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Welcome to STEMology – Show Notes

Season 1, Episode 35

Subconsciously numerate eye holes, magnetic space rubbish truck, killing to learn & panda

In today’s episode of STEMology…

David & Sophie had a discussion on how the pupils subconsciously counts for you, scientists found a way to cleans space junks using magnets, how first-person shooter games make you a better learner and panda is a great camouflage in its natural habitat

Subconsciously numerate eye holes

So it turns out that pupils can think and we don’t even know it.

Basically what’s novel in this paper and in this work is that they’ve worked out a way to move things that are not usually strongly affected by magnetic fields with magnetic fields.

Killing to learn

So what they’re saying is if you play action packed video games, you’re then primed to learn on these other kinds of tasks, which have nothing to do with playing video games.


This is the first time that the camouflage capabilities of the giant Panda has actually been tested in a series of advanced image analysis techniques, using rare photos of pandas in their natural habitat.

This is a “kind of, sort of, vaguely close” copy of the words that David & Sophie speak in this episode.

IT IS NOT 100% accurate.  We are very sorry if we have spelt something completely incorrectly.  If it means a lot to you to have it corrected, email us at stemology@ramaley.media

STEMology s1e35

[00:00:00] Sophie: Welcome to episode 35 of STEMology.

[00:00:03] David: A podcast to sharing some of the fun interesting, and sometimes just patently bizarre news in science, technology, engineering, or maths.

[00:00:10] Sophie: your hosts are Dr. David Farmer and Dr. Sophie Calabretto.

[00:00:14] David: In today’s episode of STEMology, we’ll be chatting about

[00:00:16] Sophie: subconsciously numerate eye holes, magnetic space rubbish trucks

[00:00:21] David: killing to learn and Panda.

Subconsciously numerate eye holes

[00:00:25] David: Sophie.

[00:00:26] If eyes could count,, we can apparently Sophie count with just little more than our eye holes.

[00:00:32] Sophie: but subconsciously we can subconsciously count things with our eyeholes, without even realizing.

[00:00:38] David: Yeah. So this is some work from the School of Psychology at the University of Sydney, in collaboration with the University of Pisa and Florence. And basically what they’re saying is when we passively look at a scene with a number of things in it, it changes the size of our pupils indicating that we’re looking passively at how numerous things are.

[00:00:59] Sophie: [00:01:00] Yeah. So I think pupils are interesting, right, Dave. So, so We do a lot of spontaneous perceiving as people, right? So it’s like, we’re here, we’re in the world. we spontaneously perceive things like form and size and movement and color and all this kind of stuff.

[00:01:15] And also the number of things before us. And apparently this is quite an important ability, which is fundamental to our survival. So say if I’m in, I don’t know, the African planes and there’s, are they 12 lions or one lion. It’s quite important for me to know that like straight off the bat, in terms of how many people do I have to push in front of me when I run away from the lions it’s for my survival.

[00:01:36] You know, if I’m looking at prey, I think the press release use the example of apples. How many, how many of my prey on that tree? Are there six apples, or is there one apple? And

[00:01:44] David: I am the apple hunter.

[00:01:46] Sophie: I am the apple hunter, and the question is. Where does this, where does my ability, where’s the underlying mechanism of live in my face and head that allows me to spontaneously count things.

[00:01:59] David: So we’re [00:02:00] talking about something quite specific, right? Cause we’re not talking about the intellectual, like the intellectual act of counting, as would know it. So it’s not like we have these mathematical things called numbers and we give each one a label and we stretch them out on a scale that goes all the way to infinity.

[00:02:13] And those are numbers it’s like, as you say, you look at something, you get an immediate sense of just about how many there are. And then it’s like, okay, now we can very quickly use this information for our survival.

[00:02:24] Sophie: Yeah. And it turns out it, the pupils. So the pupils, which are, I like to think of. So the Iris is like the color sphincter of the eye and the pupil is like, what the size of the butthole did

[00:02:39] David: That that analogy is perfect. In every way it’s poetic, it’s moved me. I’m overjoyed

[00:02:46] Sophie: I say, I like to think of it like that. I did maybe just think of it like that then for the first time ever, and then say it when it came into my mind and I don’t regret it. and yeah, so, normally when we think of pupils, we think of, you know, pupils, they constrict, they [00:03:00] dilate and they let more or less light in.

[00:03:01] So the whole idea is if it’s very, very dark, basically my pupils get real big to let more light in so I can see, or, you know, my iris basically contracts. So my pupil gets bigger. And then if it’s very, very bright, you know, you see this like constricting and so your pupil gets very, very small because you don’t want too much light to come in and, or you’ll get blinded.

[00:03:20] Right. So I think of when I think of the pupil,

[00:03:22] David: that’s about, right. So it’s, it’s a little bit more complicated than that. Eyes is unusual, but not unusual rather cause it’s biology, but you actually have two kinds of muscle in the iris. So you have you have muscle that’s circular. So it goes around the way it goes round in the direction of the circumference of the circle.

[00:03:37] Sophie: Yeah.

