• Skip to primary navigation
  • Skip to main content
  • Skip to footer

The Vertebrate Retina, Photoreceptors, and Color Vision

Transcript of Part 2: Photoreceptors and Image Processing II

00:00:09.14		Let's look at the arrangement of photoreceptors within the retina and explore the
00:00:14.20		significance of that arrangement for the functioning of the system.
00:00:18.09		Here's an en face view of a primate retina.
00:00:22.03		We see each of these white spots is a single rod photoreceptor cell
00:00:26.25		These are the closely packed ones, and the clear spaces with a single white spot
00:00:32.27		within it are the cone photoreceptor cells.
00:00:35.06		You can see that it's essentially a sea of rods with cones interspersed like little pebbles.
00:00:40.06		Now this is what the peripheral retina looks like - the region that's off to the side.
00:00:45.22		The retina overall is not a homogeneous structure
00:00:50.16		with respect to photoreceptor topography.
00:00:53.23		In the primate retina, including the human retina,
00:00:56.09		if we were to map across the width of the retina from one side to the other
00:01:03.00		the density of the different photoreceptor cell types,
00:01:06.16		you would see a very distinctive distribution.
00:01:10.00		So, for example, the rod photoreceptors are very abundant (that's these dots here)
00:01:16.12		are very abundant in the peripheral retina.
00:01:18.12		The cone photoreceptors down at the bottom are relatively rare in the peripheral retina,
00:01:23.20		as we saw on the previous slide.
00:01:25.24		But, towards the central retina, and especially at the
00:01:29.20		very center of the retina, called the fovea,
00:01:31.29		the cone density rises to a very high peak,
00:01:37.09		and the rod density correspondingly goes down
00:01:40.20		to essentially zero.
00:01:42.10		That's a very striking arrangement. Why is that?
00:01:46.21		It turns out that the retina of primates like ourselves
00:01:53.06		is really a two-stage structure, in the sense that
00:01:57.03		we have a high acuity zone of the retina, this very central zone,
00:02:02.25		which encompasses just a tiny fraction of the retinal surface area... less than 1%.
00:02:08.06		And the rest of the retina really exists simply to alert the central retina
00:02:14.09		that something interesting has happened.
00:02:15.22		Whenever we look at something that we're interested in,
00:02:19.20		what we do is we turn our eyes towards that object.
00:02:22.22		And, we do that because we want the object to be imaged
00:02:26.08		on the central region of the retina.
00:02:28.18		Let's look at one simple consequence of this specialization within the retina.
00:02:33.29		And that is the eye movements that allow different regions of the world in which
00:02:40.21		we're interested in to be sequentially imaged on the central retina.
00:02:43.26		And there's no better example than reading text.
00:02:46.00		So, if you're reading the page of, for example, a novel,
00:02:49.26		and you look at the eye movements that correspond to that process,
00:02:55.29		you'll see that as we look here on the horizontal axis at eye position
00:03:00.13		and on the vertical axis at time,
00:03:02.16		if we look at the way the eye moves, we'll see that it's a series of stops and starts
00:03:08.12		where the eye is initially focused on one location,
00:03:12.18		and then there's a rapid movement to a new location,
00:03:15.01		a brief pause at that place and then a rapid movement to yet another location
00:03:19.06		and so on as we read from word to word across the page.
00:03:23.20		And, of course, when we get to the end of the line, then we reset all the way back
00:03:27.18		to the beginning of the next line.
00:03:31.03		Why are we doing that? Why can't we just stare straight ahead and read?
00:03:34.29		And the answer is that, when you're reading,
00:03:37.01		you need to see the words... the letters of interest
00:03:41.02		at high acuity. And the only way to do that
00:03:43.14		is to have them fall on the very central retina.
00:03:45.24		So, your eye must continually be moving to allow
00:03:49.20		the object to interest to fall in that most central region, the fovea.
00:03:54.22		Really, the rest of the retina exists simply to get our attention
00:04:00.09		and tell us about an object that we might want to image on the fovea.
00:04:04.21		For example, if you see a fly in your peripheral vision
00:04:08.01		buzzing around the room, that will grab your attention,
00:04:10.20		and you'll perhaps turn your eye towards the fly,
00:04:13.14		but you won't really get a good look at it until you turn your eye towards it.
