Sensation and Perception Exam 2

Metamers

The examples presented thus far involve the responses of the visual system to single wavelengths. However, you may have noticed that we have referred to "wavelengths or mixtures of wavelengths." That's because we are not typically exposed to single wavelengths. Almost every light and every surface that we see is emitting or reflecting a wide range of wavelengths. A laser pointer would emit a very narrow range of wavelengths, but a more normal situation we see it shows the relative amounts of light reflected from raw and cooked hamburger. How can we discriminate the raw from the cooked? When we're studying color vision, this real-world concern gets reduced to a different question: How do our cones respond to combinations of wavelengths of light? To answer this question, consider what happens if we mix just two wavelengths. For the sake of this example, we will oversimplify by ignoring the S-cones and redrawing the M- and L-cones to make the numbers simpler. Imagine that we shine a wavelength that looks red and a wavelength that looks green onto a white piece of paper so that a mixture of both is reflected back to the eyes Suppose that the light that looks green produces 80 units of activity in the M-cones and 40 in the L-cones (remember, we are ignoring the Cooked S-cones for now). In addition, suppose that the light that looks red produces 40 units of activity in the M-cones and 80 in the L-cones. If we assume that we can add the cone responses together, then summing the "red" and "green" lights produces a response of 120 units in each cone. The absolute value is not important, because it could change if the intensity of the light changed. What is important is that these two lights, mixed together, produce a mixture that excites the L- and M-cones equally. figure: Objects in the real world reflect light across the spectrum in different amounts. This graph plots the reflectances of raw and cooked hamburger meat. Which one looks redder? Which one reflects more of the long wavelengths? figure: In (A), the long-wavelength light that looks red and the shorter-wavelength light that looks green mix together to produce the same response from the cones as does the medium-wavelength light that looks yellow in (B). If two sets of lights produce the same responses, they are metamers and must look identical, so the red plus the green will look yellow. The key point is that the rest of the nervous system knows only what the cones tell it. If the mixture of lights that look red and green produces the same cone output as the single wavelength of light that looks yellow then the mixture and the single wavelength must look identical. Mixtures of different wavelengths that look identical are called metamers. The single wavelength that produces equal M- and L-cone activity will look yellow, and the correct mixture of longer- and shorter-wavelength lights will also look yellow. Two quick warnings: 1. Mixing wavelengths does not change the physical wavelengths. If we mix 500- and 600-nm lights, the physical stimulus contains wavelengths of 500 and 600 nm. It does not contain the average (550 nm). It does not contain the sum (1100 nm) (which we would not be able to see anyway). Color mixture is a mental event, not a change in the physics of light. 2.For a mixture of a red and green to look perfectly yellow, we would have to have just the right red and just the right green. Other mixes might look a bit reddish or a bit greenish. This example generalizes to any mixture of lights. All the light reaching the retina from one patch in the visual field will be converted into three numbers by the three cone types. If those numbers are sufficiently different from the numbers in another patch, you will be able to discriminate those patches. If not, those patches will be metamers: they will look identical, even if the wavelengths are physically different. Metamers: ●Perceived color depends on ambient light. ●Metamers appear similar in one light, but different from each other when illumination changes. whats occurring is that the Light levels alter spectral power distributions (basically how much the cones can absorb certain parts of the spectrum) -in very low light = overcast sky = tuned into lower wavelengths -in very bright high light = clear noon sky = tuned into higher wavelengths

preattentive stage

The process- ing of a stimulus that occurs before selective attention is deployed to that stimulus.

spectral sensitivity

The sensitivity of a cell or a device to different wavelengths on the electromagnetic spectrum.

illusory conjunction

a false combination of the features from two or more different objects

Attention and Single Cells

how attention might change the responses of a single neuron. response enhancement sharper tuning A more radical possibility is that attention changes the preferences of a neuron. The best evidence for a change in the fundamental preferences of a neuron comes from studies of its preferences in space—that is, the size and shape of a neuron's receptive field If cells are restricting their processing to the object of attention, then sensitivity to neighboring items might be reduced as resources are withdrawn from them.

salient

if it stands out visually from its neighbors it really doesn't matter how many distractors there are. The target seems to "pop out" of the display

serial self-terminating search

items are examined one after another (serially) either until the target is found or until all items have been checked

Attention Could Enhance Neural Activity

neurons that respond to stimuli in that part of the field will become more active. This will be true even in the first stages of visual cortical processing, since those stages are influenced by attention As we progress further into the visual areas of the cortex, even larger attentional effects are seen

tritanope

An individual who suffers from color blindness that is due to the absence of S-cones.

Cone-Opponent Cells in the Retina and LGN

The earliest work on the combination of cone signals was done with fish (Svae-tichin and Macnichol, 1959). By the 1960s, Russell De Valois and others had begun to show that these sorts of signals actually exist in the lateral geniculate nucleus (LGN) of macaque monkeys. As described in Chapter 3, many ganglion cells in the retina and the LGN of the thalamus are maximally stimulated by spots of light. These cells have receptive fields with a characteristic center-surround organization. For example, some cells are excited when a light turns on in the central part of their receptive fields and inhibited when a light turns on in the surround. A similar antagonistic relationship characterizes color. Some of these retinal and LGN ganglion cells are excited by the L-cone onset in their center and inhibited by M-cone onsets in their surround. These (L - M) cells are one type of cone-opponent cell, so named because different sources of chromatic information are pitted against each other. There are also (M - L), ([M + L] - S), and (S - [M + L]) cells—just the sorts of cells we would like to have to support the repackaging of cone signals, as described in the previous section. The cells that were excited by light onset could be thought of as (L + M) cells. Thus, we have the three signals that we wanted on theoretical grounds. The actual physiology is quite complicated. As mentioned in Chapters 2 and 3, for example, the S-cone signals go through the koniocellular layers in the LGN, while the M- and L-cone opponent signals are mostly found in the parvocellular layers Suppose you could stimulate a single cone. What color would you see? Any spot of light you could put up on a screen would cover many photoreceptors. However, recall from Figure 2.12 that Roorda and Williams (1999) figured out how to use some very sophisticated optics to allow them to see and classify individual cones in the retina of a human observer. Now Roorda and his colleagues have managed to focus tiny spots of light onto individual cones and ask those observers what they see figure: A piece of the retina with the L-, M- and S-cones colored red, green, and blue. On top of many cones are white rings of varying degrees of completion. These show the responses of that cone to tiny spots of light. A full white ring, for example, means the observer always said the spot looked white. Often, as you might think, L-cones produce red responses and M-cones produce green regardless of the wavelength of stimulus (remember "univariance"). However, interestingly, much of the time, the spots look white. Amazingly, when the same cone is tested in sessions many days apart, the pattern of responses can stay quite stable. Why do we have all these "white" cones? Cones could be contributing to many circuits. For example, an L-cone could be part of an (L - M) circuit that responds to color and an (L + M) circuit that responds to brightness. Maybe the (L + M) brightness response is just stronger, so the spot looks white even though that cone might also contribute to the apparent color of a more normal patch of light. Alternatively, it could be that the response looks white because some cones just don't contribute to color sensation. This might help explain why spatial resolution (acuity, contrast sensitivity, see Chapters 2 and 3) is quite bad if you use equiluminant stimuli—stimuli that vary in color but not in luminance. It may be that many cones just don't contribute to perception of such stimuli.

L-cone

A cone that is preferentially sensitive to long wavelengths; colloquially (but not entirely accurately) known as a "red cone."

Basic Principles of Color Perception

-color is NOT a physical property only related to it -humans see a narrow 400-700nm on the electromagnetic spectrum -the color of something is due to the wavelengths of the light rays hitting the eye from that piece of the thing -most of the light we see is reflected light (bounce back away) ex. sun or lightbulb emit a broad range of wavelengths that hit many surfaces around the world -some wavelengths get absorbed and the more it absorbs = darker surface becomes -other wavelengths are reflected and into the eye -the color that reaches is a mix of wavelengths and the other surfaces and lights beforehand -color is the result of an interaction of a physical stimulus with a particular nervous system electromagnetic spectrum ●Human eye is only sensitive to a tiny band of the electromagnetic spectrum. ●As we learned, different cones more sensitive to specific wavelengths. Rods more specialized for intermediate wavelengths ●Due to chromatic aberration, blue typically never in focus (about 2 diopters off relative to red) and due to low number of blue cones never high resolution. window into the: ●Windows transmit light, walls block light. ●Our color vision is a window into the electromagnetic spectrum. ●We have made windows for ~2000 years. What makes a good one? Ability to transmit light? ●Modern windows a grand illusion. ●Windows transmit light, walls block light. ●Our color vision is a window into the electromagnetic spectrum. ●We have made windows for ~2000 years. What makes a good one? Ability to transmit light? ●Modern windows a grand illusion. basically take in certain amounts of light and leaving others out and just like color perception thats what happens Basically the color we see is due to the type of wavelength of the light reaching our eyes by reflected light So the color of a surface is due to a mix of wavelengths that reach the eye from one or more surfaces and lights mixing so its interaction of the physical with the a certain nervous system

M-cone

A cone that is preferentially sensitive to middle wavelengths; colloquially (but not entirely accurately) known as a "green cone."

S-cone

A cone that is preferentially sensitive to short wavelengths; colloquially (but not entirely accurately) known as a "blue cone."

cone-opponent cell

A cell type— found in the retina, lateral geniculate nucleus, and visual cortex—that, in effect, subtracts one type of cone input from another.

color contrast

A color perception effect in which the color of one region induces the opponent color in a neigh- boring region.

color assimilation

A color perception effect in which two colors bleed into each other, each taking on some of the chromatic quality of the other.

unrelated color

A color that can be experienced in isolation.

related color

A color, such as brown or gray, that is seen only in relation to other colors. For example, a "gray" patch in complete darkness appears white.

monocular depth cue

A depth cue that is available even when the world is viewed with one eye alone.

binocular depth cue

A depth cue that relies on information from both eyes. Stereopsis is the primary example in humans, but convergence and the ability of two eyes to see more of an object than one eye sees are also bin- ocular depth cues.

agnosia

A failure to recognize objects in spite of the ability to see them. Agnosia is typically due to brain damage.

additive color mixture

A mixture of lights. If light A and light B are both reflected from a surface to the eye, in the perception of color the effects of those two lights add together.

subtractive color mixture

A mixture of pigments. If pigments A and B mix, some of the light shining on the surface will be subtracted by A, and some by B. Only the remainder will contribute to the perception of color.

positivism

A philosophical position arguing that all we really have to go on is the evidence of the senses, so the world might be nothing more than an elaborate hallucination

realism

A philosophical position arguing that there is a real world to sense.

melanopsin

A photopigment, found in a class of photoreceptive retinal ganglion cells.

visual-field defect

A portion of the visual field with no vision or with abnor- mal vision, typically resulting from dam- age to the visual nervous system.

fusiform face area (FFA)

A region of extrastriate visual cortex in humans that is specifically and reliably activated by human faces.

parahippocampal place area (PPA)

A region of extrastriate visual cortex in humans that is specifically and reliably activated more by images of places than by other stimuli.

adapting stimulus

A stimulus whose removal produces a change in visual perception or sensitivity.

lateral geniculate nucleus (LGN)

A structure in the thalamus, part of the midbrain, that receives input from the retinal ganglion cells and has input and output connections to the visual cortex.

rod monochromat

An individual with no cones of any type. In addition to being truly color-blind, rod mono- chromats are badly visually impaired in bright light.

cone monochromat

An individual with only one cone type. Cone mono- chromats are truly color-blind.

negative afterimage

An afterimage whose polarity is the opposite of the original stimulus. Light stimuli produce dark negative afterimages. Colorsare complementary; for example, red produces green, and yellow produces blue.

response enhancement

An effect of attention on the response of a neuron in which the neuron responding to an attended stimulus gives a bigger response.

sharper tuning

An effect of attention on the response of a neuron in which the neuron responding to an attended stimulus responds more precisely. For example, a neuron that responds to lines with orientations from -20 degrees to +20 degrees might come to respond to ±10-degree lines.

rapid serial visual presentation (RSVP)

An experimental procedure in which stimuli appear in a stream at one location (typically the point of fixation) at a rapid rate (typically about 8 per second).

anomia

An inability to name objects in spite of the ability to see and recog- nize them (as shown by usage). Anomia is typically due to brain damage.

achromatopsia

An inability to perceive colors that is caused by damage to the central nervous system.

deuteranope

An individual who suf- fers from color blindness that is due to the absence of M-cones.

protanope

An individual who suffers from color blindness that is due to the absence of L-cones.

feature integration theory

Anne Treisman's theory of visual attention, which holds that a limited set of basic features can be processed in parallel preattentively, but other properties, including the correct binding of fea- tures to objects, require attention.

unique hue

Any of four colors that can be described with only a single color term: red, yellow, green, blue. Other colors (e.g., purple or orange) can also be described as compounds (reddish blue, reddish yellow).

