviously, the wealth of research

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Speaker 1: goes beyond this classic trope, but you're also not wrong.

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Speaker 1: Some of the most important studies on rat behavior have

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Speaker 1: utilized a maze, and mazes are great for examining learning,

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Speaker 1: spatial memory, and a baseline for measuring performance, cognition, and

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Speaker 1: drug studies. But what would it be like to be

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Speaker 1: a rat in a maze with no GPS to help you?

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Speaker 1: The labyrinth is often used as a horror action device.

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Speaker 1: The idea of being stuck in a maze winding up

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Speaker 1: trapped is scary and disorienting. Let's talk about a real

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Speaker 1: life labyrinth that is more bewildering and terrifying than any

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Speaker 1: sort of laboratory maze. The miles and miles of catacombs

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Speaker 1: that lurk underground. Last year, two teenagers got lost in

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Speaker 1: the Paris Catacombs. This is no hokey corn maze. There

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Speaker 1: are over two hundred miles of tunnels, some of which

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Speaker 1: had been constructed as quarries during the time of the Romans.

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Speaker 1: In the seventeen hundreds, tunnels were constructed as asco arresting

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Speaker 1: place for human skeletal remains. Due to the crisis of

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Speaker 1: overflowing cemeteries, these catacombs would come to hold the bones

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Speaker 1: of over six million people. Only a small fraction of

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Speaker 1: the tunnels are open to the public, but that doesn't

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Speaker 1: keep people from venturing into the dark forbidden areas. Needless

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Speaker 1: to say, this is a very bad idea. So imagine

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Speaker 1: being those two lost teenagers in the catacombs. It's dark, dank, cold,

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Speaker 1: and you're utterly lost. How would you get back to

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Speaker 1: the entrance by memory? There's not enough light to see landmarks,

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Speaker 1: and even if there were, there's no map to orient

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Speaker 1: yourself to. How do you know which tunnel you've been down,

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Speaker 1: which skull lined quarter you've already retraced. Humans, unlike butterflies

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Speaker 1: or cows, don't have a built in compass. We don't

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Speaker 1: have the echolocation of bats or the noses of moles,

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Speaker 1: who can see the world through vibrations. Were pretty pathetic

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Speaker 1: navigators when compared to the animal kingdom. The teenagers are

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Speaker 1: lost for three days, although to them, who knows how

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Speaker 1: long it felt. Without diurnal information from the light of

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Speaker 1: the sun, it's very hard to keep track of the

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Speaker 1: passage of time, and according to authorities, they were only

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Speaker 1: found things to search and rescue dogs. The dog's ability

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Speaker 1: to track scent was crucial. Without the help of other animals,

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Speaker 1: humans are ill prepared to navigate dark, fractal like passageways.

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Speaker 1: There have been urban myths of people dying in the catacombs.

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Speaker 1: There's a story of Masha, a woman who allegedly ventured

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Speaker 1: into the Odessa Catacombs, got lost, and died of thirst.

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Speaker 1: There's no proof of this story being true or Masha

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Speaker 1: even being a real person, so it's likely just a

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Speaker 1: legend that taps into our fears. However, there was one

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Speaker 1: life that may very likely have been claimed by the

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Speaker 1: Paris Catacombs. In seventee, Philip bet as Bet, a Parisian man,

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Speaker 1: descended into the catacombs and was never seen again until

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Speaker 1: eighteen o four, when his body was discovered in the tunnels.

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Speaker 1: So let's find out if rats fare better at mastering

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Speaker 1: the maize than our poor friend Phillybert Aspare. So, I

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Speaker 1: want to ask you to join me on an imagination journey,

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Speaker 1: which I like to do on the podcast. We right

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Speaker 1: on down to imagination station. Um, what would you do

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Speaker 1: to survive being lost? In the catacombs. Oh man, I

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Speaker 1: would die immediately where I'm so weak, I would I

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Speaker 1: think the main thing is preserving oxygen and like bodily resources, right,

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Speaker 1: so like water would probably water and oxygen. Um, I'd

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Speaker 1: start collecting my P immediately. That's smart, yea, yeah, so

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Speaker 1: how do you what? I feel like there are tricks

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Speaker 1: to like, yeah, I mean I would I would stay Like,

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Speaker 1: I would stop wandering around for one, because like, the

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Speaker 1: more you wander, the more likely you're going to get

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Speaker 1: disoriented and get further and further away. Like the minute

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Speaker 1: you know you're lost, you stop. You do not keep

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Speaker 1: moving unless you're pretty sure you know exactly like where

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Speaker 1: the exit is, and you probably don't though there's a

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Speaker 1: huge chance that you get disoriented again. Um so I

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Speaker 1: would like probably just stay there and uh. Like the

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Speaker 1: the main threats seemed to be dehydration and exposure to

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Speaker 1: the cold of the catacombs, so starting to get um hypothermia.

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Speaker 1: Um so I would try to keep warm again, immediately

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Speaker 1: start saving my P, like you know, that's like the

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Speaker 1: first thing you do. Um, hopefully you've got like a

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Speaker 1: water bottle or something you could start. Uh, it's the

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Speaker 1: first thing I done every morning, I know, like I

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Speaker 1: just save my p just in case you never you

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Speaker 1: never know, it's a waste to don't be wasteful. Um uh, yeah,

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Speaker 1: it's it's scary. It's one of those scary things to

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Speaker 1: me though, the idea of being in the dark and

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Speaker 1: not being able to navigate, like not knowing where you

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Speaker 1: are and just getting further and further into a maze. Yeah,

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Speaker 1: that's yeah, that's definitely terrifying. Do you do the thing

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Speaker 1: like I grew up in Utah and they're like addicted

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Speaker 1: to corn mazes? Do you do the thing where you

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Speaker 1: put your right hand on the wall and you like

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Speaker 1: go or do you Is that a thing? That's interesting?

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Speaker 1: So the idea being if you put your right hand,

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Speaker 1: you're not going to get turned around at least you'll yeah,

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Speaker 1: you'll still it'll still be left right, you'll have down, yeah,

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Speaker 1: or like you'll know where you've been, like you only

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Speaker 1: go right so at some point you get out. Yeah,

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Speaker 1: that's interesting. I want I don't know, like I want

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Speaker 1: if you did that in the catacombs, though, you're you'd

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Speaker 1: just be like rubbing a bunch of skulls because they

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Speaker 1: actually line the they line the corridors with human skulls,

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Speaker 1: so you're just like I feel like maybe just like

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Speaker 1: just as you're walking along, um yeah, or maybe you

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Speaker 1: could just get to know some of the skulls really well,

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Speaker 1: like really feel them, feel their facial structure, like, yeah,

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Speaker 1: this is up the skull who's got a weird missing tooth?

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Speaker 1: Or like count their teeth and that's how you like

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Speaker 1: get a landmark? Yeah yeah yeah, Or can how many

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Speaker 1: skulls are on each wall? Um, I feel like that

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Speaker 1: you won't go insane a media, Yeah, yeah you will.

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Speaker 1: That'll be okay, You'll be fine. Like it's just like, hey,

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Speaker 1: it's just me and all my friends who are skulls

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Speaker 1: several Wilson's Yeah, yeah, I don't know. I feel yeah,

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Speaker 1: I feel like you have to preserve your energy, your

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Speaker 1: p in your ox. Yeah, I keep saying oxygen. But

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Speaker 1: I don't know how the catacombs are telling you. I

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Speaker 1: think it's I think you would die from exposure. I

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Speaker 1: don't think you would necessarily. I think there's enough in

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Speaker 1: most of the I don't Yeah, I'm not sure if

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Speaker 1: it's I think the air flow is is okay enough.

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Speaker 1: It's not like the death Zone and like the upper

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Speaker 1: Mount Everest where every because like there you will just eventually, yeah,

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Speaker 1: die of airsickness and deprivation. But have you seen the

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Speaker 1: new there's like these new photos of like the really

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Speaker 1: long line to get to the peak of Mount Everest.

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Speaker 1: A lot of those people died, right yeah, yeah, because

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Speaker 1: they're just waiting and eventually you don't. You can't live there.

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Speaker 1: Your body is not meant to live at that both

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Speaker 1: the temperature and the main problem being like the lack

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Speaker 1: of oxygen. So if you wait too long, you just

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Speaker 1: you will die. Yeah, there were a lot of like

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Speaker 1: dead bodies because there were too many people up there. Yeah,

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Speaker 1: it's it's so creepy, like I can't imagine, just like

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Speaker 1: because they're talking about people just stepping over dead bodies

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Speaker 1: to get to the peak, and like the peak itself

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Speaker 1: is the size of like two ping pong tables, and

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Speaker 1: so everyone's like cramming into get their pictures and it's

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Speaker 1: just what a statement on capitalism high, I know, I know,

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Speaker 1: just uh it's and yeah, just like you just got

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Speaker 1: to get that Instagram up, dead body. Did you see

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Speaker 1: their taking pictures, like influencers are taking pictures at Chernol, Yes,

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Speaker 1: I saw that. I saw that. That's uh one of

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Speaker 1: them like popped a butt. That was Oh yeah, she

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Speaker 1: was hella naked. Yeah, that place is still radioactive. Right. Well,

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Speaker 1: So one of the most important earlier routes studies was

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Speaker 1: in seven by pt Young. It's actually pretty have you

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Speaker 1: heard of this one? It's uh, it's actually kind of

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Speaker 1: not as well known because it's weirdly it didn't get

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Speaker 1: as much attention. It didn't get as many publications. Um,

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Speaker 1: but it's one of physicists Richard Feynman's favorite. That old

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Speaker 1: creepy guy. He was huge in I went to cal

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Speaker 1: Tech for undergrad and he was like a huge person

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Speaker 1: at cal Tech. So we heard a lot of stories

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Speaker 1: about him. But everybody like he was like a physics thod.

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Speaker 1: He was like carouser, right, like kind of a womanizer.

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Speaker 1: He was a womanizer. I hadn't heard anything inappropriate, right,

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Speaker 1: not that he was like not m yeah, not non

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Speaker 1: consensual crowsing, consensual crops. So yeah, that's why I want

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Speaker 1: I won't call it creepy, but it was he was

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Speaker 1: getting it. Uh. So he he loved this study, this

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Speaker 1: seven routes study. Um. He described it like this, I'm

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Speaker 1: just going to quote him because he puts it in

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Speaker 1: it's a somewhat confusing study, and he phrased it as

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Speaker 1: it in a way that is a lot clearer than

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Speaker 1: I think I could do. So quote, Uh, this researcher,

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Speaker 1: he had a long corridor with doors all along one

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Speaker 1: side where the rats came in, and doors all along

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Speaker 1: the other side where the food was. He wanted to

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Speaker 1: see if he could train the rats to go in

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Speaker 1: at the third door down from wherever he started them off.

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Speaker 1: The question was how did the rats know because the

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Speaker 1: corridor was so beautifully built in so uniform that this

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Speaker 1: was the same door as before. So basically, you know,

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Speaker 1: this researcher is saying, like, wherever door you come out of,

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Speaker 1: the food reward is going to be three doors down

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Speaker 1: to the left from where you started. But instead the rats,

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Speaker 1: no matter where they were in the maze, wherever they

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Speaker 1: were put like door number one, two, three, four, whatever,

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Speaker 1: they would always go to the door where the food

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Speaker 1: was last, and they knew where that was. And um,

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Speaker 1: the research was trying to eliminate all of the clues

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Speaker 1: of like where they were in the maze. So he

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Speaker 1: repainted the doors so the texture of the paint strokes

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Speaker 1: were the same, because he was like, maybe they can

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Speaker 1: tell by the texture of the door, but that didn't work.

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Speaker 1: The routes still found that original door. Um. He thought

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Speaker 1: maybe they were smelling it. That's a pretty reasonable assumption.

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Speaker 1: But he masked all the scent with these really powerful

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Speaker 1: chemicals every time, so there was no way. Um. So

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Speaker 1: that didn't change anything either. Um. And he also like

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Speaker 1: covered the entire maze up because he was thinking, well,

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Speaker 1: maybe they're orienting themselves, like to the lights in the lab,

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Speaker 1: so the lights on the ceiling sort of like you know,

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Speaker 1: with their little tiny rats sextants, like going like oh

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Speaker 1: here and like um. But that didn't have any effect either.

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Speaker 1: And finally he thought, well, maybe like the sound the

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Speaker 1: floor makes, like they're they're using some kind of like

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Speaker 1: they're hearing differences in the sound the floor makes. So

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Speaker 1: he filled the entire um mazed with sand and that

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Speaker 1: masked the sound that their footsteps made and that they

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Speaker 1: couldn't find it anymore. And that was that was the thing. Um.

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Speaker 1: And what's so important about that study besides the fact

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Speaker 1: that you can't you can't trigger at like, it's so

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Speaker 1: crazy that they're just like they could find it no

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Speaker 1: matter what, and it was just this one slight like

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Speaker 1: they could hear I guess, like some slight difference in

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Speaker 1: the way their feet, like maybe it's more hollow there

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Speaker 1: or something. Um. But Uh, it's so not just in

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Speaker 1: terms of rat intelligence, but the crazy lengths you have

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Speaker 1: to go to to eliminate all of these different outside

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Speaker 1: environmental factors in a study, because that's the hardest thing

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Speaker 1: about behavioral studies is that you don't know if what

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Speaker 1: you're getting is accurate. You just have to say, like,

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Speaker 1: to the best my knowledge, it is because these animals

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Speaker 1: have there's so many variables. Like wasn't there recently um

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Speaker 1: a study about how mice react differently to experimenters that

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Speaker 1: are male versus female? Oh? Interesting? Yeah, there, I think

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Speaker 1: there's that. There's also um they're like scared of men

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Speaker 1: and so they act differently when they are And so

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Speaker 1: if you ever have an experiment that requires multiple researchers,

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Speaker 1: it has to be the same. Yeah. I think there

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Speaker 1: was also um a recent uh finding about like gender

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Speaker 1: bias in terms of the rats actual gender. So female

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Speaker 1: rats are often not used in certain medical studies because

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Speaker 1: of their um their minstrel cycles, uh are thought to like, well,

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Speaker 1: that's an inconsistent influence, but there's some concern that you know,

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Speaker 1: this may that kind of the same sort of differences

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Speaker 1: in terms of their hormones. I mean human women have

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Speaker 1: that too, so I know women are crazy what with

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Speaker 1: their minstrel luninormal bodies. Yeah yeah yeah. And also the

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Speaker 1: fact that everything is modeled after like male animals in

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Speaker 1: every It's like, this is great. Even the rats have

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Speaker 1: to be dudes. I just like how like the solution

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Speaker 1: to um the fact that rats have pesky ovaries is

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Speaker 1: just like, oh, we won't study yeah as much. Yeah, yeah,

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Speaker 1: did you? This is also like a really random like

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Speaker 1: animal fact. But there was you know how they use

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Speaker 1: like messenger messenger pigeons in like the wars and stuff.