00:04:17.20		Now let's look at eye movements in a somewhat more natural context.
00:04:22.12		And this tells us also something not only about the eye, but about the brain...
00:04:25.23		that is, about which regions of a given scene
00:04:28.16		(perhaps a more natural scene than reading of text)
00:04:31.01		grabs our attention.
00:04:32.13		And so here, for example, we're looking at eye movements
00:04:36.00		that correspond to looking at faces.
00:04:40.16		So, the top two photographs are the images that were observed,
00:04:46.04		and the bottom data points indicate where the eyes were pointed
00:04:52.16		during viewing of that image.
00:04:54.00		So, when looking at a face... for example, this young lady's face.
00:04:58.07		You can see that the eyes of the observer tend to look at her eyes,
00:05:04.11		and they also look at her mouth, they look at her nose a little bit,
00:05:06.26		maybe around the face.
00:05:07.28		If we look at a face in profile, again, you can see from where the eyes were pointing,
00:05:12.14		that they tend to look at the edges of the image--
00:05:18.02		the nose, the eyes, the chin, the mouth, and so on,
00:05:20.24		and largely neglect the central regions --
00:05:24.04		the cheeks, the neck, and so on that are perhaps of lower interest
00:05:28.11		and where less is going on.
00:05:30.09		This is a highly selective viewing strategy,
00:05:34.03		and so it really means that we're sampling the world in a way that is
00:05:38.26		far from random and far from complete.
00:05:42.00		We're sampling just subsets of any given scene.
00:05:45.17		Now, the fact that the central retina is the only place
00:05:52.08		where the image is seen with high acuity
00:05:55.21		means that, if you're going to examine, say, a non-moving object,
00:06:00.18		and you're doing it in the context of a head and a body that are moving,
00:06:05.17		perhaps, because you're walking or running or just trying to stay still,
00:06:09.11		but you're not entirely still,
00:06:10.18		there must be a mechanism that allows the eye to stabilize itself
00:06:15.11		in the context of this moving holder that is the rest of the body.
00:06:19.21		And, in fact, that mechanism exists, and it's a very powerful one.
00:06:23.22		Here's an example just showing how that sort of movement compensates
00:06:29.05		for head and body movements during walking.
00:06:31.14		Again, on the horizontal axis, we have the angular degree,
00:06:36.26		and we have plotted here, in red, the head movements associated with walking.
00:06:42.13		Of course, there's some swaying back and forth with each step.
00:06:45.00		And, as time goes on, moving up this chart,
00:06:48.28		we see that the head movements in red
00:06:53.05		and the eye movements within the head in green
00:06:56.01		are nearly mirror images of each other.
00:07:00.16		That is, the eye is moving within the socket to almost perfectly compensate
00:07:07.10		for the back and forth movement of the head.
00:07:09.26		It's completely unconscious - we do it without thinking about it.
00:07:12.29		But, the result is that the eye is pointing nearly perfectly straight ahead
00:07:18.29		during the entire walk
00:07:21.23		by virtue of this moment to moment sense of where it is deflected.
00:07:27.05		If it's deflected a little to the left, it will move a little to the right.
00:07:29.24		If it's deflected a little to the right, it will move to the left,
00:07:32.06		and this, of course, keeps the world looking relatively still
00:07:36.05		despite the fact that we are moving around.
00:07:39.27		Now let me say one final thing about the arrangement of photoreceptors
00:07:45.15		within the retina... this non-random arrangement.
00:07:49.02		And that now relates to the distribution of the different cone photoreceptors.
00:07:53.26		We haven't talked about the different kinds of cones.
00:07:56.07		We're going to talk in detail about that in the second and third lectures.
00:07:59.16		But, in the human retina, let's just for the moment note
00:08:03.19		that there are three different cone classes.
00:08:05.29		One most sensitive to longer wavelengths,
00:08:08.23		one most sensitive to medium wavelengths of light,
00:08:10.24		and one most sensitive to shorter wavelengths of light.