Physical Constraints Make Constancy Possible

As noted in the previous section, it is impossible to know which two integers are multiplied to produce 48. However, if you are told that the first number is between 9 and 14, you're saved. The first number must be 12, and the second, then, must be 4. In an analogous way, color constancy must be based on some information or assumptions that constrain the possible answers. There are many possible assumptions that could help. Suppose we assumed that, in a complex scene, the brightest region was white (Land and McCann, 1971) or that the av- erage color across the whole scene was gray (Buchsbaum, 1980). We could scale the other colors relative to these white or gray anchors. However, this can't be entirely right. Think what would happen if you were in a dark room with two spots of light on the wall: a red one and a blue one. Under a simple version of a bright-is-white theory, the brighter spot should look white and the other spot should change color. That can't be right (and theorists knew this, so the actual theories are more subtle, but they still do not work perfectly). There are other possible constraining pieces of information. Assumptionscan be made about the illuminant. For instance, natural light sources (and mostartificial ones, such as standard lightbulbs) are generally "broadband." That is,they contain many wavelengths, even if some wavelengths are not as intense asothers. Furthermore, their spectral composition curves (see Figure 5.25) are usuallysmooth; spikes at particular wavelengths are uncommon, though this generalizationis violated by some artificial light sources. Highly unnatural (e.g., monochromatic) (A) light sources make the world look highly unnatural—a fact exploited in nightclubsthe world over. Indeed, with a monochromatic source, color vision is impossible,though not much broadband light is needed to get color back. Assumptions can be made about surfaces. Real surfaces also tend to be broadband in their reflectances (recall the hamburger distributions of Figure 5.6). It would be very unlikely, for example, to find a surface that reflected 100% of 535-nm light, 0% of 538-nm light, and 100% again of 540-nm light. Even surfaces that look like single wavelengths of light typically reflect a wide range of wavelengths. That is, the red bars in Figure 5.25 may be metameric with some- thing like a 600-nm light, but that region is sending many other wavelengths to your eye. There are other limits on the reflectance of real surfaces. The whitest surface rarely reflects more than 95% of any wavelength, and the blackest rarely reflects less than 5%. The brightest thing in the visual field is likely to be white. A "specular reflection" (like the shiny spot on a billiard ball) has a wavelength composition very similar to that of the illuminant. Any of these facts might help. This brings us back to the dress introduced at the start of the chapter. Why does that dress look white and gold or black and blue? We are not really sure, but it seems that people are making different assumptions about the light source in the original photograph (Figure 5.26A). Some people assume that the illuminant is white (Figure 5.26B). They would see the dress as black and blue (the actual colors). Others may be assuming a more complicated situation with one diffuse blue light and a more direct, more yellow light (Figure 5.26C). This could persuade those observers to see the black as a sort of golden brown and the blue as a bluish white. Then, when asked about the cloth, they might report (as would the author of this bit of the text) that the dress looks Figure: What color is this dress? (A) The colorof this dress may not look the same to different people because each observer is making different assumptions about the nature of the light shining on the dress. (B) Maybe the light is just broadband white light. Then the dress appears to be blue and black. (C) Maybe there area couple of light sources: one bluish and the other more yellow. Then the dress could appear to be white and gold. (B, C after Macknik and Martinez-Conde, 2015.) like a white-and-gold dress under a bluish light. If you asked such an observer to match the color of the lighter stripes, the patch that matched the color might be quite blue. Remember, "Are these cut from the same cloth?" and "Are these the same color?" are different questions (Arend and Reeves, 1986). The real mystery about the dress is that it looks dramatically different to different people. Normally, this does not happen. You might disagree with your friend about whether a shirt is bluish green or greenish blue, but if both of you are normal trichromats, it would be very unusual for you to think the shirt was dark blue while your friend insisted on white. To be sure, there are ambiguous stimuli (remember the Necker cube of Figure 4.24). In those cases, even if the two interpretations are very different, one person can see them both. Usually, perception will alternate between the possibilities. It is unusual for one person to see one possibility and to be unable to see the other (sounds like politics, not perception). As this is being written, the dress remains a hot topic (Brainard and Hurlbert, 2015; Gegenfurtner, Bloj, and Toscani, 2015; Lafer-Sousa, Hermann, and Conway, 2015; Wallisch, 2017), but we don't have a clear explanation for the dramatic differences between perceptions. Nevertheless, we think it must be telling us something significant about the mechanisms of color perception in complex scenes. Assumptions about illumination are not the only route to color constancy. Other assumptions can be made about the structure of the world. Sharp borders in an image are almost always the result of boundaries between surfaces, not boundaries between light sources. Thus, if you see something that looks pink next to something that looks golden, it is very unlikely to be the result of cleverly placed pink and golden light sources (unless you're in a theater, perhaps). Shadow borders can be an exception to this rule (Adelson, 1993; Cavanagh and Leclerc, 1989). A shadow can produce a sharp edge that is unrelated to any change in the underlying surface. However, the change across a shadow border is typically a change in brightness and not a change in the chromatic properties of the regions. Figure 5.27: The visual system "knows" that brightness changes across a shadow boundary but hue does not. As a result, the difference in (A) looks more like a shadow than the one in (B). In Figure 5.27A, you can easily imagine that you're looking at three rectangles with a circular patch of shadow, lying across their middles. Not so in Figure 5.27B; though the top and bottom in Figure 5.27B are brighter than the middle, the darker region does not look like a shadow, because for that darkening to be the result of a shadow, the shadow would have to be coincidentally aligned with a set of hue changes. Our implicit knowledge of these sorts of constraints helps us sort out the visual world. Cues like this, perhaps in clever combination, can be used to solve the otherwise unsolvable problem of color constancy (Smithson, 2005). All of these assumptions about the state of the world might sound a lot like the "prior proba- bilities" that are important in Bayesian theories of the sort introduced in Chapter 4. Bayesian ideas have been put to work to explain color constancy (Brainard, 2009; Brainard and Freeman, 1997). The hard part, whether you're working in an explicitly Bayesian framework or not, is to get the prior probabilities exactly right (Foster, 2011). What are the precise assumptions that give us our level of color constancy? We are still not sure. standing in a kitchen illuminated by a light source composed primarily of short and middle wavelengths (Figure 5.28: The strawberries in a complex scene will look red even if there is not much long-wavelength light in the illuminant.) . None of the surfaces in your visual field will reflect much long-wavelength light, because there isn't much of this light to be reflected. But given the assumption that the illumination is evenly distrib- uted around the room, you will still perceive the strawberries as red, because they reflect more long-wavelength light than any other surface does.

Adaptation and Afterimages

Color contrast effects show how the spatial relations between colors can influence color appearance. Temporal relations matter, too. What you saw before has an influence on the color you see now. You already know this from the discussion of light adaptation in Chapter 2. Adapting to a bright light makes a moderate light look darker. Adapting to darkness would make that same moderate light appear brighter. Now let's extend that principle to color. Adaptation can be color-specific, as we see in the phenomenon of negative afterimages. If you look at one color for a few seconds, a subsequently viewed achromatic region will appear to take on a color opposite to the original color. We can call the first colored stimulus the adapting stimulus. The illusory color that is seen afterward is the negative afterimage. The principle is illustrated in Figure 5.24: To understand what negative afterimages are, study the image in (A) and convince yourself that the ring of circles is gray. Now stare at the black dot in (B). After 10 seconds or so, shift your fixation to the black dot in (A). The circles should now look col- ored. This is a negative afterimage. Why does it happen? (If it didn't happen, try fixating more rigorously. Really look at the black dot.) Now try with a real scene. Fixate the black dot in (C), and then flick your eyes to the same spot in (D). You should see a washed-out version of the colors in (E). Figure 5.24A consists of a circle of gray spots. Now, stare at the black dot at the center of Figure 5.24B and consider what happens as you expose one bit of your retina and visual system to the red dot at the top of Figure 5.24B. The L-cones will be more stimulated than the M- or S-cones. L+/M- opponent processes will be stimulated. You will see "red." When you move your eyes back to fixate on the black dot at the center of Figure 5.24A, the red is withdrawn from that area of the visual field. The L-cones will be more adapted than M- or S-cones, as will the later processes in the retina and brain that were more stimulated by the red spot. Adapted processes behave as though they are somewhat tired. They respond less vigorously than unadapted processes. The result is a bit like what would happen if you held a pendulum up and released it. The red-green opponent color mechanism swings back toward the neutral point, overshoots this point, and slides over to the green side. As a consequence, the gray spot appears greenish until the opponent mechanism settles back to the neutral point. If you look at the green dot at the bottom of Figure 5.24B and then look back at the gray image (Figure 5.24A), you will see the result of pushing the red-green mechanism in the other direction. Other colors will produce other results, which you should now be able to predict. In Figures 5.24C-E, you can try this with a real scene. If you stare at the black dot in Figure 5.24C and then quickly move your eyes to the same dot in Figure 5.24D, you will see a pale, tinted version of the real photograph (Figure 5.24E), though this effect will be more dramatic if you look at the version in Web Activity 5.4: Afterimages. Notice that we are not attributing negative afterimages to just the cones or just one set of cone- or color-opponent processes. Adaptation occurs at multiple sites in the nervous system, though the primary generators are in the retina ex. stare at four colors middle dot then look at white background results in: Retinal fatigue creates after-images of complementary colors (where you see the complementary colors)

Color contrast and Assimilation

Color contrast: color of one region induces the opponent color of in a neighboring region.Notice how the center green square looks greener with a red background? Color Assimilation: two colors bleed into each other, each taking on some of the chromatic quality of the other.Notice in the second column how the yellow squares appear reddish (top) and greenish (bottom).

basic color terms

Color words that are single words (like "blue," not "sky blue"), are used with high frequency, and have meanings that are agreed upon by speakers of a language.

Metamers (definition)

Different mixtures of wavelengths that look identical. More generally, any pair of stimuli that are perceived as identical in spite of physical differences.

Avian color vision

Eagles and other birds of prey can see four to five times farther than the average human can, meaning they have 20/5 or 20/4 vision. ●For mammals, primates have great color vision. ●Avian cones can contain oil droplets of different colors that help distinguish color. ●Avian cones are often 'double cones' that are sensitive to different wavelengths: effectively integrating color like primate ganglion cells.