[00:03:38] David: So if that muscle constricts, then the pupil get smaller. But you also have muscle that’s radial. So that’s muscle that goes from the inside of the Iris outside of the iris. So that constricts, the pupil gets bigger. And I think the one that’s important apparently from my Wikipedia reading and other important research that I did.

[00:03:54] the one that’s important to the pupil that are be flicks is the radial one. So it’s actually, yeah. it’s a constriction [00:04:00] of the muscle that makes the Iris bigger. But Yeah. that’s probably happening in concert with the relaxation of the muscle that goes right in the way.

[00:04:07] Sophie: Right. Yeah. so the idea is they wanted to know, basically the question that they asked was where the perceived numerosity modulates to pupillary light response.

[00:04:16] So they grew up a group of participants. And this, I find very funny. These basically we had six male participants, 10 female participants, mean age of 30. They all had normal or corrected to normal vision. So, you know, normal or wore glasses to make them normal. And they were presented with images and bought the images where were a variable number of dots that were either isolated or connected to go into it.

[00:04:36] And second, but basically they were told to just like, observe the patterns passively. So don’t count them, just like sit there. And so they were like looking at these people’s eyes as they were passively observing this. I reckon the second someone goes, don’t count the dots. Just sit there passively.

[00:04:51] David: difficult.

[00:04:52] Sophie: I don’t know how you do it all.

[00:04:53] I’d just be like, I’m not counting the dots. I’m not, it’s like, you know, when you have to, like, I don’t know, keep

[00:04:59] David: listeners. Hey [00:05:00] listeners. Don’t think about elephants.

[00:05:01] Sophie: Right. Or even you go to have an eye test and they just like, keep your eyes open. It’s like, all I want to do is blink. I’ve never needed so much before in my life.

[00:05:09] And so I just that and went, I would be rubbish at this experiment because all I’d want to do is count all the dots and I wouldn’t be able to keep my eyes just passively observing

[00:05:16] David: Yes. So they looked at whether the pupillary response to numerosity was impacted in any way by the number of dots. And it is.

[00:05:25] is short answer. When they looked at bright regions with dark spots on them, then if you had higher perceived numerosity than the Iris contracted less.

[00:05:38] Sophie: They found that. The diameter of the participants pupils varied according to perceived number of dots. So it was greatest. Like the diameter of the pupil was greatest i.E biggest when the perceived numbers were high. And when they perceived fewer objects, then basically the pupil radius was

[00:05:56] lowest i.e you know, you’ve got your smallest pupil. I [00:06:00] thought the fun thing in here, they also dealt with the, um, like the illusion of the connecting dots. I really liked that. So I was like, you know, if you’ve got like a, you’ve got a bunch of dots and, you know, there’s 10 of them and you look at them and you can sort of see about 10.

[00:06:10] If you connect one dot to another, And not, that’s usually got two dots connected with a straight line. So it looks like a little mini dumbbell. You’ve still got 10 dots, but you’ve kind of got like five objects. And so like it turns out that we actually it’s a well-known illusion, but it’s true. Like, we actually perceive that as fewer things, when you look at it, I mean, when you passively looking at it, because I guess but you’re not really looking at them dots.

[00:06:31] You’re looking at objects, you’re perceiving rather than perceiving dumbbells. and so they did. It looks like they did apparently a bunch of things. So there was a they did a first pupillometry experiment. They also did something with numerosity discrimination, and then they did a second pupillometry experiment with a psychophysics experiment linked to it.

[00:06:53] And I’m gonna be honest, Dave, I tried to understand the difference between all these things and I didn’t really know. So basically they had [00:07:00] participants observe the patterns passively. We had 18 or 20 dots. Sometimes they were joined. Sometimes they weren’t joined and they basically measured the pupil diameter.

[00:07:09] It was monitored at 500 Hertz with an EyeLink 1000 system, which I did not

[00:07:13] David: fine. That’s what I would call it though.

[00:07:15] Sophie: But, yeah, and I presume that measures the size of your pupil and the results showed that, although the number of pixels, which are black and white were the same for all patterns, the diameter of the pupil, the participants pupils did vary with perceived number of dots.

[00:07:28] So it turns out that, um, pupils can think, and we don’t even know it.

[00:07:32] David: Yeah. Well, so The pupils can think, but it’s still the brain. So what, when we were talking about the different kinds of muscle in the eye and how they contract.. So when you shine light on the eye, that information goes to the brainstem, which is a very old and ancient part of the brain.

[00:07:45] And then that sends an almost like it’s a really apparently very simple reflex that causes the pupil to constrict when the light is shone on it. So that’s mediated by the brainstem and that’s the pupillary light reflex that they do to you. If you’re, you know, If you get knocked down by a car or [00:08:00] something, and they shine a light in your eyes to make sure that your pupils dilate, because the reason they do that is to make sure that your brainstem is okay basically.

[00:08:06] So it’s basically, it’s a function for very fundamental brain. It’s a test for very fundamental brain function. So what this suggests is that that reflex is happening and that this, the numerosity, the perceived numerosity, or rather than not perceive numerosity. Cause apparently it happens passively is impacting the way that that reflex works.