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Speaker 1: There was one I think in World War Two that

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Speaker 1: like was given like this metal because it saved like

401
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Speaker 1: two people and like it did all these like crazy things.

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Speaker 1: And it's in the Spy Museum in d C. And

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Speaker 1: they have a whole section on these pigeons and uh

404
00:22:51,000 --> 00:22:55,640
Speaker 1: so it's just like Pigeon of Valor, right, and it's

405
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Speaker 1: it's lauded for its work and they keep miss gendering

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Speaker 1: it as male. Oh like indifferent interest pigeon. Yeah. Yeah,

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Speaker 1: behind every great male pigeon is it's actually it is

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Speaker 1: actually just a female pigeon though. That's that's inferior. That's

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Speaker 1: always frustrating, the h the assumption of certain like the

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Speaker 1: assumption that like the more um, I don't know, physically

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Speaker 1: capable animals or males, it's often the females that are, uh,

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Speaker 1: we just endure. I mean, I think what you're saying

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Speaker 1: is we do need to bring feminism to pigeons. Yeah. Yeah,

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Speaker 1: I mean I feel like we could learn a lot.

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Speaker 1: We would, We could learn from each other. Who would

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Speaker 1: learn the most? Humans are pigeons. Humans from pigeons, we'd

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Speaker 1: learn more. I feel like we would, Yeah, for sure.

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Speaker 1: So So back to rats. Um, pigeons of the ground,

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Speaker 1: the pigeons of the yes, the pigeons of the sewers,

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Speaker 1: the pigeons of the feet, is what I was gonna say,

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Speaker 1: but then I realized that doesn't really makes sense. Um.

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Speaker 1: So in more contemporary science, our ability to be precise

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Speaker 1: has gotten much more high tech. So um, as you

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Speaker 1: well know, researchers can now use neurotechnology to record rat

425
00:24:17,960 --> 00:24:20,879
Speaker 1: brains and give nearly real time read out where the

426
00:24:20,960 --> 00:24:23,919
Speaker 1: rats think they are in a maze. So you know

427
00:24:24,000 --> 00:24:26,560
Speaker 1: that the technology of being able to put a bunch

428
00:24:26,600 --> 00:24:30,600
Speaker 1: of sensors like you kind of implanted into the rat

429
00:24:30,640 --> 00:24:32,679
Speaker 1: brain and then it gives a feed out that's not

430
00:24:32,840 --> 00:24:36,199
Speaker 1: super new, but the more real time aspect of it

431
00:24:36,240 --> 00:24:39,080
Speaker 1: where you can it's instead of taking a lot of

432
00:24:39,080 --> 00:24:41,840
Speaker 1: time for that data to be interpreted and processed, it's

433
00:24:41,880 --> 00:24:44,119
Speaker 1: becoming more and more to the point where you can

434
00:24:44,359 --> 00:24:47,320
Speaker 1: UM basically see where the rat thinks it is in

435
00:24:47,359 --> 00:24:49,560
Speaker 1: the maze as it's in the maze at the same

436
00:24:49,600 --> 00:24:53,320
Speaker 1: time as where it is in the maze. That's like, UM, like,

437
00:24:53,520 --> 00:24:58,280
Speaker 1: have you heard of Karl Desroth and optogenetics. I have

438
00:24:58,440 --> 00:25:01,919
Speaker 1: heard of opto genetics, but have not heard of this

439
00:25:01,960 --> 00:25:04,960
Speaker 1: specific guy. He um figured out a way to use

440
00:25:05,080 --> 00:25:08,520
Speaker 1: a light to control the brains. Yes, actually, actually maybe

441
00:25:09,160 --> 00:25:10,719
Speaker 1: I say, I don't know who this guy is, but

442
00:25:10,800 --> 00:25:14,880
Speaker 1: I may actually know. Yeah, he's at Stanford. He's That

443
00:25:14,960 --> 00:25:19,159
Speaker 1: field became revolutionized because you could basically trigger different cells

444
00:25:19,240 --> 00:25:23,120
Speaker 1: to activate using light in real time. UM and that

445
00:25:23,200 --> 00:25:25,640
Speaker 1: was a huge breakthrough. And then he had another break

446
00:25:25,640 --> 00:25:29,960
Speaker 1: through later UM with clear which was turning the brain clear.

447
00:25:30,119 --> 00:25:35,679
Speaker 1: But but opti genetics was was very like vital to

448
00:25:35,760 --> 00:25:38,239
Speaker 1: this field. And there you know, there are a lot

449
00:25:38,240 --> 00:25:40,920
Speaker 1: of animal studies with that. We're actually going to talk

450
00:25:40,960 --> 00:25:43,840
Speaker 1: about one. But no, no, that's it's good because like

451
00:25:43,880 --> 00:25:46,480
Speaker 1: it is a uh when I when I was looking

452
00:25:46,520 --> 00:25:48,560
Speaker 1: at it, is took a little while for it to

453
00:25:48,640 --> 00:25:51,760
Speaker 1: think in what it actually was because it's you genetically

454
00:25:51,800 --> 00:25:56,719
Speaker 1: modify the animal beforehand such that it's uh, neurons can

455
00:25:56,800 --> 00:25:59,119
Speaker 1: be manipulated with light and then you get the study.

456
00:25:59,520 --> 00:26:01,760
Speaker 1: But well, we have a pretty fun study that that

457
00:26:01,880 --> 00:26:07,119
Speaker 1: uses that technique. Um. So, uh. One thing I wonder

458
00:26:07,160 --> 00:26:10,399
Speaker 1: about when rats are running through maze is it's always

459
00:26:10,400 --> 00:26:13,640
Speaker 1: like it's easy to anthropomorphies like that they feel frustration

460
00:26:14,000 --> 00:26:18,200
Speaker 1: or um. But there is a there is a study

461
00:26:18,240 --> 00:26:21,840
Speaker 1: that looks at whether they feel regret for bad choices. Um.

462
00:26:21,880 --> 00:26:25,359
Speaker 1: And of course, with any of these behavioral studies, regrets

463
00:26:25,400 --> 00:26:30,520
Speaker 1: a very um, you know, kind of uh amorphous concept.

464
00:26:31,280 --> 00:26:33,320
Speaker 1: It's they probably look at like what lights up in

465
00:26:33,359 --> 00:26:35,760
Speaker 1: the brain right exactly. So to say that it is

466
00:26:35,840 --> 00:26:39,439
Speaker 1: exactly regret is that's a little subjective. But it's like,

467
00:26:39,480 --> 00:26:42,800
Speaker 1: how do each of us see blue right exactly? Um?

468
00:26:42,880 --> 00:26:45,080
Speaker 1: But you know, to the extent that we can figure

469
00:26:45,080 --> 00:26:48,199
Speaker 1: that out. They've they did a study, a University of

470
00:26:48,280 --> 00:26:52,159
Speaker 1: Minneapolis study that measured brain responses and rats as they

471
00:26:52,280 --> 00:26:56,040
Speaker 1: navigated a prize maze. Um. The rats basically had to

472
00:26:56,080 --> 00:27:00,520
Speaker 1: gamble which door to choose. Um. If the rats choose wrong, uh,

473
00:27:00,560 --> 00:27:03,480
Speaker 1: they had to wait longer for the food food rewards.

474
00:27:03,480 --> 00:27:06,239
Speaker 1: So and then they had like a audio cue that

475
00:27:06,640 --> 00:27:09,960
Speaker 1: basically would tell them like they were trained to associate

476
00:27:09,960 --> 00:27:13,399
Speaker 1: different audio cues with different lengths of waiting times. Like. Uh,

477
00:27:13,920 --> 00:27:16,360
Speaker 1: I when I was reading this study, I just imagined

478
00:27:16,359 --> 00:27:18,480
Speaker 1: the Jeopardy song like playing, and they're like, oh man,

479
00:27:18,520 --> 00:27:20,639
Speaker 1: I have to wait like two minutes for my food.

480
00:27:21,320 --> 00:27:23,240
Speaker 1: But it was like certain tones and then one tone

481
00:27:23,280 --> 00:27:25,360
Speaker 1: would mean you have to wait forty five seconds. One

482
00:27:25,359 --> 00:27:27,520
Speaker 1: tone me and you wait sixty seconds. So obviously they

483
00:27:27,520 --> 00:27:31,160
Speaker 1: want the food now. So longer wait times equals sadder rat,

484
00:27:31,320 --> 00:27:36,399
Speaker 1: just like me when I'm waiting for like uber eats too. Yeah. Um,

485
00:27:36,480 --> 00:27:39,280
Speaker 1: so that those the regret was more when they had

486
00:27:39,320 --> 00:27:41,560
Speaker 1: to wait longer. Yes, So what would happen is they

487
00:27:41,560 --> 00:27:44,480
Speaker 1: would go to a prize door and they weren't actual doors,

488
00:27:44,520 --> 00:27:47,560
Speaker 1: but it's like the it's a little bit like weird

489
00:27:47,560 --> 00:27:50,159
Speaker 1: to explain. They're like these like spokes in the maze

490
00:27:50,160 --> 00:27:53,040
Speaker 1: where they like they activated it, then the waiting period

491
00:27:53,040 --> 00:27:56,000
Speaker 1: would happen. But yeah, so they would approach one, they'd

492
00:27:56,000 --> 00:27:58,600
Speaker 1: hear this tone where they'd have to wait like thirty seconds.

493
00:27:58,600 --> 00:28:00,440
Speaker 1: They're like, oh, I don't want to wait thirty seconds.

494
00:28:00,840 --> 00:28:03,320
Speaker 1: So they'd go to the next door and they're like, well, actually,

495
00:28:03,400 --> 00:28:05,399
Speaker 1: at this door, you have to wait a full minute.

496
00:28:05,800 --> 00:28:07,480
Speaker 1: And then they're like, ah, damn it, I should have

497
00:28:07,520 --> 00:28:09,520
Speaker 1: stuck with that first door where I only had to

498
00:28:09,520 --> 00:28:11,760
Speaker 1: wait thirty seconds. So they would look back at the

499
00:28:11,840 --> 00:28:15,320
Speaker 1: first door and the um their brain pattern would light

500
00:28:15,400 --> 00:28:17,720
Speaker 1: up with the same pattern that they had when they

501
00:28:17,760 --> 00:28:20,879
Speaker 1: initially approached that first door and made the decision to

502
00:28:20,960 --> 00:28:26,600
Speaker 1: leave that first door. UM. So what the researchers interpreted

503
00:28:26,680 --> 00:28:30,080
Speaker 1: this as showing like they look back and consider this

504
00:28:30,160 --> 00:28:33,399
Speaker 1: old choice and maybe as a form of regret of

505
00:28:33,880 --> 00:28:37,480
Speaker 1: like oh I should have done that. Um. It's obviously

506
00:28:37,600 --> 00:28:40,560
Speaker 1: it's hard to say what the rat feels in the

507
00:28:40,640 --> 00:28:44,320
Speaker 1: moment if it is regret. Um, if they're you know

508
00:28:44,920 --> 00:28:48,920
Speaker 1: what rat regret feels like, But I can I can

509
00:28:49,000 --> 00:28:51,760
Speaker 1: buy it. I think they could in some you know,

510
00:28:51,960 --> 00:28:56,000
Speaker 1: different way. Yeah, yeah, I feel like they could. Um. There,

511
00:28:56,120 --> 00:28:58,280
Speaker 1: I will say like after having worked with rats, they're

512
00:28:58,320 --> 00:29:00,680
Speaker 1: like really smart, and I like, really you love them

513
00:29:01,120 --> 00:29:04,080
Speaker 1: as animals and they're very easy to work with. Um.

514
00:29:04,120 --> 00:29:06,440
Speaker 1: And so you do when you're talking about like anthropomorphizing them,

515
00:29:06,480 --> 00:29:09,080
Speaker 1: you totally do that. Yeah. There were so many rats

516
00:29:09,160 --> 00:29:11,800
Speaker 1: named Pinky in the brain, like at the place that

517
00:29:11,840 --> 00:29:13,960
Speaker 1: I was working, Like so many, even a couple of

518
00:29:13,960 --> 00:29:16,640
Speaker 1: mine where it named that um. There is um. The

519
00:29:16,720 --> 00:29:18,680
Speaker 1: second thing that there is. I remember when I was

520
00:29:18,680 --> 00:29:21,320
Speaker 1: interviewing for Master's programs, there was a guy at RICE

521
00:29:21,440 --> 00:29:25,320
Speaker 1: who was working on a really interesting study UM with

522
00:29:25,320 --> 00:29:29,080
Speaker 1: with erasing their memories. We're going to talk about talking about.

523
00:29:29,960 --> 00:29:31,800
Speaker 1: I don't know if it's the same one, but we'll

524
00:29:31,840 --> 00:29:34,200
Speaker 1: talk about that. And if it's a different study, please

525
00:29:34,200 --> 00:29:36,840
Speaker 1: tell me about that. Funny. This is great, This is great.

526
00:29:40,400 --> 00:29:44,200
Speaker 1: So maybe rats can show regret, but can they show empathy?