00:08:12.24		The medium and longer wave receptors (and I'll call these M and L),
00:08:17.06		are, in fact, rather similar to one another, with largely overlapping absorbances,
00:08:23.08		whereas the shorter wave (which I'll call S),
00:08:26.08		is really quite different. It absorbs at wavelengths quite down
00:08:30.18		towards the short end of the visible spectrum.
00:08:33.13		And, as a consequence, any given image which contains
00:08:38.16		wavelengths throughout the visible spectrum,
00:08:41.12		and which therefore would be exciting all the different photoreceptor cells,
00:08:45.26		will, when it arrives at the retina, not be perfectly focused.
00:08:50.13		Let's think about why that is.
00:08:51.24		It's because any lens, including our own lens,
00:08:55.17		has some degree of what's called chromatic aberration.
00:08:58.16		That is, the image being focused for a given wavelength,
00:09:03.05		is not going to be focused perfectly for some other wavelength.
00:09:07.24		And so, for example, as shown here, in this upper image,
00:09:12.25		if this word red, in red letters, is focused perfectly on the retina,
00:09:18.02		so that it's sharply focused and a point in the outer world
00:09:21.19		comes to a point on the retina,
00:09:23.01		then blue light, coming from the same region of space,
00:09:28.07		would be focused actually ahead of the retina,
00:09:30.18		so that the word blue here, as shown on the right,
00:09:33.09		would be a bit blurry - a bit out of focus.
00:09:35.23		How does the retina deal with this problem?
00:09:38.11		It turns out that in the very central fovea, the S cones have been excluded.
00:09:43.19		Only M and L cones populate that region.
00:09:47.02		That's the region with the very highest acuity.
00:09:50.00		And, what the retina has done... what the eye has done,
00:09:52.24		is simply eliminate the short wave end of the spectrum
00:09:59.00		for that most high acuity zone of the retina.
00:10:03.12		So, by taking a narrow cut of wavelengths,
00:10:07.05		we're effectively getting a sharper image,
00:10:09.01		of course at the price of having a less colorful image.
00:10:12.25		Now let's talk about how the image is processed in the retina.
00:10:19.07		We mentioned at the very beginning of the lecture,
00:10:20.27		that the retina is really different from a film in a camera,
00:10:24.16		in the sense that it is not just detecting the image,
00:10:29.03		and sending that detected image to the brain,
00:10:32.10		but it's processing it and extracting from it those aspects
00:10:35.28		that are of greatest interest to the organism.
00:10:37.24		Let's look at one of those feature extractors here.
00:10:41.19		This can be illustrated with this classic illusion, the so-called Hering illusion.
00:10:45.10		I hope you can see that at the intersections, where these white streets come together,
00:10:50.21		there are illusory black dots.
00:10:53.21		Actually, not on the one that you might focus on,
00:10:56.10		but on the ones that strike your peripheral retina.
00:10:59.01		I'll leave it as a homework exercise to figure out why that should be so.
00:11:03.22		But, if you can't see these as clearly as I can,
00:11:07.29		I invite you to produce an illusion of just this sort on a home computer.
00:11:12.04		You can do it either with black squares and white streets in between
00:11:15.20		or the reverse, if you do it with white squares and black streets,
00:11:19.02		you'll see little white dots at the intersections of the streets.
00:11:22.04		And this illusion has been known for well over a century,
00:11:26.08		and it turns out to be fully explained by a peculiar type of spatial organization
00:11:34.06		of the receptive fields of ganglion cells.
00:11:37.11		Now, let me define receptive field.
00:11:38.27		That refers to that zone of primary photoreceptor input
00:11:45.12		which influences the output of the retinal ganglion cells.
00:11:48.07		The classic retinal ganglion cell receptive field is as shown here.
00:11:53.07		There's a little zone of retina (So, we're looking now en face at the retina...)
00:11:56.22		There's a little zone of retina where illumination excites the ganglion cell
00:12:01.12		and would increase, for instance, the firing of action potentials.
00:12:04.15		And then there is an annulus around it (a donut)
00:12:07.29		where light activation of photoreceptors actually inhibits the ganglion cell.
00:12:15.08		This is essentially a contrast detector.