The History of Trichromatic Theory

From what you've read so far in this book, you would be forgiven for supposing that clever anatomists and physiologists identified the three cone types and built the trichromatic theory of color vision from there. Indeed, there have been beautiful experiments of this sort: For instance, Schnapf, Kraft, and Baylor (1987) managed to record the activity of single photoreceptors. Nathans, Thomas, and Hogness (1986) found the genes that code for the different photopigments. David Williams and his students even developed a method for photographing and identifying different cone types in the living human eye. Such research has cemented our understanding of the physical basis of trichromacy, but the basic theory was established by psychophysical experimentation. The theorizing started with Isaac Newton's great discovery that a prism would break up sunlight into the spectrum of hues, and a second prism would put the spectrum back together into light that looked white. In 1666, Newton understood that "the rays to speak properly are not coloured" (from Opticks, published originally in 1704). Newton knew that color is a mental event. The three-dimensional nature of the experience of color was worked out in the nineteenth century by Thomas Young (1773-1829) and subsequently by Hermann von Helmholtz (1821-1894). In their honor, trichromatic theory is often called the "Young-Helmholtz theory." James Clerk Maxwell (1831-1879) developed a color-matching technique that was central to Helmholtz's work on this topic. (Somehow Maxwell missed having his name attached, or we would have the Young-Maxwell-Helmholtz theory.) Maxwell's technique is illustrated in figure: In a modern version of Max- well's color-matching experiment, a color is presented on the left. On the right, the observer adjusts a mixture of the three lights to match the color on the left. The observer in a modern version of Maxwell's experiments would try to use different amounts of "primary" colored lights (e.g., the lights looking red, green, and blue on the right side of the figure) to exactly match another reference color (e.g., the light looking cyan, or bluish, on the left side). The central observation from these experiments was that only three mixing lights are needed to match any reference light. Two primaries are not enough, and four are more than are needed. Long before physiology could prove it, these results led Young and Helmholtz to deduce that three different color mechanisms must limit the human experience of color. This history of color -1st dude is Newton's prism study refracted (bent) white light with a prism and demonstrated it was composed of the rainbow of wavelengths. ●Crucially, any single one of these pure monochromatic colors could not be refracted into a different color. ●Crucially, a second prism that combined all the colors would create a white light. -2nd due is James Clerk Maxwell was most influential physicist since Newton (at least according to Einstein). ●Unified magnetic, electric and light as being waves in the electromagnetic spectrum, infers existence radio waves. ●Mixing 3 colors on spinning top could emulate any solid color. ●Developed color triangle. ●Maxwell in 1861: Take three black and white photos of the same scene: each with a different color filter (red, green, blue). ●Use three projectors to display images, placing appropriate filter in front of each projector.

What Is Color Vision Good For?

Having introduced some of the basics and some of the complications of color vision, we will wrap up this chapter by asking about the usefulness of color vision. The ability to use wavelength information has evolved several times in several ways during the course of time. Evolutionary theory tells us that, for this to be true, color vision must provide an advantage that makes it worth the trouble. Color is not an absolute requirement. If we could not make wavelength discriminations, we could still identify a lion (Figure 5.29A: Do humans need color vision? (A) A black-and-white lion is still a lion,), and we could still find our way through the forest (Figure 5.29B: and (B) you could still find a path in the woods without color vision.). Although color vision might make the lion stand out a bit better from the background, we would be much more impaired if we lacked "orientation vision" or "motion vision." Across the animal kingdom, however, there seem to be at least two realms of behavior where color vision is especially useful: eating and sex. More generally, color vision seems to be particularly helpful in visual search tasks (see Chapter 7). Having color vision does seem to make it easier to find candidate foods and to discriminate good food from bad food. Comparing the two versions of Figure 5.30: Finding a raspberry is easier if you have color vision, as is deciding if that berry is ripe. , it is clear that finding berries is easier with color vision. Notice also that it makes it easier to decide which of these berries is ripe. You may recall that we mentioned this possibility when discussing the L- and M-cones, earlier in the chapter. Most diurnal animals have two photopigments: roughly an S-cone and an LM-cone. Some primates have evolved separate L and M photopigments and the neural circuitry to exploit the rather small differences between the responses of those cone types. There has been considerable debate regarding whether this really conveys an advantage in telling ripe from unripe fruit or distinguishing subtle differences among green leaves. Some have argued that shape and texture information, along with dichromatic color vision, are good enough for these purposes. How can we find out? In some monkey species (e.g., capuchins) is a mix of dichromats and trichromats (as in the human species). Do the trichromats have an advantage in hunting for food? Rather than test this in capuchins, Melin et al. (2013) carefully simulated six varieties of capuchin color vision in human observers and had those observers search for capuchin food in photographs of those fruits as they would appear in the Costa Rican forests where the monkeys live. The result was a clear advantage for trichromatic vision. The food-color vision connection is not limited to primates. The colors of wildflowers did not develop to please the aesthetic sense of humans. As a general rule, they are advertisements to bees and other insects, offering to trade food for sex (well, at least for pollination of the flower). In fact, many flowers have dramatic patterns that we cannot see, because they are variations in the reflection of short-wavelength (ultraviolet) light, which is outside our range. Bees can see these short wavelengths, and it is the bees that flowers have evolved to attract (Figure 5.31: Color vision in different species. (A) These black-eyed Susans are shown as they are seen with human photoreceptors and color vision. (B) A honeybee can see UV light. In UV, the flower's "black eye" (or maybe the pupil of that eye) is much larger—a better target for the bee. (Courtesy of Tom Eisner.) ). While we're on the subject of the relationship of plants and animals, it is worth a detour to point out that plants exploit several sensory systems in an effort to attract the attention of the right animal. The smell of a flower is intended as a signal to pollinators, and even the shape of specialized leaves can create an auditory beacon to attract echolocating bats (Simon et al., 2011). In addition to searching for and assessing food, animals spend significant time and effort searching for and assessing potential mates. Here, too, color plays a central role. Colorful displays—from the dramatic patterns on tropical fish (Figure 5.32A: The colors of animals—from (A) tropical fish) to the tail of a peacock (Figure 5.32B to (B) peacocks to) to the face of the mandrill (Figure 5.32C man- drills—are often advertisements to potential mates.)—are all sexual signals. What makes the male peacock that has the most colorful tail the most desirable mate for a female peacock? We can't ask her, of course, but a colorful tail might somehow indicate that this peacock's genes are better than his competitors'. A female peacock that sees the world in black and white won't be able to perceive this information and will therefore be at an evolutionary disadvantage. In another example that we mentioned earlier, the development of separate L- and M-cones may have made primate color vision particularly well suited to detecting the amount of blood in a blushing or blanched cheek (Changizi, Zhang, and Shimojo, 2006). Color vision is accomplished in different ways in different species. We are trichromats, with three different types of cone photoreceptors. Dogs appear to be dichromats, with two types of cone photoreceptors (Neitz, Geist, and Jacobs, 1989). Chickens, surprisingly enough, turn out to be tetrachromats, with four (Okano, Fukada, and Yoshizawa, 1995). There is not much gain in information if the number of cone types is increased beyond 3 or 4 (Maloney, 1986), which probably explains why octachromats or dodecachromats—individuals with 8 or 12 types of cones—are very rare. Rare, but not nonexistent. If you have some time, look into the case of the mantis shrimp, a wonderfully eccentric beast with at least 12 types of photoreceptors (Marshall and Arikawa, 2014). Our S-, M-, and L-cones are different because they contain different photopigments (Figure 5.33A: Two ways to make photoreceptors with different spectral sensitivities. (A) Our S-, M-, and L-cones are different because they contain different photopigments. (B) Some animals have only one type of photopigment. These animals can have color vision because colored oil droplets sitting on top of photoreceptors create groups of pho- toreceptors with different sensitivities to wavelength.). It is also possible to use a single photopigment to create more than one functional type of cone. The trick is to put a different filter in front of each type of cone so that some wavelengths are subtracted before light reaches the photoreceptor (Figure 5.33B: (B) Some animals have only one type of photopigment. These animals can have color vision because colored oil droplets sitting on top of photoreceptors create groups of pho- toreceptors with different sensitivities to wavelength.) . A cone with a reddish oil droplet in front of it will respond more vigorously to long-wavelength light than will a cone covered by a greenish droplet. Chicks and other birds have these droplets, as do a variety of reptiles. Even fireflies get into this act in a limited way. Fireflies signal each other with bioluminescence: they make their own light. Different species have differ- ent lights, and each species' visual system appears to be tuned to its particular wavelength signature. A combination of a photopigment and a colored filter makes signals of conspecifics (members of the same species) appear brighter than the flashes of other fireflies in the vicinity (Cronin et al., 2000). With this sort of visual system, the firefly will never appreciate the palette of colors in a sunset. But it will be able to locate an appropriate mate, and that, after all, was the pressure shaping the development of this limited sensitivity to wavelength. what is color good for? -segmenting predator, prey from background (natural selection) -selecting ripe fruit (natural selection) -advertising and evaluating fitness (sexual selection)

A Different Ganglion Cell Helps to Keep Track of Day and Night

If you have ever flown a significant distance east or west, you have probably experienced jet lag. The time on your cell phone clock changed by a few hours one way or the other, but your internal clock didn't get the message and remained on your home time. Over a couple of days, you adjusted to the new time. The primary force adjusting your internal circadian clock is sunlight. Interestingly, mice bred to have no functioning photoreceptors continued to entrain their circadian clocks to light. Some apparently blind humans also seem to have that ability. How could this be? There has been speculation about photoreceptors in the skin, but in recent years, we have learned that there is a previously unknown photosensitive cell in the retina. It is a ganglion cell. It receives input from the rods and the cones, but it also contains its own photopigment, melanopsin, so it can detect light even when normal photoreceptors are absent. You don't consciously see the outputs of this cell. Its outputs go to centers in the brain that control functions such as your circadian clock. It is as if two (or more) visual systems were occupying your body, sharing the same eyes. Indeed, under the right circumstances, a few seconds of blue light can have an impact on cognitive function, even in individuals who have no conscious light perception, via this melanopsin-based system.

exogenous cue

In directing attention, a cue that is located out (exo) at the desired final location of attention. seem to summon attention automatically by virtue of their physical salience. while the exogenous cue could drag you to the right, whether you wanted to go there or not. cues can be valid or invalid.

endogenous cue

In directing attention, an endogenous cue is located in (endo) or near the current location of attention instructions that can be voluntarily obeyed. The endogenous cue says "go right," so you go right, cues can be valid or invalid.

parietal lobe

In each cerebral hemi- sphere, a lobe that lies toward the top of the brain between the frontal and occipital lobes.

qualia

In philosophy, a private con- scious experience of sensation or perception.

extinction

In reference to visual attention, the inability to perceive a stimulus to one side of the point of fix- ation (e.g., to the right) in the presence of another stimulus, typically in a com- parable position in the other visual field (e.g., on the left side). However, if there is competition from a similar object in the ipsilesional field (the same side as the lesion), then the two objects compete for attention and the ipsilesional object wins.

cultural relativism

In sensation and perception, the idea that basic percep- tual experiences (e.g., color perception) may be determined in part by the cul- tural environment.

stereopsis

The ability to use binocu- lar disparity as a cue to depth.