527
00:29:44,560 --> 00:29:46,720
Speaker 1: What a rat help you out in a dire maze

528
00:29:46,760 --> 00:29:51,120
Speaker 1: situation or help you escape the catacombs? Well? I wouldn't

529
00:29:51,120 --> 00:29:53,160
Speaker 1: bet my life on it, but there may be some

530
00:29:53,280 --> 00:29:57,680
Speaker 1: hope in A pet rat named Fido reportedly saved a

531
00:29:57,720 --> 00:30:00,880
Speaker 1: family from their burning home. As oakes started to fill

532
00:30:00,920 --> 00:30:04,280
Speaker 1: their house, Fido escaped his cage, but instead of getting

533
00:30:04,280 --> 00:30:07,000
Speaker 1: out of there, like well a rat office sinking ship,

534
00:30:07,360 --> 00:30:10,760
Speaker 1: Fido went upstairs and scratched on the bedroom door. The

535
00:30:10,800 --> 00:30:14,840
Speaker 1: scratching awoke the family and they managed to escape the kicker.

536
00:30:14,960 --> 00:30:18,200
Speaker 1: The family had a dog who did absolutely nothing to help,

537
00:30:18,720 --> 00:30:21,560
Speaker 1: so everyone survived and thanked fight of the rat for

538
00:30:21,640 --> 00:30:25,080
Speaker 1: saving them. So I guess Fido kind of beats the

539
00:30:25,120 --> 00:30:28,200
Speaker 1: dog in terms of the family's favorite pet now, But

540
00:30:28,600 --> 00:30:32,800
Speaker 1: in fact, is this actually empathy? A study in looked

541
00:30:32,840 --> 00:30:36,080
Speaker 1: at the rescue behavior in rats. When placed in a

542
00:30:36,120 --> 00:30:38,800
Speaker 1: new environment, they'll often try to scratch it a tube

543
00:30:38,800 --> 00:30:42,280
Speaker 1: in which another rat is trapped, in an apparent attempt

544
00:30:42,400 --> 00:30:46,040
Speaker 1: to save them. However, the researchers concluded that the behavior

545
00:30:46,080 --> 00:30:49,240
Speaker 1: may be motivated more by a desire for social contact

546
00:30:49,280 --> 00:30:52,600
Speaker 1: and comfort rather than an actual attempt to quote rescue

547
00:30:52,640 --> 00:30:56,280
Speaker 1: their companion. The rats seemed to touch the tube regardless

548
00:30:56,280 --> 00:30:59,680
Speaker 1: of whether they're touching actually free the rat, seeming instead

549
00:30:59,720 --> 00:31:03,280
Speaker 1: to be interested in interacting with the trapped raft. So

550
00:31:03,560 --> 00:31:07,840
Speaker 1: maybe Fido's heroism was due to his desire to seek reassurance.

551
00:31:07,880 --> 00:31:12,640
Speaker 1: And friendship. But that's almost even cuter. Well, rats, we're

552
00:31:12,680 --> 00:31:14,480
Speaker 1: gonna have to take a quick break, but we'll be

553
00:31:14,520 --> 00:31:18,080
Speaker 1: back soon with some more rat tails who getting hooked

554
00:31:18,120 --> 00:31:20,440
Speaker 1: on co came? These are rat tails? Do do do?

555
00:31:25,440 --> 00:31:28,440
Speaker 1: Rats and humans actually share a lot of qualities. Were

556
00:31:28,480 --> 00:31:33,120
Speaker 1: both mammals highly intelligent and social. And here's another interesting quirk.

557
00:31:33,640 --> 00:31:39,000
Speaker 1: Rats and humans tend to forget distracting memories. Scientific review

558
00:31:39,040 --> 00:31:41,720
Speaker 1: discussed how rats and humans both have the ability to

559
00:31:41,800 --> 00:31:46,000
Speaker 1: selectively forget information or memories that are distracting and perhaps

560
00:31:46,160 --> 00:31:49,120
Speaker 1: non beneficial. A study found that the act of re

561
00:31:49,240 --> 00:31:53,440
Speaker 1: calling certain memories and humans replaces related but tangential memories,

562
00:31:53,760 --> 00:31:57,160
Speaker 1: actively destroying any memory that interviews with our recall of

563
00:31:57,200 --> 00:31:59,960
Speaker 1: the search for memories, So basically, you have any memory

564
00:32:00,080 --> 00:32:01,920
Speaker 1: that gets in the way of you trying to remember

565
00:32:02,000 --> 00:32:06,240
Speaker 1: something related, those memories are destroyed. Bulldozered rats have this

566
00:32:06,320 --> 00:32:09,840
Speaker 1: habit as well when introduced to new objects. If one

567
00:32:09,880 --> 00:32:12,600
Speaker 1: object is given more importance, like they're trained to keep

568
00:32:12,600 --> 00:32:16,080
Speaker 1: remembering it, they'll quickly forget about the other object, much

569
00:32:16,120 --> 00:32:19,120
Speaker 1: more quickly than in control conditions where both objects are

570
00:32:19,160 --> 00:32:22,960
Speaker 1: of equal importance. So in a sense, we're constantly doing

571
00:32:23,000 --> 00:32:26,560
Speaker 1: Eternal Sunshine of the Spotless Mind on ourselves, erasing memories

572
00:32:26,600 --> 00:32:30,640
Speaker 1: that are undesirable. But are there more extreme science fiction

573
00:32:30,720 --> 00:32:35,080
Speaker 1: e things going on in RAT research? So back to

574
00:32:35,120 --> 00:32:38,720
Speaker 1: your imagination station? Do you think you would all like,

575
00:32:39,000 --> 00:32:41,560
Speaker 1: have you seen Eternal Sunshine of the Spotless Mind? I

576
00:32:41,640 --> 00:32:44,520
Speaker 1: actually tweeted a joke about it, like early day or

577
00:32:44,560 --> 00:32:47,440
Speaker 1: the day before. I was like, crazy, how in how

578
00:32:47,520 --> 00:32:51,680
Speaker 1: in that movie? Uh, they totally raised Jim Carrey's memories

579
00:32:51,720 --> 00:32:59,320
Speaker 1: of how vaccines were weird, that is weird. Somehow germ

580
00:32:59,400 --> 00:33:03,960
Speaker 1: theory got mixed up in his romance. Did you see

581
00:33:04,000 --> 00:33:06,280
Speaker 1: Jessica Biel just came out as anti back. I think

582
00:33:06,280 --> 00:33:09,600
Speaker 1: she's taking it back, but really I don't know pedaling

583
00:33:09,600 --> 00:33:11,440
Speaker 1: on that, yeah, I think because she got a lot

584
00:33:11,560 --> 00:33:16,520
Speaker 1: of backlash. Yeah, So she's introducing a sort of um

585
00:33:16,720 --> 00:33:19,280
Speaker 1: something that will run that will run counter to the

586
00:33:19,320 --> 00:33:22,160
Speaker 1: negativity that's coming in, that will sort of attach itself

587
00:33:22,200 --> 00:33:26,040
Speaker 1: to the negativity and kind of destroy that negativity, you

588
00:33:26,080 --> 00:33:28,479
Speaker 1: know what I mean? Yeah? Yeah, yeah, like trying to

589
00:33:28,520 --> 00:33:33,640
Speaker 1: like detect it protect herself right exactly. So it's like good,

590
00:33:33,760 --> 00:33:36,600
Speaker 1: it's like good to kind of train her PR team

591
00:33:36,680 --> 00:33:40,840
Speaker 1: to basically know how to protect her against certain threats

592
00:33:40,880 --> 00:33:44,880
Speaker 1: to her popularity, and then later when this happens again,

593
00:33:45,200 --> 00:33:48,240
Speaker 1: they'll actually be really good at protecting her again. Another

594
00:33:48,360 --> 00:33:54,520
Speaker 1: pr nightmare. Amazing how that? Yeah that was your vaccine joke. Guys,

595
00:33:57,240 --> 00:34:03,440
Speaker 1: We're hip, We're hip, cool podcast. I was just like, normal, heaven,

596
00:34:03,720 --> 00:34:08,719
Speaker 1: your children are going to heaven. I feel I don't know,

597
00:34:08,800 --> 00:34:11,200
Speaker 1: I feel so bad for those kids. Now, those kids

598
00:34:11,200 --> 00:34:15,799
Speaker 1: are eighteen and getting vaccinated themselves. They're rebelling by getting vaccinated. Yeah,

599
00:34:15,960 --> 00:34:18,359
Speaker 1: it's so cool. They're like talking about how stupid their

600
00:34:18,400 --> 00:34:22,000
Speaker 1: parents are. They are correct. The fact that they survived

601
00:34:22,080 --> 00:34:25,160
Speaker 1: is like crazy to me, I know, surviving all the

602
00:34:25,160 --> 00:34:30,080
Speaker 1: way to eighteen with no vaccinations. Um, I mean yeah,

603
00:34:30,160 --> 00:34:32,960
Speaker 1: it's I love that. It's like the rebellious thing to

604
00:34:33,000 --> 00:34:34,960
Speaker 1: do now is to be healthy, go to the doctor,

605
00:34:35,040 --> 00:34:37,840
Speaker 1: get your vaccines. Yeah, to like be be helly in

606
00:34:37,840 --> 00:34:40,520
Speaker 1: that way. People are rebelling by going to therapy. People

607
00:34:40,520 --> 00:34:43,080
Speaker 1: like kids are rebelling now by being politically active and

608
00:34:43,120 --> 00:34:46,840
Speaker 1: like holding activist rallies and getting Yeah, I mean that's

609
00:34:47,040 --> 00:34:49,319
Speaker 1: kind of case for a while. Yeah. That's the thing

610
00:34:49,400 --> 00:34:52,000
Speaker 1: is I think young people are maligned, but I think

611
00:34:52,239 --> 00:34:56,320
Speaker 1: they are. Also they've always been responsible young, good people.

612
00:34:56,640 --> 00:35:00,640
Speaker 1: I mean there's always there's always the dabbing, you know, dabbing.

613
00:35:00,760 --> 00:35:03,279
Speaker 1: There's some form of dabbing in every generation and that's

614
00:35:03,280 --> 00:35:08,600
Speaker 1: fine as long as they're getting vaccinated. Um so uh so,

615
00:35:08,640 --> 00:35:13,160
Speaker 1: would you do eternal sunshine of the spotless mind on yourself? Like? Like,

616
00:35:13,200 --> 00:35:18,480
Speaker 1: would you ever um wanna like race um either bad

617
00:35:18,520 --> 00:35:22,480
Speaker 1: habits or bad memories because it is a vaccination against

618
00:35:22,640 --> 00:35:26,759
Speaker 1: future pain. That's a really good way of thinking of it.

619
00:35:26,880 --> 00:35:29,080
Speaker 1: I've never thought of it that way. If I did

620
00:35:29,120 --> 00:35:30,959
Speaker 1: not have the information I have now, I'd be making

621
00:35:31,040 --> 00:35:33,799
Speaker 1: so many more mistakes. That is such a good point, Yeah,

622
00:35:33,840 --> 00:35:39,360
Speaker 1: because it that is basically what memory is for, to

623
00:35:39,400 --> 00:35:43,480
Speaker 1: train ourselves to be better at doing life in the future.

624
00:35:43,680 --> 00:35:48,120
Speaker 1: That's why you remember um emotionally traumatic moments traumatic moments

625
00:35:48,120 --> 00:35:51,040
Speaker 1: in general more than other moments, right, because like what's

626
00:35:51,120 --> 00:35:55,480
Speaker 1: more important to remember eating a sandwich and everything was

627
00:35:55,560 --> 00:35:58,520
Speaker 1: fine or that time you took a huge bite of

628
00:35:58,560 --> 00:36:00,759
Speaker 1: a sandwich and you almost choked death alone in your

629
00:36:00,760 --> 00:36:03,600
Speaker 1: own apartment. Yeah. I think I think also works the

630
00:36:03,600 --> 00:36:07,919
Speaker 1: opposite way, where when it is too sad, you black out,

631
00:36:08,400 --> 00:36:11,319
Speaker 1: and I think that that is also protective. Yes, that

632
00:36:11,480 --> 00:36:14,160
Speaker 1: is true. Yeah, yeah, where where it's like if it's

633
00:36:14,960 --> 00:36:19,600
Speaker 1: if it's just generally unhelpful, um, but it's like super unpleasant,

634
00:36:20,120 --> 00:36:22,560
Speaker 1: we we kind of prune those memories as well. Yeah,

635
00:36:22,840 --> 00:36:25,000
Speaker 1: Like I just like I think of like extremely traumatic

636
00:36:25,080 --> 00:36:29,080
Speaker 1: moments where people like forget, they'll they'll disassociate, disassociate, and

637
00:36:29,120 --> 00:36:32,200
Speaker 1: it is like a survival technique because it's not not

638
00:36:32,280 --> 00:36:35,879
Speaker 1: really helping. Like if you're just being tormented at some point,

639
00:36:36,080 --> 00:36:39,040
Speaker 1: it's not you're not learning anything that's helpful. It's just

640
00:36:39,440 --> 00:36:42,600
Speaker 1: hurting pain. Yeah. There's also that thing of where like

641
00:36:42,760 --> 00:36:47,400
Speaker 1: children like toddlers and younger in emergencies, they fall asleep.

642
00:36:47,680 --> 00:36:49,920
Speaker 1: Oh that's so that's so weird because that's almost like,

643
00:36:50,120 --> 00:36:55,000
Speaker 1: um the what's it called the catatonia and um like

644
00:36:55,040 --> 00:36:57,480
Speaker 1: when you startle those goats of those cat kittens that

645
00:36:57,600 --> 00:37:00,640
Speaker 1: have that disorder where um, I think it's like catatonia

646
00:37:00,719 --> 00:37:03,840
Speaker 1: myopia or something like that. Um, we're like clap and

647
00:37:03,840 --> 00:37:06,360
Speaker 1: then they fall over because like they're startled and somehow

648
00:37:06,600 --> 00:37:08,920
Speaker 1: they don't actually I don't think they fall unconscious. It's

649
00:37:08,960 --> 00:37:12,160
Speaker 1: just all their muscles like spasm and they fall over. Yeah,

650
00:37:12,440 --> 00:37:14,319
Speaker 1: they yeah, so, but it is. I think it is

651
00:37:14,360 --> 00:37:16,360
Speaker 1: like a protective thing for the parents because then the

652
00:37:16,480 --> 00:37:19,480
Speaker 1: child won't cry. Interesting, So it is it is like

653
00:37:19,680 --> 00:37:23,160
Speaker 1: the best state for a child to take a baby

654
00:37:23,680 --> 00:37:27,400
Speaker 1: when something bad is happening is for them to sleep soundly. Yes, yes,

655
00:37:27,480 --> 00:37:29,919
Speaker 1: so that their parents can be quiet and like take

656
00:37:30,000 --> 00:37:33,040
Speaker 1: them and rescue them. Yeah. That is so interesting. It's

657
00:37:33,080 --> 00:37:36,040
Speaker 1: like like they're going into sleep mode so they can

658
00:37:36,080 --> 00:37:41,680
Speaker 1: be easily transported airplane mode. So I want to talk

659
00:37:41,719 --> 00:37:46,680
Speaker 1: about eternal Sunshine of Spotless rat minds, um, just imagine

660
00:37:47,239 --> 00:37:49,480
Speaker 1: Jim carrying that movie is a giant rat or do

661
00:37:49,560 --> 00:37:53,279
Speaker 1: we have to imagine? I don't hate him that much.