00:12:18.18		The ganglion cell is looking for spatial differences in illumination in the retina,
00:12:23.26		and let's see how this explains that illusion.
00:12:28.17		Here I've shown, superimposed on a little region of that Hering illusion
00:12:34.28		one of the receptive fields of a ganglion cell,
00:12:39.00		where, in the center, the ganglion cell is looking at the light
00:12:44.15		that falls at one of these intersections
00:12:46.05		between white streets, and I think we can see that for this particular cell,
00:12:50.17		not only is the center being illuminated, which would tend to excite the cell,
00:12:53.16		but a rather substantial chunk of the surround on the right and left sides
00:12:59.02		above and below is also being illuminated, so the result would be
00:13:02.25		that this cell would be substantially suppressed
00:13:06.00		by that illumination of the inhibitory surround.
00:13:09.20		What happens if we look at a ganglion cell that’s little off to the right?
00:13:14.15		Its center is also fully illuminated, but now I think you can appreciate
00:13:18.16		a bit less of the surround is illuminated than was the case for the first cell.
00:13:23.18		But now, if we look at a cell that is even further off to the right,
00:13:28.15		where we see the center is illuminated,
00:13:31.01		but now the surround is only minimally illuminated...
00:13:34.07		just this horizontal region here is receiving light,
00:13:38.10		I think we can appreciate that this cell would be substantially more active,
00:13:41.18		because the central region being fully illuminated is countered by
00:13:46.14		only a modest amount of inhibitory illumination just from the left and right sides.
00:13:51.04		So, the brain would perceive, or the retina would perceive,
00:13:56.28		this zone of white street as brighter, relative to the zone over here,
00:14:04.08		because the ganglion cells, seeing the street that's off to the right
00:14:08.09		or off to the left side,
00:14:10.10		would have greater overall activity than would the ganglion cell
00:14:15.11		that was centered over this intersection here.
00:14:20.12		And that is the source of that illusion of blackness...
00:14:24.02		that sort of fuzzy blackness at the intersections between the streets.
00:14:27.16		I should just say that this idea of center-surround ganglion cells was really predicted
00:14:34.09		by this illusion many years before those cells
00:14:36.28		were actually identified electrophysiologically.
00:14:39.21		Now this leads us to a larger question related to image processing in the visual system,
00:14:52.06		and here we're going to move a little bit beyond the retina, into the brain.
00:14:55.00		But, if what the retina and also the brain are looking for,
00:15:00.01		among other things, are differences
00:15:02.11		between one region of the image and another region,
00:15:06.07		as illustrated by those center-surround ganglion cells,
00:15:09.01		then it stands to reason that schematics of the sort that bring out those differences
00:15:17.22		might resonate in some way - might have a special meaning
00:15:21.02		in terms of their information content for our visual system.
00:15:25.03		And that is exactly the case.
00:15:26.12		So, for example, we see here on the left, a photograph of a hand
00:15:30.29		with fingers about to snap, and on the right,
00:15:34.02		we see a schematic here, just a line drawing of the same kind of hand
00:15:40.08		with the fingers about to do like this (snap).
00:15:43.11		And, we immediately recognize in this simple line drawing what's going on.
00:15:48.06		But that's actually kind of surprising that we should recognize
00:15:52.21		this image of a hand so readily,
00:15:55.19		after all, on a pixel by pixel basis, this little schematic
00:15:59.14		bears very little resemblance to the photograph.
00:16:02.03		The schematic shows black lines that outline each object -
00:16:08.04		each finger and the palm of the hand,
00:16:09.27		but the real world isn't like that at all.
00:16:12.09		In fact, the real world is made of shapes with shadows
00:16:15.22		and complex changes in illumination--
00:16:18.13		as shown by that photograph--very different on a pixel by pixel basis
00:16:22.04		from this schematic.
00:16:23.17		Why is it that the schematic is so immediately recognizable?
00:16:27.28		I think almost certainly it's because the schematic is essentially the processed version
00:16:33.17		that our retina and our brain has created from the real image.
00:16:37.29		We've fed the visual system by giving it this schematic
00:16:41.20		a version which is already part way up the visual chain of command.
00:16:46.03		Let's see another example of this.