Opponent Colors

In the nineteenth century, Ewald Hering (1834-1918) described a curious feature of color vision: some combinations of colors seem to be perceptually "illegal." We can have a bluish green, a reddish yellow (which we would call "orange"), or a bluish red (which we would call "purple"), but reddish green and bluish yellow don't exist. Red and green are, in some fashion, opposed to each other, as are blue and yellow. Young and Helmholtz described a trichromatic theory with three basic colors (red, green, and blue); Hering's opponent color theory had four basic colors in two opponent pairs: red versus green, and blue versus yellow. A black-versus-white component formed a third opponent pair. Figure: Hering's idea of opponent colors. Hering noted that all the colors on the "color circle" (the center ring) could be represented by two pairs of opposing colors: blue versus yellow, and red versus green (shown in the outer ring). Thus, a color could be a reddish yellow or a bluish green, but not a reddish green or a bluish yellow. illustrates this idea. The center ring shows the hue dimension of HSB space, wrapped into the color circle as in Figure 5.14B. The outer ring offers a cartoon of four color mechanisms in two pairs. The inner patch on the center left looks yellow, in opponent color theory, because it stimulates the yellow pole of the yellow-blue opponency and does not stimulate either red or green. Move a bit counterclockwise, and the orange patches add increasing red to the decreasing yellow. Leo Hurvich and Dorothea Jameson (1957) revived Hering's ideas and developed one way to quantify this opponency. The method, called hue cancellation, is shown in Figure: A hue cancellation exper- iment starts with a color—here, bluish green (A) and a greener hue (B)—and attempts to determine how much of the opponent color of one of the starting color's components must be added to eliminate any hint of that component from the starting color. In this example, the observer adds red to cancel green (B, E), leaving only the blue component of the bluish green (C, E). In this example we start with a light that appears to be a bluish green. We can cancel the perceptual greenness by adding its opponent color, red. We measure the amount of a light that looks red that is needed to just remove all traces of green. The result will appear neither red nor green. It will be a shade of blue. Since it is a mix of lights that look bluish green and red, it will be a rather desaturated-looking blue. If we do this for a greener color, it will take more added light that looks red to cancel the green, and the result will be an even fainter blue remnant. If we started with pure green, once it was cancelled, the result would be an achromatic patch because there would be no blue to be left over. We could cancel the perception of blue in our bluish-green patches by adding light that looked yellow. In this case, the remnant would appear green. If we did hue cancellation experiments for lights across the spectrum, we would get results that look like those in Figure 5.17: Results from a hue cancellation experiment. The locations where the hue cancellations cross the neutral midpoint are the locations of the unique blue and yellow (A) and green (B) hues—for example, the green hue with no hint of blue or yellow in it. "Unique red" is not defined by just one spectral locus. (After Hurvich and Jameson, 1957.) —not exactly like these, because these are Dorothea Jameson's data and everyone has slightly different results. If we start at about 400 nm, the lights look reddish blue (or violet) and we need to add some green and some yellow to cancel them. But look what happens at about 470 nm. Here is a light that has no red or green to cancel; it looks perfectly blue. This location on the spectrum is known as unique blue. Continuing to scan along the spectrum in the figure reveals the locations of two other unique hues—hues that can be described with only a single color term. Only four hues can be described in this way. As you might guess, they are red, green, yellow, and blue. Only three of them have unique loci on the spectrum. All the very long wavelengths look red (with, maybe, a touch of residual yellow). The two crossings of the red-green function provide the loci of unique blue and unique yellow (Figure 5.17A). The point where the blue-yellow function crosses from positive to negative is the locus of unique green, the green that has no blue or yellow in it (and, of course, no red) (Figure 5.17B). There are excellent exemplars of other colors, such as orange or purple, but they are not unique in the same way; orange, no matter how pure, can still be described as a reddish yellow. We can think of gray as a unique hue, too (or antihue). It is what you get if you cancel both red-green and blue-yellow (Webster, 2017). This opponent-process, color-appearance story sounds rather similar to the (L - M) and ([L + M] - S) story. However, (L - M) is not the same as red versus green, and ([L + M] - S) is not the same as yellow versus blue. If this were the case, an ([L + M] - S) cell should be a yellow-blue cell: a cell that would be maximally excited by unique yellow and maximally inhibited by unique blue. However, that ([L + M] - S) cell would actually be stimulated most strongly by a yellowish-greenish hue and least by a purplish hue. The (L - M) cells aren't in quite the right place either. The L-cone end of the axis is near perceptual red, but the M-cone end is a bluish green. Krauskopf, Williams, and Heeley (1982) call these endpoints the "cardinal directions" in color space, but they are not perceptual red, green, yellow, and blue. We need at least three steps to get to color appearance. These are shown in figure: Three steps to color per- ception. (A) Detection: light is differen- tially absorbed by three photopigments in the cones. (B) Discrimination: differ- ences are taken between cone types, creating cone-opponent mechanisms, important for wavelength discriminations. (C) Appearance: further recombination of the signals creates color-opponent pro- cesses that support the color-opponent nature of color appearance. (After Stock- man and Brainard, 2010.) First, three cones detect light in different ranges of wavelengths. Then, opponent processes measure the differences in activity between cone types. Finally, some further transformations are needed to create the color opponency described by Hering (Stockman and Brainard, 2010) Color Opposition ●Johann Wolfgang von Goethe (1749 -1832) proposed the color wheel which diametrically opposed colors. ●Ewald Hering (1834-1918) discovered the opponent color theory in which all perceptions of color are outputs of three opposition colors ○red-green ○blue-yellow ○black-white

scene-based guidance

Information in our understanding of scenes that helps us find specific objects in scenes (e.g., objects do not float in air, faucets are near sinks).

Color Constancy

Let us return to the zebra. The fact that the picture looks black, white, and green whether viewed inside or outside is an illustration of color constancy, the tenden- cy for the colors of objects to appear relatively unchanged in spite of substantial changes in the lighting conditions. All the color figures in this book will seem to have more or less the same colors wherever you read the book (though there is an entire research area hidden in what we might mean by "more or less"; Foster, 2011) Figure 5.25: The same surface (A) illuminated by two different lights (B) will generate two different patterns of activity in the S-, M-, and L-cones (C-E). However, the surface will appear to be the same color under both illuminants. This phenomenon is known as color con- stancy. (After Smithson, 2005.) illustrates why color constancy is yet another difficult problem for the visual system to solve. The heart of the problem is that the illuminant the light that illuminates a surface, is not constant. Lighting changes as we go from indoors to outdoors or as the sun moves from the horizon to high in the sky. Figure 5.25A shows the spectral reflectance function for a surface—the percentage of each wavelength that is reflected from a particular surface. With its preponderance of long and short wavelengths and that dip in the middle wavelengths, this surface probably looks purplish. Let's call it "lilac." Figure 5.25B shows the spectral power distribution—the relative amount of light at different visible wavelengths—of two different types of "daylight": sunlight and skylight. Sunlight is a yellowish light, richer in middle and long wavelengths; skylight is more bluish, with more short-wavelength energy. Figure 5.25C shows that the light reflected to our eyes is the product of the surface and the illumination. For example, a surface might reflect 90% of 650-nm light, but no 650-nm light would reach the eye unless there was some in the original illumination. Figure 5.25D, E show that those two different products of surface and illumination are converted into two different sets of three numbers by the L-, M- and S-cones. Here's the problem. Even though the three numbers from the three cones are different in the two conditions, that lilac-colored surface will look lilac under both illuminants. White paper will look white. A banana will look yellow. This color constancy is beneficial because we want to know the color of the object. Under normal circumstances, we don't care about the spectral composition of the lights. color constancy: ●Color cast depends on ambient light. ○ The same object will reflect different color depending on illumination. ○ Our brains (and cameras) need to compensate for this. ex. Koffka Ring Illusion ●We compensate for brightness. ●Ring (correctly) perceived as same brightness when connected. ●Gap creates illusion of brightness difference ●When aligned offset we get the sense of transparency (connectedness) ex. blue and black vs white and gold dress (depends on light illuminance causing that perception of different colors)

The Problem with the Illuminant

Let's think about color constancy as a math problem. In simple terms, we have an illuminant (call it I) and a surface (S). As shown in Figure 5.25C, what we can sense is a result—the product of I × S—but what we want to know is S. It is as if we were given a number (say, 48), told that it is the product of two other numbers, and asked to guess what those two numbers might be. The answer could be 12 and 4. Or it could be 16 and 3. Or 6 and 8. Given just the number 48, we cannot solve the problem. Nevertheless, given the result of I × S, the visual system does a pretty good job of figuring out S. We sometimes talk about "discounting" the illuminant as if our whole goal were to throw away the I term and just see the surface color. However, this is not quite right. For instance, you can get different answers if you point to two patches under two different illuminants and ask if these two were "cut from the same cloth" or if they are the "same color" (Arend and Reeves, 1986). If you just discarded the illuminant information, these answers would be the same—but they are not. Similarly, you can tell the difference between a scene lit by the morning sun and a scene illuminated by the sun at high noon. Thus, not only can you recover the color of the surface, but you also know something about the illuminant. How do you do this?

binocular summation

The combina- tion (or "summation") of signals from both eyes in ways that make perfor- mance on many tasks better than with either eye alone.

From the Color of Lights to a World of Color

Now for a bit of depressing news. The material presented so far in this chapter about color is quite complex, but the sad fact is that it has only addressed the relatively simple problems related to the detection, discrimination, and appear- ance of isolated lights. We pointed to the limits of this approach to color when we noted that there are no "brown" lights. A surface looks brown when there are other, typically brighter surfaces in the neighborhood. A nonspectral color like brown just scratches the surface of the puzzles that must be solved if we want to understand color in the world. Think about this. Figure 5.22: This zebra looks like a black-and-white beast in a green field whether you are looking at this book under a dim yellow bulb inside, under the bright sky outside, or for that matter, on your computer screen. How does that work? shows a black-and-white zebra on grass. Let us assume that you are looking at this in print (the story would be different on a screen). The black, white, and green are products of reflection from paper, covered with specific inks. Let us suppose you are reading this deep in the bowels of the library, by the light of a yellowish incandescent lightbulb. Now, you take the book outside and continue to read in sunlight. The number of photons coming from the page to your eye is now thousands of times greater than it was inside. Outside, a patch of "black" stripe is now sending much more energy to your photoreceptors than did a patch of "white" stripe inside. Moreover, the mix of wavelengths reflected from the grass will be very different if the light source is the sun rather than a dim bulb. Nev- ertheless, the grass looks green and the zebra looks black-and-white in both locations. How (and why) do you do that? Let's start by just putting colors next to each other. We live in a world where regions of one color abut regions of another, and this proximity changes the appearance of colors, as Figure 5.23: Color contrast and color assimilation. (A) In color contrast, the central square takes on chromatic attri- butes that are opposite those of the surround, so the green central square looks greener on the red background than on the green background. (B) In color assimilation, colors blend together locally. So, in the second column the yellow squares look a bit reddish in the upper square and a bit greenish in the lower square. (From Stockman and Brainard, 2010.) shows. In color contrast effects, the color of one region induces the opponent color in a neighboring region. Thus, in Figure 5.23A the yellow surround weakens the yellow of a central square and strengthens the blue. In color assimilation effects, two colors bleed into each other, each taking on some of the chromatic quality of the other. So, in Figure 5.23B the blue in the first column looks reddish or purplish in the top image and greenish on the bottom. Not only can other colors in the scene alter the color of a target region, but scenes can contain colors that cannot be experienced in isolation. Though it may be hard to believe unless you try it, you cannot sit in complete darkness and see a gray light, all by itself. That light will look white if seen as an isolated or unrelated color. To be seen as gray, it must be seen in relationship to other patches of color. Thus it is a related color. Brown is another related color. We can distinguish a few thousand unrelated colors. Allowing for context effects is what boosts the number of distinguishable colors to the millions (Shevell, 2003). Color induction: Perceived color influenced by 1. surround 2. spatial frequency