662
00:37:53,320 --> 00:37:55,839
Speaker 1: I do hate him for the anti vax. I don't

663
00:37:55,880 --> 00:37:58,560
Speaker 1: like the anti vax stuff. But as a comedian, he's

664
00:37:58,600 --> 00:38:01,399
Speaker 1: like such a hero. He's a great He's a great

665
00:38:01,480 --> 00:38:06,560
Speaker 1: dramatic actor too. It's amazing. So rats who I'm starting

666
00:38:06,560 --> 00:38:08,480
Speaker 1: to like them more and more the more more I

667
00:38:08,560 --> 00:38:11,800
Speaker 1: learned about them. In some ways, they're better than people.

668
00:38:12,400 --> 00:38:16,960
Speaker 1: But there's researchers at the University of Pittsburgh set up

669
00:38:16,960 --> 00:38:20,279
Speaker 1: a Pavlovian experiment involving rats. Now I think most people

670
00:38:20,360 --> 00:38:23,480
Speaker 1: kind of know what with the Pavlovian responses. But just

671
00:38:23,520 --> 00:38:27,120
Speaker 1: a quick refreshers the study where you ring a bell,

672
00:38:27,480 --> 00:38:29,759
Speaker 1: dog gets food. Now every time you ring the bell,

673
00:38:29,840 --> 00:38:32,399
Speaker 1: the dog rules everyone. I think every pet owner knows

674
00:38:32,440 --> 00:38:36,799
Speaker 1: what this is. You know, any like slight like package crinkling,

675
00:38:36,880 --> 00:38:40,040
Speaker 1: like the quietest crinkling of any package in my dog

676
00:38:40,120 --> 00:38:46,359
Speaker 1: just like well like she's there, suddenly there appears like magic. Um. So,

677
00:38:46,640 --> 00:38:51,640
Speaker 1: in this study, researchers got rats addicted to cocaine, which

678
00:38:51,640 --> 00:38:55,400
Speaker 1: happens a lot in uh in rat research. Um. But

679
00:38:55,840 --> 00:38:58,880
Speaker 1: so they were given an audio visual cue that Harold

680
00:38:59,120 --> 00:39:02,799
Speaker 1: Harold did their cocaine hit. Um. So, you know, a

681
00:39:02,800 --> 00:39:05,319
Speaker 1: bell would ring and they'd get some cocaine. The start

682
00:39:05,360 --> 00:39:12,320
Speaker 1: wearing leather jacket. Um. So, researchers found that there's increased

683
00:39:12,360 --> 00:39:16,440
Speaker 1: activity between the thalamus and amygdala associated with the rats

684
00:39:16,480 --> 00:39:21,000
Speaker 1: anticipation of the cocaine. Eventually, just seeing or hearing the

685
00:39:21,120 --> 00:39:23,959
Speaker 1: queue made their brains go bonkers, like their brains are like, yeah,

686
00:39:23,960 --> 00:39:29,960
Speaker 1: it's cocaine time. Um. And so the researchers used optogenetics

687
00:39:30,120 --> 00:39:33,880
Speaker 1: to selectively turn off the neurons associated with their drug craving.

688
00:39:33,960 --> 00:39:36,439
Speaker 1: So we talked about this a little bit earlier. It's

689
00:39:36,480 --> 00:39:41,160
Speaker 1: the process of genetically modifying neurons so such that there's

690
00:39:41,600 --> 00:39:44,880
Speaker 1: a sensitive delights. So they'll the lab will create this

691
00:39:44,920 --> 00:39:49,719
Speaker 1: little rat baby that and their neurons have been modified

692
00:39:49,760 --> 00:39:53,560
Speaker 1: such that you can kind of implant these these lights

693
00:39:53,560 --> 00:39:56,840
Speaker 1: into their brains and then once you turn on the

694
00:39:56,920 --> 00:39:59,240
Speaker 1: light or turn off a light, it affects the functioning

695
00:39:59,320 --> 00:40:02,920
Speaker 1: of the neurons. UM. Uh do you know, like do

696
00:40:02,960 --> 00:40:06,399
Speaker 1: you know anything about like how they how they found that,

697
00:40:06,560 --> 00:40:10,720
Speaker 1: like they could do this, like how the genetics of Uh.

698
00:40:10,760 --> 00:40:13,600
Speaker 1: I used to remember the history of it. But I

699
00:40:13,640 --> 00:40:17,680
Speaker 1: mean a lot of these experiments that are run in general,

700
00:40:17,719 --> 00:40:21,719
Speaker 1: are run on genetically modified animals. UM. So that part

701
00:40:21,800 --> 00:40:26,320
Speaker 1: I think, like that's not unusual, that's not novel. UM.

702
00:40:26,360 --> 00:40:28,160
Speaker 1: But I used to know the history of it because

703
00:40:28,200 --> 00:40:30,719
Speaker 1: it was so captivating and it was. But it's been

704
00:40:30,719 --> 00:40:32,600
Speaker 1: a while since I looked at it. UM. But they

705
00:40:32,719 --> 00:40:35,560
Speaker 1: I guess they looked. I mean it had to be

706
00:40:35,600 --> 00:40:40,640
Speaker 1: like a light sensitive UM mechanism UM, And so I

707
00:40:40,680 --> 00:40:42,600
Speaker 1: think I forget how they attached it. Let me, I

708
00:40:42,600 --> 00:40:45,359
Speaker 1: don't know, but uh, but yeah, it was like it

709
00:40:45,400 --> 00:40:50,040
Speaker 1: was a huge discovery. Like everybody who is anywhere near

710
00:40:50,120 --> 00:40:54,560
Speaker 1: biology knows Carl Desseron right, because you can then, um,

711
00:40:54,760 --> 00:40:57,799
Speaker 1: do you sort of selective quote unquote brain damage that's

712
00:40:57,800 --> 00:41:00,520
Speaker 1: actually not damaging the brain, Like you can turn it

713
00:41:00,600 --> 00:41:03,400
Speaker 1: back off. It's not is that right? Like it's not

714
00:41:03,440 --> 00:41:06,480
Speaker 1: a permanently UM. It's literally like you could shine a

715
00:41:06,600 --> 00:41:10,400
Speaker 1: light and then it changes the activation of whatever the

716
00:41:10,440 --> 00:41:13,480
Speaker 1: target is, yeah, like instantly, and and then it can

717
00:41:13,520 --> 00:41:15,880
Speaker 1: also be turned back off. You can look at YouTube

718
00:41:15,920 --> 00:41:20,800
Speaker 1: videos of this happening. Yeah, so they I mean channel

719
00:41:20,840 --> 00:41:25,319
Speaker 1: rhodopsin and all these rhodopsin um proteins are have been

720
00:41:25,360 --> 00:41:28,040
Speaker 1: known to be light sensitive UM, and so I think

721
00:41:28,360 --> 00:41:31,759
Speaker 1: he knew that and then you know, like figured out

722
00:41:31,800 --> 00:41:35,520
Speaker 1: a way to control UM these neurons and so like

723
00:41:35,640 --> 00:41:39,479
Speaker 1: putting it together is uh. I think the key like

724
00:41:39,760 --> 00:41:44,040
Speaker 1: the idea that he could use use proteins in that

725
00:41:44,080 --> 00:41:48,160
Speaker 1: way was the big, big, big leap. That's so cool.

726
00:41:48,640 --> 00:41:52,480
Speaker 1: So uh so they have these uh, these rats and

727
00:41:52,560 --> 00:41:59,040
Speaker 1: they use the optogenetics to UM turn off the the

728
00:41:59,160 --> 00:42:03,319
Speaker 1: um uh parts of the brain that that part like

729
00:42:03,360 --> 00:42:07,000
Speaker 1: between the thalamus and the amygdala where that would show

730
00:42:07,000 --> 00:42:09,960
Speaker 1: excitement every time that the Q would happen. So like

731
00:42:10,040 --> 00:42:13,320
Speaker 1: they have the the like bell rings are like expecting cocaine.

732
00:42:13,320 --> 00:42:16,560
Speaker 1: They get really excited. Then they turn a light on,

733
00:42:16,800 --> 00:42:19,920
Speaker 1: you know, like like turn off those neurons. Uh. And

734
00:42:19,960 --> 00:42:24,080
Speaker 1: then suddenly like they were able to completely remove the

735
00:42:24,200 --> 00:42:28,120
Speaker 1: rats excitement for cocaine. So um just like no, except

736
00:42:28,120 --> 00:42:30,520
Speaker 1: like they would play that like, hey, Cocaine's a come

737
00:42:30,560 --> 00:42:33,080
Speaker 1: in bell and then the rats showed no response, like

738
00:42:33,120 --> 00:42:37,640
Speaker 1: no response in the brain. Uh like because like beforehand,

739
00:42:37,640 --> 00:42:41,160
Speaker 1: their brains were going absolutely crazy, like totally firing, like

740
00:42:41,280 --> 00:42:43,480
Speaker 1: super excited for that head of cocaine and then now

741
00:42:43,560 --> 00:42:46,640
Speaker 1: just like nothing, just like totally cool and fine. And

742
00:42:46,920 --> 00:42:50,640
Speaker 1: it's really kind of a cool thing because um, especially

743
00:42:50,680 --> 00:42:54,000
Speaker 1: like we're you know, as maybe we start to shift

744
00:42:54,040 --> 00:42:56,359
Speaker 1: the paradigm of how we see addiction as like a

745
00:42:56,400 --> 00:43:00,200
Speaker 1: medical issue and not like a moral failing. Um. You know,

746
00:43:00,239 --> 00:43:04,200
Speaker 1: the research into how you treat addiction, it's really important.

747
00:43:04,200 --> 00:43:06,680
Speaker 1: And this is I mean, it's way far away from

748
00:43:06,719 --> 00:43:09,080
Speaker 1: ever being able to be applied to humans because we

749
00:43:09,120 --> 00:43:14,000
Speaker 1: don't have like we haven't started like having um opt

750
00:43:14,080 --> 00:43:17,040
Speaker 1: genetic babies that you know, where you can turn human

751
00:43:17,040 --> 00:43:20,840
Speaker 1: brains off with lights, but it is, um it it

752
00:43:20,880 --> 00:43:24,680
Speaker 1: does show that that's a black Marror episode. Oh they

753
00:43:24,680 --> 00:43:27,279
Speaker 1: should do that. It's like, what if you turn your

754
00:43:27,360 --> 00:43:34,520
Speaker 1: light off, the brain turned off, or if you turn

755
00:43:34,600 --> 00:43:37,640
Speaker 1: a light, I mean your brain just turned off. That

756
00:43:37,640 --> 00:43:40,160
Speaker 1: That show is really good and like coming from a

757
00:43:40,160 --> 00:43:45,719
Speaker 1: person who worked on neural probes, like, it's so fun watch. Yeah, yeah, yeah,

758
00:43:45,800 --> 00:43:48,239
Speaker 1: So I can't get over the pig fucking episode though.

759
00:43:48,440 --> 00:43:50,879
Speaker 1: That was the first one that was a rough one

760
00:43:50,920 --> 00:43:53,880
Speaker 1: to start on Bold. I liked it. I was like,

761
00:43:53,960 --> 00:44:00,480
Speaker 1: what but yeah, um so I think, like, uh, I

762
00:44:00,520 --> 00:44:05,759
Speaker 1: feel like that could happen. It definitely feels like right now.

763
00:44:06,520 --> 00:44:09,240
Speaker 1: So so, um, I look at it in terms of, like, um,

764
00:44:09,280 --> 00:44:12,000
Speaker 1: the neural probe stuff, because that's what I'm more familiar with,

765
00:44:12,360 --> 00:44:16,960
Speaker 1: Like with Parkinson's disease, there's deep brain stimulations, um there.

766
00:44:17,000 --> 00:44:20,480
Speaker 1: You know, there's other ways of moving prosthetics and stuff.

767
00:44:20,520 --> 00:44:23,760
Speaker 1: With neural probes electrodes, youre in certain to the brain

768
00:44:23,840 --> 00:44:27,160
Speaker 1: and then you can use a computer to like translate,

769
00:44:27,520 --> 00:44:31,319
Speaker 1: like there's an ex striking an external device that and

770
00:44:31,360 --> 00:44:34,120
Speaker 1: like it it actually is it's using feedback from the

771
00:44:34,160 --> 00:44:37,239
Speaker 1: brain and then it since adjusting. Yeah, but basically you

772
00:44:37,280 --> 00:44:40,239
Speaker 1: think and you move a prosthetic, right and so so

773
00:44:40,600 --> 00:44:42,920
Speaker 1: there are That's the whole point of like a lot

774
00:44:42,960 --> 00:44:45,000
Speaker 1: of the field is to like figure out where things

775
00:44:45,880 --> 00:44:50,120
Speaker 1: where neuron spiking causes an external reaction and and taking

776
00:44:50,120 --> 00:44:54,120
Speaker 1: in a sensory information as well. UM so speaking, so

777
00:44:54,160 --> 00:44:56,160
Speaker 1: talking about it in terms of that, like we have

778
00:44:56,280 --> 00:44:58,200
Speaker 1: come a long way with figuring that out, Like there

779
00:44:58,239 --> 00:45:00,680
Speaker 1: are people wearing those prosthetics and a fitting from it

780
00:45:00,719 --> 00:45:03,680
Speaker 1: now and deep brain stimulation in Parkinson's disease has been

781
00:45:03,920 --> 00:45:06,279
Speaker 1: proven to be Like there are videos of that too

782
00:45:06,280 --> 00:45:09,480
Speaker 1: where people are shaking. You turn it on, they stop shaking. UM.

