Dr. L., a young neuropsychologist, was presenting the case of Mrs. R. to a group of medical students doing a rotation in the neurology department at the medical center. The chief of the department had shown them Mrs. R.’s CT scans, and now Dr. L. was addressing the students. He told them that Mrs. R.’s stroke had not impaired her ability to talk or to move about, but it had affected her vision.

A nurse ushered Mrs. R. into the room and helped her find a seat at the end of the table.

“How are you, Mrs. R.?” asked Dr. L.

“I’m fine. I’ve been home for a month now, and I can do just about everything that I did before I had my stroke.”

“Good. How is your vision?”

“Well, I’m afraid that’s still a problem.”

“What seems to give you the most trouble?”

“I just don’t seem to be able to recognize things. When I’m working in my kitchen, I know what everything is as long as no one moves anything. A few times my husband tried to help me by putting things away, and I couldn’t see them any more.” She laughed. “Well, I could see them, but I just couldn’t say what they were.”

Dr. L. took some objects out of a paper bag and placed them on the table in front of her.

“Can you tell me what these are?” he asked. “No,” he said, “please don’t touch them.”

Mrs. R. stared intently at the objects. “No, I can’t rightly say what they are.”

Dr. L. pointed to one of them, a wristwatch. “Tell me what you see here,” he said.

Mrs. R. looked thoughtful, turning her head one way and then the other. “Well, I see something round, and it has two things attached to it, one on the top and one on the bottom.” She continued to stare at it. “There are some things inside the circle, I think, but I can’t make out what they are.”

“Pick it up.”

She did so, made a wry face, and said, “Oh. It’s a wristwatch.” At Dr. L.’s request, she picked up the rest of the objects, one by one, and identified each of them correctly.

“Do you have trouble recognizing people, too?” asked Dr. L.

“Oh, yes!” she sighed. “While I was still in the hospital, my husband and my son both came in to see me, and I couldn’t tell who was who until my husband said something—then I could tell which direction his voice was coming from. Now I’ve trained myself to recognize my husband. I can usually see his glasses and his bald head, but I have to work at it. And I’ve been fooled a few times.” She laughed. “One of our neighbors is bald and wears glasses, too, and one day when he and his wife were visiting us, I thought he was my husband, so I called him ‘honey.’ It was a little embarrassing at first, but everyone understood.”

“What does a face look like to you?” asked Dr. L.

“Well, I know that it’s a face, because I can usually see the eyes, and it’s on top of a body. I can see a body pretty well, by how it moves.” She paused a moment. “Oh, yes, I forgot, sometimes I can recognize a person by how he moves. You know, you can often recognize friends by the way they walk, even when they’re far away. I can still do that. That’s funny, isn’t it? I can’t see people’s faces very well, but I can recognize the way they walk.”

Dr. L. made some movements with his hands. “Can you tell what I’m pretending to do?” he asked.

“Yes, you’re mixing something—like some cake batter.”

He mimed the gestures of turning a key, writing, and dealing out playing cards, and Mrs. R. recognized them without any difficulty.

“Do you have any trouble reading?” he asked.

“Well, a little, but I don’t do too badly.”

Dr. L. handed her a magazine, and she began to read the article aloud—somewhat hesitantly but accurately. “Why is it,” she asked, “that I can see the words all right but have so much trouble with thingsand with people’s faces?”

As we saw in Chapter 3 , the brain performs two major functions: It controls the movements of the muscles, producing useful behaviors, and it regulates the body’s internal environment. To perform both these tasks, the brain must be informed about what is happening both in the external environment and within the body. Such information is received by the sensory systems. This chapter and the next are devoted to a discussion of the ways in which sensory organs detect changes in the environment and the ways in which the brain interprets neural signals from these organs.

We receive information about the environment from sensory receptors —specialized neurons that detect a variety of physical events. (Do not confuse sensory receptors with receptors for neurotransmitters, neuromodulators, and hormones. Sensory receptors are specialized neurons, and the other types of receptors are specialized proteins that bind with certain molecules.) Stimuli impinge on the receptors and, through various processes, alter their membrane potentials. This process is known as sensory transduction because sensory events are transduced (“transferred”) into changes in the cells’ membrane potential. These electrical changes are called receptor potentials . Most receptors lack axons; a portion of their somatic membrane forms synapses with the dendrites of other neurons. Receptor potentials affect the release of neurotransmitters and hence modify the pattern of firing in neurons with which these cells form synapses. Ultimately, the information reaches the brain.

sensory receptor A specialized neuron that detects a particular category of physical events.

sensory transduction The process by which sensory stimuli are transduced into slow, graded receptor potentials.

receptor potential A slow, graded electrical potential produced by a receptor cell in response to a physical stimulus.

FIGURE 6.1 The Electromagnetic Spectrum

People often say that we have five senses: sight, hearing, smell, taste, and touch. Actually, we have more than five, but even experts disagree about how the lines between the various categories should be drawn. Certainly, we should add the vestibular senses; as well as providing us with auditory information, the inner ear supplies information about head orientation and movement. The sense of touch (or, more accurately, somatosensation) detects changes in pressure, warmth, cold, vibration, limb position, and several different kinds of events that damage tissue (that is, produce pain). Everyone agrees that we can detect all of these stimuli; the issue is whether we should say that they are detected by separate senses.

This chapter considers vision, the sensory modality that receives the most attention from psychologists, anatomists, and physiologists. One reason for this attention derives from the fascinating complexity of the sensory organs of vision and the relatively large proportion of the brain that is devoted to the analysis of visual information. Approximately 20 percent of the cerebral cortex plays a direct role in the analysis of visual information (Wandell, Dumoulin, and Brewer, 2007 ). Another reason, I am sure, is that vision is so important to us as individuals. A natural fascination with such a rich source of information about the world leads to curiosity about how this sensory modality works. Chapter 7 deals with the other sensory modalities: audition, the vestibular senses, the somatosenses, gustation, and olfaction.

The Stimulus

As we all know, our eyes detect the presence of light. For humans, light is a narrow band of the spectrum of electromagnetic radiation. Electromagnetic radiation with a wavelength of between 380 and 760 nm (a nanometer, nm, is one-billionth of a meter) is visible to us. (See Figure 6.1 . ) Other animals can detect different ranges of electromagnetic radiation. For example, honeybees can detect differences in ultraviolet radiation reflected by flowers that appear white to us. The range of wavelengths we call light is not qualitatively different from the rest of the electromagnetic spectrum; it is simply the part of the continuum that we humans can see.

The perceived color of light is determined by three dimensions: hue, saturation, and brightness. Light travels at a constant speed of approximately 300,000 kilometers (186,000 miles) per second. Thus, if the frequency of oscillation of the wave varies, the distance between the peaks of the waves will vary similarly but in inverse fashion. Slower oscillations lead to longer wavelengths, and faster ones lead to shorter wavelengths. Wavelength determines the first of the three perceptual dimensions of light: hue . The visible spectrum displays the range of hues that our eyes can detect.

hue One of the perceptual dimensions of color; the dominant wavelength.

Light can also vary in intensity, which corresponds to the second perceptual dimension of light: brightness . If the intensity of the electromagnetic radiation is increased, the apparent brightness increases, too. The third dimension, saturation , refers to the relative purity of the light that is being perceived. If all the radiation is of one wavelength, the perceived color is pure, or fully saturated. Conversely, if the radiation contains all visible wavelengths, it produces no sensation of hue—it appears white. Colors with intermediate amounts of saturation consist of different mixtures of wavelengths. Figure 6.2 shows some color samples, all with the same hue but with different levels of brightness and saturation. (See Figure 6.2 . )

brightness One of the perceptual dimensions of color; intensity.

saturation One of the perceptual dimensions of color; purity.

FIGURE 6.2 Saturation and Brightness

This figure shows examples of colors with the same dominant wavelength (hue) but different levels of saturations or brightness.