Attention May Change the Way Neurons Talk to Each Other

One possibility is that this binding and coordination of areas involves synchronizing the temporal patterns of activity in those areas One role of attention appears to be to control what is synchronized with what. Baldauf and Desimone (2014) showed that attention to faces or places could change whether FFA or PPA were synchronized with a third brain area. attention may serve to desynchronize neurons. If neurons are synchronized, it is as if they are all doing more or less the same thing. A group of properly desynchronized neurons can more accurately represent a stimulus if different neurons contribute different bits of information

Language and Color

Putting aside the finer points of philosophy, suppose you are in a clothing store and you find a shirt that you like but you want a different color. You might go to the clerk and ask, "Do you have this in blue?" You simply assume that you and that clerk agree about the meaning of blue. You can discriminate on the order of millions of different colors, but you don't have a separate word for each of these. There is a vast range of color words, but in looking for that shirt, you would not typically ask for "azuline" or "cerulean" (both, varieties of blue). You would use a word like blue that almost every speaker of your language would use quickly and consistently in naming colors. These colors names are the basic color terms of the language. What makes a color term basic? Berlin and Kay (1969) asserted that it must be common (like red and not like beige), not an object or substance name (excluding bronze and olive), and not a compound word (no blue green or light purple). This classification is a little subjective (is beige that uncommon?), but Berlin and Kay argued that, in English, these rules yield a list of 11 terms: red, green, blue, yellow, black, white, gray, orange, purple, brown, and pink. Interestingly, the numbers of "basic" color terms differ dramatically across cultures, down to as few as 2 or 3. At one time it was thought that the differing numbers of basic color terms in different languages meant that color categori- zation was arbitrary. This notion was called cultural relativism, meaning that each group was free to create its own linguistic map of color space. Berlin and Kay's important discovery was that the various maps used in different cultures are actually rather similar (Lindsey and Brown, 2006). After surveying many languages, they found that the 11 basic color terms in English are about as many as any group possesses. Of course, the words themselves differ. Red becomes rouge in French or adom in Hebrew. Moreover, languages do not select randomly among the possible color terms. If a language has only two basic color terms, speakers of the language divide colors into "light" and "dark." If a language has three color terms (one chromatic term beyond light and dark), what do you think the third usually is? If you guessed red, you are correct. Typically, the fourth color term would be yellow, then green and blue. This ordering is not absolute, but you won't find a language with, for example, just purple, green, and gray as its basic color terms. How do new basic color terms emerge? Berlin and Kay argued that a big color term is partitioned into two smaller terms. Levinson (2000) suggested that new basic terms tend to emerge at the boundary between two existing color terms, in the area where neither existing term works well. In fact, both processes may be at work. Lindsey and Brown (2014) looked at the use of color words in American English by asking 51 Americans to name the color of each of 330 color patches (like paint chips). These observers were told to use a single word. That word had to be a word that could be used for anything of that color (you can't have a "blond" car, can you?). Lindsey and Brown were looking for the sort of word you might use in everyday speech to name the color of a car or a shirt. Those 51 Americans used 122 terms for 330 colors. Everyone used the basic 11, but there was evidence that American English might be moving beyond the 11 basics. A color term teal may become basic. It emerges in the no man's land of colors that are neither blue nor green. A term like purple may eventually be partitioned, with a term like lavender or lilac taking over some of the purple real estate in color space figure: When Lindsey and Brown (2014) asked Americans to name color patches, everyone used the 11 "basic" color names. The other color terms were used by fewer and fewer people, going toward the bottom of the graph. A few of these other terms, like peach and teal, may be considered basic color names over time. If our set of basic color terms increased, would that change the way we see color? If a language has only two or three basic color terms, do its speakers see colors differently than we do with our 11 basic terms? Eleanor Rosch (Heider, 1972) studied this question among the Dani of New Guinea, a tribe whose language has only two basic color terms: mola for light-warm colors and mili for dark-cool colors. Now, it is hard enough to ask your neighbor to define the experience of "blue" and then to ask if that is the same as your experience of "blue." It is much more difficult to ask these questions across a great cultural divide. But there are tricks, as the experiment illustrated in Figure 5.20: It is easier to remember which of two colors you have seen if the choices are categorically different. For example, suppose you had to remember the "blue" patch shown at the top of each part of the figure. Picking between two "blues," as in (A), would be rather hard. The task would be easier if one of the choices were "blue" and the other "green," as in (B), even if the distances in color space were the same in the two cases. demonstrates. Suppose you are shown a bluish color chip and asked to remember it. Then you are shown two test chips and asked to pick the color you saw before. Obviously, the less similar the two test colors are, the easier this task is. But more important, you will do better if the wrong choice is on the other side of a color categorical boundary. Color boundaries are sharper than you might think. If you show people a collection of colors and ask, "Which are blue and which are green?" people do the task without much difficulty. If you have to remember a color, as in the task shown in Figure 5.20, you are likely to give it a label like "green" or "blue." If the next color has the same label, you are more likely to be confused than if it has a different label. Rosch found that the Dani's performance on such tasks reflected the same color boundaries, even when their language did not recognize the distinction between the two colors (a Dani might use the term mili for all the colors in Figure 5.20 but would still do better with the task in Figure 5.20B). This finding leads to the conclusion that color perception is not especially influenced by culture and language; blue and green are seen as categorically different, even if one's language does not employ color terms to express this difference. In the late 1990s, Debi Roberson went up the Sepik River in New Guinea to study the Berinmo, whose language, like the Dani's, has a limited set of basic color terms. Unlike previously studied groups, the Berinmo have terms that form novel boundaries in color space. For example, their nol/wor distinction lies in the middle of colors we categorize as green, and may roughly distinguish live from dead or dying foliage. Moreover, when the Berinmo did the color memory task, they performed better across their nol-wor boundary than across the blue-green boundary. English speakers showed the opposite result (Davidoff, Davies, and Roberson, 1999). You might think that this was just about the role of language in memory. If you call a spot "blue" or "green," the word acts as a cue when you see two spots and only one of them is green. However, color boundaries have an influence, even if the task doesn't have a memory component. As you can see in Figure: Observers see four color patches and sim- ply locate the one patch that is different from the other three. Responses are faster and more accurate when the two colors cross a color boundary—in this case, between red and brown. Witzel and Gegenfurtner (2016) measured how long it took to say which one of four patches was of a different color. They used four different pairs of colors selected from red and brown patches. Each patch was separated from the next patch by two just noticeable differences. (If you don't remember "just noticeable differences," revisit Chapter 1 page 7). They found that people were faster to identify the different one and more accurate when the patches were separated by the border between red and brown than when both patches were within the "red" or "brown" category. So the color names matter, even when you compare two colors side by side.

koniocellular

Referring to cells in the koniocellular layer of the lateral genic- ulate nucleus of the thalamus. Konio from the Greek for "dust" referring to the appearance of the cells.

parvocellular

Referring to cells in the parvocellular layers of the lateral genic- ulate nucleus of the thalamus. Parvo from the Greek for "small" referring to the size of the cells.

phototopic

Referring to light intensities that are bright enough to stimulate the cone receptors and bright enough to "saturate" the rod receptors (that is, drive them to their maximum responses). Photopic vision-Light is bright enough to stimulate cone receptors and bright enough to saturate rod

scoptopic

Referring to light intensities that are bright enough to stimulate the rod receptors but too dim to stimulate the cone receptors. Scotopic (night) vision-Light is bright enough to stimulate rod receptors but not enough to stimulate cone receptors (night vision)

monocular

Referring to one eye.

equiluminant

Referring to stimuli that vary in color but not in luminance.

circadian

Referring to the biological cycle that recurs approximately every 24 hours, even in the absence of cues to time of day (via light, clocks, etc.).

mesopic

Referring to the middle range of light intensities.

tetrachromatic

Referring to the rare situation (in humans, at least) where the color of any light is defined by the rela- tionships of four numbers—the outputs of those four receptor types.

binocular

Referring to two eyes.

Step 3: Color Appearance

Returning to the visual system that is giving rise to our conscious perception of the visual world, we have seen that the three cones detect a range of wavelengths. Rods make a small, important contribution to color vision, but only in fairly dim light. Interestingly, the three cone types and the rods are all active in a middle range of light intensities (call the mesopic range) but the visual system never developed "tetrachromacy," color perception based on four photoreceptor types (but we will return to this topic later in this chapter, in the section on genetic differences). The retina and LGN contain cells that have repackaged the three cone signals into cone-opponent difference signals that constrain our ability to see differences between regions. Now is the time to think about what colors will be perceived. color appearance: ●Terms: ○Color Space ■3D space of color based on the three cone types ○Achromatic ■Lacking color (Black, White or Gray) ●HSL is one way to describe colors ○Hue (want is the dominant color) ■The chromatic aspect of color (yellow, red, blue, ect) ○Saturation (how pure is the hue: washed out or vibrant) ■The strength of the hue. (Greyish-Pink-Red) ○Lightness/Luminance: (brightness) ■The perceived intensity of the color (light blue vs dark blue)

Feature Searches (are efficient)

Search for a target defined by a single attribute, such as a salient color or orientation.

guided search

Search in which attention can be restricted to a subset of possible items on the basis of infor- mation about the target item's basic features (e.g., its color).

parallel search

Search in which mul- tiple stimuli are processed at the same time. the objects no longer look like three-dimensional bricks and no longer differ in their apparent ori- entation in depth. Without that distinguishing feature, the light-green-on-top target is harder to find.

3 steps to color perception

Several problems must be solved in order to go from physical wavelengths to the perception of color. 1. Detection. Wavelengths must be detected. 2. Discrimination. We must be able to tell the difference between one wave-length (or mixture of wavelengths) and another. 3. Appearance. We want to assign perceived colors to lights and surfaces in the world. Moreover, we want those perceived colors to go with the object (that rose looks red) and not to change dramatically as the viewing conditions change (that rose should remain red in sun and shadow, for example). We can say at the outset that color researchers have pretty convincing accounts of how color detection and discrimination work. Color appearance is a trickier problem

The Nonselective Pathway Computes Ensemble Statistics

That is, you know that a range of fish orientations is present. They are not all point- ed in exactly the same direction. To know these things, you are computing ensemble statistics

binding problem

The challenge of tying different attributes of visual stimuli (e.g., color, orientation, motion), which are handled by different brain circuits, to the appropriate object so that we perceive a unified object (e.g., red, vertical, moving right).