783
00:45:09,600 --> 00:45:13,960
Speaker 1: What is so when when they turn on the stimulator stimulator?

784
00:45:14,000 --> 00:45:17,080
Speaker 1: What is that is that? UM? Is that's using UM

785
00:45:17,360 --> 00:45:21,239
Speaker 1: like an electrical pulse or yeah, so yeah, that's how

786
00:45:21,440 --> 00:45:26,239
Speaker 1: UM neurons work is their electrochemical They send action potentials

787
00:45:26,320 --> 00:45:29,200
Speaker 1: through like a singular cell UM and then between cells

788
00:45:29,239 --> 00:45:32,160
Speaker 1: that can be like electric or electrical or chemical UM

789
00:45:32,160 --> 00:45:35,680
Speaker 1: with the neurotransmitters that jump from cell to cell and

790
00:45:35,840 --> 00:45:40,439
Speaker 1: activate the next cells UM electrical potential UM and so yeah,

791
00:45:40,480 --> 00:45:44,319
Speaker 1: so it's all electrochemical UM. So you you can send

792
00:45:44,320 --> 00:45:48,480
Speaker 1: and receive once you're able to like translate the signals, right,

793
00:45:48,520 --> 00:45:52,560
Speaker 1: those neural signals um. And so it's like it's interesting.

794
00:45:52,560 --> 00:45:56,920
Speaker 1: So with Parkinson's, like they just send like these signals

795
00:45:56,920 --> 00:45:59,239
Speaker 1: out and they don't know exactly how it works, but

796
00:45:59,239 --> 00:46:02,400
Speaker 1: they know that it does. Yes. And so there's that case.

797
00:46:02,440 --> 00:46:04,279
Speaker 1: And then there's a case with the press aetics where

798
00:46:04,280 --> 00:46:06,040
Speaker 1: it like it seems to be like a little bit

799
00:46:06,120 --> 00:46:09,160
Speaker 1: better mapped out, um, but it's still like you know,

800
00:46:09,200 --> 00:46:11,920
Speaker 1: they're still it's still rough around the edges. UM. So

801
00:46:12,000 --> 00:46:15,120
Speaker 1: I do think like we're closer to this than people. Yeah,

802
00:46:15,320 --> 00:46:18,040
Speaker 1: I think like we're closer to like some parts of

803
00:46:18,080 --> 00:46:21,800
Speaker 1: Black Mirror, right. And also yeah, in in in talking

804
00:46:21,840 --> 00:46:24,799
Speaker 1: about addiction, being able to find the areas of the

805
00:46:24,800 --> 00:46:28,719
Speaker 1: brain that are responsible for addiction and responsible for finding

806
00:46:29,600 --> 00:46:32,880
Speaker 1: or making the connection like um, because one of the

807
00:46:32,920 --> 00:46:35,640
Speaker 1: things with addictions that makes it so hard for people too.

808
00:46:35,800 --> 00:46:38,040
Speaker 1: And this can be any kind of like drug addiction.

809
00:46:38,080 --> 00:46:41,120
Speaker 1: It can be gambling or even food addictions, well especially

810
00:46:41,160 --> 00:46:45,800
Speaker 1: food addictions where um, you'll be triggered by some um

811
00:46:46,000 --> 00:46:49,520
Speaker 1: something in your environment that makes you, um want to

812
00:46:49,560 --> 00:46:52,480
Speaker 1: go do the behavior. It can be emotional. It can

813
00:46:52,520 --> 00:46:56,239
Speaker 1: be something like um, you know, depression or anxiety. It

814
00:46:56,239 --> 00:47:00,600
Speaker 1: could be simply smelling food or being addict sino and

815
00:47:01,080 --> 00:47:05,240
Speaker 1: like hearing the sounds of a casino and then that'll UM.

816
00:47:05,440 --> 00:47:08,080
Speaker 1: And like obviously with with severe drug addictions, it's a

817
00:47:08,160 --> 00:47:12,960
Speaker 1: much there's a stronger UM physiological physical feeling UM to

818
00:47:13,080 --> 00:47:17,360
Speaker 1: the addiction. But it's I think like this rat study

819
00:47:17,440 --> 00:47:20,600
Speaker 1: is showing us that we can interrupt that feedback loop

820
00:47:20,920 --> 00:47:26,640
Speaker 1: of of UM, the the addictive nature of a substance

821
00:47:26,640 --> 00:47:30,560
Speaker 1: and then the response to a stimulus relating to that

822
00:47:30,640 --> 00:47:33,840
Speaker 1: addictive substance. Yeah, and I think what you said earlier

823
00:47:33,920 --> 00:47:37,280
Speaker 1: about UM understanding that it is like a biological response

824
00:47:37,440 --> 00:47:40,920
Speaker 1: is very It's a smart UM understanding of like the

825
00:47:41,000 --> 00:47:45,440
Speaker 1: socio the sociological impact UM. It's kind of like when people,

826
00:47:45,719 --> 00:47:47,440
Speaker 1: I mean this isn't as well known, but like when

827
00:47:47,480 --> 00:47:49,320
Speaker 1: people found out like that there is like a gene

828
00:47:49,320 --> 00:47:53,120
Speaker 1: that UH tends to occur more in people who are

829
00:47:53,160 --> 00:47:55,960
Speaker 1: more violent. UM. And so then you you do realize

830
00:47:55,960 --> 00:47:57,640
Speaker 1: that these a lot of these are like on and

831
00:47:57,680 --> 00:47:59,799
Speaker 1: off switch. Is it's definitely more complex than that. There's

832
00:47:59,840 --> 00:48:03,320
Speaker 1: like lot more going on, but once you have control

833
00:48:03,400 --> 00:48:06,240
Speaker 1: over that, then you it's it's just how like pharmacological

834
00:48:06,239 --> 00:48:09,759
Speaker 1: stuff works. Right, It's like you there are receptors things

835
00:48:09,800 --> 00:48:12,640
Speaker 1: bind two and that causes this physical response that alleviates

836
00:48:12,640 --> 00:48:14,799
Speaker 1: your headache, that does all these things right, so that

837
00:48:14,840 --> 00:48:17,240
Speaker 1: it's the same way, but it's just like different techniques

838
00:48:17,360 --> 00:48:21,000
Speaker 1: used to manipulate these cells. So it could be like

839
00:48:21,320 --> 00:48:24,239
Speaker 1: you know, like pharmaceutical medicine that we are used to

840
00:48:24,280 --> 00:48:28,680
Speaker 1: taking and comfortable with taking versus uh, you know, optogenizing

841
00:48:29,600 --> 00:48:34,240
Speaker 1: stuff versus probes. Yeah, I mean it is. Yeah, pharmaceuticals

842
00:48:34,280 --> 00:48:37,759
Speaker 1: do you literally interact with your cells, bind to them physically,

843
00:48:38,400 --> 00:48:42,319
Speaker 1: you know, alter your in Pharmaceuticals that have to do

844
00:48:42,360 --> 00:48:46,799
Speaker 1: with UM, you know, say like UM mental health will

845
00:48:46,840 --> 00:48:49,759
Speaker 1: also like physically interact with your brain. So I know

846
00:48:49,840 --> 00:48:56,880
Speaker 1: that certain certain things like UM, the the sort of

847
00:48:57,840 --> 00:49:01,239
Speaker 1: cranial stimulation things where it's like the there's both, Like

848
00:49:01,280 --> 00:49:06,959
Speaker 1: the magnetic stimulation in the can be kind of creepy. Yeah,

849
00:49:07,000 --> 00:49:11,879
Speaker 1: there's transcarnial uh yeah, magnetic stimulationstics teams and then that's

850
00:49:11,920 --> 00:49:16,640
Speaker 1: but that's not the one. There's also the uh, the

851
00:49:16,680 --> 00:49:20,560
Speaker 1: electrical stimulation that's used for like depression, UM. And so

852
00:49:20,680 --> 00:49:23,080
Speaker 1: it's like when you hear about those things, it seems

853
00:49:23,120 --> 00:49:26,480
Speaker 1: kind of creepy because it's like this physics. You're like

854
00:49:26,480 --> 00:49:29,799
Speaker 1: like it's all it's all physical. Yeah. Yeah, so it's

855
00:49:29,800 --> 00:49:31,719
Speaker 1: all like that's what that's what I mean Like with

856
00:49:31,760 --> 00:49:35,440
Speaker 1: the with taking drugs, that like is something that is

857
00:49:35,600 --> 00:49:39,480
Speaker 1: ingested that then causes a series of events. But in

858
00:49:39,520 --> 00:49:43,399
Speaker 1: the same way that those electrical triggers also caused those

859
00:49:43,400 --> 00:49:46,640
Speaker 1: action potentials and the neurons. So it is it does

860
00:49:46,719 --> 00:49:48,799
Speaker 1: have to do with like receptor binding in one case

861
00:49:48,840 --> 00:49:51,000
Speaker 1: and the other case it's just like stimulating and also

862
00:49:51,160 --> 00:49:55,400
Speaker 1: chemical and electrical or in terms of the brain just

863
00:49:55,640 --> 00:50:00,000
Speaker 1: completely linked. It's like very closely because it's um the um.

864
00:50:00,040 --> 00:50:05,399
Speaker 1: The neurotransmitter activity is what causes the electrical impulse impulse exactly. Yeah,

865
00:50:05,440 --> 00:50:09,320
Speaker 1: so that the neurotransmitters work between synapses or within a synapse,

866
00:50:09,360 --> 00:50:12,760
Speaker 1: which is between cells, right, So one cell sends its

867
00:50:12,800 --> 00:50:15,839
Speaker 1: its message along through its its own body, and then

868
00:50:15,880 --> 00:50:19,600
Speaker 1: that causes neurotransmitters to be expelled from one cell, hit

869
00:50:19,640 --> 00:50:23,080
Speaker 1: the other cell and then cause another impulse. Yeah. So

870
00:50:23,120 --> 00:50:26,319
Speaker 1: it is all very closely linked. And everybody is very

871
00:50:26,360 --> 00:50:29,920
Speaker 1: complex and all these things. Uh, each like disease disorder

872
00:50:30,200 --> 00:50:33,799
Speaker 1: has like an optimal treatment and so it depends on

873
00:50:33,920 --> 00:50:36,080
Speaker 1: the person in the disease, the disorder. But it's all

874
00:50:36,120 --> 00:50:38,600
Speaker 1: it all is. It is like you know, turning switches.

875
00:50:39,360 --> 00:50:42,240
Speaker 1: It's just I think it's just like a root Goldberg

876
00:50:42,280 --> 00:50:45,719
Speaker 1: machine or I think and I think when people think

877
00:50:45,719 --> 00:50:49,360
Speaker 1: about um like uh t mass or or or like

878
00:50:49,400 --> 00:50:52,840
Speaker 1: quote unquote electrotherapy, it brings to mind sort of the

879
00:50:53,719 --> 00:50:56,680
Speaker 1: requiem for a dream thing where someone's like in a

880
00:50:56,680 --> 00:50:59,200
Speaker 1: lot of pain, or like the idea of because there

881
00:50:59,280 --> 00:51:02,200
Speaker 1: used to be you know, psychology has gone under some

882
00:51:02,800 --> 00:51:05,640
Speaker 1: pretty big changes in terms of being a lot more

883
00:51:05,760 --> 00:51:08,520
Speaker 1: humane um and so there were there were like bad

884
00:51:08,560 --> 00:51:11,040
Speaker 1: practices like using sort of but it was a version

885
00:51:11,080 --> 00:51:13,359
Speaker 1: therapy that is like the bad one where it's like

886
00:51:13,600 --> 00:51:17,280
Speaker 1: you you try to get someone to like stop someone

887
00:51:17,400 --> 00:51:21,200
Speaker 1: from um, you know, being sexual by like shocking them,

888
00:51:21,200 --> 00:51:25,040
Speaker 1: And that's not that's not really what we're like a punishment,

889
00:51:25,719 --> 00:51:27,400
Speaker 1: that's that's yeah, that's not what we're talking about. What

890
00:51:27,480 --> 00:51:29,839
Speaker 1: we're talking about is like literally like changing the way

891
00:51:29,920 --> 00:51:33,600
Speaker 1: you think by causing these because that's that's also like

892
00:51:33,760 --> 00:51:36,640
Speaker 1: that's how we learn. And we were talking earlier about memories.

893
00:51:36,960 --> 00:51:41,120
Speaker 1: Synapses can be like weak or strong, and the balance

894
00:51:41,160 --> 00:51:44,000
Speaker 1: of that is necessary. So you want some synapses too

895
00:51:44,080 --> 00:51:45,520
Speaker 1: weak in in order for you to be able to

896
00:51:45,600 --> 00:51:48,760
Speaker 1: learn other things. It's plasticity. It's like it's how learning

897
00:51:48,880 --> 00:51:51,480
Speaker 1: learning works, and so like there's like the short term

898
00:51:51,480 --> 00:51:54,400
Speaker 1: way of like you know, having these things triggered so

899
00:51:54,480 --> 00:51:56,520
Speaker 1: that it changes like how you think like in the moment,

900
00:51:56,880 --> 00:51:59,440
Speaker 1: but then that change that should affect a change longer

901
00:51:59,560 --> 00:52:02,480
Speaker 1: term and how your brain functions. Right, It's like it's

902
00:52:02,520 --> 00:52:04,719
Speaker 1: like a water channel. So like if you have sort

903
00:52:04,719 --> 00:52:06,839
Speaker 1: of these different channels and you're trying to get water

904
00:52:06,920 --> 00:52:08,600
Speaker 1: to flow through them, and that would be sort of

905
00:52:08,640 --> 00:52:11,680
Speaker 1: a pattern of activation in the brain. If you have

906
00:52:11,760 --> 00:52:14,040
Speaker 1: like one channel that's really bad, like say like a

907
00:52:14,080 --> 00:52:17,360
Speaker 1: bad habit, like an addictive behavior or like um a

908
00:52:17,440 --> 00:52:22,040
Speaker 1: sort of depressive mental mancher or something um that and

909
00:52:22,239 --> 00:52:24,359
Speaker 1: it gets used a lot, and then that sort of

910
00:52:24,440 --> 00:52:26,440
Speaker 1: channel is going to get really deep that groove, and

911
00:52:26,480 --> 00:52:29,200
Speaker 1: then the water is gonna want to flow through there. Um.