[00:08:27] So they say it’s the pupils, but really it’s the brainstem. But the question becomes what part of the brain is talking to the brainstem to produce this? So I think a really interesting experiment would have been to do this in unconscious people whose eyes are open.

[00:08:40] Sophie: Oh, creepy Dave.

[00:08:43] David: Yeah.

[00:08:43] Yeah. So if you have people who are unconscious, so they’re, they’re not, you know, they’re definitely not physically perceiving anything. Does this effect persist because it does that tells you something about the regions of the brain that are responsible,

[00:08:56] Sophie: Are you going to contact our friends at the University of Sydney in collaboration with the [00:09:00] Universities of Pisa and Florence with your idea? Or just,

[00:09:03] David: I think they might say that’s creepy. I think they might say that’s creepy and not ethical in some way.

[00:09:08] Sophie: uh, speaking of a creeping unethical? No, that’s not a good segue. So I,

[00:09:12] David: have you’d have to recruit patients who sleep with their eyes open.

[00:09:15] Sophie: Can you, is there some, is there a, okay. I need all pharmacologists to write to STEMology.. Is there a drug we can give people where they basically get like a little bit unconscious with their eyes open? is that a, can we make that happen?

[00:09:28] David: I think you could do any range of anesthetics with eyes opened, but I think putting people under general anesthetic is generally a bit of an ethical no-no if you

[00:09:36] it’s dangerous, quite hard on the kidneys.

[00:09:38] Sophie: But surely if we’ve got ethics approval for this experiment, this

[00:09:43] David: Well, I’m I’m we’ll just get it We’ll just ask for it. We’ll

[00:09:45] Sophie: That’s fine. I hear it’s very easy to get ethics approval for everything, but know what else I learnt Dave? Cause I was learning about pupils pupils and obviously we’ve talked about this like light action, but apparently there’s research that has shown that, pupils can basically constrict or dilate

[00:09:59] [00:10:00] because of a bunch of other weird things, or apparently it may indicate interest in the subject of attention or arousal. Sexual stimulation, uncertainty, decision conflict, errors, or increasing cognitive load or demand. There’s been research that has shown that your pupils react to all those different things.

[00:10:18] But I just think I get worried when they list things in the order of sexual stimulation and then uncertainty and arousal oh, and decision conflict, just, they could have just put that in a different way, but also Dave, fun fact pupils contract immediately before REM sleep.

[00:10:34] David: I was not aware of that. See to, in order to do that, they must’ve had someone’s eyes open

[00:10:39] Sophie: That’s true. Okay. We should all look that paper up and we can, talk afterwards. And also my final fun fact about a pupil. Apparently you can intentionally condition a pupillary response as a Pavlovian response to some stimuli.

[00:10:52] David: There you go. think I read that too. So if you show people a picture of the sun, even if it’s not super bright than their pupils [00:11:00] constricts, because they perceive it as something that’s very bright,

[00:11:02] Sophie: I think that’s

[00:11:03] David: which

[00:11:04] Sophie: bit. Yeah. Anyway, but yeah. So it turns out that your brainstem and your pupils together, friends for life, subconsciously counting things and modulating the pupillary response so that you don’t get eaten by a lion

[00:11:16] David: Or an apple.

[00:11:17] Sophie: Or an apple. If only apple was had pupils and they wouldn’t be eaten by me.

Magnetic Space Rubbish Trucks

[00:11:23] Sophie: Dave,

[00:11:34] David: Sophie, speaking of numerosity, did you know there are 27,000 pieces of space debris bigger than the size of a softball currently orbiting the earth?

[00:11:43] Sophie: I did know that, but did you know that they are traveling at speeds up to 28,000 kilometers per hour, which is fast enough for a small trunk to damage a satellite or spacecraft like an intergalactic cannon ball Oh,

[00:11:57] David: I did know that. I did know that. That. Did you [00:12:00] know that mechanical engineers have discovered a method of manipulating orbiting space degree with the use spinning magnets, allowing agencies more dextrous movement and clearing a space Juncker repairing satellite

[00:12:09] Sophie: I did know that. Yes. Dave, tell me about space magnets from the University of Utah, cleaning up our galaxy that we have turned into a trash pile.

[00:12:19] David: So there’s a lot of trash in space and because we want to keep going into space and we’re going to keep sending up more stuff, which is going to contribute more junk. Eventually we’re going to need to clean up some of it, because as you mentioned, very, silly Sophie, some of it’s going very fast.

[00:12:33] We’ll take chunks out of whatever you send up there.

[00:12:36] Sophie: I just interject right now really cleaning up space junk so you can shoot more space junk into space. To me seems like equivalent of like literally throwing up so you can eat more. Like, it just seems real. You just like, we’ve made this huge problem. We want to continue making problems, but we’re going to need to like remedy a few things first.