Anatomy of the Visual System

For an individual to see, an image must be focused on the retina, the inner lining of the eye. This image causes changes in the electrical activity of millions of neurons in the retina, which results in messages being sent through the optic nerves to the rest of the brain. (I said “the rest” because the retina is actually part of the brain; it and the optic nerve are in the central—not peripheral—nervous system.) This section describes the anatomy of the eyes, the photoreceptors in the retina that detect the presence of light, and the connections between the retina and the brain.

The Eyes

The eyes are suspended in the orbits, bony pockets in the front of the skull. They are held in place and moved by six extraocular muscles attached to the tough, white outer coat of the eye called the sclera. (See Figure 6.3 . ) Normally, we cannot look behind our eyeballs and see these muscles because their attachments to the eyes are hidden by the conjunctiva. These mucous membranes line the eyelid and fold back to attach to the eye (thus preventing a contact lens that has slipped off the cornea from “falling behind the eye”). Figure 6.4 illustrates the anatomy of the eye. (See Figure 6.4 . )

FIGURE 6.3 The Extraocular Muscles, Which Move the Eyes

The eyes make three types of movements: vergence movements, saccadic movements, and pursuit movements. Vergence movements are cooperative movements that keep both eyes fixed on the same target—or, more precisely, that keep the image of the target object on corresponding parts of the two retinas. If you hold up a finger in front of your face, look at it, and then bring your finger closer to your face, your eyes will make vergence movements toward your nose. If you then look at an object on the other side of the room, your eyes will rotate outward, and you will see two separate blurry images of your finger.

vergence movement The cooperative movement of the eyes, which ensures that the image of an object falls on identical portions of both retinas.

When you scan the scene in front of you, your gaze does not roam slowly and steadily across its features. Instead, your eyes make jerky saccadic movements —you shift your gaze abruptly from one point to another. (Saccade comes from the French word for “jerk.”) When you read a line in this book, your eyes stop several times, moving very quickly between each stop. You cannot consciously control the speed of movement between stops; during each saccade the eyes move as fast as they can. Only by performing a pursuit movement —say, by looking at your finger while you move it around—can you make your eyes move more slowly.

saccadic movement ( suh kad ik ) The rapid, jerky movement of the eyes used in scanning a visual scene.

pursuit movement The movement that the eyes make to maintain an image of a moving object on the fovea.

FIGURE 6.4 The Human Eye

The white outer layer of most of the eye, the sclera, is opaque and does not permit entry of light. However, the cornea, the outer layer at the front of the eye, is transparent. The amount of light that enters is regulated by the size of the pupil, which is an opening in the iris, the pigmented ring of muscles situated behind the cornea. The lens, situated immediately behind the iris, consists of a series of transparent, onionlike layers. Its shape can be altered by contraction of the ciliary muscles. These changes in shape permit the eye to focus images of near or distant objects on the retina—a process called accommodation .

accommodation Changes in the thickness of the lens of the eye, accomplished by the ciliary muscles, that focus images of near or distant objects on the retina.

After passing through the lens, light traverses the main part of the eye, which is filled with vitreous humor(“glassy liquid”), a clear, gelatinous substance. After passing through the vitreous humor, light falls on the retina , the interior lining of the back of the eye. In the retina are located the receptor cells, the rods and cones (named for their shapes), collectively known as photoreceptors .

retina The neural tissue and photoreceptive cells located on the inner surface of the posterior portion of the eye.

rod One of the receptor cells of the retina; sensitive to light of low intensity.

cone One of the receptor cells of the retina; maximally sensitive to one of three different wavelengths of light and hence encodes color vision.

photoreceptor One of the receptor cells of the retina; transduces photic energy into electrical potentials.

The human retina contains approximately 120 million rods and 6 million cones. Although they are greatly outnumbered by rods, cones provide us with most of the visual information about our environment. In particular, they are responsible for our daytime vision. They provide us with information about small features in the environment and thus are the source of vision of the highest sharpness, or acuity (from the Latin acus, “needle”). The fovea , or central region of the retina, which mediates our most acute vision, contains only cones. Cones are also responsible for color vision—our ability to discriminate light of different wavelengths. Although rods do not detect different colors and provide vision of poor acuity, they are more sensitive to light. In a very dimly lighted environment we use our rod vision; therefore, in very dim light we are color-blind and lack foveal vision. (See Table 6.1 . )

fovea (foe vee a ) The region of the retina that mediates the most acute vision of birds and higher mammals. Color-sensitive cones constitute the only type of photoreceptor found in the fovea.

Another feature of the retina is the optic disk , where the axons conveying visual information gather together and leave the eye through the optic nerve. The optic disk produces a blind spot because no receptors are located there. We do not normally perceive our blind spots, but their presence can be demonstrated. If you have not found yours, you may want to try the exercise described in Figure 6.5 .

optic disk The location of the exit point from the retina of the fibers of the ganglion cells that form the optic nerve; responsible for the blind spot.

TABLE 6.1 Locations and Response Characteristics of Photoreceptors

Cones	Rods
Most prevalent in the central retina; found in the fovea	Most prevalent in the peripheral retina; not found in the fovea
Sensitive to moderate to high levels of light	Sensitive to low levels of light
Provide information about hue	Provide only monochromatic information
Provide excellent acuity	Provide poor acuity

Close examination of the retina shows that it consists of several layers of neuron cell bodies, their axons and dendrites, and the photoreceptors. Figure 6.6 illustrates a cross section through the primate retina, which is divided into three main layers: the photoreceptive layer, the bipolar cell layer, and the ganglion cell layer. Note that the photoreceptors are at the back of the retina; light must pass through the overlying layers to get to them. Fortunately, these layers are transparent. (See Figure 6.6 . )

The photoreceptors form synapses with bipolar cells , neurons whose two arms connect the shallowest and deepest layers of the retina. In turn, bipolar cells form synapses with the ganglion cells , neurons whose axons travel through the optic nerves (the second cranial nerves) and carry visual information into the rest of the brain. In addition, the retina contains horizontal cells and amacrine cells , both of which transmit information in a direction parallel to the surface of the retina and thus combine messages from adjacent photoreceptors. (Look again at Figure 6.6 . )

bipolar cell A bipolar neuron located in the middle layer of the retina, conveying information from the photoreceptors to the ganglion cells.

ganglion cell A neuron located in the retina that receives visual information from bipolar cells; its axons give rise to the optic nerve.

horizontal cell A neuron in the retina that interconnects adjacent photoreceptors and the outer processes of the bipolar cells.

amacrine cell (amm a krine ) A neuron in the retina that interconnects adjacent ganglion cells and the inner processes of the bipolar cells.

The primate retina contains approximately 55 different types of neurons: one type of rod, three types of cones, two types of horizontal cells, ten types of bipolar cells, 24–29 types of amacrine cells, and 10–15 types of ganglion cells (Masland, 2001 ).

Photoreceptors

Figure 6.7 shows a drawing of two rods and a cone. Note that each photoreceptor consists of an outer segment connected by a cilium to the inner segment, which contains the nucleus. (See Figure 6.7 . ) The outer segment contains several hundred lamellae , or thin plates of membrane. (Lamella is the diminutive form of lamina, “thin layer.”)

lamella A layer of membrane containing photopigments; found in rods and cones of the retina.

FIGURE 6.5 A Test for the Blind Spot

With your left eye closed, look at the plus sign with your right eye and move the page nearer to and farther from you. When the page is about 20 cm from your face, the green circle disappears because its image falls on the blind spot of your right eye.

FIGURE 6.6 Details of Retinal Circuitry

(Adapted from Dowling, J. E., and Boycott, B. B. Proceedings of the Royal Society of London, B, 1966, 166, 80–111.)