912
00:52:29,239 --> 00:52:31,239
Speaker 1: So if you can weaken that channel kind of like

913
00:52:31,360 --> 00:52:34,200
Speaker 1: you know, block it off, then you can um strengthen

914
00:52:34,280 --> 00:52:38,200
Speaker 1: the other uh neural networks, the other patterns of activation

915
00:52:38,239 --> 00:52:40,839
Speaker 1: that are more desirable. Yeah, that's a really good way

916
00:52:40,840 --> 00:52:44,600
Speaker 1: of putting it. Yeah, it's it's probably an over much

917
00:52:44,640 --> 00:52:46,920
Speaker 1: overly simplified, but it's one way that helps me to

918
00:52:46,920 --> 00:52:51,879
Speaker 1: think about it. If you're wondering if there's a real

919
00:52:51,960 --> 00:52:55,680
Speaker 1: life example of an eternal sunshine situation, there is, and

920
00:52:55,760 --> 00:52:58,920
Speaker 1: it's a bizarre, tragic tail. You may be familiar with

921
00:52:58,960 --> 00:53:02,400
Speaker 1: retrograde amnesia. It it's often the subject of soap operas.

922
00:53:02,520 --> 00:53:06,320
Speaker 1: You suffer a traumatic accident and forget everything before that accident,

923
00:53:06,920 --> 00:53:09,279
Speaker 1: and tower a great amnesia is the flip side of that.

924
00:53:09,719 --> 00:53:12,640
Speaker 1: After the traumatic event or brain injury, you are unable

925
00:53:12,640 --> 00:53:15,840
Speaker 1: to form new memories. So you may have some memory

926
00:53:15,880 --> 00:53:19,200
Speaker 1: of your past preserved from before the accident, but imagine

927
00:53:19,200 --> 00:53:21,839
Speaker 1: what it's like to be unable to form new memories.

928
00:53:22,280 --> 00:53:24,840
Speaker 1: Every day is the first day of your life, literally

929
00:53:24,960 --> 00:53:27,960
Speaker 1: in a horrifying way. This is such a rare condition

930
00:53:27,960 --> 00:53:31,040
Speaker 1: that each clinical case is notable. One such case is

931
00:53:31,040 --> 00:53:34,200
Speaker 1: that of Clive Wearing. He was a brilliant musicologist and

932
00:53:34,280 --> 00:53:38,440
Speaker 1: conductor before contracting a virus that attacked his central nervous system.

933
00:53:38,480 --> 00:53:41,520
Speaker 1: The brain damage gave him enter a great amnesia. He

934
00:53:41,640 --> 00:53:44,160
Speaker 1: was still able to remember certain things about his life,

935
00:53:44,520 --> 00:53:47,200
Speaker 1: his love for his wife, certain old memories, and he

936
00:53:47,200 --> 00:53:51,120
Speaker 1: could still play complex piano and organ pieces. However, his

937
00:53:51,200 --> 00:53:53,800
Speaker 1: ability to form new memories last only about seven to

938
00:53:53,920 --> 00:53:57,480
Speaker 1: thirty seconds, so he's living basically half a minute at

939
00:53:57,480 --> 00:54:00,840
Speaker 1: a time. Every day, he wakes up every thirty seconds.

940
00:54:01,200 --> 00:54:04,680
Speaker 1: His diary is a terrifying glimpse into his reality. Pages

941
00:54:04,719 --> 00:54:07,279
Speaker 1: and pages of frantic writing about waking up for the

942
00:54:07,320 --> 00:54:13,240
Speaker 1: first time, over and over. Use just a small sample. Am,

943
00:54:13,280 --> 00:54:17,280
Speaker 1: now I am really completely awake. Nine oh six am.

944
00:54:17,320 --> 00:54:22,360
Speaker 1: Now I am perfectly overwhelmingly awake. Four am. Now I

945
00:54:22,400 --> 00:54:26,160
Speaker 1: am superlatively actually awake. We'll be right back after a

946
00:54:26,160 --> 00:54:35,920
Speaker 1: few messages, so we're back, Uh, Paulo VI, I kind

947
00:54:35,920 --> 00:54:37,439
Speaker 1: of want to talk to you a little bit more

948
00:54:37,480 --> 00:54:40,600
Speaker 1: about your work. Um. What you do kind of goes

949
00:54:40,680 --> 00:54:44,319
Speaker 1: over my head a little bit, admittedly, UM, but it

950
00:54:44,400 --> 00:54:46,720
Speaker 1: is really cool. We talked a little bit about it earlier,

951
00:54:46,719 --> 00:54:51,960
Speaker 1: where we established that UM using um uh, the genetic

952
00:54:52,000 --> 00:54:55,719
Speaker 1: algorithms is it's not the genetics of an animal, it's

953
00:54:55,760 --> 00:55:00,319
Speaker 1: that the algorithms themselves, these computer model models have sort

954
00:55:00,360 --> 00:55:05,880
Speaker 1: of a um computational genome. UM. And Uh, you wrote

955
00:55:05,880 --> 00:55:09,279
Speaker 1: to me, UM something that when I read it, UM,

956
00:55:09,600 --> 00:55:14,120
Speaker 1: I think my ears just like started to implode, and

957
00:55:14,480 --> 00:55:17,000
Speaker 1: my eyes kind of struveled, and like, as I was

958
00:55:17,040 --> 00:55:21,560
Speaker 1: trying to understand, you say, you use evolutionary algorithm to

959
00:55:21,600 --> 00:55:25,360
Speaker 1: constrain biophysical parameters of different cell types in this overall

960
00:55:25,440 --> 00:55:29,160
Speaker 1: large scale model of the rat himp a campus UM,

961
00:55:29,200 --> 00:55:32,520
Speaker 1: which it's I mean it's for I'm not trying to

962
00:55:32,560 --> 00:55:35,400
Speaker 1: be glib here. That sounds insanely cool, and from what

963
00:55:35,480 --> 00:55:38,279
Speaker 1: you've talked about, it is super cool. So you, like

964
00:55:38,360 --> 00:55:42,759
Speaker 1: we talked about earlier, you're breeding these computational models and

965
00:55:42,800 --> 00:55:45,279
Speaker 1: then you have this new model and you're trying to

966
00:55:45,360 --> 00:55:48,879
Speaker 1: match it to seeing if this new new model with

967
00:55:48,960 --> 00:55:50,680
Speaker 1: like the new you compare all of them at the

968
00:55:50,719 --> 00:55:52,759
Speaker 1: same time. I see it. So you have a population

969
00:55:52,920 --> 00:55:55,239
Speaker 1: and you're, um, you're like, I'm going to take the

970
00:55:55,280 --> 00:55:57,640
Speaker 1: top Let's say you have a population of a hundred

971
00:55:57,680 --> 00:55:59,960
Speaker 1: and you're like, I'm going to te d models And

972
00:56:00,000 --> 00:56:01,680
Speaker 1: you're like, I'm going to take the ten best models,

973
00:56:01,719 --> 00:56:06,680
Speaker 1: meaning the ten that most America's top models. I've been

974
00:56:06,800 --> 00:56:12,799
Speaker 1: Katie Golden UM. And you take the top ten and

975
00:56:12,840 --> 00:56:14,239
Speaker 1: you're like, I'm going to move that on to the

976
00:56:14,239 --> 00:56:17,239
Speaker 1: next generation. UM and so, and then you have like

977
00:56:17,520 --> 00:56:21,319
Speaker 1: random you can do it different ways, but basically, you

978
00:56:21,480 --> 00:56:24,040
Speaker 1: evaluate how good a model is by how closely it's

979
00:56:24,040 --> 00:56:29,279
Speaker 1: outputs match experimental values of these characteristics, which are UM,

980
00:56:29,600 --> 00:56:33,720
Speaker 1: you know, input resistance, bike, frequency, adaptation, all these characteristics

981
00:56:33,760 --> 00:56:37,440
Speaker 1: of neurons UM. And so you're you you match these

982
00:56:37,480 --> 00:56:41,359
Speaker 1: outputs UM, the inputs change, that's what changes between each

983
00:56:41,400 --> 00:56:43,840
Speaker 1: model UM and so you're just it's just a way

984
00:56:43,880 --> 00:56:48,200
Speaker 1: of exploring tweaking the inputs in a more in a faster,

985
00:56:48,360 --> 00:56:50,399
Speaker 1: more efficient way. So you're like, I'm going to take

986
00:56:50,440 --> 00:56:53,320
Speaker 1: the best ones, some combination of that might be better

987
00:56:53,400 --> 00:56:55,840
Speaker 1: than the parents, right, and so I'm not going to

988
00:56:55,920 --> 00:56:58,360
Speaker 1: focus on the ones that aren't. Sort of like breeding

989
00:56:58,400 --> 00:57:04,719
Speaker 1: dog breeds, yeah, but more morally, right, So you're not

990
00:57:04,800 --> 00:57:07,560
Speaker 1: You're not taking a computer model and bringing it so

991
00:57:07,600 --> 00:57:09,960
Speaker 1: that every time it sneezes, its eyeballs pop out of

992
00:57:10,000 --> 00:57:13,480
Speaker 1: its school. I mean we could if if that what

993
00:57:13,920 --> 00:57:16,280
Speaker 1: if that makes you feel powerful? Yeah, I mean it

994
00:57:16,400 --> 00:57:19,600
Speaker 1: kind of. You do sort of sound like the progenitors

995
00:57:19,640 --> 00:57:22,440
Speaker 1: of a new computer race, Like are you are you

996
00:57:22,480 --> 00:57:25,480
Speaker 1: at all worried that they're going to start to like

997
00:57:26,040 --> 00:57:28,880
Speaker 1: develop a consciousness rise up to you know that that

998
00:57:28,960 --> 00:57:33,320
Speaker 1: whole no Okay, not at all where it is. It's

999
00:57:33,360 --> 00:57:36,600
Speaker 1: literally like you have to manually program it's so tedious. Yeah,

1000
00:57:36,800 --> 00:57:39,680
Speaker 1: so it's like um and then yeah, so it's not

1001
00:57:40,720 --> 00:57:42,680
Speaker 1: I don't know and I didn't create it. It's a

1002
00:57:42,920 --> 00:57:45,640
Speaker 1: type of program that's been established that I'm just utilizing

1003
00:57:45,680 --> 00:57:50,360
Speaker 1: for my purposes. Um. But yeah, so I mean that's

1004
00:57:50,400 --> 00:57:53,160
Speaker 1: the basis of it. Um, And you can apply this.

1005
00:57:53,240 --> 00:57:55,200
Speaker 1: It's a it's a technique, it's a process. It's a

1006
00:57:55,240 --> 00:57:59,840
Speaker 1: way of getting closer to the output that we want, right. Um. Yeah,

1007
00:58:00,040 --> 00:58:04,200
Speaker 1: that's that's basically it. Um. So you're also a stand

1008
00:58:04,240 --> 00:58:09,240
Speaker 1: up comedian um. Um. And now this is a pretty

1009
00:58:09,280 --> 00:58:13,600
Speaker 1: hypocritical uh question coming from me, but um, you know

1010
00:58:13,760 --> 00:58:16,120
Speaker 1: it is kind of an interesting thing to me, like

1011
00:58:16,200 --> 00:58:21,040
Speaker 1: the like, UM, scientists getting into comedy and vice versa. UM,

1012
00:58:21,160 --> 00:58:24,040
Speaker 1: Like how do you feel like these two fields kind

1013
00:58:24,040 --> 00:58:28,280
Speaker 1: of influence each other? Like does your um, your your

1014
00:58:28,320 --> 00:58:30,600
Speaker 1: research influence your comedy? Do you kind of like to

1015
00:58:30,680 --> 00:58:36,400
Speaker 1: keep them separate, like you know, like uh, party upfront businesses? Yeah,

1016
00:58:36,520 --> 00:58:39,560
Speaker 1: kind of like that. Um. It definitely helps to have

1017
00:58:40,400 --> 00:58:45,200
Speaker 1: UM So I love both um and having like my

1018
00:58:45,240 --> 00:58:48,840
Speaker 1: school friends and having this more technical thing to focus

1019
00:58:48,840 --> 00:58:52,400
Speaker 1: on helps when I feel like to drowning in comedy. UM,

1020
00:58:52,440 --> 00:58:56,520
Speaker 1: but I absolutely love comedy, and I think I think

1021
00:58:56,600 --> 00:58:58,440
Speaker 1: in general, like my education and the way that I

1022
00:58:58,480 --> 00:59:02,600
Speaker 1: think has informed my voice, and my voice informs my comedy.

1023
00:59:03,000 --> 00:59:06,240
Speaker 1: Um and so like now I'm trying to utilize my

1024
00:59:06,320 --> 00:59:09,080
Speaker 1: expertise and use that in my comedy. So I have

1025
00:59:09,120 --> 00:59:11,960
Speaker 1: like the web series that I'm doing, um, Dirty Science,

1026
00:59:11,960 --> 00:59:14,200
Speaker 1: and it's like on my YouTube, my Instagram, and I'm

1027
00:59:14,320 --> 00:59:17,360
Speaker 1: using that to like kind of spread men like scientific

1028
00:59:17,360 --> 00:59:20,280
Speaker 1: information but through a comedic lens. And I think that

1029
00:59:20,480 --> 00:59:22,680
Speaker 1: helps me stand out. And so like people people like

1030
00:59:22,720 --> 00:59:24,959
Speaker 1: you have like asked me to do this, um, which

1031
00:59:25,000 --> 00:59:27,800
Speaker 1: is gonna like you know, like give me more opportunities.