[00:12:54] David: Yeah. I guess so. So it says the, um, space junk and industrial complex, I think, [00:13:00] whereby one fuels the other and then the space junk cleaning industry gets bigger and bigger because the space junk industry gets bigger and bigger and so on and so on and wars are fought and humanity ends and that’s it for us.

[00:13:11] Sophie: It’s a shame anyway. Sorry, Dave. I interrupted. Continue. Tell me about

[00:13:15] David: No. So this is, a project by the University of Utah and a team of researchers who have discovered a method of manipulating orbital debris with spinning magnets. Now what’s cool about this is obviously if something is magnetic like a magnetic metal, then you can manipulate it with a magnet that is kind of given right.

[00:13:33] that makes sense.

[00:13:35] Sophie: yeah. magnets manipulate magnets. Yeah.

[00:13:37] David: Yes, that makes sense. But lots of space stuff has made out of aluminium, which is not ferromagnetic, which means it doesn’t interact well with magnetic fields.

[00:13:45] just,

[00:13:46] Sophie: yeah. So a metal is conductive cause it’s got, electrons that are free to move around, but yet you’ve got a lot of metals that aren’t magnet. If all metals are magnetic we’d be in trouble, I think

[00:13:54] David: Yes, that would be a different world. so basically what’s novel in this paper and in this work is [00:14:00] that they’ve worked out a way to move things that are not usually strongly affected by magnetic fields with magnetic fields.

[00:14:09] Sophie: so it’s actually a very, very simple. So reading that because when I first heard about the story I went, that sounds nuts, but really they’re just using Faraday’s law. So the whole idea is that we’ve got a metal that is non magnetised, but it’s conductive cause so it’s got those free electrons.

[00:14:23] And so basically when you subject something like that to a changing magnetic field, You basically get the electrons within the metal to circulate. So the whole idea is a changing magnetic field generates an electric current in a conductor. Like that’s the basis of this and that’s just Faraday’s law. Or I think it’s actually called Faraday’s law of induction.

[00:14:40] So changing magnetic field makes these electrons move, moving electrons, themselves, create a magnetic field. So by basically getting a changing magnetic field, applying it to this metal. It produces a magnetic field within the metal. And that’s what we call an electromagnets. We sort of had these two types of magnets in existence.

[00:14:58] The permanent magnet that [00:15:00] you talked about before, which basically is just an object that produces a magnetic field, but requires no power. Whereas an electromagnet is basically just a magnet that Is produced by an electric current. So you take a piece of meadow, you turn it into a magnet with this electric current.

[00:15:14] And then as you said before, then magnets can interact with magnets. So basically you’re turning these non magnets into electromagnet and then using magnets to control those. So you can apply force and torque and torque is just force in a rotational way. And so apparently though Dave, this idea is not new necessarily, but the new part is the fact that they’ve managed to actually move it in all directions.

[00:15:36] So apparently in the past they could do this with one degree of freedom i.e pushing it. So it’s like if you’ve got something and you just pushing it along, whereas using multiple magnetic field sources in a coordinated fashion, and I looked at how they did that in the paper and it made my eyes bleed. they’ve managed to get the full six degrees of freedom.

[00:15:54] So that’s the three degrees. So three degrees are translation. So it’s just moving in the X, Y and [00:16:00] Z directions i.e up, down. Left right forward, backwards, but then they’ve also got the three types of rotation as well. So, you know, you can go from like rotating from your X to your Y, Z to your Y..

[00:16:12] And then your X to your Z.. And I did learn that these all have different names. So Dave and I, you’ve got to indulge me second cause you know, I

[00:16:17] David: yeah. Go, go, go,

[00:16:18] Sophie: So apparently translation in the Y direction in the Y axis, this is called a heaving. Translation in the heaving. Translation, the X direction is called surging. Translation in the Z direction is called swaying.

[00:16:31] Right? then the road.

[00:16:32] David: Who came up with these?

[00:16:33] Sophie: I don’t know. And then the rotation ones are a bit, I’ve heard these words at least, but I’d never act like, well, my years of physics education, I never actually knew exactly which one was, which, but rotation from X to Y is called pitch. X to Z is Yaw, as Y A W and then Z to Y is called roll.

[00:16:51] Anyway. But yes, the whole idea is now rather than just using magnets in a simpler way and to push something, they can now basically manipulate these in [00:17:00] 3D in every way we need them to, which means that we can essentially control any metal thing, we can turn it into a magnet and then control it with magnets with six degrees of freedom, which is excellent.

[00:17:10] David: Yes. And the example they give of that, which is a pretty good example is if you had a damage satellites that’s been, I don’t know, hit by a piece of space junk, then if it’s tumbling through space, what you’d be able to do is not just push it and say push it into the Earth’s atmosphere to burn up. So it doesn’t cause a problem.

[00:17:25] You could actually stop it from spinning by applying force using this magnetic array and then get your robotic arm onto it and start to fix it.

[00:17:32] Sophie: Yeah.

[00:17:33] Cause they said, you know, if your robotic arm tried to grab that before it stopped spinning. What you’re most likely going to do is like snap off your robotic arm and then create more space debris, which is counter productive in this particular arena.