00:16:48.04		Here, in this self-portrait by Pablo Picasso,
00:16:51.27		we see the remarkable effectiveness of simple portrait sketches.
00:16:56.21		Why is this so immediately recognizable as a face?
00:16:59.15		And, in fact, it's recognizable as Picasso's face.
00:17:01.21		Again, even though, on a pixel-by-pixel basis, it's very different
00:17:06.01		from a real human face, it has abstracted the information
00:17:10.16		in just the way that our brain abstracts it as well.
00:17:13.10		Now there are other things that the visual system is interested in,
00:17:17.17		besides changes in intensity over space.
00:17:22.08		It's also interested in changes over time and the combination of time and space.
00:17:28.11		Let's look at an example of time changes.
00:17:31.16		Here are the responses of a retinal ganglion cell in the rabbit retina,
00:17:37.06		to an object moving in various different directions.
00:17:40.17		The directions of motion across the receptive field
00:17:42.28		are indicated by the arrows in the center here,
00:17:45.23		and the responses that go with each of those directions of motion
00:17:49.14		are indicated by the cluster of action potentials
00:17:54.04		recorded from that cell that are indicated adjacent to the corresponding arrow.
00:17:59.07		Now, I think you can appreciate that motion in the upward direction
00:18:05.00		and directions that are close to it are especially effective,
00:18:08.04		but motion in the downward direction gives essentially no response.
00:18:12.13		So, this is a cell that is interested in movement,
00:18:16.05		and in particular, in movement in a given direction.
00:18:19.14		Having cells of this kind makes perfect sense in a visual system.
00:18:24.29		Because of course, things that are moving in the world are of interest to the organism.
00:18:29.02		If you're a rabbit, you're interested in things like hawks that might be moving,
00:18:33.14		and so having cells that respond selectively to motion
00:18:37.21		as opposed to simply the world that is unchanging,
00:18:41.11		has obvious selective value.
00:18:43.04		And I think we can all appreciate that in our own, everyday experience,
00:18:46.24		For example, if a fly or some other object is moving in the peripheral visual field,
00:18:52.06		that immediately catches our attention and it does so in a way
00:18:56.21		that non-moving objects do not.
00:18:58.17		Now this brings us to a potential paradox.
00:19:02.07		As we saw a few minutes ago, the head and body are generally moving
00:19:07.15		around in space as we walk or go around our daily business,
00:19:12.09		and even though there are compensatory eye movements
00:19:14.13		which work quite well to keep the eye largely oriented in space,
00:19:20.00		despite those movements, the compensatory eye movements are not
00:19:23.19		perfect, and so the eye is constantly wiggling around back and forth in space,
00:19:28.19		and therefore the image which falls upon the retina
00:19:31.20		is constantly wiggling back and forth in the reverse direction.
00:19:36.06		So, if our retinas were composed of direction-selective cells
00:19:43.10		of the sort we've seen here,
00:19:44.21		and these cells are the ones that are supposed to tell us
00:19:48.06		what's moving out there in the world,
00:19:49.28		we have a problem that these cells would be constantly stimulated simply by
00:19:54.23		the motion that's referable to head, body, and eye motion, that is self motion.
00:20:00.18		What's the solution to that?
00:20:02.16		It turns out that the solution is a yet cleverer set of cells.
00:20:07.12		Again, motion-selective cells, but they respond only
00:20:11.28		to local motion and not to global motion.
00:20:14.26		And so, for example, if we deliver to one of these cells, within its receptive field,
00:20:22.11		a grating of alternating black and white stripes which are moving from left to right,
00:20:28.09		so they are just flowing across the visual field,
00:20:32.20		the cell gives a very brisk response if that's all that is being presented to the retina.
00:20:39.14		That is, the surrounding region of retina is being presented simply
00:20:43.03		with a non-moving grey background.
00:20:45.16		The cell loves this stimulus and responds vigorously.
00:20:49.09		But what if we simply unmasked the rest of the retina
00:20:54.29		and show it the same set of bars moving from left to right.
00:20:58.20		Now, the receptive field of the cell--this central circular region--
00:21:04.10		hasn't changed at all in terms of the stimulus being delivered to it.