1032
00:59:27,960 --> 00:59:31,120
Speaker 1: Um and uh. And I do get booked on certain

1033
00:59:31,160 --> 00:59:33,360
Speaker 1: things because of my scientific background, but they want my

1034
00:59:33,400 --> 00:59:35,280
Speaker 1: comedic voice. They just like want someone who can like

1035
00:59:35,360 --> 00:59:40,920
Speaker 1: navigate both realms. UM. I would say my research is

1036
00:59:41,240 --> 00:59:43,800
Speaker 1: not imformant, Like it's hard because like my comedy is

1037
00:59:43,800 --> 00:59:46,800
Speaker 1: like my personality. So it's like I get along with

1038
00:59:46,800 --> 00:59:50,520
Speaker 1: people because of like how I because of my chom

1039
00:59:50,560 --> 00:59:52,480
Speaker 1: like I don't know, So it's like it's weird to

1040
00:59:52,560 --> 00:59:55,080
Speaker 1: say that it influences. But actually I do think it

1041
00:59:55,080 --> 00:59:58,080
Speaker 1: does because like I kind of look at presentations like sets,

1042
00:59:58,640 --> 01:00:00,760
Speaker 1: and so I'm like, oh, like I've bomb this meeting.

1043
01:00:01,400 --> 01:00:04,000
Speaker 1: I killed that presentation after so like I kind of

1044
01:00:04,000 --> 01:00:07,200
Speaker 1: like look at it like that, and I actually, um, recently,

1045
01:00:07,200 --> 01:00:09,840
Speaker 1: it's really dumb because like I am not getting enough

1046
01:00:09,840 --> 01:00:12,600
Speaker 1: work done, which is another way my comedy influences my research.

1047
01:00:13,280 --> 01:00:16,360
Speaker 1: It's a it's a good procrastination tool. It's it's amazing,

1048
01:00:16,400 --> 01:00:18,960
Speaker 1: and it's also like it's ramping it's been ramping up

1049
01:00:18,960 --> 01:00:21,520
Speaker 1: for a while, and it's really hard to balance two

1050
01:00:21,560 --> 01:00:25,000
Speaker 1: full time careers UM, and so that's that's been really difficult.

1051
01:00:25,000 --> 01:00:27,280
Speaker 1: But it did help me, like when this post her award,

1052
01:00:27,320 --> 01:00:29,760
Speaker 1: because I helped in my communication skills and like feeling

1053
01:00:29,760 --> 01:00:32,520
Speaker 1: more confident when I like I wasn't assure of something

1054
01:00:32,720 --> 01:00:34,520
Speaker 1: or well, you know. It's interesting because one of my

1055
01:00:34,680 --> 01:00:39,320
Speaker 1: first ever improv classes it was at UCSD and most

1056
01:00:39,400 --> 01:00:42,840
Speaker 1: of the other students were grad students and uh either

1057
01:00:42,960 --> 01:00:47,040
Speaker 1: science or um a social science field UM or business

1058
01:00:47,440 --> 01:00:50,040
Speaker 1: not none of them like we're really interested in And

1059
01:00:50,120 --> 01:00:52,720
Speaker 1: at the time I was not thinking I was going

1060
01:00:52,760 --> 01:00:56,600
Speaker 1: into a comedy field. I was working doing pharmaceutical e learning.

1061
01:00:56,600 --> 01:00:58,600
Speaker 1: I wasn't like, I was just like I need I

1062
01:00:58,640 --> 01:01:02,560
Speaker 1: need a break from like talking about diabetes all days.

1063
01:01:03,560 --> 01:01:06,840
Speaker 1: Uh yeah it is. Um. I think like improv especially

1064
01:01:06,880 --> 01:01:09,400
Speaker 1: is like a little bit more inaccessible because it requires

1065
01:01:09,640 --> 01:01:12,080
Speaker 1: a lot of money to take classes. But there are

1066
01:01:12,080 --> 01:01:14,640
Speaker 1: certain areas I do think that we don't realize, Like

1067
01:01:14,800 --> 01:01:18,200
Speaker 1: there are a lot of comedians that come from privilege. Um,

1068
01:01:18,360 --> 01:01:20,520
Speaker 1: more so than I think anybody wants. My friend Chris Autums,

1069
01:01:20,520 --> 01:01:22,920
Speaker 1: who is really funny, I was talking about this, Um,

1070
01:01:22,960 --> 01:01:25,560
Speaker 1: it's more so than any of us are willing to

1071
01:01:25,600 --> 01:01:27,720
Speaker 1: admit because like we're allowed to have, like we have

1072
01:01:27,760 --> 01:01:30,240
Speaker 1: a safety network and so we're allowed to take career

1073
01:01:30,320 --> 01:01:33,080
Speaker 1: risks like this. Not saying that all comedians are. There

1074
01:01:33,080 --> 01:01:35,160
Speaker 1: are plenty of comedians that have come from like really difficult.

1075
01:01:35,360 --> 01:01:38,200
Speaker 1: Tiffany Hattish is like a prime example of someone who's

1076
01:01:38,240 --> 01:01:41,760
Speaker 1: like succeeded in the face of like all this, you know,

1077
01:01:41,840 --> 01:01:45,560
Speaker 1: all these obstacles. It's just like it's not comedy. Is

1078
01:01:45,560 --> 01:01:48,320
Speaker 1: not like you're not automatically going to get a payoff

1079
01:01:48,320 --> 01:01:51,520
Speaker 1: from it. So if you don't have like if you're

1080
01:01:51,560 --> 01:01:53,880
Speaker 1: if either you don't have your family as a safety

1081
01:01:53,920 --> 01:01:56,680
Speaker 1: net or like you know, or like a crude wealth

1082
01:01:56,760 --> 01:02:00,320
Speaker 1: or something you you know, I mean, you're gonna get

1083
01:02:00,360 --> 01:02:03,280
Speaker 1: punished for trying to pursue your dream because you don't

1084
01:02:03,400 --> 01:02:07,400
Speaker 1: have uh that kind of like backup in case. Like it. Also,

1085
01:02:07,440 --> 01:02:08,960
Speaker 1: it's like I know that a lot of my comedian

1086
01:02:08,960 --> 01:02:11,920
Speaker 1: friends are suffering, like they are like poor, um. But

1087
01:02:12,000 --> 01:02:14,240
Speaker 1: it's like if ship hit the fan, if they ended

1088
01:02:14,320 --> 01:02:16,120
Speaker 1: up in the hospital, like somebody would be there for

1089
01:02:16,240 --> 01:02:19,360
Speaker 1: them exactly, whereas I think that there are there are

1090
01:02:19,400 --> 01:02:22,200
Speaker 1: comedians who like there wouldn't be anybody for them, like

1091
01:02:22,240 --> 01:02:26,080
Speaker 1: they would be they would be totally truly fucked um.

1092
01:02:26,120 --> 01:02:28,320
Speaker 1: And it's interesting to see like in different places like

1093
01:02:28,360 --> 01:02:31,040
Speaker 1: in in the Bay Area, like a lot of improvisers

1094
01:02:31,040 --> 01:02:33,080
Speaker 1: they're all tech people. They have the time and the

1095
01:02:33,080 --> 01:02:35,720
Speaker 1: money to do it. In India they're all like tech

1096
01:02:35,760 --> 01:02:38,400
Speaker 1: and engineering people because they have the time and money

1097
01:02:38,440 --> 01:02:40,760
Speaker 1: and access to do it. Um. So it is like

1098
01:02:40,840 --> 01:02:43,720
Speaker 1: really interesting seeing like where people come from based on

1099
01:02:43,760 --> 01:02:47,919
Speaker 1: where they are and like, um, what what access they have? UM.

1100
01:02:47,960 --> 01:02:50,800
Speaker 1: I don't know why I'm saying all this, what I mean,

1101
01:02:51,040 --> 01:02:53,280
Speaker 1: I think it's a really interesting point because I think

1102
01:02:53,320 --> 01:02:58,600
Speaker 1: that um, it does kind of shape, uh what sort

1103
01:02:58,640 --> 01:03:01,320
Speaker 1: of the topics of what comedy is about. So we

1104
01:03:01,320 --> 01:03:04,720
Speaker 1: we have kind of a probably in more narrow scope

1105
01:03:04,840 --> 01:03:08,840
Speaker 1: of comedy that does come from like, um, you know

1106
01:03:08,880 --> 01:03:12,400
Speaker 1: a lot of people who went to uh like graduated

1107
01:03:12,440 --> 01:03:17,120
Speaker 1: from college and have this specific perspective. Yeah. Um, you

1108
01:03:17,200 --> 01:03:19,080
Speaker 1: know people talk about like SNL is like they have

1109
01:03:19,160 --> 01:03:21,680
Speaker 1: like the Harvard people and the other people. Right, Yeah,

1110
01:03:21,680 --> 01:03:23,600
Speaker 1: it's kind of like that, but like, yeah, I'm trying

1111
01:03:23,600 --> 01:03:26,120
Speaker 1: to in terms of how it is informing my comedy.

1112
01:03:26,160 --> 01:03:29,520
Speaker 1: I'm trying to incorporate more in my stand up, specifically

1113
01:03:29,720 --> 01:03:32,800
Speaker 1: more of my perspective with um science and engineering. And

1114
01:03:33,200 --> 01:03:35,320
Speaker 1: it's it's hard to find a way to like educate people.

1115
01:03:35,360 --> 01:03:37,920
Speaker 1: Sometimes I use as a decoy to like have a punchline.

1116
01:03:38,240 --> 01:03:41,200
Speaker 1: We like, look how smart I am? This pussy joke,

1117
01:03:41,280 --> 01:03:43,800
Speaker 1: like you know, like and then that let that pussy

1118
01:03:43,880 --> 01:03:46,600
Speaker 1: joke in. Yeah. I mean that's what I hope to

1119
01:03:46,640 --> 01:03:48,960
Speaker 1: do with this podcast is like we bait you in

1120
01:03:49,080 --> 01:03:52,080
Speaker 1: as it being a comedy pod or an educational We're

1121
01:03:52,120 --> 01:03:53,640
Speaker 1: like we're going to educate you and then just a

1122
01:03:53,760 --> 01:03:56,720
Speaker 1: pussy joke that right and there. Yeah, So that's kind

1123
01:03:56,720 --> 01:03:59,160
Speaker 1: of like kind of what I do, um, but I'm

1124
01:03:59,160 --> 01:04:01,000
Speaker 1: trying to do the opposite. Actually is just like because

1125
01:04:01,000 --> 01:04:03,440
Speaker 1: most of me is like the silly fun like we're

1126
01:04:03,560 --> 01:04:06,480
Speaker 1: just like a comedian. It's like that's me. And then

1127
01:04:06,520 --> 01:04:09,320
Speaker 1: I try but like what I'm interested in is like

1128
01:04:09,400 --> 01:04:11,240
Speaker 1: all of these things that sometimes are it's hard for

1129
01:04:11,280 --> 01:04:13,240
Speaker 1: people to hear um, and so then I try to

1130
01:04:13,280 --> 01:04:16,000
Speaker 1: like slip that in there. Um. I think comedy can

1131
01:04:16,040 --> 01:04:21,520
Speaker 1: definitely take the edge off serious topics like unvaccinated children,

1132
01:04:21,560 --> 01:04:25,360
Speaker 1: for example, and mental health issues like just all sorts

1133
01:04:25,400 --> 01:04:28,840
Speaker 1: of things um and so yes, and also like de stigmatized.

1134
01:04:28,880 --> 01:04:31,920
Speaker 1: So like, because I feel like one of the one

1135
01:04:31,920 --> 01:04:34,120
Speaker 1: of the cool things about comedy is like it seems

1136
01:04:34,120 --> 01:04:37,440
Speaker 1: like most or at least many comedians have had to

1137
01:04:37,480 --> 01:04:40,040
Speaker 1: deal with some kind of mental health issues, and so

1138
01:04:40,080 --> 01:04:44,439
Speaker 1: that it's, uh, it's really destigmatizing because it's just talking

1139
01:04:44,440 --> 01:04:46,120
Speaker 1: about it, like hey, look, you know, I am just

1140
01:04:46,240 --> 01:04:48,400
Speaker 1: a person and then I have this thing, and like,

1141
01:04:48,640 --> 01:04:51,200
Speaker 1: you know, by kind of making it where you can

1142
01:04:51,640 --> 01:04:53,680
Speaker 1: you can sort of do a set on it or

1143
01:04:54,360 --> 01:04:56,680
Speaker 1: have some humor about it, it's really kind of and

1144
01:04:56,720 --> 01:04:58,840
Speaker 1: I think it makes it a lot easier for other

1145
01:04:58,960 --> 01:05:01,360
Speaker 1: people to go like, hey, know, it's like I have O. C.

1146
01:05:01,480 --> 01:05:03,640
Speaker 1: D two Like, hey, yeah, I've done like I've done

1147
01:05:03,640 --> 01:05:06,320
Speaker 1: those weird things too. Yeah. And it's also like finding

1148
01:05:06,320 --> 01:05:08,840
Speaker 1: the funny like makes people like, yeah, it definitely finds

1149
01:05:08,840 --> 01:05:11,120
Speaker 1: the community within it and like makes them relating stuff.

