[00:00:04] Speaker A: Id the Future, a podcast about evolution and intelligent design.
[00:00:12] Speaker B: Welcome to id of the future. I'm your host, Andrew McDermott. Well, today I'm sitting down with physicist Doctor Brian Miller to discuss the intelligent design of human vision. Doctor Miller is a senior fellow of Discovery Institute's center for Science and Culture, newly minted senior fellow, where he serves as research coordinator. There he holds a b's in physics with a minor in engineering from MIT and a PhD in physics from Duke University.
He helps to manage the CSCS ID 3.0 research program, and he speaks internationally on the topics of intelligent design and the impact of worldviews on society. Brian, welcome back to the show.
[00:00:51] Speaker C: It's a pleasure to be back.
[00:00:52] Speaker B: Well, you have a presentation you've been developing and presenting recently on the intelligent design of human vision, and I'd like to share with listeners some of your insights into this amazing system. There's so much to cover, of course, on vision, so we will come back to this in a second conversation. But today I thought we'd start by refreshing our memory about the evolutionary scenario for how the vertebrate eye came about. Let me also note here that even Charles Darwin himself acknowledged how absurd it would be to imagine human vision coming from an undirected process like natural selection in the origin of species. He said. To suppose that the eye, with all its inimitable contrivances for adjusting the focus to different distances, for admitting different amounts of light, and for the correction of spherical and chromatic aberration, could have been formed by natural selection seems, I freely confess, absurd in the highest degree. But that hasn't stopped proponents of darwinian evolution from coming up with an evolutionary path to get from light sensitive spots to crisp images produced by spherical eyes. In previous articles, Brian, you've noted that these evolutionary proposals score high on imagination and flare, but low on empirical evidence and thoughtful analysis. Can you tell us more?
[00:02:09] Speaker C: Yes, it's really interesting that you mentioned Charles Darwin, because he recognizes many that there's clear evidence of design in the eye, and it really does not seem possible that it could come about incrementally. But he and others have come up with an evolutionary scenario. The basic idea is they say it started with a simple eye spot, maybe just a photoreceptor connected to a processing system. Then they imagined that perhaps more photoreceptors formed, and then it formed inside of a cavity so it could detect shadows, that maybe there was a pinhole, so that could help to focus the image in a crude way. Then perhaps a transparent medium took place that could help focus the light, then perhaps a lens and then a fully functional eye. So this way, they imagined this step by step process for it to evolve. The challenge is that this scenario completely collapses when you look at the biological details. So, for instance, a photoreceptor is a very, very complex of molecular machinery in a cell. And what happens? You have to have multiple proteins in multiple systems working together for a photoreceptor to properly detect light and then transmit it to some system that can process the image or process the data. And, of course, then even if you had, let's say, a simple photoreceptor in a cell, the photoreceptor would still have to be integrated to the cell to know what to do with the detection of photons. And the other stages are no easier. So, for instance, if you want a lens, that's a very, very complex system, also, because you have to have the manufacturing in place to create the lens, which we'll talk about more. And that, again, would require just thousands and thousands of new, highly coordinated and specific mutations. And, of course, if you had half of that manufacturing in place, the lens wouldn't be transparent, so it actually be debilitating to the organism. So the early mutations would be quickly lost. And then going to a fully functional, camera like eye that we have requires more steps that are irreducibly complex. So, again, the scenario sounds great if you talk about it in terms of a story, but it, like most evolutionary scenarios, completely collapses when you look at the actual details.
[00:04:16] Speaker B: Okay, that makes sense. Yeah. And they call it an aimless, creative process, but it's one thing to come up with this, you know, neat little story. It's another thing to actually have it make sense in the biological world. Now, Brian, you're part of a project called the Engineering Research group. Can you tell us about that briefly? Yes.
[00:04:34] Speaker C: So I am working with biologists and engineers, and the group is headed by Steve Laughman, who's a systems engineer. And we're having this conversation take place between the two different disciplines in order to understand how can engineering principles, patterns be used to gain a deeper understanding of biology? And what we're also doing is we're conducting research on specific biological systems, applying specific engineering principles or patterns. So, for instance, we're looking at how communication strategies used in human communication can be used to help us understand communication in biological systems. This will lead, and has led and will continue to lead into peer reviewed publications. And ultimately, it's going to lead to the fully developed theory of biological design that we can help give a manual to future researchers to better do research in biology, because then they'll start, they'll know in advance what are the engineering principles that are at play and what are common patterns that they'll see over and over again.
[00:05:34] Speaker B: And, you know, when people look back at the history of biology, I think they will really see this as a turning point. You know, starting to look at biological systems through an engineering, you know, standpoint, it does seem very helpful when you're looking at goals and constraints and dependencies. You know, it's just like this cascade of problems you have to solve. And if you start there, it helps you reverse engineer systems and help you unpack what's really going on.