00:21:07.26		Completely unchanged.
00:21:10.14		The only thing that's different is that now the surround is doing exactly the same thing.
00:21:14.12		What happens? The cell immediately falls silent.
00:21:18.03		That is, this is a cell which wants to see a little region of the world moving
00:21:26.07		against a background that is not moving.
00:21:28.25		And if everything moves at the same time, that's not an interesting stimulus.
00:21:33.07		This is perfect because this is just the kind of cell that would tell a rabbit,
00:21:38.13		for example, if a hawk were flying across the sky,
00:21:42.10		but not if the rabbit had turned its eye a little bit,
00:21:46.01		and all the trees and bushes and everything else whose images
00:21:50.06		are impinging on the retina
00:21:51.08		had moved across the retinal surface.
00:21:54.11		And, of course, that's just what the rabbit wants to know.
00:21:56.21		There's a very beautiful illusion -- the so-called Ouchi illusion,
00:22:00.26		named after the Japanese artist who initially derived this
00:22:06.02		which nicely illustrates this kind of motion-sensitive ganglion cells effect
00:22:11.21		on our visual system.
00:22:12.25		In the Ouchi illusion, and I've shown them here at two different magnifications,
00:22:18.01		because depending on how you're viewing this lecture,
00:22:20.21		one or the other might be more effective.
00:22:22.27		In the Ouchi illusion, there are a series of horizontal black and white bricks
00:22:28.21		which encompass the surrounding zone and are most of the image,
00:22:33.10		and then in the circular center is essentially the same thing,
00:22:37.01		except it's been flipped vertically.
00:22:38.27		And I think you can appreciate that the central region seems to float around
00:22:42.28		and bounce back and forth in a way somewhat different from the surround,
00:22:46.19		as if it's almost autonomous from the surround.
00:22:49.08		The origin of that motion is the spontaneous eye movements which we have all the time.
00:22:56.17		Our eyes are constantly jiggling around in their sockets.
00:22:59.03		And, because of that jiggling, the direction-selective ganglion cells
00:23:06.01		are differentially activated by the center versus the surround of this illusion.
00:23:12.06		So, for example, if the eye were to make a horizontal excursion,
00:23:18.16		these horizontal brick stimuli would be less provocative to those cells on average.
00:23:24.12		Why? Because a small horizontal movement leaves much of the visual field
00:23:29.27		with the same stimulus - the same little black zone that was stimulated
00:23:33.16		when the eye was at one location will now, if the eye moves a tiny bit horizontally,
00:23:38.10		still be stimulating in a black region of the new location.
00:23:44.08		But the vertically arranged bricks will have a relatively larger effect
00:23:48.29		for a given horizontal eye movement
00:23:50.18		because that little movement will be more likely to shift the gaze
00:23:54.27		from, say a black zone to a white zone or a white zone to a black zone.
00:23:58.10		Now, I think you can appreciate that the reverse is true for vertical eye movements.
00:24:01.27		So, that causes the visual system to make this segregation of the central circular zone
00:24:10.18		from the surround and it gives it this sort of bouncing back and forth...
00:24:14.07		this sort of autonomy that grabs our attention and makes this illusion so effective.
00:24:19.01		To continue with our theme of looking at how the visual system
00:24:24.07		extracts information from the scene,
00:24:27.06		we can ask whether having two eyes gives us some special advantage
00:24:31.00		over an organism that might have only one.
00:24:34.23		And the answer is yes, having two eyes does confer a special advantage.
00:24:37.27		And, in particular, it allows us to determine the depth of an object.
00:24:42.11		That is, its distance away from ourselves,
00:24:45.07		using the mechanism called stereoscopic depth perception.
00:24:49.05		Let's see how that works.
00:24:50.24		So, if we have two points in the visual world,
00:24:55.10		one here on the right, and one here on the left,
00:24:58.12		I think you can see by simple geometric optics
00:25:00.25		would be imaged on the retinas of the two eyes
00:25:04.04		shown down at the bottom.
00:25:06.11		And, of course, the left point is imaged to the right on the eye,
00:25:10.26		because the image is reversed when it's imaged on the retina,
00:25:14.09		and the right point is imaged on the left side.