From Retina to Brain: Repackaging the Information

The cones in the retina are the neural substrate for detection of lights. What is the neural basis for discriminating between lights with different wavelength composition? To tell the difference between different lights, the nervous system will look at differences in the activities of the three cone types. This work begins in the retina. We could send separate L, M, and S signals to the brain, but that approach would be less useful than one might think. For example, the L- and M-cones have very similar sensitivities (see Figure 5.1), so most of the time they are in close agreement: L says, "Lots of light coming from location X." "Yes, lots of light coming from location X," M agrees. Computing differences between cone responses turns out to be a much more useful way to transmit information to the brain. The nervous system computes two differences: (L - M) and ([L + M] - S). Why these two? Comparisons across species suggest that the comparison between S-cones and an LM-cone happened first, perhaps 500 million years ago (Mollon, 1989). Then, about 40 million years ago, the LM-cone split into very similar L- and M- cones, and the difference between those cone types turned out to be useful. We don't know exactly why the L-M comparison became important in color vision, but there are theories. For example, blushing and turning pale are useful signals to observe, and our specific photopigments may have evolved to help us see those signals (Changizi, Zhang, and Shimojo, 2006) by making it possible to discriminate different amounts of blood in skin. That is not the only possibility. The (L - M) difference may also be very useful if you want to tell the difference between fruit and leaves, different shades of green in the foliage, or the ripeness of a berry. In addition to (L - M), we could create (L - S) and (M - S) signals. However, because L and M are so similar, a single comparison between S and (L + M) can capture almost the same information that would be found in (L - S) and (M - S) signals. Finally, combining L and M signals is a pretty good measure of the intensity of the light (S-cones make a rather small contribution to our per- ception of brightness). Thus, on theoretical grounds, it might be wise to convert the three cone signals into three new signals—(L - M), ([L + M] - S), and (L + M) and the visual system does something reasonably close to this. Repacking Retina Information ●Signal input for the detection of light start at the retina and is "repacked" before sent to the brain (L,M,&S signal) ● Computing difference between signal strengths ○(L+M) tells us about luminance ○i.e. (L-M) is good at telling if fruit is ripe ○([L+M]-S) distinguish blue and yellow ●Combining signals to determine brightness (luminance) ●In the end your color system relies on three basic signals; (L+M), (L-M), ([L+M]-S)

binocular disparity

The differences between the two retinal images of the same scene. Disparity is the basis for stereopsis, a vivid perception of the three-dimensionality of the world that is not available with monocular vision.

principal of univariance

The fact that an infinite set of different wave- length-intensity combinations can elicit exactly the same response from a single type of photoreceptor. One photoreceptor type cannot make color discriminations based on wavelength.

probability summation

The increased detection probability based on the statistical advantage of having two (or more) detectors rather than one.

Genetic Differences in Color Vision

The individual differences described in the previous two sections are either small or, in the case of inverted qualia (singular quale), hypothetical. Under most cir- cumstances, if you declare two lights to be metamerically matched, those around you will generally agree, even if we can't make definitive statements about their qualia. There will be some variation between individuals. For example, unique green can vary between observers from at least 495 to 530 nm (Nerger, Volbrecht, and Ayde, 1995). Some of these differences will be due to factors like age, which turns the lens of the eye yellow (Werner, Peterzell, and Scheetz, 1990). To a first approximation, however, your performance on standard measures of color vision will be the same as others'. However, there is a significant exception to this universality of color matching. Some 8% of the male population and 0.5% of the female population have a form of color vision deficiency commonly known as "color blindness," in which there is a malfunction in one or more of the genes coding the three cone photopigments. It's a "guy thing" because the genes that code for the M- and L-cone photopigments are on the X chromosome (Nathans, 1986). Males have only one copy of the X chromosome, so if one is defective, the male in question will have a problem. Females have two copies and can have normal color vision even if one copy is abnormal. In fact, some women can end up with four different cone pigments, and in very rare cases, that produces tetrachromatic color vision—color vision based on four numbers per patch of light (Jordan et al., 2010). Such individuals may actually see colors that trichromats cannot see. The S-cone photopigment is coded elsewhere, so everyone has two copies, and therefore S-cone color deficiencies are rare There are a number of different types of color blindness. One determining factor is the type of cone affected. A second factor is the type of defect; either the photopigment for that cone type is anomalous (different from the norm) or the cone type is missing altogether. Although we call people who are missing one cone type "color-blind," it is a mistake to think that this means they cannot see colors at all. As you will recall, if you have all three cones with their standard photopigments, you need three primary colors to make a metameric match with an arbitrary patch of color. If you have two cone types rather than three, the normally three-dimensional color space becomes a two-dimensional space. The world will still be seen in color, but you will have a "flatter" color experience, different from that of people with normal color vision. Because M- and L-cone defects are the most common, most color-blind in- dividuals have difficulty discriminating lights in the middle-to-long-wavelength range. For example, consider the wavelengths 560 and 610 nm. Neither of these lights activates S-cones very much, and the L-cones fire at about the same rate for both. But most of us can distinguish the lights on the basis of the M-cone outputs they elicit, which will be higher for the 560-nm light than for the 610-nm light (you can confirm these assertions by consulting Figure 5.5). English-speaking trichromats would label the colors of these two lights as "green" and "reddish orange," respectively. Now consider a deuteranope, someone who has no M-cones. His photoreceptor output to these two lights will be identical. Following our maxim that the rest of the visual system knows only what the photoreceptors tell it, 560- and 610-nm lights must and will be classified as the same color by our deuteranopic individual. A protanope—someone who has no L-cones—will have a different set of color matches based on the outputs of his two cone types (M and S). And a tritanope—with no S-cones—will be different again. Genetic factors can also make people color-anomalous. Color-anomalous individuals typically have three cone photopigments, but two of them are so similar that these individuals experience the world in much the same way as individuals with only two cone types. We actually have some notion of exactly what the world looks like to col- or-deficient individuals, because there are a few, very rare cases of individuals who are color-blind in only one eye. They can compare what they see with the color-blind eye to what they see with the normal eye, enabling us to reconstruct the appearance of the color-blind world (MacLeod and Lennie, 1976). True color blindness does occur but it is very unusual. It is possible to be a cone monochromat, with only one type of cone in the retina. Cone monochromats (who also have rods) live in a one-dimensional color space, seeing the world only in shades of gray. Even more visually impaired are rod monochromats, who are missing cones altogether. Because the rods work well only in dim light and are generally absent in the fovea, these individuals not only fail to discriminate colors, but also have very poor acuity and serious difficulties seeing under normal daylight conditions. We already mentioned one other very interesting class of color blindness, coming not from photoreceptor problems but from damage to the visual cortex. Lesions of specific parts of the visual cortex beyond primary visual cortex can cause achromatopsia. An achromatopsic individual sees the world as drained of color, even while showing evidence that wavelength information is processed at earlier stages in the visual pathway. Brain lesions can also produce various forms of color agnosia or anomia (Oxbury, Oxbury, and Humphrey, 1969). In agnosia, the patient can see something but fails to know what it is. Anomia is an inability to name—in this case, an inability to name colors. A patient with anomia might be able to pick the banana that "looks right" but unable to report that the banana is or should be yellow. color blindness: The discovery of color blindness not until 1798! Dalton studied color blindness. ○Correctly: suggested color blindness was hereditary (both he and his brother were color blind). ○Incorrectly: Suggested it was due to blueish vitreous humor. ■Those with normal vision have similar problems when viewing scenes with blue-tinted glasses. ■To test, had his own eyes posthumously dissected. color blindess more in males due to X chromosome: ●Long wavelength (red) cones anomalous(meaning not firing normally) (Protanomaly, PA 1% of men) or absent (Protanopia, P 1% of males) ●Medium (green) cones absent (Deuteranopia, 1% of men) or anomalous Deuteranomaly (5% of men so most common type) ●Short (blue) cones absent (Tritanopia) or anomalous (Tritanomaly) - very rare, not sex linked (chromosome 7) Cure for common color blindness: Notch filter glasses help accentuate red/green boundary filtering out other parts of the electromagnetic spectrum

spectral reflectance function

The percentage of a particular wavelength that is reflected from a surface.

reflectance

The percentage of light hitting a surface that is reflected and not absorbed into the surface. Typically reflectance is given as a function of wavelength.

spectral power distribution

The physical energy in a light as a function of wavelength.

neutral point

The point at which an opponent color mechanism is gen- erating no signal. If red-green and blue-yellow mechanisms are at their neutral points, a stimulus will appear achromatic. (The black-white process has no neutral point.)

the issue in chapter 6

The problem that the visual system needs to solve is how to construct a three-dimensional world based on the inverted images on the retina of each eye. Parallel lines in the world do not necessarily remain parallel in the retinal image, as Figure 6.1 illustrates: the Euclidean geometry of the three-dimensional world turns into something quite different on the curved, two-dimensional retina. In the Euclidean world, the angles of a triangle add up to 180 degrees. In the non-Euclidean world of the retinal image, this need not be so. The angles of triangles don't always add up to 180 degrees. The retinal area occupied by an object gets smaller as the object moves farther away from the eyeball. What all this means is that if we want to appreciate the three-dimensional world, we have to reconstruct it from the distorted retinal input. To be more precise, as a general rule our visual experience is a reconstruction of the world based on two distorted inputs: the two distinct retinal images. Close your left eye, stretch your left arm out in front of you, and hold up your left index finger. Then hold up your right index finger about 6 inches in front of your face so that it appears to be positioned just to the left of the left index finger, as illustrated in Figure 6.2: The two retinal images of a three-dimensional world are not the same. Now, quickly open your left eye and close your right eye. If you positioned your fingers properly, your right finger will jump to the other side of your left finger. Although this demonstration is designed to exaggerate the different views of your two eyes, the point is a general one: the two retinal images always differ. They differ because your two eyeballs (and their two retinas) are in slightly different places in your head. Just as you and the person standing next to you see somewhat different views of the world, so do your two eyes. Much of this chapter will be devoted to explaining how the visual system goes to quite elaborate lengths both to exploit and to reconcile these differences. Why have two eyes at all? Perhaps most fundamentally, having two eyes confers the same evolutionary advantage as having two lungs or two kidneys: you can lose one eye and still be able to see. A second advantage to doubling the number of eyes is that they enable you to see more of the world. This is especially true for animals like rabbits who have lateral eyes on the sides of their heads. A rabbit can actually see for 360 degrees around its head Figure 6.3 : Comparing rabbit and human visual fields. (A) A rabbit's lateral eyes can see all around its head. It can even see above its head, making its visual field something like a planetarium dome (with ears). (B) The human visual field is more like a windshield covering a large region in front of the eyes. Light purple indicates the entire visual field; dark purple, the part of the visual field seen by both eyes. This explains why it is so hard to sneak up on a rabbit. Moreover, the rabbit can also see straight up above its head Humans, with frontal eyes, still see more of the world with two eyes than with one. Our visual field is limited to about 190 degrees from left to right, 110 degrees of which is covered by both eyes (Figure 6.3B). The field is more restricted vertically: about 140 degrees, 60 degrees up to a limit defined by your eyebrows and 80 degrees down to your cheeks The exact size of your visual field will be limited by the specific anatomy of your cheeks and eyebrows. Overlapping, frontal, binocular visual fields give predator animals such as humans a better chance to spot small, fast-moving objects in front of them that might provide dinner. Prey animals, like rabbits, are often those with very wide visual fields allowing them to monitor the whole scene for predators. With frontal eyes and overlapping visual fields, you also get the advantage of two detectors looking at the same thing. For example, if two independent people each had a 50% chance of missing a target, the chance that both of them would miss it would be 50% × 50% = 25%. So the chance that at least one of the two would find that target is 100% - 25%, or 75%. This is known as probability summation. Something similar happens if the two eyes both look for the same hard-to-see target. In vision, this would be called binocular summation (Blake, Sloane, and Fox, 1981). Binocular summation may have provided the evolutionary pressure that first moved eyes toward the front of some birds' and mammals' faces. Under most circumstances, we do not get complete probability summa- tion. The increase from a 50% chance to a 75% chance assumes two completely independent observers, but our two eyes are not independent, because they are embedded in one person. Nevertheless, we will do better at many tasks with two eyes than with just one Once the eyes moved to the front, though, evolution found an additional use for overlapping visual fields. Try this: Take the top off a pen and hold the top in one hand and the pen in the other. Hold both about a foot in front of your face, with your elbows bent. Now, close one eye and try to quickly put the cap on the pen. Repeat the same task with both eyes open. For most (but not all) people, this task is easier with two eyes than with one. This is a quick demonstration of the usefulness of binocular disparity—the differences between the two retinal images of the same world. Disparity is the basis for a vivid perception of the three-dimensionality of the world that is not available with purely monocular (one-eyed) vision. The technical term for this binocular perception of depth is stereopsis. The geometric and physiological bases for stereopsis are the topic of a large portion of this chapter (see also I. P. Howard and Rogers, 2001). Stereopsis is special in that it can provide very-high-resolution depth information, in the absence of other cues (McKee, 1983). When you decide you need a break from reading this chapter, take that break with one eye closed. You should be able to notice the loss of stereopsis (and of part of your visual field), but a period of one-eyed visual experience will also make it clear that stereopsis is not a necessary condition for depth perception or space perception. Rabbits do very well with very little binocular vision, and painters and movie directors manage to convey realistic impressions of depth on flat canvases and movie screens. On the other hand, stereopsis does add a richness to perception of the three-dimensional world, as vividly described by Oliver Sacks (2006) in his article about "Stereo Sue," a neuroscientist who regained stereopsis at the age of 48. We will talk about her later. In this chapter, first we describe the set of monocular depth cues to three- dimensional space. After that, we turn to the more complicated topic of binocular stereopsis, a binocular depth cue. Finally, we consider how the various cues are combined to produce a unified perception of space.