Let’s consider the nature of transduction of visual information. The first step in the chain of events that leads to visual perception involves a special chemical called a photopigment. Photopigments are special molecules embedded in the membrane of the lamellae; a single human rod contains approximately 10 million of them. The molecules consist of two parts: an opsin (a protein) and retinal (a lipid). There are several forms of opsin; for example, the photopigment of human rods, rhodopsin , consists of rod opsinplus retinal. (Rhod- refers to the Greek rhodon, “rose,” not to rod. Before it is bleached by the action of light, rhodopsin has a pinkish hue.) Retinal is synthesized from vitamin A, which explains why carrots, which are rich in this vitamin, are said to be good for your eyesight.

photopigment A protein dye bonded to retinal, a substance derived from vitamin A; responsible for transduction of visual information.

opsin (opp sin ) A class of protein that, together with retinal, constitutes the photopigments.

retinal (rett i nahl ) A chemical synthesized from vitamin A; joins with an opsin to form a photopigment.

rhodopsin ( roh dopp sin ) A particular opsin found in rods.

FIGURE 6.7 Photoreceptors

When a molecule of rhodopsin is exposed to light, it breaks into its two constituents: rod opsin and retinal. When that happens, the rod opsin changes from its rosy color to a pale yellow; hence, we say that the light bleaches the photopigment. The splitting of the photopigment produces the receptor potential: hyperpolarization of the membrane of the photoreceptor.

In the vertebrate retina, photoreceptors provide input to both bipolar cells and horizontal cells. Figure 6.8 shows the neural circuitry from a photoreceptor to a ganglion cell. The circuitry is much simplified and omits the horizontal cells and amacrine cells. The first two types of cells in the circuit—photoreceptors and bipolar cells—do not produce action potentials. Instead, their release of the neurotransmitter (glutamate) is regulated by the value of their membrane potential; depolarizations increase the release, and hyperpolarizations decrease it. The contents of the circles indicate what would be seen on an oscilloscope screen recording changes in the cells’ membrane potentials in response to a spot of light shining on the photoreceptor.

The hyperpolarizing effect of light on the membrane of a photoreceptor is shown in the left circle. In the dark, photoreceptors constantly release their neurotransmitter. When light strikes molecules of the photopigment, the hyperpolarization that ensues reduces the amount of neurotransmitter released by the photoreceptor. Because the neurotransmitter normally hyperpolarizes the dendrites of the bipolar cell, a reduction in its release causes the membrane of the bipolar cell to depolarize. Thus, light hyperpolarizes the photoreceptor and depolarizes the bipolar cell. (See Figure 6.8 . ) The depolarization of the bipolar cell causes it to release more neurotransmitter, which depolarizes the membrane of the ganglion cell and raises this cell’s rate of firing. Thus, light shining on the photoreceptor excites the ganglion cell and increases the rate of firing of its axon.

The circuit shown in Figure 6.8 illustrates a ganglion cell whose firing rate increases in response to light. As we will see, other ganglion cells decrease their firing rate in response to light. These neurons are connected to bipolar cells that form different types of synapses with the photoreceptors. The functions of these two types of circuits are discussed in the next section, “Coding of Visual Information in the Retina.”

FIGURE 6.8 Neural Circuitry in the Retina

Light striking a photoreceptor produces a hyperpolarization, so the photoreceptor releases less neurotransmitter. Because the neurotransmitter normally hyperpolarizes the membrane of the bipolar cell, the reduction causes a depolarization. This depolarization causes the bipolar cell to release more neurotransmitter, which excites the ganglion cell.

(Adapted from Dowling, J. E., in The Neurosciences: Fourth Study Program, edited by F. O. Schmitt and F. G. Worden. Cambridge, Mass.: MIT Press, 1979.)

Connections Between Eye and Brain

The axons of the retinal ganglion cells bring information to the rest of the brain. They ascend through the optic nerves and reach the dorsal lateral geniculate nucleus (LGN) of the thalamus. This nucleus receives its name from its resemblance to a bent knee (genu is Latin for “knee”). It contains six layers of neurons, each of which receives input from only one eye. The neurons in the two inner layers contain cell bodies that are larger than those in the outer four layers. For this reason the inner two layers are called the magnocellular layers , and the outer four layers are called the parvocellular layers (parvo- refers to the small size of the cells). A third set of neurons in the koniocellular sublayers are found ventral to each of the magnocellular and parvocellular layers. (Konis is Greek word for “dust.”) As we will see later, these three sets of layers belong to different systems, which are responsible for the analysis of different types of visual information. They receive input from different types of retinal ganglion cells. (See Figure 6.9 . )

dorsal lateral geniculate nucleus (LGN) A group of cell bodies within the lateral geniculate body of the thalamus; receives inputs from the retina and projects to the primary visual cortex.

magnocellular layer One of the inner two layers of neurons in the dorsal lateral geniculate nucleus; transmits information necessary for the perception of form, movement, depth, and small differences in brightness to the primary visual cortex.

parvocellular layer One of the four outer layers of neurons in the dorsal lateral geniculate nucleus; transmits information necessary for perception of color and fine details to the primary visual cortex.

koniocellular sublayer ( koh nee oh sell yew lur ) One of the sublayers of neurons in the dorsal lateral geniculate nucleus found ventral to each of the magnocellular and parvocellular layers; transmits information from short-wavelength (“blue”) cones to the primary visual cortex.

The neurons in the LGN send their axons through a pathway known as the optic radiations to the primary visual cortex—the region surrounding the calcarine fissure (calcarine means “spur-shaped”), a horizontal fissure located in the medial and posterior occipital lobe. The primary visual cortex is often called the striate cortex because it contains a dark-staining layer (striation) of cells. (See Figure 6.10 . )

calcarine fissure (kal ka rine ) A horizontal fissure on the inner surface of the posterior cerebral cortex; the location of the primary visual cortex.

striate cortex (stry ate ) The primary visual cortex.

FIGURE 6.9 Lateral Geniculate Nucleus

This photomicrograph shows a section through the right lateral geniculate nucleus of a rhesus monkey (cresyl violet stain). Layers 1, 4, and 6 receive input from the contralateral (left) eye, and layers 2, 3, and 5 receive input from the ipsilateral (right) eye. Layers 1 and 2 are the magnocellular layers; layers 3–6 are the parvocellular layers. The koniocellular sublayers are found ventral to each of the parvocellular and magnocellular layers. The receptive fields of all six principal layers are in almost perfect registration; cells located along the line of the unlabeled arrow have receptive fields centered on the same point.

(Photomicrograph from Hubel, D. H., Wiesel, T. N., and Le Vay, S. Philosophical Transactions of the Royal Society of London, B, 1977, 278, 131–163. Reprinted with permission.)

Figure 6.11 shows a diagrammatical view of a horizontal section of the human brain. The optic nerves join together at the base of the brain to form the X-shaped optic chiasm (khiasma is the Greek for “cross”). There, axons from ganglion cells serving the inner halves of the retina (the nasal sides) cross through the chiasm and ascend to the LGN on the opposite side of the brain. The axons from the outer halves of the retina (the temporal sides) remain on the same side of the brain. (See Figure 6.11 . ) The lens inverts the image of the world projected on the retina (and similarly reverses left and right). Therefore, because the axons from the nasal halves of the retinas cross to the other side of the brain, each hemisphere receives information from the contralateral half (opposite side) of the visual scene. That is, if a person looks straight ahead, the right hemisphere receives information from the left half of the visual field, and the left hemisphere receives information from the right. It is not correct to say that each hemisphere receives visual information solely from the contralateral eye. (Look again at Figure 6.11 . )

optic chiasm (ky az’ m ) A cross-shaped connection between the optic nerves, located below the base of the brain, just anterior to the pituitary gland.

Besides the primary retino-geniculo-cortical pathway, fibers from the retina take several other pathways. For example, one pathway to the hypothalamus synchronizes an animal’s activity cycles to the 24-hour rhythms of day and night. (We will study this system in Chapter 9 .) Other pathways, especially those that travel to the optic tectum and the pretectal nuclei, coordinate eye movements, control the muscles of the iris (and thus the size of the pupil) and the ciliary muscles (which control the lens), and help to direct our attention to sudden movements in the periphery of our visual field.