00:25:16.12		And those two images fall on what would be called corresponding points.
00:25:21.07		There are corresponding points for the right spot,
00:25:24.19		and there are corresponding points for the left spot.
00:25:26.23		So, there's nothing surprising here.
00:25:29.08		But, now let's ask, what if there is a pair of points that differ
00:25:35.21		not by virtue of their position left and right,
00:25:38.29		but by virtue of the fact that one is further and one is nearer to the viewer.
00:25:43.02		Now, if you think about it, what the geometric optics tells us
00:25:48.17		is that the innermost point is imaged further to the periphery
00:25:56.07		on the left retina and also further to the periphery on the right retina.
00:26:01.00		That is, the corresponding points for this closer object
00:26:08.07		lie on opposite sides of the corresponding points for the further object.
00:26:13.19		That's this more distant one here, which is now imaged on points
00:26:19.03		that are closer in - more nasal - on the two retinae.
00:26:23.00		Now that is a bit more confusing, perhaps, for the brain to figure out.
00:26:30.10		What the brain has to do is figure out that the image that it sees for that nearer point
00:26:37.29		on the left retina and on the right retina - those two images
00:26:41.25		are really representing the same object. This is the so-called correspondence problem.
00:26:46.02		And because it falls on these different sides of that further object's image,
00:26:51.29		therefore, it must be nearer.
00:26:56.09		The nearer one can be detected as nearer by virtue of the fact
00:27:01.13		that its corresponding points are lateral,
00:27:04.06		and the further one can be detected as being further
00:27:07.11		by virtue of the fact that its corresponding points are more nasal.
00:27:10.21		Now that's a very difficult computational problem, as it turns out.
00:27:15.00		It's not fully understood how it's done.
00:27:17.03		But, it's a very powerful system, and it gives us very accurate depth perception.
00:27:22.16		As we consider further how the brain analyzes the visual scene,
00:27:29.00		we eventually reach a point where that information is so complex,
00:27:33.26		that the analysis requires not just an assumption-less series of steps,
00:27:40.22		but a series of steps that involve imposing some order
00:27:43.19		related to our previous experience of visual information.
00:27:47.25		Let me give you an example of this.
00:27:49.01		Here we see a way in which we analyze depth from shading,
00:27:53.26		and we have included in that analysis a hidden assumption.
00:27:57.26		And the hidden assumption is that this three dimensional object
00:28:01.20		has been illuminated from above.
00:28:03.18		That's a natural assumption because ,in general, the sun, the classic illuminant,
00:28:09.11		(the classic source of illumination) is above.
00:28:11.20		And so, when we see an object, generally, shadows are below,
00:28:15.22		and the rounded object can be analyzed in part based on the pattern of its shadows,
00:28:22.15		with that correct assumption that the sun is sitting above it.
00:28:27.05		So here we have, just to illustrate this type of analysis,
00:28:31.12		two images of cuneiform writing.
00:28:34.18		In fact, these two images are the same image. The one on the right
00:28:40.08		differs from the one on the left only by virtue of having been rotated 180 degrees.
00:28:45.22		And yet, I think, we can appreciate that the one on the left looks
00:28:50.12		as if the cuneiform is coming out at us from the plane,
00:28:54.15		and the one on the right looks like the cuneiform is receding into the plane.
00:28:58.20		And, of course, the reason is that when we see the shadows on the left,
00:29:03.02		we immediately assume that the sunlight is from above, and therefore,
00:29:07.04		since the shadows are below, the objects are coming out,
00:29:10.08		and on the right side, the reverse -- the shadows,
00:29:14.15		if we assume the illumination is from the top,
00:29:17.11		would tell us that the objects have receded into the plane.
00:29:20.22		OK, that's obviously a learned assumption...
00:29:23.29		it's really an unconsciously learned assumption.
00:29:25.20		And, it's generally a correct one.
00:29:28.21		Let's look at another way in which learned responses
00:29:34.02		affect the way we analyze a scene.
00:29:36.28		Of course, we've moved around in a world of three dimensions,
00:29:40.02		and we're very tuned into the depth of 3D scenes.