[00:17:44] David: So in the research they show, they did a simulation of the six degrees of freedom work, but they also actually did a study, an experimental study with three degrees of freedom where they moved the copper ball around on a plastic wrap and a tank of water. And [00:18:00] I didn’t know what to make of it. I believe that they could do it, but I didn’t know how impressed to be by it.

[00:18:04] Sophie: I have some thoughts on both the experiments and the computations, So, so what they did is, yeah, so basically they looked at all the equations, they came up with a model for this, and then they did some finite element analysis. That’s fine. Finite element is just a way of meshing something up where you look at your domain and you turn it into like a mesh of finite elements in element being a shape.

[00:18:24] But they used ANSYS for it. Dave and ANSYS is very expensive engineering software that I would, on average, frown upon because it’s what I refer to as one of these blackbox software. So it will cost you like, and this is ballpark because some of these things they’ll be like $10,000, $20,000 for licenses.

[00:18:42] They compute very complicated things. And what you do is you put in a problem and it spits out an answer and you don’t see how anything works within that black box. And as someone who did computational fluid mechanics, but as a mathematician, we like to know the equations we’re working with. We like to know the assumptions.

[00:18:58] We like to know everything about it. [00:19:00] If I’ve insulted any ANSYS users. Please write in to stemology@ramaley.media. You probably won’t change my mind, but like, I’m very happy for you to vent. So they used ANSYS, which I, I don’t love, but then they went “Okay now we’re going to do this experimentally.” And again, so they used, it was a 25 millimeter copper sphere.

[00:19:18] So the radius was 25 millimeters. So 2.5 centimeters and they basically managed to, as you said, I think they rotated the sphere and then they could also. move it in a square. So it’s three degrees of freedom. So one rotation, two translations. there was they said the natural next step would be to consider hollow spheres and other simple geometric objects like cuboids or cylinders.

[00:19:39] That’s fine. They talk nothing about scale up Dave. So 2.5 centimeter sphere. Yes. You can move that. Great. Like what power of magnets do you need to use to move a piece of space junk? This was the bit that, you know, and I guess we, you know, we talked about, I think you said that we’ve got our [00:20:00] 27,000 pieces of space debris, which a bigger than a softball, like just have a whack with a softball size thing, just to see and that was the thing that frustrated me.

[00:20:08] It’s like, this is great, conceptually, that seems really good. But there was no discussion, at least none that I could find about if we were to scale this up to actually be feasible in space, like what types of power. Like how much electricity you putting into that changing magnetic field to produce these things that you’re going to move space junk.

[00:20:24] And I don’t know, Dave, I can’t answer that question.

[00:20:27] David: The other thing I wondered about was presumably the shape of something would be quite important and would it not become very complicated to manipulate something that’s not spherical, that’s say satellite shaped in terms of where you’re going to induce the currents and how exactly that’s going to translate to forest.

[00:20:43] Wouldn’t that be quite a difficult thing?

[00:20:44] Sophie: Interesting question. It might be. Yeah. I just, I think I felt like there was not enough information in this paper for me. and I had many questions, but not enough for me to contact anyone, but yeah. So I liked the idea. The idea is great. We’re using. So Faraday. He was [00:21:00] around a long time ago, still solving problems for us.

[00:21:03] And basically, yeah, we’re basically turning metal into an electromagnet and then we’re moving that electromagnet with other magnets. It’s like a great idea. I’d be interesting to see how this develops, because at this stage I just, I feel like it’s very theoretical and yes, we have a proof of concept, but not in a useful space way yet

[00:21:19] David: Yeah

Killing to learn

[00:21:30] Sophie: Dave. Did you know that playing first person shooters makes you better at learning to learn?

[00:21:35] David: Well, now I do Sophie. apparently the, so this is a story all about, as you say, learning to learn.

[00:21:40] It’s a story about generalization of learning.

[00:21:42] Sophie: Yes.

[00:21:43] David: So it’s a story about before we talk about what exactly the done with the video games, we should talk a little bit about the whole idea about the type of learning.

[00:21:50] So the, the analogy that the authors give is imagine taking someone and putting them through physical training to make them more athletic. Now let’s say you just throw them onto a rugby pitch. [00:22:00]

[00:22:00] Sophie: Yeah,

[00:22:01] David: they won’t know any of the rules for rugby, so they’re not going to be great rugby players, but because they’ve got that athleticism, they’ll still be better at rugby than someone who hasn’t been through the training regime.

[00:22:11] Sophie: Yeah, exactly.

[00:22:13] David: So basically they’re making an analogy here for cognitive learning. And particularly what they’ve looked at here is working memory. And

[00:22:22] Sophie: And perception, I think

[00:22:24] David: Yeah. So what they’re saying here is that playing action packed video games, specifically first person shooters. And as it turns out, quite old first person shooters, improves performance on these, cognitive tasks.

[00:22:37] So what they’re saying is if you play action packed video games, you’re then primed to learn on these other kinds of tasks, which have nothing to do with playing video games.