The Limits of the Rainbow

The spectrum that you can see in the "hue" slider is like the rainbow/wavelength spectrum There are hues that you can see that do not exist on the wavelength spectrum. Figure:If you combine lights that look red and blue, the result will look purple, but there is no purple on the spectrum (A). Purples are nonspectral colors that join the ends of the spectrum into a color circle (B). (figure of the hue slider: A color picker may offer several ways to specify a color in a three-dimen- sional color space. These "sliders" from Microsoft® PowerPoint® are based on an RGB (red, green, blue) set of primaries in (A, C) and on HSB (hue, saturation, brightness) in(B, D).) This figure illustrates how this comes to be. For example, suppose we combined a pure 420-nm light with a pure 680-nm light. This combination would strongly stimulate the L- and S-cones and produce minimal stimulation in the M-cones. No single wavelength of light could do that, but such a mixture is visible and must look like something. In fact, such mixtures will produce purplish magentas that seem to us to lie naturally between red and blue on a color circle. When we wrap the wavelength spectrum into a color circle, we join the red and blue ends with a set of colors that are called nonspectral hues—hues that can arise only from mixtures of wavelengths. While we are on the topic, it is worth noting that there are other commonly perceived colors that are not included in the spectrum's "all the colors of the rainbow." Brown is one such color. There are no brown lights. Brown is seen when a mixture of wavelengths that would look yellow, greenish yellow, or orange is seen in the company of other, brighter patches of color. You cannot see an isolated brown light in the dark

Individual Differences in Color Perception

Thus far, with a little caution, we have been talking about color as if we all see colors the same way, but do we? This is one of those questions that everyone asks at one point or another, often as a child. Like many "childish" questions, this has been the topic of much decidedly unchildlike philosophical work.

attentional blink (AB)

The tendency not to perceive or respond to the second of two different target stimuli amid a rapid stream of distracting stimuli if the observer has responded to the target stimulus 200-500 milliseconds before the second stimulus is presented. we can see that there are several "speed limits" in our mental life. The single-target RSVP experiments tell us that if we are just watching a stream of stimuli over time, we can speed along at a rate of many images per second (perhaps as many as 75 per second; Potter et al., 2014). A much slower rate describes the speed with which we can act on items, even if we are just grabbing them to report about their presence

color constancy (definition)

The tendency of a surface to appear the same color under a fairly wide range of illuminants.

opponent color theory

The theory that perception of color is based on the output of three mechanisms, each of them resulting from an opponency between two colors: red-green, blue-yellow, and black-white.

trichromacy or trichromatic theory of color vision

The theory that the color of any light is defined in our visual system by the relationships of three numbers—the outputs of three receptor types now known to be the three cones. Also called the Young-Helm- holtz theory.

color space

The three-dimensional space, established because color perception is based on the outputs of three cone types, that describes the set of all colors.

A Brief Digression into Lights, Filters, and Finger Paints

The ubiquity of video screens in the twenty-first century may make color mixing and metamers reasonably intuitive. If you've never done so, find a magnifying glass and take a very close look at a yellow patch on your computer screen. You'll find that the patch is actually composed of thousands of intermixed red and green dots. The "red + green = yellow" formula is an example of additive color mixture because we are taking one wavelength or set of wavelengths and adding it to another. For most of us, color mixture begins in kindergarten or earlier, with paints. In that world, red plus green doesn't make yellow; that mixture typically looks brown. A finger paint, or any other pigment, looks a particular color because it absorbs some wavelengths, subtracting them from the broadband ("white") light falling on a surface covered with the pigment. When a toddler smears together red and green, almost all wavelengths are absorbed by one pigment or the other, so we perceive the subtractive color mixture as a dark color like brown. Actually, finger paint mixtures are rather complicated, with some pigment particles sitting next to each other and effectively adding their reflected light to the result. Other particles occlude each other, and still others are engaged in other complex interactions. Colored filters, like those you might put over stage lights, are a cleaner example of subtractive color mixture. Figure: In this example of subtractive color mixture, "white"—broadband—light is passed through two filters. The first one absorbs shorter wavelengths, transmitting a mix of wavelengths that looks yellow. The second absorbs longer wavelengths and the shortest wavelengths, transmit- ting a mix that looks blue. The wavelengths that can pass through both filters without being subtracted are a middle range of wavelengths that appear green. This figure shows how a subtractive mixture of yellow and blue filters would subtract wavelengths, leaving only wavelengths in a middle range, which appear green. An additive mixture of lights that look blue and yellow will look white (if you have exactly the right blue and yellow)(below figure) because that combination produces a mix of wavelengths that stimulate the three cone types roughly equally. In figure: If we shine a light that looks blue and a light that looks yellow on the same patch of paper, the wavelengths will add, producing an additive color mixture. Remember that the light that looks yellow is equivalent to a mix of a long wavelength and a medium wavelength, so blue plus yellow results in a mix of short, medium, and long wavelengths. The mixture looks white (or gray, if it is not the brightest patch in view). Additive color mixture with paints is possible. Georges Seurat (1859-1891) and other Postimpressionist artists of the late nineteenth century experimented with Pointillism, a style of painting that involved creating many hues by placing small spots of just a few colors in different textures. Figure: Pointillism is usually illustrated with a picture by Seurat. For variety, here is a painting by Paul Signac (1863-1935) using additive color mixture in the manner of a modern computer monitor. Thus, from a distance the domes look grayish, even if, up close, they are composed of spots of blue, green, and white. Viewing the painting up close, as illustrated in the figure, we can see each individual dot of color. Like the red, green, and blue phosphor dots on a computer monitor, the dots in the painting combine additively to produce a wide range of colors. Thus, from a distance the water's surface is appropriately silvery gray even if, up close, it is composed of spots of blue and yellow that would not be seen on the surface of a (clean) harbor. Light vs Pigment Illumination: Additive Mixture ●Blue Light + Green Light = Cyan (bluer) Light ●Lights, computer screens Reflection: Subtractive Mixture ●Pigments, dyes, ink ●Acts like filter: Green paint absorbs most of the non-green light, blue paint absorbs most of the non-blue. ●Blue Paint + Green Paint =Forest Green (Greener) Paint

ipsilesional field

The visual field on the same side as a brain lesion.

contralesional field

The visual field on the side opposite a brain lesion. For example, points to the left of fixation are contralesional to damage in the right hemisphere of the brain.

Three Numbers, Many Colors

To begin, it is useful to have a system for talking about all the colors we can see. Because we have exactly three different types of cone photoreceptors, the light reaching any part of the retina will be translated into three responses, one for each local population of cones. After that translation, the rest of the nervous system cannot glean anything more about the physical wavelengths of the light. If the light rays reflecting off two isolated surfaces produce the same set of cone responses, the two surfaces must and will appear to be exactly the same color. They will be metamers, even if their physical characteristics are quite different. Thus, it is possible to produce bloodred color on the page without mimicking the physical properties of blood. Working with just three numbers might not sound very promising, but it has been estimated that, with this system, we can discriminate the surfaces of more than 2 million different colors A lot of these colors are lightness variations of what we would colloquially consider the same color: bloodred in dim light, bloodred in brighter light, and so on. But even if we ignore lightness, we can still distinguish about 26,000 colors Unless you're in paint sales, you don't know this many distinctive color names, but you could tell two different colors apart even if they were both named "pea green" or "sky blue." We need some way to talk about all those colors in an orderly way. We can describe each of the colors in the spectrum with a single number—for example, the light's wavelength. Going beyond the spectrum, we have a three-dimensional color space analogous to a three-dimensional physical space. Of course, it doesn't make much sense to talk about height, length, and width in color space, but the space that contains the set of all perceptible colors has three dimensions based on the three numbers that come from the outputs of the three cone types.

double opponent cells and color boundaries

Used for edge detection/sharpness. But think about the consequences of double-opponent cells and some color combinations...

Two Pathways to Scene Perception

Visual experience can be thought of as a combination of the work of a selective and a nonselective pathway The initial stages of vision are shared by both pathways. The spotlight of attention is associated with the selective pathway Specific locations or objects are selected for the processing that allows for binding and object recognition. The visual world outside the current spotlight is brought to you via the nonselective pathway

The Trichromatic Solution

We can detect differences between wavelengths or mixtures of wavelengths precisely because we have more than one kind of cone photoreceptor. Figure shows how, with our three cone types, we can tell the difference between lights of different wavelengths. Look at the three cones' responses to the two wave- lengths—450 and 625 nm—that produced the same response from the single cone in Figure 5.3. The two wavelengths of light still produce the same response from that type of cone, now revealed to be the M-cone. However, these two wavelengths produce different outputs from the L-cones and S-cones. any wavelength from about 420 to 660 nm produces a unique set of three responses from the three cone types. This combined signal, a triplet of numbers for each "pixel" in the visual field, can be used as the basis for color vision. (We can see from about 400 to 700 nm, but the very long and very short wavelengths each stimulate only one type of cone.) The two wavelengths that produce the same response from one type of cone (M) produce different pat- terns of responses across the three types of cones (S, M, and L). In our discussion of the univariance problem, we noted that we can make any wavelength produce the same response as any other from a single cone type. That is not a problem in the three-cone world of human color vision. A specific light produces a specific set of three responses from the three cone types. Suppose that the light produces twice as much M response as S response and twice as much S response as L response. If we increase the intensity of the light, the response sizes will change but the relationships will not. There will still be twice as much M response as S response and twice as much S response as L response, and those relationships will define our response to the light and, eventually, the color that we see. (Think what might happen if the lights were really bright.) The idea that ability to discriminate one light from another is defined in our visual system by the relationships among three numbers is the heart of trichromacy, or more elaborately, the trichromatic theory of color vision. Trichromatic theory ●In 1802 Thomas Young suggested vision due to 3 types of photoreceptors. ●Hermann von Helmholtz developed the theory further in 1850: that the three types of cone photoreceptors could be classified as short-preferring (blue), middle-preferring (green), and long-preferring (red), according to their response to the wavelengths of light striking the retina. ●Only proven in 1959! ROY G BIV (questions to think about) ●Why does violet (very short wavelengths) look reddish? ●Why does violet look identical to purple? (combining red and blue illumination)

Step 2: Color Discrimination

We can detect wavelengths between 400 and 700 nm, but how do we distinguish, for example, between lights of 450, 550, and 625 nm? To see how discrimina- tion differs from detection, let's examine the response of a single photorecep- tor to a single wavelength of light. Graph shows how one kind of human photoreceptor responds to light of a specific wavelength while the intensity of the light is held constant. Because of the properties of the photopigment in the photoreceptor cell, 400-nm light produces only a small response in each cell of this type, 500-nm light produces a greater response, and 550-nm light even more. However, 600-nm light produces less than the maximal response, and 650-nm light produces a minimal response. Light of 625 nm produces a response of moderate strength. A single photoreceptor shows different responses to lights of different wavelengths but the same intensity. A 625-nm light of this inten- sity, indicated by the arrow, produces a response midway between the maxi- mum and minimum responses. Color Discrimination from Opposition of Colors ●Color discrimination based on relative firing rates of different types of cells. Step 1: The trichromatic stage: Trichromatic cone cells respond positively to one or three frequencies exhibited by photons arriving on their surface Step 2: Opponent Process Stage: The three color channels are discovered by nearby opponent cells. Opponent cells tuned to luminosity are excited by red, green, and blue color signals Cg cells are excited by red and blue and inhibited by green. Cb cells are excited by red and green and inhibited by blue

Fatigue and Habituation

We can make colors disappear.Stare at the fixation dot. ●Roughly isoluminant colors with ill defined edges begin to fade out.