FIGURE 6.10 Striate Cortex

This photomicrograph shows a cross section through the striate cortex of a rhesus macaque monkey. The ends of the striate cortex are shown by arrows.

(From Hubel, D. H., and Wiesel, T. N. Proceedings of the Royal Society of London, B, 1977, 198, 1–59. Reprinted with permission.)

FIGURE 6.11 The Primary Visual Pathway

SECTION SUMMARY: The Stimulus and Anatomy of the Visual System

Light consists of electromagnetic radiation, similar to radio waves but of a different frequency and wavelength. Color can vary in three perceptual dimensions: hue, brightness, and saturation, which correspond to the physical dimensions of wavelength, intensity, and purity.

The photoreceptors in the retina—the rods and the cones—detect light. Muscles move the eyes so that images of particular parts of the environment fall on the retina. Accommodation is accomplished by the ciliary muscles, which change the shape of the lens. Photoreceptors communicate through synapses with bipolar cells, which communicate through synapses with ganglion cells. In addition, horizontal cells and amacrine cells combine messages from adjacent photoreceptors.

When light strikes a molecule of photopigment in a photoreceptor, the retinal molecule detaches from the opsin molecule. This detachment hyperpolarizes the membrane of the photoreceptor. As a result, the rate of firing of the ganglion cell changes, signaling the detection of light.

Visual information from the retina reaches the striate cortex surrounding the calcarine fissure after being relayed through the magnocellular, parvocellular, and koniocellular layers of the LGN. Several other regions of the brain, including the hypothalamus and the tectum, also receive visual information. These regions help to regulate activity during the day–night cycle, coordinate eye and head movements, control attention to visual stimuli, and regulate the size of the pupils.

■ THOUGHT QUESTION

People who try to see faint, distant lights at night are often advised to look just to the side of the location where they expect to see the lights. Can you explain the reason for this advice?

Coding of Visual Information in the Retina

This section describes the way in which cells of the retina encode information they receive from the photoreceptors.

Coding of Light and Dark

One of the most important methods for studying the physiology of the visual system is the use of microelectrodes to record the electrical activity of single neurons. As we saw in the previous section, some ganglion cells become excited when light falls on the photoreceptors that communicate with them. The receptive field of a neuron in the visual system is the part of the visual field that an individual neuron “sees”—that is, the place in which a visual stimulus must be located to produce a response in that neuron. Obviously, the location of the receptive field of a particular neuron depends on the location of the photoreceptors that provide it with visual information. If a neuron receives information from photoreceptors located in the fovea, its receptive field will be at the fixation point—the point at which the eye is looking. If the neuron receives information from photoreceptors located in the periphery of the retina, its receptive field will be located off to one side.

receptive field That portion of the visual field in which the presentation of visual stimuli will produce an alteration in the firing rate of a particular neuron.

At the periphery of the retina many individual receptors converge on a single ganglion cell, bringing information from a relatively large area of the retina—and hence a relatively large area of the visual field. However, the fovea contains approximately equal numbers of ganglion cells and cones. These receptor-to-axon relationships explain the fact that our foveal (central) vision is very acute but our peripheral vision is much less precise. (See Figure 6.12 . )

Over seventy years ago, Hartline ( 1938 ) discovered that the frog retina contained three types of ganglion cells. ON cells responded with an excitatory burst when the retina was illuminated, OFF cells responded when the light was turned off, and ON/OFF cells responded briefly when the light went on and again when it went off. Kuffler ( 1952 , 1953 ), recording from ganglion cells in the retina of the cat, discovered that their receptive field consists of a roughly circular center, surrounded by a ring. Stimulation of the center or surrounding fields had contrary effects: ON cells were excited by light falling in the central field (center) and were inhibited by light falling in the surrounding field (surround), whereas OFF cells responded in the opposite manner. ON/OFF ganglion cells were briefly excited when light was turned on or off. In primates most of these ON/OFF cells project primarily to the superior colliculus, which is primarily involved in visual reflexes in response to moving or suddenly-appearing stimuli (Schiller and Malpeli, 1977 ); thus, these cells do not appear to play a direct role in form perception. (See Figure 6.13 . )

FIGURE 6.12 Foveal Versus Peripheral Acuity

Ganglion cells in the fovea receive input from a smaller number of photoreceptors than those in the periphery and hence provide more acute visual information.

Figure 6.13 also illustrates a rebound effect that occurs when the light is turned off again. Neurons whose firing is inhibited while the light is on will show a brief burst of excitation when it is turned off. In contrast, neurons whose firing is increased will show a brief period of inhibition when the light is turned off. (Look again at Figure 6.13 . )

The two major categories of ganglion cells (ON and OFF) and the organization of their receptive fields into contrasting center and surround provide useful information to the rest of the visual system. Let us consider these two types of ganglion cells first. As Schiller ( 1992 ) notes, ganglion cells normally fire at a relatively low rate. Then, when the level of illumination in the center of their receptive field increases or decreases (for example, when an object moves or the eye makes a saccade), they signal the change. In particular, ON cells signal increases and OFF cells signal decreases, but both signal them by an increased rate of firing. Such a system is particularly efficient. Theoretically, a single type of ganglion cell could fire at an intermediate rate and signal changes in the level of illumination by increases or decreases in rate of firing. However, in this case, the average rate of firing of the one million axons in each optic nerve would have to be much higher.

FIGURE 6.13 ON and OFF Ganglion Cells

This figure shows responses of ON and OFF ganglion cells to stimuli presented in the center or the surround of the receptive field.

(Adapted from Kuffler, S. W. Cold Spring Harbor Symposium for Quantitative Biology, 1952, 17, 281–292.)

Several studies have shown that ON cells and OFF cells do, indeed, signal different kinds of information. Schiller, Sandell, and Maunsell ( 1986 ) injected monkeys with a drug that selectively blocks synaptic transmission in ON bipolar cells. They found that the animals had difficulty detecting spots that were made brighter than the background but had no difficulty detecting spots that were slightly darker than the background. In addition, Dolan and Schiller ( 1989 ) found that an injection of this drug completely blocked vision in very dim light, which is normally mediated by rods. Thus, rod bipolar cells must all be of the ON type. (If you think about it, that arrangement makes sense; in very dim light we are more likely to see brighter objects against a dark background than dark objects against a light background.)

The second characteristic of the receptive fields of ganglion cells—their center-surround organization—enhances our ability to detect the outlines of objects even when the contrast between the object and the background is low. Figure 6.14 illustrates this phenomenon. This figure shows six gray squares arranged in order of brightness. The right side of each square looks lighter than the left side, which makes the borders between the squares stand out. But these exaggerated borders do not exist in the illustration; they are added by our visual system because of the center-surround organization of the receptive fields of the retinal ganglion cells. (See Figure 6.14 . )

Figure 6.15 explains how this phenomenon works. We see the centers and surrounds of the receptive fields of several ganglion cells. (In reality these receptive fields would be overlapping, but the simplified arrangement is easier to understand. This example also includes only ON cells—again, for the sake of simplicity.) The image of the transition between lighter and darker regions falls across some of these receptive fields. The cells whose centers are located in the brighter region but whose surrounds are located at least partially in the darker region will have the highest rate of firing. (See Figure 6.15 . )

FIGURE 6.14 Enhancement of Contrast

Although each gray square is of uniform darkness, the right edge of each square looks somewhat lighter, and the left edge looks somewhat darker. This effect appears to be caused by the opponent center-surround arrangement of the receptive fields of the retinal ganglion cells.

FIGURE 6.15 Neural Basis of Enhancement of Contrast

This figure shows a schematic explanation of the phenomenon shown in Figure 6.14 . Only ON cells are shown; OFF cells are responsible for the darker appearance of the left side of the darker square.