[00:22:47] Sophie: Yeah, which is really interesting. So what they did is they had, 25 participants from the University of Rochester in New York, and then they had 52 participants at the University of Geneva in Switzerland. And what they did is they separated these groups into roughly [00:23:00] equal groups. I say, roughly because 25 is not exactly divisible by two and they assign them to play 45 hours of either the action video games.

[00:23:09] Well then other games that unfolded a different pace without relying so much on visual attention and reaction speed. So the action games they played were call of duty one call of duty two and half-life, and then the other games they played were Sims three zoo tycoon and Viva pinata, and actually funny Dave, cause I like playing games, but I find first person shooters so stressful. Like to me, that not enjoyable game. I can’t do it. And so I was looking at these and I was like, I have not played call of duty one. I’ve not played call of duty two. I’ve not played half-life. I have played SIMS three, I have played viva pinata. I haven’t played zoo tycoon, but was a big fan of SIM, safari and rollercoaster tycoon.

[00:23:46] And I feel like if those, you get zoo tycoon. And, but yes, I, before they got them to play these games, what they did is they had these, the visual perception and the working memory tasks, and they tested people in these tasks and apparently, [00:24:00] both groups were relatively, even in their initial tests.

[00:24:03] Then they got them to play these 45 hours of either the action video games or the other games. and they found that the action game players like straightaway had an advantage and they had sort of improved learning in these tasks. And then that just got better, I think, by the sounds of it. So it was like straight away.

[00:24:21] It was good. And then they improved faster at tasks, than people who played the other game.

[00:24:27] David: Yes. That was my understanding. Although with the orientation learning tas anyway, they seem to eventually achieve the same performance, but the action video game players were initially better. they had an advantage to begin with Yeah. I really think there should be a disclaimer with this, that it’s not just action games.

[00:24:43] it’s relatively old action games. Also, Sophie, I know you’ve never played half-life but if you did play half-life, it should come with a trigger warning for you because it’s about pretending to be a scientist. And as someone who’s exited scientific academia can be kind of triggering. Um, although as soon as you like become a soldier, who’s saving the world,

[00:24:59] it’s pretty [00:25:00] good.

[00:25:00] Sophie: So it’s scary for me in two ways. One, I find first person shooter scary anyway, and two as an ex academic scientist. It’s like that second kind of scary.

[00:25:09] David: Absolutely. It’s, it’s scary in that. like the game, you kind of open a portal to another dimension and allow all these aliens to come in right before you, like, if it’s like, if you weren’t doing that, you would have been filling out a grant application and that would

[00:25:23] Sophie: I mean, it would be more interesting. but I feel like it wouldn’t be any less painful than doing a grant application. So, um, yeah, maybe academics would make great space aliens soldiers.

[00:25:32] David: that’s definitely the point being made by this game, I think. I think it strong case for that Anyway.

[00:25:38] Sophie: in reality. Anyway, go on Dave.

[00:25:40] David: So yeah, it seems like apparently action game players are better able to perform these kinds of tasks, at least initially. one thing we should say as a relatively low number of participants, think they ended up with only, 15. Once people have been excluded at the end.

[00:25:56] Sophie: Yeah,

[00:25:56] David: and the P numbers were pretty marginal. So I believe [00:26:00] some of the differences, but they were pretty spread. The data were pretty spread in terms of what they saw. So definitely worth another look, but definitely not like you shouldn’t take this as unequivocal evidences yet, but having said that they only asked people to play 45 hours of video games and to play no more than five hours a week. So. I mean, that’s pretty compared to what some people do bearing in mind that we now have a world with professional gamers in it. pretty minor in terms of what people might do.

[00:26:30] Sophie: I’m going to be honest. Like I reckon I easily play more than five hours a week games. it’s not that hard. Yeah.

[00:26:36] David: Absolutely. So the fact that they’ve actually seen something with what seems like a relatively mild intervention over weeks is pretty

[00:26:42] Sophie: Yeah. and the whole idea is then we could look at, you know, as we’ve said, the games, the action games that improved these cognitive sort of functions, They were a little bit violent, but then there’s discussions of like, maybe, you know, if you can work out exactly what about the game is beneficial.

[00:26:55] You could maybe create these kinds of learning games that don’t require [00:27:00] children to go around and kill aliens, like big, scary guns with things running at you from everywhere.

[00:27:05] David: The problem is that those kinds of games are objectively the most fun.

[00:27:08] Sophie: Well, and that, except for I go, so it’s like so stressful. Cause I was trying to think of, I was like, have I ever played a scary game? I think, I think, I can’t remember. I’ve talked about this before, at least on stem ology, but like I can get the scariest game I’ve ever tried to play was that original, like X-Files game, which isn’t even like a first person shooter.

[00:27:23] It’s like one of those interactive like move and point and click adventure games. And we’re like seven CD ROMs. And I got to the bit where like you’re in an abandoned building, there’s like a shape. It’s like scary. The spooky music, spooky lighting. There’s a shape-shifting alien who wants to kill you with a stiletto? so this is back when I turned the monitor off and I walked away and then I came back in 10 minutes later and I just like hard rebooted the computer with a button and I never went back and that was like 15 year old.