The Principle of Univariance

We know that different wavelengths of light give rise to different experiences of color, and the varying responses of this photoreceptor to different wavelengths could provide a basis for color vision. But there is a problem, Suppose we change the wavelength from 625 nm to 450 nm. Figure 5.3 shows that an equal amount of 450-nm light will produce the same response from this photoreceptor that 625-nm light does. If we were looking at the output of the photoreceptor, we would have no way of distin- guishing between the two lights. But when we look with a normal human color vision system, the 625-nm light looks orange and the 450-nm light looks bluish. Lights of 450 and 625 nm both elicit the same response from the photoreceptor whose responses are shown here and in Figure 5.3. This situation illustrates the principle of univariance. Actually, the problem is much worse. Remember that Figures 5.2 and 5.3 represent the photoreceptor's response rate when all wavelengths are presented at the same intensity. Under these conditions, the graphs indicate that light at either 450 or 625 nm produces a response lower than the peak response obtained at about 535 nm. If we had a 535-nm light, we could reduce its intensity until it produced exactly the same level of response from our photoreceptor as the 450- or 625-nm light did at the higher intensity. Indeed, we could take a "white light" or any mix of wavelengths and, by properly adjusting the intensity, get exactly the same response out of the photoreceptor. Thus, when it comes to seeing color, the output of a single photoreceptor is completely ambiguous. An infinite set of different wavelength-intensity combinations can elicit exactly the same response, so the output of a single photoreceptor cannot by itself tell us anything about the wavelengths stimulating it. This constraint is known as the principle of univariance. Obviously, the human visual system has solved the problem, but not under all circumstances. Univariance explains the lack of color in dimly lit scenes. Remember that there is only one type of rod photoreceptor. All rods contain the same type of photopigment molecule: rhodopsin. Thus, they all have the same sensitivity to wavelength. As a consequence, although it is possible to tell light from dark under scotopic conditions, the problem of univariance makes it impossible to discriminate colors. Our nighttime color blindness is one hint that color is psychophysical and not physical. The world seen under a bright moon has not been physically drained of color. The same mix of wavelengths that produces color perception during the day remains present on that moonlit night, but we fail to see colors under dim illuminants like moonlight, because dim light stimulates only the rods, and the output of that single variety of photoreceptor does not permit color vision. The moonlit world appears drained of color because we have only one type of rod photoreceptor transducing light under these scotopic conditions. With just one type of photoreceptor, we cannot make discriminations based on wavelength, so we cannot see color. Receptor Univariance ●The probability of firing depends on intensity and wavelength. ●Example: M-cell fires at same rate for ○bright cyan (500nm) ○dim green (534nm) ○bright orange (580nm) ●Determining color requires comparing relative firing rates of different types of cones.

Color in the Visual Cortex

We know that transformations that produce perceived color take place in the visual cortex; it is not clear how the physiology gives rise to perception. Many cells in cortex are interested in color but do not seem to linearly add and subtract inputs from different cone types. There is some evidence that they add and subtract in a nonlinear manner. For instance, a cell might be responding to something more like (L2 - M2 + S2), though exactly how this would produce the colors we see remains a topic for future work. For now, we may have to agree with Conway (2014, p. 201): "There is still no good neural explanation for Hering's psychologically important colors ... or ... the unique hues often associated with them." Is there a specific area or are there areas in the brains of monkeys and humans specialized for color vision? In the 1980s, evidence favored a separate pathway for color. There were "blobs" in V1 where cells did not seem interested in orientation but seemed very interested in color. The blobs sent output to "thin stripe" regions in V2 (Livingstone and Hubel, 1988) and from there to V4, an area that Semir Zeki argued was specialized for color, with cells that responded not to wavelength but to perceived color. More recently, functional imaging in monkeys has been used to uncover color hot spots in visual cortex. These have been named "globs" (really!) and cells in these regions also seem more interested in color than do cells outside. However, although these anatomical pathways are there, it has become less clear that we can separate color processing from other perceptual processes in cortical anatomy. Indeed, you may remember from Chapter 4 that V4 is a popular candidate for a "shape" area. This doesn't mean that it is uninterested in color, but merely that it is probably not only interested in color. Modern imaging studies show some areas of the human visual cortex that seem particularly interested in color, but we can't record from single cells in humans under most circumstances. Perhaps the best evidence for specialized brain areas for color in humans comes from certain cases of achromatopsia, a loss of color vision after brain damage. People with achromatopsia may be able to find the boundaries between regions of different colors, but they cannot report what those colors might be. In these individuals, vision is largely intact, while the experience of color seems specifically impaired. color in the visual cortex: ●Single opponent cell ●Double opponent cells ●Used for edge detection/sharpness cortical area or V4: ●Ventral stream (what pathway bottom of brain) sensitive to color. ●In particular, area V4 sensitive to color. ●Injury to V4 leads to achromatopsia: inability to see colors despite healthy retinal vision (very rare)

Many Searches Are Inefficient

When the target and distractors in a visual search task contain the same ba- sic features the search is inefficent

color-anomalous

better term for the commonly used term color-blind. Most "color-blind" individuals can still make discriminations based on wave- length. Those discriminations are differ- ent from the norm—that is, anomalous.

neglect

in reference to a neurologi- cal symptom, in visual attention: 1. The inability to attend or respond to stimuli in the contralesional visual field (typ- ically, the left field after right parietal damage). 2. Ignoring half of the body or half of an object.

conjunction search

search for a target defined by the presence of two or more attributes (e.g., a red, vertical target among red horizontal and blue vertical distractors).

Impossible Chimerical Colors

simultaneously see two colors in one Stygian blue (simultaneously deep blue and black) Self Luminous red (simultaneously red and brighter then white) Hyperbolic orange (more then 100% color saturation)

illuminant

the light that illuminates a surface

Attention Could Enhance the Processing of a Specific Type of Stimulus

we are attending not on the basis of spatial location, but on the basis of a stimulus property. Attentional selection can also activate parts of the brain that are specialized for the processing for one or another type of stimulus.

Step 1: Color Detection

we have three types of cone photoreceptors. These cones differ in the photopigment they carry, and as a result, they differ in their sensitivity to light of different wavelengths. a graph shows those sensitivities as a function of wavelength. Each cone type is named for the location of the peak of its sensitivity on the spectrum: The cones that have a peak at about 420 nm are known as short-wavelength cones, or S-cones. The medium-wavelength cones, or M-cones, peak at about 535 nm. Long-wavelength cones, or L-cones, peak at about 565 nm. cones are not exclusively sensitive to different parts of the spectrum. That is, even though the L-cone is maximally sensitive at about 565 nm, the M-cone can detect that wavelength as well. Their spectral sensitivities overlap. S-cones are relatively rare, and they are less sensitive than M- and L-cones. The combination of sensitivities of the three types of cones gives us our overall ability to detect wavelengths from about 400 nm to about 700 nm cones work at photopic (daylight) light levels. We have one type of rod photoreceptor; it works in scotopic (dimmer) light and has a somewhat different sensitivity profile, peaking at about 500 nm. how do we detect color ●Three color receptors (Ch 2): ○Highly concentrated in fovea ○'Red' and 'Green' most common ○Peak actually violet, green and yellow. ○Each very broad range of sensitivity. why so few blue cones or short cones ●"Red" and "Green" cones have very similar response, but "Blue" is very different. One would expect an optimal design would have a lot of "Blue" cones to provide better color. ●Yet blue receptors very rare. Why? for now think of tuning and that these blu cones actually blur our vision a bit Chromatic aberration ●Simple lenses act like a prism. ●This means that only one wavelength will be perfectly in focus. ●Human visual system designed for ripe fruit (orange/yellow) wavelengths ●Blue inherently out of focus, so blue receptors help with color discrimination but not acuity. No need for lots of them. ●Advantage of seeing the world with rose/orange colored glasses (blue blockers): block blurry blues. Color detection process ●S-Cones - peak 420nm ●M-Cones - peak 535nm ●L-Cones- peak 565nm ●Rods ~498nm○Not sensitive to red light○Red cockpit lights

color vision in evolution

●All vertebrates except placental mammals have double cones: pair sensitive to different wavelengths effectively like primate ganglion cells. ●Placental mammals also lack oil droplets. ●Many mammals have no cones at all (armadillo, bats, hedgehogs) ●It appears that mammals went through a spell of being nocturnal only. ●It is thought that primates recently developed color vision almost as good as our ancestors had 300 million years ago.

How do animals see color?

●Birds are true tetrachromats ●Dogs and most mammals are dichromats (Similar to R/G colorblind in humans) ●Cats are trichromats (but lower proportion of cones to rods) Also UV lights

Color resolution

●Our brain uses brightness to identify edges. ○Top image: high resolution color and brightness. ○Middle: Subsampled brightness ○Bottom: Subsampled color information ●We are very sensitive to intensity edges, but relatively blind to color boundaries. ●Extreme example: even heavily blurred colors look OK. ●This feature exploited by TV and computer images that reduce bandwidth by subsampling color information. ●Exploited with hand painted postcards prior to inexpensive color printing. Simulations: TV screens and digital cameras designed for human vision. ●Screens use Red, Green, Blue elements ●Camera's use Red, Green, Blue sensors. ●Camera's ability impaired to mimic human vision ('hot mirror' filters infrared) ●Camera acquires twice as many samples for green as red or blue, providing luminance accuracy at expense of color. ●A Starling would not be impressed.

Commission Internationale de l'Elcairage (CIE)

●The edges of the CIE represent colors that can be represented by single wavelengths (monochromatic light) ●We can divide this into four categories based on four primary psychological hues) (B/Y, Magenta/G) Basically continues talking about chromaticity

The color purple

●Violet is a pure spectral color - very short wave length. ●Purple is not a spectral color - mixture of blue and red light. ●Human eye can not distinguish between violet and some purples.

adaptation

●When looking at perimeter ○Purple dot turns gray. ●Stair at center: ○Purple dot turns green ○Eventually purple disappears: we only see a rotating green dot. At this point we no longer perceive the real colors yet still perceive illusory color.