Coding of Color

So far, we have been examining the monochromatic properties of ganglion cells—that is, their responses to light and dark. But, of course, objects in our environment selectively absorb some wavelengths of light and reflect others, which, to our eyes, gives them different colors. The retinas of humans and many species of nonhuman primates contain three different types of cones, which provides them (and us) with the most elaborate form of color vision (Jacobs, 1996 ; Hunt et al., 1998 ). Although monochromatic (black-and-white) vision is perfectly adequate for most purposes, color vision gave our primate ancestors the ability to distinguish ripe fruit from unripe fruit and made it more difficult for other animals to hide themselves by means of camouflage (Mollon, 1989 ). In fact, the photopigments of primates with three types of cones seem well suited for distinguishing red and yellow fruits against a background of green foliage (Regan et al., 2001 ).

COLOR MIXING

Various theories of color vision have been proposed for many years—long before it was possible to disprove or validate them by physiological means. In 1802, Thomas Young, a British physicist and physician, proposed that the eye detected different colors because it contained three types of receptors, each sensitive to a single hue. His theory was referred to as the trichromatic (three-color) theory. It was suggested by the fact that for a human observer any color can be reproduced by mixing various quantities of three colors judiciously selected from different points along the spectrum.

I must emphasize that color mixing is different from pigment mixing. If we combine yellow and blue pigments (as when we mix paints), the resulting mixture is green. Color mixing refers to the addition of two or more light sources. If we shine a beam of red light and a beam of bluish green light together on a white screen, we will see yellow light. If we mix yellow and blue light, we get white light. When white appears on a color television screen or computer monitor, it actually consists of tiny dots of red, blue, and green light. (See Figure 6.16 . )

Another fact of color perception suggested to a German physiologist, Ewald Hering ( 1905/1965 ), that hue might be represented in the visual system as opponent colors; red versus green and yellow versus blue. People interested in color perception have long regarded yellow, blue, red, and green as primary colors—colors that seem unique and do not appear to be blends of other colors. (Black and white are primary, too, but we perceive them as colorless.) All other colors can be described as mixtures of these primary colors. The trichromatic system cannot explain why yellow is included in this group—why it is perceived as a pure color. In addition, some colors appear to blend, whereas others do not. For example, one can speak of a bluish green or a yellowish green, and orange appears to have both red and yellow qualities. Purple resembles both red and blue. But try to imagine a reddish green or a bluish yellow. It is impossible; these colors seem to be opposite to each other. Again, these facts are not explained by the trichromatic theory. As we shall see in the following section, the visual system uses both trichromatic and opponent-color systems to encode information related to color.

PHOTORECEPTORS: TRICHROMATIC CODING

Physiological investigations of retinal photoreceptors in higher primates have found that Young was right: Three different types of photoreceptors (three different types of cones) are responsible for color vision. Investigators have studied the absorption characteristics of individual photoreceptors, determining the amount of light of different wavelengths that is absorbed by the photopigments. These characteristics are controlled by the particular opsin a photoreceptor contains; different opsins absorb particular wavelengths more readily. Figure 6.17 shows the absorption characteristics of the four types of photoreceptors in the human retina: rods and the three types of cones. (See Figure 6.17 . )

FIGURE 6.16 Additive Color Mixing and Paint Mixing

When blue, red, and green lights of the proper intensity are all shone together, the result is white light. When red, blue, and yellow paints are mixed together, the result is a dark gray.

The peak sensitivities of the three types of cones are approximately 420 nm (blue-violet), 530 nm (green), and 560 nm (yellow-green). The peak sensitivity of the short-wavelength cone is actually 440 nm in the intact eye because the lens absorbs some short-wavelength light. For convenience the short-, medium-, and long-wavelength cones are traditionally called “blue,” “green,” and “red” cones, respectively.

FIGURE 6.17 Absorbance of Light by Rods and Cones

The graph shows the relative absorbance of light of various wavelengths by rods and the three types of cones in the human retina.

(Based on data from Dartnall, Bowmaker, and Mollon, 1983.)

Evidence suggests that the first cone opsin to evolve was most sensitive to long wavelengths of light. These “red” cones were supplemented by the evolution of “blue” cones, which provides the limited color vision found in most mammals (Haverkamp et al., 2005 ). The trichromatic color vision found in humans and Old World monkeys was made possible when the “red” opsin gene was duplicated and one of the copies mutated into the gene that produced the “green” opsin (Solomon and Lennie, 2007 ). (You will recall from Chapter 3 that an important factor in evolutionary development is genetic duplication, which permits the process of natural selection to “experiment” with mutations of the extra gene. In this case the gene for the old “red” opsin was retained, and the new gene for the “green” opsin produced a third category of color-sensitive cones).

Genetic defects in color vision result from anomalies in one or more of the three types of cones (Wissinger and Sharpe, 1998 ; Nathans, 1999 ). The first two kinds of defective color vision described here involve genes on the X chromosome; thus, because males have only one X chromosome, they are much more likely to have this disorder. (Females are likely to have a normal gene on one of their X chromosomes, which compensates for the defective one.) People with protanopia (“first-color defect”) confuse red and green. They see the world in shades of yellow and blue; both red and green look yellowish to them. Their visual acuity is normal, which suggests that their retinas do not lack “red” or “green” cones. This fact and their sensitivity to lights of different wavelengths suggest that their “red” cones are filled with “green” cone opsin. People with deuteranopia (“second-color defect”) also confuse red and green and also have normal visual acuity. Their “green” cones appear to be filled with “red” cone opsin. (In other words, their vision is dichromatic, or “two color,” like that of our ancestors and most present-day mammals.)

protanopia ( pro tan owe pee a ) An inherited form of defective color vision in which red and green hues are confused; “red” cones are filled with “green” cone opsin.

deuteranopia ( dew ter an owe pee a ) An inherited form of defective color vision in which red and green hues are confused; “green” cones are filled with “red” cone opsin.

Mancuso et al. ( 2009 ) attempted to perform gene therapy on adult squirrel monkeys whose retinas lacked the gene for “red” cone pigment. Although most female squirrel monkeys have trichromatic color vision, males have only dichromatic vision and cannot distinguish red from green. Mancuso and her colleagues used a genetically modified virus to insert a human gene for the pigment of that “red” cone into the retinas of male monkeys. Color vision tests before and after surgery confirmed that the gene insertion converted the monkeys from dichromats into trichromats: They could now distinguish between red and green.

Tritanopia (“third-color defect”) is rare, affecting fewer than 1 in 10,000 people. This disorder involves a faulty gene that is not located on an X chromosome; thus, it is equally prevalent in males and females. People with tritanopia have difficulty with hues of short wavelengths and see the world in greens and reds. To them a clear blue sky is a bright green, and yellow looks pink. Their retinas lack “blue” cones. Because the retina contains so few of these cones, their absence does not noticeably affect visual acuity.

tritanopia ( try tan owe pee a ) An inherited form of defective color vision in which hues with short wavelengths are confused; “blue” cones are either lacking or faulty.

RETINAL GANGLION CELLS: OPPONENT-PROCESS CODING

At the level of the retinal ganglion cell the three-color code gets translated into an opponent-color system. Daw ( 1968 ) and Gouras ( 1968 ) found that these neurons respond specifically to pairs of primary colors, with red opposing green and blue opposing yellow. Thus, the retina contains two kinds of color-sensitive ganglion cells: red-green and yellow-blue. Some color-sensitive ganglion cells respond in a center-surround fashion. For example, a cell might be excited by red and inhibited by green in the center of their receptive field while showing the opposite response in the surrounding ring. (See Figure 6.18 . ) Other ganglion cells that receive input from cones do not respond differentially to different wavelengths but simply encode relative brightness in the center and surround. These cells serve as “black-and-white detectors.”