[00:27:49] So I couldn’t like, that was scary enough for me. I can do things with violence, but just when they’re at you. Whereas I prefer like, you know, a bit of an age of empires, age of mythology, I build up my army. I go and slaughter everyone around [00:28:00] me, especially if the AI taunts me, but like, it’s just not quite as at me.

[00:28:04] I mean, basically what I’ve said is I’m a good commander. I’m not a good soldier.

[00:28:08] David: I think that’s absolutely what you’re saying. And one of the questions I think raised here is do playing those kinds of simulation kind of games. Do they prime you to learn in other scenarios? So if you give people a managerial task,

[00:28:19] Sophie: Mm,

[00:28:20] David: Are they better primed to perform that task than someone who’s trained on a first person shooter?

[00:28:24] Sophie: Interesting. I’m a great multitasker, Dave, maybe it’s because I games where I got people. I had to collect resources and kill everyone at the same time. And now I’m great at that in real life too.

[00:28:35] David: Raising villages to the ground.

[00:28:37] Sophie: OnlyI get taunted by a computer though, because obviously I take it very personally, so yeah. Action games. Good for learning.


[00:28:43] Sophie: All right. I’ve got a good one here, Dave, you ready?

[00:28:55] David: Yeah.

[00:28:56] Sophie: Dave? Did you know that Pandas actually good at camouflaging despite being rubbish at [00:29:00] survival in almost every other way,

[00:29:02] David: What a segue. I did know that Sophie

[00:29:04] Sophie: So Dave, this is the first time that the camouflage capabilities of the giant Panda has actually been tested in a series of advanced image analysis techniques, using rare photos of pandas in their natural habitat. And it turns out from a predator’s perspective in their natural habitats, giant pandas are actually pretty good at camouflaging, but we’re just used to seeing them like sitting in greenfields in zoos, in which case we’re like, what animals at protecting themselves.

[00:29:31] David: Yeah. So the real question is here is why are pandas black and white? Because evolution tends to kind of optimize stuff. You know, it tends to kind of make sure these things are got right. So yeah, when you look at a Panda in a zoo enclosure, surrounded by bamboo on a few rocks, you’re

[00:29:47] Sophie: This is why you guys are dying. This is why all you’re raised in captivity, but it’s like turns out we just gave him them wrong zoo habitat.

[00:29:54] David: Yeah. Also have sex once in a while. but as it turns out from a predator’s perspective, [00:30:00] that’s a predator of the Panda, not the like predator from the film predator, because then obviously it would just glow.

[00:30:05] Sophie: All I can say is like, I bet he can catch a panda though.

[00:30:08] David: I bet he can catch a Panda. I bet he would catch a Panda, but he would only do it if the Panda was armed.

[00:30:12] Sophie: That’s true.

[00:30:13] David: I say he, it’s never really clear what sex the predator is.

[00:30:16] Sophie: I’d be in a minimal violent Dave. Sorry.

[00:30:20] David: anyway. but from a predators perspective, and here we’re talking about big cats and big dogs, apparently the Panda is rather well camouflaged. So at longer viewing distances, apparently they show high edge disruption.

[00:30:32] So basically the the animal is well broken up by the patchiness that it shows the black white patchiness. Whereas when you get close up to it. The colors that it displays the black and the white and the intermediate are actually quite good for matching up with waxy specular, lighting of foliage during summer and snow during the winter.

[00:30:54] Sophie: Yeah, exactly. So they’ve said that, the darker fur helps you blend in with tree trunks and dark forest. And then you’ve [00:31:00] got these either the specular lighting in the summer, and that’s like, sort of corresponds to the lighter bits on the panda and then also like snow in winter. And then you said there’s also, yeah, we’ve kind of got these brown patches, which are sort of an intermediate camouflage color.

[00:31:12] And I just think it was like, I really liked in the paper they even went here are pictures of a panda in a zoo. Here are pictures of pandas in their natural habitat. I was like already in natural habitat, harder to see, like just knocked. I was like, we can criticize pandas for a bunch of things, but. No more may we criticize patterns of being rubbish at camouflage when we have put them in these like weird habitats, but yeah, they did this.

[00:31:34] So it was like pretty interesting. So they got, as you said, we’ve got a few conclusions from it, but yeah, so they did this thing called quantitative color and pattern analysis and basically weighs up. Color and pattern essentially. And so it’s how the color is a place near each other, how well defined the edges are.

[00:31:50] And, but they also then did this to a bunch of other animals that were either thought to be good or bad at, camouflaging. And it turns out that Pandas like a [00:32:00] really steadily in the middles. I think in at least the group of animals they looked at the best at camouflaging was the fat sand rat and the worst at camouflaging with the green, and black dark frog.