1150
01:05:11,360 --> 01:05:15,920
Speaker 1: And it is like interesting especially being um, like Indian American,

1151
01:05:16,240 --> 01:05:20,360
Speaker 1: like having family who's like more conservative or whatever, like

1152
01:05:20,440 --> 01:05:23,960
Speaker 1: be accepting of it. It's just so much more validating

1153
01:05:24,000 --> 01:05:27,360
Speaker 1: because you're like, oh, like they can't it's hard for

1154
01:05:27,400 --> 01:05:29,520
Speaker 1: them to reject like the truth. They can tell you

1155
01:05:29,560 --> 01:05:31,520
Speaker 1: not to talk about it, but they can't deny it,

1156
01:05:32,040 --> 01:05:35,120
Speaker 1: so it's it's hard. It's it's interesting seeing people come

1157
01:05:35,160 --> 01:05:38,400
Speaker 1: out of the woodworks and support you when you expect

1158
01:05:38,400 --> 01:05:40,560
Speaker 1: when like your whole life people have been telling you

1159
01:05:40,600 --> 01:05:43,040
Speaker 1: to stop talking about it, you know what I mean, Like,

1160
01:05:43,160 --> 01:05:45,560
Speaker 1: but through a comedic lens, they're like, oh, like this

1161
01:05:45,680 --> 01:05:48,280
Speaker 1: is like your truth. It is funny, Like I feel

1162
01:05:48,320 --> 01:05:51,680
Speaker 1: this way too, um and finally like it's channeled somewhere,

1163
01:05:52,680 --> 01:05:57,120
Speaker 1: but yeah, yeah, I think it kind of. Um, it's

1164
01:05:57,160 --> 01:06:01,280
Speaker 1: a it's it's such a disarming kind of medium that

1165
01:06:01,360 --> 01:06:05,320
Speaker 1: it's both good for like like educating on like scientific

1166
01:06:05,360 --> 01:06:08,400
Speaker 1: topics but also social issues like kind of like you know,

1167
01:06:08,440 --> 01:06:13,560
Speaker 1: you you're sort of it's people are um, they're they're disarmed,

1168
01:06:13,600 --> 01:06:16,240
Speaker 1: They're not they're dukes, aren't up. They're not like prepared

1169
01:06:16,320 --> 01:06:18,640
Speaker 1: to like sort of reject what you're saying off the bat,

1170
01:06:18,760 --> 01:06:21,360
Speaker 1: because you know, you're you're kind of like, hey, you know,

1171
01:06:21,360 --> 01:06:23,040
Speaker 1: I'm going to tell you a joke, like this is

1172
01:06:23,080 --> 01:06:26,200
Speaker 1: a this is a positive interaction already and there, and

1173
01:06:26,240 --> 01:06:29,720
Speaker 1: you're you're acknowledging your behavior, yes, and you're not just

1174
01:06:29,920 --> 01:06:31,840
Speaker 1: you know, lashing out at other people. You're like, I'm

1175
01:06:31,920 --> 01:06:35,160
Speaker 1: this Like Billboard does a really good job of like

1176
01:06:35,160 --> 01:06:36,720
Speaker 1: like I like him a lot um. He does a

1177
01:06:36,720 --> 01:06:39,080
Speaker 1: really good job of Like he's like I'm like in

1178
01:06:39,080 --> 01:06:41,280
Speaker 1: my head, I'm like angry and like racist and all

1179
01:06:41,280 --> 01:06:43,560
Speaker 1: these things. But that doesn't mean that you have to

1180
01:06:43,600 --> 01:06:46,920
Speaker 1: continue to be that way. And like he's like like

1181
01:06:47,000 --> 01:06:49,800
Speaker 1: I always say, like whenever I think like a racist

1182
01:06:49,840 --> 01:06:51,480
Speaker 1: thought or like you know, because we all have that

1183
01:06:51,600 --> 01:06:54,760
Speaker 1: programming in us, it's like just on learning everything. I'm

1184
01:06:54,800 --> 01:06:57,240
Speaker 1: like my brain's dump Like I'm just like, oh my

1185
01:06:57,320 --> 01:06:59,600
Speaker 1: brain did that stupid thing. I gotta got a thing

1186
01:06:59,640 --> 01:07:02,080
Speaker 1: about that. And I learned that um. So it's like

1187
01:07:02,240 --> 01:07:05,360
Speaker 1: it's a fun way of like acknowledging all these flaws

1188
01:07:05,400 --> 01:07:08,840
Speaker 1: that we all have and like not ostracizing people for it.

1189
01:07:09,200 --> 01:07:11,040
Speaker 1: That's a really good way to put it. It's it's

1190
01:07:11,080 --> 01:07:14,520
Speaker 1: like you're you're introspecting in a way that's safe because

1191
01:07:14,560 --> 01:07:18,040
Speaker 1: like in comedy, you can make fun of yourself and

1192
01:07:18,480 --> 01:07:21,800
Speaker 1: think about all of your flaws without it feeling really

1193
01:07:21,840 --> 01:07:25,400
Speaker 1: sort of like self loathing or judgmental. It's more it's like,

1194
01:07:25,760 --> 01:07:29,360
Speaker 1: you know, hey, I'm I do you know, like uh,

1195
01:07:29,400 --> 01:07:31,520
Speaker 1: every time I go to bed, I like stare at

1196
01:07:31,520 --> 01:07:33,640
Speaker 1: the stove for five minutes because I'm worried for some

1197
01:07:33,680 --> 01:07:35,720
Speaker 1: reason the nons are going to be on suddenly, Can

1198
01:07:35,760 --> 01:07:37,480
Speaker 1: I have O C D? And it's like, you know,

1199
01:07:37,520 --> 01:07:40,600
Speaker 1: instead of like being like oh, that's that makes me

1200
01:07:40,760 --> 01:07:43,840
Speaker 1: weird or like crazy, it's like it's it's actually really

1201
01:07:43,880 --> 01:07:46,240
Speaker 1: funny when you think about it, like like when you

1202
01:07:46,320 --> 01:07:49,600
Speaker 1: talk like using humor to be like yeah, like no,

1203
01:07:49,760 --> 01:07:52,120
Speaker 1: I just did a really Dirk Gass thing the other day,

1204
01:07:52,200 --> 01:07:54,760
Speaker 1: or I did like like I think my thinking is

1205
01:07:54,800 --> 01:07:57,680
Speaker 1: like really weird in this way, and then it's I think, yeah,

1206
01:07:57,720 --> 01:08:01,120
Speaker 1: the key thing um for any progress in any society,

1207
01:08:01,200 --> 01:08:05,400
Speaker 1: it's like observing and acknowledging what's happening. And I think, yeah,

1208
01:08:05,480 --> 01:08:08,720
Speaker 1: doing that on an individual level means that your heart

1209
01:08:08,800 --> 01:08:13,439
Speaker 1: is in the right place, even if your mind sometimes isn't. Yeah, yeah,

1210
01:08:13,600 --> 01:08:17,680
Speaker 1: I hope. I also hope that um comedy can you know,

1211
01:08:17,720 --> 01:08:21,320
Speaker 1: because we were talking about the the anti vaccine movement

1212
01:08:21,439 --> 01:08:25,479
Speaker 1: and sort of these almost like and it's the same

1213
01:08:25,479 --> 01:08:28,920
Speaker 1: thing with climate denial or in just like generally denying

1214
01:08:29,000 --> 01:08:32,719
Speaker 1: science exists, that maybe comedy is a way too, because

1215
01:08:32,760 --> 01:08:35,439
Speaker 1: I think people have this perception of science being this

1216
01:08:35,600 --> 01:08:39,480
Speaker 1: like very sort of like just a bunch of like um,

1217
01:08:39,520 --> 01:08:43,479
Speaker 1: you know, stodgy old men in lab coats, and you know,

1218
01:08:43,560 --> 01:08:48,120
Speaker 1: they're totally serious, and some of them are, of course, yes,

1219
01:08:48,160 --> 01:08:52,160
Speaker 1: plenty of them are, but kidding this is hot mic

1220
01:08:52,800 --> 01:08:56,960
Speaker 1: mic um. But yeah, And and this idea that it's

1221
01:08:57,000 --> 01:09:00,720
Speaker 1: this like really snotty, snobby kind of in flirt well,

1222
01:09:00,760 --> 01:09:04,759
Speaker 1: and again that is sometimes true. But I think that

1223
01:09:05,000 --> 01:09:09,080
Speaker 1: having that outreach of comedy of researcher, you know, it's like,

1224
01:09:09,120 --> 01:09:11,519
Speaker 1: it's so cool to me that you're researcher and you're

1225
01:09:11,520 --> 01:09:14,320
Speaker 1: doing comedy, because that makes it I mean, like I'm

1226
01:09:14,320 --> 01:09:18,160
Speaker 1: a dumbass, just like I just studied this one thing. Yeah,

1227
01:09:18,400 --> 01:09:23,280
Speaker 1: you're a dumbass who studies computational models that of the round,

1228
01:09:23,320 --> 01:09:27,639
Speaker 1: hippcap real dumbass with ship. I don't know what I'm doing.

1229
01:09:28,360 --> 01:09:30,760
Speaker 1: Just do the next thing. I try different things. I

1230
01:09:30,800 --> 01:09:34,680
Speaker 1: don't know. Well, thank you so much for coming on.

1231
01:09:34,720 --> 01:09:38,439
Speaker 1: This has been really really incredible, and it's so uh

1232
01:09:38,439 --> 01:09:40,680
Speaker 1: it's really an honor to meet you and to talk

1233
01:09:40,760 --> 01:09:48,280
Speaker 1: to you because let's just start laughing. Yeah, that's to

1234
01:09:48,320 --> 01:09:52,559
Speaker 1: meet you. You're lovely. Thank you. I've literally done nothing

1235
01:09:52,600 --> 01:09:57,560
Speaker 1: for science. Uh, but you're a wonderful person. Than what

1236
01:09:57,600 --> 01:09:59,200
Speaker 1: are you talking about? This whole thing is what do

1237
01:09:59,200 --> 01:10:02,080
Speaker 1: you mean you're on? That's what the funk are you

1238
01:10:02,120 --> 01:10:08,240
Speaker 1: talking about. I've collected people's spit for science. There you go,

1239
01:10:08,360 --> 01:10:12,000
Speaker 1: and you're doing this and and a diarrhea monkey, a

1240
01:10:12,040 --> 01:10:14,599
Speaker 1: contentent Tamarin that had diarrhea had to take care of it.

1241
01:10:16,360 --> 01:10:20,360
Speaker 1: A noble act a noble act um. So, but I

1242
01:10:20,360 --> 01:10:22,720
Speaker 1: do think outreach is the most important thing, because none

1243
01:10:22,760 --> 01:10:25,360
Speaker 1: of our ship will work if or get to people,

1244
01:10:25,520 --> 01:10:27,760
Speaker 1: and none of it will matter if policy doesn't change,

1245
01:10:27,760 --> 01:10:32,000
Speaker 1: and policy is influenced by people who don't know about something, Yeah,

1246
01:10:32,360 --> 01:10:35,000
Speaker 1: and so we need people to influence it in a

1247
01:10:35,000 --> 01:10:39,560
Speaker 1: possible way. Yeah, it's a great point. Um, so you

1248
01:10:39,640 --> 01:10:42,280
Speaker 1: got anything to plug? You mentioned some of your cool stuff.

1249
01:10:42,320 --> 01:10:45,200
Speaker 1: You're in the thing. Um. I have Dirty Science, which

1250
01:10:45,240 --> 01:10:47,160
Speaker 1: is my web series and you can find that on YouTube,

1251
01:10:47,400 --> 01:10:50,240
Speaker 1: YouTube dot com, slash Paula Vigan all In comedy and

1252
01:10:50,320 --> 01:10:52,439
Speaker 1: spell my name p A l l A v I

1253
01:10:52,560 --> 01:10:54,960
Speaker 1: g U n A l A n And I'm at

1254
01:10:55,040 --> 01:10:59,040
Speaker 1: Paula Vigan al In on Instagram, Facebook, Twitter, um, and

1255
01:10:59,080 --> 01:11:01,479
Speaker 1: that's my website as well. I also run a couple

1256
01:11:01,520 --> 01:11:04,080
Speaker 1: of comedy shows and have my own podcast. Um So

1257
01:11:04,200 --> 01:11:07,240
Speaker 1: I run a facial recognition comedy and oversharing comedy and

1258
01:11:07,240 --> 01:11:10,439
Speaker 1: then Facial Recognition Comedy has its own podcast, Ed's whale.

1259
01:11:10,560 --> 01:11:17,880
Speaker 1: So don't do a thing and you'll also do research? How? What? How? Yeah?

1260
01:11:18,200 --> 01:11:23,000
Speaker 1: I imagine so. Well. You can follow us on Twitter

1261
01:11:23,280 --> 01:11:26,439
Speaker 1: at Creature feet Pod. Not like feet doesn't gross feet

1262
01:11:26,439 --> 01:11:29,280
Speaker 1: but feet f e T. Really didn't think that went

1263
01:11:29,280 --> 01:11:32,240
Speaker 1: through when I was making that Twitter handle. Uh, there's

1264
01:11:32,320 --> 01:11:38,120
Speaker 1: Creature feature Pod dot com at Creature feet Pod, Instagrams.

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Speaker 1: It's you know, it's all out there. I'm Katie Golden

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Speaker 1: at Katie Golden on Twitter, I'm also propered rights um

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01:11:45,960 --> 01:11:50,520
Speaker 1: or vice versa. Who's who's to say who's controlling whom

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Speaker 1: in that situation? It's I pretend to be a bird

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Speaker 1: on Twitter. Um uh and wait your bird right? Yeah,

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Speaker 1: I know that thing. Wait that's like a famous one.

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Speaker 1: Oh my god. Yes, I've definitely like responded to your ship.

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Speaker 1: Oh my god, I love this. Uh this Jerry Springer

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Speaker 1: twist at the end. I've been the bird the whole time,

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Speaker 1: pro bird right. Yes, it's uh, that's actually how I

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Speaker 1: got into comedy. It's through through the so many people.

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Speaker 1: Are you kidding me? This is you? Yes, Holy sh it,

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Speaker 1: forget everything I've said. She has four followers and it

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Speaker 1: is hilarious. What's funny is that bird personality of made

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Speaker 1: will always overshadow me as a huge like it. I'll

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Speaker 1: never come close to that bird fame like nobody nobody

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Speaker 1: gives a ship who like Katie Golden has nobody cares.

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Speaker 1: My god, I cannot believe that is totally changing my

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Speaker 1: view of you entirely. And you thought I was a

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Speaker 1: serious person. Oh my goodness. Well, thank you guys so

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Speaker 1: much for listening. I feel truly privileged to have been

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Speaker 1: allowed in your ear holes. But really, seriously, I'm thankful

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Speaker 1: for everyone who listens to the show. UM. Combining comedy

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Speaker 1: and evolutionary biology is kind of a weird, nerdy idea,

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Speaker 1: and I'm super lucky to have listeners like you. UM.

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Speaker 1: If you're enjoying the pod and want to help me out,

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01:13:19,080 --> 01:13:22,560
Speaker 1: please please please leave a writing or review and subscribe.

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Speaker 1: It really does help out immensely. Thank you, Thank you

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Speaker 1: so much, you guys. I'll be back in your ear

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Speaker 1: holes again next Wednesday. I'm still reading these tweets, and

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Speaker 1: thanks to the Space Classics for their awesome song Exo Lumina.