[00:06:01] Speaker C: You're absolutely right. And what is interesting to note is it's not just people within the intelligent design community. If you go to systems biology, for instance, more and more you're seeing engineers working with biologists, because people realize that the engineering insights helps you to gain a much deeper understanding of these biological systems. And what's also amazing is you're seeing the assumptions that biologists originally had based on evolutionary theory being increasingly abandoned and being replaced with design based theories. So, for instance, evolutionary theory predicts that biology shouldn't have an overarching design logic. It's very reductionistic. It looks primarily at individual reactions, individuals, cells. And then they assume that just serendipitously, they came together haphazardly to produce something a bit more complex. But they've historically assumed that it should often be poorly designed, suboptimal, and it should have very little similarities to human engineering. But what's turned out is the truth is the exact opposite. The more engineers work with biologists, the more they realize that you really see this top down design, where it looks like a mind planned everything in advance, so that every component works seamlessly and optimally with every other component. And they also see the same engineering principles that we used employed in life, like negative feedback, control loops, integrative feedback loops, four bar linkages. The main difference is that biology does it better than what we do. And also what's particularly helpful is the role of systems engineers, because systems engineers realize that when you have a system that's composed of multiple different systems working together, there are certain things you have to worry about. Things like interfaces, communication protocols, risk management and maintenance. And then when our engineers like Steve Laughman and Howard Glixman wrote their book, your design body, they see these very same principles at play. So when you look at something like hearing, what you see is you have risk management. So you have these bones that act like a lever arm to transmit sound from the air to the system that transmits it to the brain. And a big challenge is that if the sound is too loud, it could damage the machinery of hearing. So you have these very specialized muscles that prevent the bones from moving if the sound becomes too large. And that's exactly what risk management is, is the designer anticipated this danger and planned those muscles in advance to mitigate that danger. So when I could speak for hours on principles of system engineering. But what's amazing is when systems engineers look at life, they can literally think the thoughts of the designer that the designer had, because they can see how the designer was applying the same principles they use in the design of life.
[00:08:47] Speaker B: Wow. And I'm glad you mentioned that book, your design body. Laughman and Glixman, they go into detail about the different systems involved in the human eye. They dedicate a whole chapter to it. I like what they call it this a chemically fine tuned biomechanical, electro optical signal processing vision interpretation system, also known as our eyes. So they had some really cool insights. Anything else you want to share about what they pulled out in their chapter?
[00:09:15] Speaker C: Yeah. And this is a beautiful example of how engineering helps you to understand life, because they asked the question as a systems engineer, Steve Laughman, working with a medical doctor, Howard Glixman, how can you bring these insights together to understand systems such as vision? And then what they found is that the same logic that we use in, let's say, digital cameras can be used as a template to understand the logic of vision, because you see the same basic components. You have a lens, which focuses light. You have an aperture that changes size to allow different amounts of light in. You have the lens, which changes to focus the image. You've got photoreceptors that digitizes the image. And then you've got an image processing system, just like we see in human engineering. But also what's really amazing is they talked about how engineering principles are at play. So, for instance, Steve likes to talk about what's called the push pull principle. And that's very often you both have to have a system that does one thing and also that does the opposite. And a great example in the book is the issue of the photoreceptor, because a photoreceptor is a cell. And in that cell, you have specialized disks. And in those discs, you have rhodopsin proteins. In the rhodopsin proteins, you have this molecule called retinol. And the way the photoreceptor works is a photon will interact with the retinal molecule, causing it to change its shape. And that causes a very complex cascade of chemical signals that emerge, which then will eventually lead to a neural impulse to the brain. But the problem is, when a photon changes the shape of the retinol, you need a completely different system to reset it, to put it back in the original state so it can detect another photon. So, again, these are two completely different systems. One acts in the response of a photon. That's the push, but you also have to reset it, which is the pull. And also what happens is you have to be concerned about a lot of different things, like fuel. And this is a point that when a systems engineer designs a system, they have to make sure it has the energy it needs. And when you look at the visual system, you see an incredibly complicated and brilliant system where photoreceptors touch what's called the retinal pigment epithelium, which then supplies the right nutrients, the right raw materials at the right time to allow the photoreceptor to keep in operation. So Laughman's engineering expertise, combined with Glixman's biological expertise allow them to gain insights on the visual system that they would not have had without bringing those two disciplines together.
[00:11:52] Speaker B: Right. Yeah. Well, I wonder if here, with our remaining time, you can walk us through some of the processes that are going on as we see something and how the brain processes that and also how the eye processes that.