The response characteristics of retinal ganglion cells to light of different wavelengths are obviously determined by the particular circuits that connect the three types of cones with the two types of ganglion cells. These circuits involve different types of bipolar cells, amacrine cells, and horizontal cells.

FIGURE 6.18 Receptive Fields of Color-Sensitive Ganglion Cells

When a portion of the receptive field is illuminated with the color shown, the cell’s rate of firing increases. When a portion is illuminated with the complementary color, the cell’s rate of firing decreases.

Figure 6.19 helps to explain how particular hues are detected by the “red,” “green,” and “blue” cones and translated into excitation or inhibition of the red-green and yellow-blue ganglion cells. The diagram does not show the actual neural circuitry, which includes the retinal neurons that connect the cones with the ganglion cells. The arrows in Figure 6.19 refer merely to the effects of the light falling on the retina.

Detection and coding of pure red, green, or blue light is the easiest to understand. For example, red light excites “red” cones, which causes the excitation of red-green ganglion cells. (See Figure 6.19a . ) Green light excites “green” cones, which causes the inhibition of red-green cells. (See Figure 6.19b . ) But consider the effect of yellow light. Because the wavelength that produces the sensation of yellow is intermediate between the wavelengths that produce red and green, it will stimulate both “red” and “green” cones about equally. Yellow-blue ganglion cells are excited by both “red” and “green” cones, so their rate of firing increases. However, red-green ganglion cells are excited by red and inhibited by green, so their firing rate does not change. The brain detects an increased firing rate from the axons of yellow-blue ganglion cells, which it interprets as yellow. (See Figure 6.19c . ) Blue light simply inhibits the activity of yellow-blue ganglion cells. (See Figure 6.19d . )

The opponent-color system employed by the ganglion cells explains why we cannot perceive a reddish green or a bluish yellow: An axon that signals red or green (or yellow or blue) can either increase or decrease its rate of firing; it cannot do both at the same time. A reddish green would have to be signaled by a ganglion cell firing slowly and rapidly at the same time, which is obviously impossible.

FIGURE 6.19 Color Coding in the Retina

(a) Red light stimulating a “red” cone, which causes excitation of a red-green ganglion cell. (b) Green light stimulating a “green” cone, which causes inhibition of a red-green ganglion cell. (c) Yellow light stimulating “red” and “green” cones equally but not affecting “blue” cones. The stimulation of “red” and “green” cones causes excitation of a yellow-blue ganglion cell. (d) Blue light stimulating a “blue” cone, which causes inhibition of a yellow-blue ganglion cell. The arrows labeled E and I represent neural circuitry within the retina that translates excitation of a cone into excitation or inhibition of a ganglion cell. For clarity, only some of the circuits are shown.

ADAPTATION: NEGATIVE AFTERIMAGES

Figure 6.20 demonstrates an interesting property of the visual system: the formation of a negative afterimage . Stare at the cross in the center of the image on the left for approximately 30 seconds. Then quickly look at the cross in the center of the white rectangle to the right. You will have a fleeting experience of seeing the red and green colors of a radish—colors that are complementary, or opposite, to the ones on the left. (See Figure 6.20 . ) Complementary items go together to make up a whole. In this context complementary colors are those that make white (or shades of gray) when added together. ( Simulate complementary colors in MyPsychLab to see this phenomenon even more vividly.)

negative afterimage The image seen after a portion of the retina is exposed to an intense visual stimulus; consists of colors complementary to those of the physical stimulus.

complementary colors Colors that make white or gray when mixed together.

The most important cause of negative afterimages is adaptation in the rate of firing of retinal ganglion cells. When ganglion cells are excited or inhibited for a prolonged period of time, they later show a rebound effect, firing faster or slower than normal. For example, the green of the radish in Figure 6.20 inhibits some red-green ganglion cells. When this region of the retina is then stimulated with the neutral-colored light reflected off the white rectangle, the red-green ganglion cells—no longer inhibited by the green light—fire faster than normal. Thus, we see a red afterimage of the radish.

FIGURE 6.20 A Negative Afterimage

Stare for approximately 30 seconds at the plus sign in the center of the left figure; then quickly transfer your gaze to the plus sign in the center of the right figure. You will see colors that are complementary to the originals.

SECTION SUMMARY: Coding of Visual Information in the Retina

Recordings of the electrical activity of single neurons in the retina indicate that each ganglion cell receives information from photoreceptors—just one in the fovea and many more in the periphery. The receptive field of most retinal ganglion cells consists of two concentric circles, with the cells becoming excited when light falls in one region and becoming inhibited when it falls in the other. This arrangement enhances the ability of the nervous system to detect contrasts in brightness. ON cells are excited by light in the center, and OFF cells are excited by light in the surround. ON cells detect light objects against dark backgrounds; OFF cells detect dark objects against light backgrounds. ON/OFF cells play an important role in responding to movement.

Color vision occurs as a result of information provided by three types of cones, each of which is sensitive to light of a certain wavelength: long, medium, or short. The absorption characteristics of the cones are determined by the particular opsin that their photopigment contains. Most forms of defective color vision appear to be caused by alterations in cone opsins. The “red” cones of people with protanopia are filled with “green” cone opsin, and the “green” cones of people with deuteranopia are filled with “red” cone opsin. The retinas of people with tritanopia appear to lack “blue” cones. An attempt at gene therapy successfully converted the dichromatic vision of male squirrel monkeys into trichromatic vision.

Most color-sensitive ganglion cells respond in an opposing center-surround fashion to the pairs of primary colors: red and green, and blue and yellow. The responses of these neurons is determined by the retinal circuitry connecting them with the photoreceptors. Negative afterimages produced by staring at a colored stimulus and then looking at a neutral background provide an image with colors complementary to the original stimulus. This phenomenon is caused by adaptation of retinal cells that show rebound activity in the opposite direction from that produced by sight of the original stimulus.

■ THOUGHT QUESTION

Why is color vision useful? Birds, some fish, and some primates have full, three-cone color vision. Considering our own species, what other benefits (besides the ability to recognize ripe fruit, which I mentioned earlier in this section) might come from the evolution of color vision?

Analysis of Visual Information: Role of the Striate Cortex

The retinal ganglion cells encode information about the relative amounts of light falling on the center and surround regions of their receptive field and, in many cases, about the wavelength of that light. The striate cortex performs additional processing of this information, which it then transmits to the visual association cortex.

Anatomy of the Striate Cortex

The striate cortex consists of six principal layers (and several sublayers), arranged in bands parallel to the surface. These layers contain the nuclei of cell bodies and dendritic trees that show up as bands of light or dark in sections of tissue that have been dyed with a cell-body stain. (See Figure 6.21 . )

If we consider the striate cortex of one hemisphere as a whole—if we imagine that we remove it and spread it out on a flat surface—we find that it contains a map of the contralateral half of the visual field. (Remember that each side of the brain sees the opposite side of the visual field.) The map is distorted; approximately 25 percent of the striate cortex is devoted to the analysis of information from the fovea, which represents a small part of the visual field. (The area of the visual field seen by the fovea is approximately the size of a large grape held at arm’s length.)

The pioneering studies of David Hubel and Torsten Wiesel at Harvard University during the 1960s began a revolution in the study of the physiology of visual perception (see Hubel and Wiesel, 1977 , 1979 ). Hubel and Wiesel discovered that neurons in the visual cortex did not simply respond to spots of light; they selectively responded to specific features of the visual world. That is, the neural circuitry within the visual cortex combines information from several sources (for example, from axons carrying information received from several different ganglion cells) in such a way as to detect features that are larger than the receptive field of a single ganglion cell or a single cell in the LGN. The following subsections describe the visual characteristics that researchers have studied so far: orientation and movement, spatial frequency, retinal disparity, and color.