00:29:44.18		We see some objects as closer, some as further away,
00:29:47.15		In this little cartoon, we certainly get a strong sense
00:29:51.13		that the walls form a zigzag pattern.
00:29:54.12		coming out and receding. Of course, the gentlemen who are pictured here
00:29:59.01		by virtue of their size reinforce that sense.
00:30:03.20		And now, if we look at these heavy black bars -
00:30:07.00		the one that's here and the one over here to the left,
00:30:10.10		we get a strong sense, because we have assumed their different depths,
00:30:16.29		that the bar that is to the right is a smaller object than the bar that is to the left.
00:30:22.23		And, of course, in the context of this three-dimensional scene, that is perfectly right.
00:30:27.22		But, in fact, if you measure the size of these two bars in this particular image...
00:30:33.16		you simply put a ruler next to this bar and a ruler next to that bar,
00:30:37.28		they're exactly the same size.
00:30:39.19		Now, what's most striking is that once one knows that,
00:30:43.12		even if you hold two rulers next to them,
00:30:46.05		and you prove to yourself that that is the case,
00:30:49.18		you have a very difficult time convincing your visual system that that's true.
00:30:54.05		That differential in size is a very strong and persistent sense,
00:31:00.25		and again, it's a learned sense,
00:31:04.09		and it is learned in the context of depth perception in the real world.
00:31:08.20		Now, let's look at one particularly famous example of this sort
00:31:14.00		of imposing of order upon a scene.
00:31:16.28		This is a commemorative vase that we're seeing here.
00:31:20.17		It was made to honor Prince Philip and Queen Elizabeth,
00:31:23.22		and I think you can appreciate that, not only is it a beautiful white vase,
00:31:28.11		but on one side is the profile of Queen Elizabeth -
00:31:31.29		that is the shape of the vase makes her profile,
00:31:34.23		and on the other side is the profile of Prince Philip.
00:31:37.23		That's the nose right here, here's a nose right here.
00:31:40.16		And, the striking thing about the vase, as you view it and look for those profiles,
00:31:48.22		is that the sense that it is a white vase or the sense
00:31:54.00		that it is two faces looking at each other,
00:31:57.01		seem to be mutually exclusive. That is, you can see one or you can see the other,
00:32:02.18		but it's hard to see them both or see them fully
00:32:06.06		and see them in a dominant way at the same time.
00:32:09.01		And this is just an example, again, of the visual system
00:32:11.29		imposing some order on the scene.
00:32:14.10		It's trying to choose which of those two somewhat exclusive ways
00:32:19.01		of looking at the scene is the correct one.
00:32:20.27		Let's look at one final example.
00:32:23.23		And this is a series of four sketches that Pablo Picasso drew of a bull
00:32:29.02		(one of his favorite subjects).
00:32:30.16		I think you can appreciate that going from the upper left to the lower right,
00:32:35.11		we're seeing an increasingly abstract version of a bull.
00:32:39.00		Yet the striking aspect of this set of drawings is that, despite the abstraction,
00:32:45.02		we immediately recognize it for what it is.
00:32:47.27		Now, like those simple line drawings, I think this begs the question why that should be so...
00:32:53.26		why the visual brain is so good at these sets of abstract images as representing a bull.
00:33:02.18		And, like those line drawings, I think our conclusion would be that
00:33:06.08		what Picasso has hit upon is that these almost child-like drawings
00:33:10.27		are really the internal representation
00:33:13.12		of the bull. And that we have created a simplified, abstracted version in our heads,
00:33:18.11		our memory trace of what a bull is that really is this kind of drawing
00:33:24.13		and therefore resonates immediately with it.

This material is based upon work supported by the National Science Foundation and the National Institute of General Medical Sciences under Grant No. 2122350 and 1 R25 GM139147. Any opinion, finding, conclusion, or recommendation expressed in these videos are solely those of the speakers and do not necessarily represent the views of the Science Communication Lab/iBiology, the National Science Foundation, the National Institutes of Health, or other Science Communication Lab funders.

© 2023 - 2006 iBiology · All content under CC BY-NC-ND 3.0 license · Privacy Policy · Terms of Use · Usage Policy
 

Power by iBiology