You know, how do we go from light gathering to a high definition image? I know you've touched on some of the systems, but just sort of walk us through it. You know, when our eyes lay on something in the outside world around us, what is going on?
[00:12:25] Speaker C: You know, certainly I'll go through several details, so people who are listening should prepare themselves, because I'm going to go through a pretty extensive list of what's happening, but I think you'll find it extremely fascinating. So, first of all, what happens is light goes through the cornea, which is an outer layer that protects the eye. And what happens that cornea has to be strong enough to protect the eye, but also has to be transparent. So it has very specialized cells that are transparent. And then when the light goes through the cornea, it has to go through the iris. And the iris is what allows there to be a pupil. So there's a little hole that allows the light through. And the iris has very, very specialized muscles. So one set of muscles will make the pupil larger and another set of muscles will make the pupil smaller. So obviously, it gets larger if it's dimmer light, and it's smaller if there's more light. Also, what happens is, when the light goes through the pupil, it eventually will go to the retina. And the retina is the array of photodetectors. It's composed of about 100 million rods, which detect light for night vision. And about 6 million cones that can detect color. You have three different cones, and the different types of cones will detect different frequencies of light, which is why we have color vision. Now. Also in the center of the retina, you have the fovea, which is where you have the highest concentration of cones. That's where the sharpest image is produced. And that's why the network there is considerably different from the edge. Because the strategy in the middle is to process high resolution images, while the strategy around the edges is more just to see things like motion and potential dangers. Also, what happens is you have a very complex neural network where the photoreceptors will send signals to a highly complex array of neural cells, which are different, that do very complicated pre processing of the image. And then they eventually will send the signals after being processed into multiple neural wires that go directly to the brain through what's called the optic nerve. And then in the brain, you have a very complex neural network that then will take the digitized image. It was digitized because of the photoreceptors. And then it'll reconstruct the image, figure out what it is, and then send the image to higher centers of the brain, which then determine what to do with it. Now, in addition to that, what happens is you've got highly specialized muscles around the eye, which allowed the eye to move in every direction. In fact, one of the most complex muscles has to use what's called the trochlea, because the muscle has to pull in a very unusual direction. And there's really nothing to attach a muscle tool to in the eye socket. So there's a little piece of cartilage that acts like a pulley. So the tendon of a muscle goes through that little pulley. And that allows the muscle to pull at a very specific angle that's necessary. And also what you have is a very complex feedback system where your brain connects to what are called your semicircular canals, which help the body to determine motion. And that's sent back into your muscle system so that the eye always adjusts so it focuses on the same image. And if it didn't do that, everything would be blurry, but this extremely complex feedback loop allows you to see a sharp image. So that's really just touching on the surface, but that gives you a small picture of the incredibly complex system of systems that amount to vision.
[00:15:46] Speaker B: Sure. And, you know, this is all happening in picoseconds, and we'll unpack that next time. You know, it's just such a quick system. It's happening in virtual real time so that there's no lag between what you see and how you're processing it in your brain. When we come back next time, we're going to talk about whether the system is irreducible, complex. We'll look at some of the arguments where people are saying, ah, the eye is actually badly designed, you know, and some of the refutation there that puts those arguments in their place. So still lots to come and lots to look at when it comes to engineering principles as well. Well, any closing thoughts before we finish up here, Brian?
[00:16:28] Speaker C: Well, one thing that's really beautiful about the eye is it envision is it brings together virtually every engineering principle you can possibly imagine. And it even connects with the fine tuning of the laws of nature, because vision would not work if the wavelength of light were too large or too small. So it just so happens that our planet is designed to allow the right light through, namely visible light and heat. The wavelength of light is just perfect for high resolution vision, and then our eyes are perfectly designed to process that information. So what you see with vision is a convergence of virtually every type of fine tuning and design that we see throughout nature.
[00:17:09] Speaker B: Right. And Darwin certainly was picking up on this and probably why he thought, yeah, this is a bit of an absurd notion to put this down to natural processes. Well, he may be more right than he thought back then anyway. You know, it's. It's great to talk to you, Brian, but there's just so much, and I don't want to throw too much on our listeners all at once. So, as we said, big topic, but we will come back to this. Let's close this episode now, but look for a part two where we'll add more to this topic. By the way, in the show notes for this episode, I'll include links to some of Brian's writing on this issue on human vision, and also where you can find a whole chapter on it in your design body. You can find the show notes with more resources for every episode, as well as our massive archive of previous
[email protected]. Dot for the show for id the future, I'm Andrew McDermott with Brian Miller. Thanks for listening.
[00:18:09] Speaker A: Visit
[email protected] and intelligentdesign.org dot this program is Copyright Discovery Institute and recorded by center for Science and Culture.