FIGURE 6.21 Striate Cortex

This photomicrograph of a small section of striate cortex shows the six principal layers. The letter W refers to the white matter that underlies the visual cortex; beneath the white matter is layer VI of the striate cortex on the opposite side of the gyrus.

(From Hubel, D. H., and Wiesel, T. N. Proceedings of the Royal Society of London, B, 1977, 198, 1–59. Reprinted with permission.)

FIGURE 6.22 Orientation Sensitivity

An orientation-sensitive neuron in the striate cortex will become active only when a line of a particular orientation appears within its receptive field. For example, the neuron depicted in this figure responds best to a bar that is vertically oriented.

(Adapted from Hubel, D. H., and Wiesel, T. N. Journal of Physiology [London], 1959, 148, 574–591.)

Orientation and Movement

Most neurons in the striate cortex are sensitive to orientation. That is, if a line or an edge (the border of a light and a dark region) is positioned in the cell’s receptive field and rotated around its center, the cell will respond only when the line is in a particular position—a particular orientation. Some neurons respond best to a vertical line, some to a horizontal line, and some to a line oriented somewhere in between. Figure 6.22 shows the responses of a neuron in the striate cortex when lines were presented at various orientations. As you can see, this neuron responded best when a vertical line was presented in its receptive field. (See Figure 6.22 . )

FIGURE 6.23 Orientation-Sensitive Neurons

This figure shows the response characteristics of neurons in the primary visual cortex: (a) simple cell, (b) complex cell, (c) hypercomplex cell.

Some orientation-sensitive neurons have receptive fields organized in an opponent fashion. Hubel and Wiesel referred to them as simple cells . For example, a line of a particular orientation (say, a dark 45° line against a white background) might excite a cell if placed in the center of the receptive field but inhibit the cell if moved away from the center. (See Figure 6.23a . ) Another type of neuron, which the researchers referred to as a complex cell , also responded best to a line of a particular orientation but did not show an inhibitory surround; that is, it continued to respond while the line was moved within the receptive field. In fact, many complex cells increased their rate of firing when the line was moved perpendicular to its angle of orientation—often only in one direction. Thus, these neurons also served as movement detectors. In addition, complex cells responded equally well to white lines against black backgrounds and black lines against white backgrounds. (See Figure 6.23b . ) Finally, hypercomplex cells responded to lines of a particular orientation but had an inhibitory region at the end (or ends) of the lines, which meant that the cells detected the location of ends of lines of a particular orientation. (See Figure 6.23c . )

simple cell An orientation-sensitive neuron in the striate cortex whose receptive field is organized in an opponent fashion.

complex cell A neuron in the visual cortex that responds to the presence of a line segment with a particular orientation located within its receptive field, especially when the line moves perpendicularly to its orientation.

hypercomplex cell A neuron in the visual cortex that responds to the presence of a line segment with a particular orientation that ends at a particular point within the cell’s receptive field.

FIGURE 6.24 Parallel Gratings

This figure compared two kinds of gratings: (a) Square-wave grating, and (b) sine-wave grating.

Spatial Frequency

Although the early studies by Hubel and Wiesel suggested that neurons in the primary visual cortex detected lines and edges, subsequent research found that they actually responded best to sine-wave gratings (De Valois, Albrecht, and Thorell, 1978 ). Figure 6.24 compares a sine-wave grating with a more familiar square-wave grating. A square-wave grating consists of a simple set of rectangular bars that vary in brightness; the brightness along the length of a line perpendicular to them would vary in a stepwise (square-wave) fashion. (See Figure 6.24a . ) A sine-wave grating looks like a series of fuzzy, unfocused parallel bars. Along any line perpendicular to the long axis of the grating, the brightness varies according to a sine-wave function. (See Figure 6.24b . )

sine-wave grating A series of straight parallel bands varying continuously in brightness according to a sine-wave function, along a line perpendicular to their lengths.

FIGURE 6.25 Visual Angle and Spatial Frequency

Angles are drawn between the sine waves, with the apex at the viewer’s eye. The visual angle between adjacent sine waves is smaller when the waves are closer together.

A sine-wave grating is designated by its spatial frequency. We are accustomed to the expression of frequencies (for example, of sound waves or radio waves) in terms of time or distance (such as cycles per second or wavelength in cycles per meter). But because the image of a stimulus on the retina varies in size according to how close it is to the eye, the visual angle is generally used instead of the physical distance between adjacent cycles. Thus, the spatial frequency of a sine-wave grating is its variation in brightness measured in cycles per degree of visual angle. (See Figure 6.25 . )

spatial frequency The relative width of the bands in a sine-wave grating, measured in cycles per degree of visual angle.

Most neurons in the striate cortex respond best when a sine-wave grating of a particular spatial frequency is placed in the appropriate part of the visual field. Different neurons detect different spatial frequencies. For orientation-sensitive neurons the grating must be aligned at the appropriate angle of orientation. Albrecht ( 1978 ) mapped the shapes of receptive fields of simple cells by observing their response while moving a very thin flickering line of the appropriate orientation through their receptive fields. He found that many of them had multiple inhibitory and excitatory regions surrounding the center. The profile of the excitatory and inhibitory regions of the receptive fields of such neurons looked like a modulated sine wave—precisely what would be needed to detect a few cycles of a sine-wave grating. (See Figure 6.26 . ) In most cases a neuron’s receptive field is large enough to include between 1.5 and 3.5 cycles of the grating (De Valois, Thorell, and Albrecht, 1985 ).

What is the point of having neural circuits that analyze spatial frequency? A complete answer requires some rather complicated mathematics, so I will give a simplified one here. (If you are interested, you can consult a classic book by De Valois and De Valois, 1988 .) Consider the types of information provided by high and low spatial frequencies. Small objects, details within a large object, and large objects with sharp edges provide a signal rich in high frequencies, whereas large areas of light and dark are represented by low frequencies. An image that is deficient in high-frequency information looks fuzzy and out of focus, like the image seen by a nearsighted person who is not wearing corrective lenses. This image still provides much information about forms and objects in the environment; thus, the most important visual information is that contained in low spatial frequencies. When low-frequency information is removed, the shapes of images are very difficult to perceive. (As we will see, the evolutionary older magnocellular system provides low-frequency information.)

FIGURE 6.26 The Experiment by Albrecht, 1978

(a) The stimulus presented to the animal. (b) The response of a simple cell in the primary visual cortex.

(Adapted from De Valois, R. L., and De Valois, K. K. Spatial Vision. New York: Oxford University Press, 1988.)

Many experiments have confirmed that the concept of spatial frequency plays a central role in visual perception, and mathematical models have shown that the information present in a scene can be represented very efficiently if it is first encoded in terms of spatial frequency. Thus, the brain probably represents the information in a similar way. Here I will describe just one example to help show the validity of the concept. Look at the two pictures in Figure 6.27 . You can see that the picture on the right looks much more like the face of Abraham Lincoln, the nineteenth-century U.S. President, than the one on the left does. Yet the two pictures contain the same information. The creators of the pictures, Harmon and Julesz ( 1973 ), used a computer to construct the figure on the left, which consists of a series of squares, each representing the average brightness of a portion of a picture of Lincoln. The one on the right is simply a transformation of the first one in which high frequencies have been removed. Sharp edges contain high spatial frequencies, so the transformation eliminates them. In the case of the picture on the left, these frequencies have nothing to do with the information contained in the original picture; thus, they can be seen as visual “noise.” The filtration process (accomplished by a computer) removes this noise—and makes the image much clearer to the human visual system. Presumably, the high frequencies produced by the edges of the squares in the left figure stimulate neurons in the striate cortex that are tuned to high spatial frequencies. When the visual association cortex receives this noisy information, it has difficulty perceiving the underlying form.

FIGURE 6.27 Spatial Filtering

The two pictures contain the same amount of low-frequency information, but extraneous high-frequency information has been filtered from the picture on the right. If you look at the pictures from across the room, they look identical.