[00:32:10] But the Panda was just like real casually, just like in the center of being good at bat. And yeah. And so what they did is apparently doing this kind of analysis helps you understand what the Panda would look like to like a predator as well, because they do weird things with like filters and stuff, but I didn’t quite understand, but yeah, it turns out that with this modeling technique, we’ve shown that they really good at that disruption from afar and then close up.

[00:32:32] They actually, their colors match quite well with th

[00:32:35] David: Yeah. So what they did, I didn’t understand all of the filtering either. Cause it got very intense very quickly, one of the things they looked at dichromatic images and then said, this is basically how, fillets and canines would see. And my superficial understanding of that is that because those animals are well adapted to see at nighttime, they don’t have very good color vision

[00:32:58] so already, like, [00:33:00] if you can say that an animal doesn’t really see very well in color, you can begin to see how being black and white doesn’t really matter so much if your predators can’t see color,

[00:33:09] Sophie: Yeah, exactly.

[00:33:10] David: and producing, as we’ve talked about on the podcast before producing color as an animal producing a particular color for you to be as energetically expensive.

[00:33:19] So if you have to do that, I can, well, very well be advantageous to not bother.

[00:33:23] Sophie: No, I’m glad you brought up energy, Dave. Cause I think, you know, one of the biggest criticism, I’m just about to defend pandas in a pretty aggressive way. Cause I felt bad, you know, like I don’t really care about a panda normally. And I was like, okay, we’re being very unfair in terms of this, you know, camouflage.

[00:33:35] So maybe there are other things that we’re being unfair about in terms of Panda. And one of the criticisms is this giant animal that has to eat an insane amount of bamboo every day. Turns out Dave that, so they’ve got evidence. At least 5,000 years ago, pandas started exclusively eating bamboo. Previous to that they ate sort of, you like insects and like other things. Cause fossil pandas can be found in China as far back as 8 [00:34:00] million years ago. And then the first direct ancestor of the Panda, and this is the giant Panda found in Spain and they dated it to 11.6 million years ago. So 5,000 in the scheme of 11.6 million years ago, it’s only very recently that they become useless in terms of what they have to consume. And you think maybe they just got tired of existing. Do you know what I mean? It’s like they did the Ark. They’ve been around for a long time. And now they’re just like, you know what? We don’t want to have to catch bugs anymore.

[00:34:28] We just want to like lays about an eight hour bamboo. And so I just said, you know, that was just another, um, another reason why we just took, which is too critical of pandas, Dave, know why I’m so defensive for them right now.

[00:34:38] David: It must be presumably it’s an interesting bit of evolutionary biology because what evolutionary biology tends to tell us. And again, I’m not an expert in this is that the generalists tend to win out.

[00:34:48] So animals that are nevertheless that can survive in a variety of different environmental conditions, tend to be the ones that succeed in the end because they’re adaptable.

[00:34:57] you go from being fairly general to [00:35:00] eating, as you say, insects and whatever else they eat. to exclusively eating one plant having 14 hours a day, consuming that plant to get the energy from it because they can’t actually digest it that well.

[00:35:12] Sophie: Yeah.

[00:35:12] David: what was the evolutionary that drove that change?

[00:35:16] That must be really interesting.

[00:35:18] Sophie: Yeah.

[00:35:18] no, I agree. And then I also ended up in, uh, you know, I like words. I was like, panda is an interesting word. Dave, did you know the word Panda was borrowed into English from French, but there’s no conclusive explanation aura of the origin of the French word Panda.

[00:35:33] But

[00:35:33] the closest candidate they reckon is the Nepali word Ponya possibly referring to the adapted wrist bone of the red Panda, which is native to Nepal.

[00:35:43] And the Western word originally applied this name to the red Panda. So we steal Ponya from the Nepalese for a red Panda. And then now, then apparently that moved to Panda, it’s called something to a different donor. Like apparently the etymology of this word is very unclear. And I also said that was.

[00:35:59] David: I think that’s [00:36:00] fascinating. We’ve been apparently talking about pandas for quite a long time, then.

[00:36:03] Sophie: Well, yeah, we haven’t yet. We still don’t know all these things about them, but, but we do know now very good at camouflaging. Be nice that.

[00:36:10] David: Be nice to the pandas. I really enjoyed this story. I really enjoyed it because for two reasons, one is, um, as you say, like they just kind of, a lot of their evidence is just showing us the pictures and saying, look,

[00:36:20] Sophie: Yeah. Enable even those pictures of that from beginning of the paper, I was like, I don’t need to read anymore. You’re right. Like in the natural habitat, they all will camouflage

[00:36:27] David: and they make an interesting point, which is that part of the issue of us thinking that pandas aren’t well camouflaged is that we just don’t see them in their natural environment that much, because so endangered.

[00:36:37] Sophie: Because they’re so endangered.

[00:36:38] David: just don’t often see them photographed in that way. So you just wouldn’t initially think, and that’s what makes this whole thing counter-intuitive even though they do their fancy modeling comes out the other way, which is joyous.

[00:36:49] Sophie: joyous. Good on your pandas. at STEMology we’re big supporters of you.

[00:36:52] David: Good job. Keep doing you.