If you want to watch the effect of filtering the extraneous high-frequency noise, try the following demonstration. Put the book down and look at the pictures in Figure 6.27 from across the room. The distance “erases” the high frequencies, because they exceed the resolving power of the eye, and the two pictures look identical. Now walk toward the book, focusing on the left figure. As you get closer, the higher frequencies reappear, and this picture looks less and less like the face of Lincoln. (Look again at Figure 6.27 . )

Retinal Disparity

We perceive depth by many means, most of which involve cues that can be detected monocularly, that is, by one eye alone. For example, perspective, relative retinal size, loss of detail through the effects of atmospheric haze, and relative apparent movement of retinal images as we move our heads all contribute to depth perception and do not require binocular vision. However, binocular vision provides a vivid perception of depth through the process of stereoscopic vision, or stereopsis. If you have used a stereoscope (such as a View-Master) or have seen a three-dimensional movie, you know what I mean. Stereopsis is particularly important in the visual guidance of fine movements of the hands and fingers, such as we use when we thread a needle.

Most neurons in the striate cortex are binocular—that is, they respond to visual stimulation of either eye. Many of these binocular cells, especially those found in a layer that receives information from the magnocellular system, have response patterns that appear to contribute to the perception of depth (Poggio and Poggio, 1984 ). In most cases the cells respond most vigorously when each eye sees a stimulus in a slightly different location. That is, the neurons respond to retinal disparity , a stimulus that produces images on slightly different parts of the retina of each eye. This is exactly the information that is needed for stereopsis: Each eye sees a three-dimensional scene slightly differently, and the presence of retinal disparity indicates differences in the distance of objects from the observer.

retinal disparity The fact that points on objects located at different distances from the observer will fall on slightly different locations on the two retinas; provides the basis for stereopsis.

Color

In the striate cortex, information from color-sensitive ganglion cells is transmitted, through the parvocellular and koniocellular layers of the LGN, to special cells grouped together in cytochrome oxidase (CO) blobs . CO blobs were discovered by Wong-Riley ( 1978 ), who found that a stain for cytochrome oxidase, an enzyme that is present in mitochondria, showed a patchy distribution. (The presence of high levels of cytochrome oxidase in a cell indicates that the cell normally has a high rate of metabolism.) Subsequent research with the stain (Horton and Hubel, 1980 ; Humphrey and Hendrickson, 1980 ) revealed the presence of a polka-dot pattern of dark columns extending through layers 2 and 3 and (more faintly) layers 5 and 6. The columns are oval in cross section, approximately 150 × 200 μm in diameter, and spaced at 0.5-mm intervals (Fitzpatrick, Itoh, and Diamond, 1983 ; Livingstone and Hubel, 1987).

cytochrome oxidase (CO) blob The central region of a module of the primary visual cortex, revealed by a stain for cytochrome oxidase; contains wavelength-sensitive neurons; part of the parvocellular system.

Figure 6.28 shows a photomicrograph of a slice through the striate cortex (also called V1 because it is the first area of visual cortex) and an adjacent area of visual association cortex (area V2) of a macaque monkey. The visual cortex has been flattened out and stained for the mitochondrial enzyme. You can clearly see the CO blobs within the striate cortex. The distribution of CO-rich neurons in area V2 consists of three kinds of stripes: thick stripes, thin stripes, and pale stripes. The thick and thin stripes stain heavily for cytochrome oxidase; the pale stripes do not. (See Figure 6.28 . )

FIGURE 6.28 Blobs and Stripes in Visual Cortex

A photomicrograph (actually, a montage of several different tissue sections) showing a slice through the primary visual cortex (area V1) and a region of visual association cortex (V2) of a macaque monkey, stained for cytochrome oxidase. Area V1 shows spots (“blobs”), and area V2 shows three types of stripes: thick, thin (both dark), and pale.

Researchers previously believed that the parvocellular system transmitted all information pertaining to color to the striate cortex. However, we now know that the parvocellular system receives information only from “red” and “green” cones; additional information from “blue” cones is transmitted through the koniocellular system (Hendry and Yoshioka, 1994 ; Chatterjee and Callaway, 2003 ).

To summarize, neurons in the striate cortex respond to several different features of a visual stimulus, including orientation, movement, spatial frequency, retinal disparity, and color. Now let us turn our attention to the way in which this information is organized within the striate cortex.

Modular Organization of the Striate Cortex

Most investigators believe that the brain is organized in modules, which probably range in size from a hundred thousand to a few million neurons. Each module receives information from other modules, performs some calculations, and then passes the results to other modules. In recent years, investigators have been learning the characteristics of the modules that are found in the visual cortex.

The striate cortex is divided into approximately 2500 modules, each approximately 0.5 × 0.7 mm and containing approximately 150,000 neurons. The neurons in each module are devoted to the analysis of various features contained in one very small portion of the visual field. Collectively, these modules receive information from the entire visual field, the individual modules serving like the tiles in a mosaic mural. Input from the parvocellular, koniocellular, and magnocellular layers of the LGN is received by different sublayers of the striate cortex: The parvocellular input is received by layer 4Cβ, the magnocellular input is received by layer 4Cα, and the koniocellular input is received by layers 2 and 3 (Nassi and Callaway, 2009 ).

The modules actually consist of two segments, each surrounding a CO blob. Neurons located within the blobs have a special function: Most of them are sensitive to color, and all of them are sensitive to low spatial frequencies. They are relatively insensitive to other visual features: They do not respond selectively to different orientations and have relatively large receptive fields, which means that they do not provide information useful for form perception. In addition, their receptive fields are monocular—they receive visual information from only one eye (Kaas and Collins, 2001 ; Landisman and Ts’o, 2002 ).

Outside the CO blob, neurons show sensitivity to orientation, movement, spatial frequency, and binocular disparity, but most do not respond to color (Livingstone and Hubel, 1984 ; Born and Tootell, 1991 ; Edwards, Purpura, and Kaplan, 1995 ). Each half of the module receives input from only one eye, but the circuitry within the module combines the information from both eyes, which means that most of the neurons are binocular. Depending on their locations within the module, neurons receive varying percentages of input from each of the eyes.

If we record from neurons anywhere within a single module, we will find that their receptive fields overlap. Thus, all the neurons in a module analyze information from the same region of the visual field. Furthermore, if we insert a microelectrode straight down into an interblob region of the striate cortex (that is, in a location in a module outside one of the CO blobs), we will find both simple and complex cells, but all of the orientation-sensitive cells will respond to lines of the same orientation. In addition, they will all have the same ocular dominance —that is, the same percentage of input from each of the eyes. If we move our electrode around the module, we will find that these two characteristics—orientation sensitivity and ocular dominance—vary systematically and are arranged at right angles to each other. (See Figure 6.29 . )

ocular dominance The extent to which a particular neuron receives more input from one eye than from the other.

FIGURE 6.29 One Module of the Primary Visual Cortex

FIGURE 6.30 Organization of Spatial Frequency

Optimal spatial frequency of neurons in striate cortex is shown as a function of the distance of the neuron from the center of the nearest cytochrome oxidase blob.

(Based on data from Edwards, Pupura, and Kaplan, 1995.)

How does spatial frequency fit into this organization? Edwards, Purpura, and Kaplan ( 1995 ) found that neurons within the CO blobs responded to low spatial frequencies but were sensitive to small differences in brightness. Outside the blobs, sensitivity to spatial frequency varied with the distance from the center of the nearest blob. Higher frequencies were associated with greater distances. (See Figure 6.30 . ) However, neurons outside the blobs were less sensitive to contrast; the difference between the bright and dark areas of the sine-wave grating had to be greater for these neurons than for neurons within the blobs.

SECTION SUMMARY: Analysis of Visual Information: Role of the Striate Cortex

The striate cortex (area V1) consists of six layers and several sublayers. Visual information is received from the magnocellular, parvocellular, and koniocellular layers of the dorsal lateral geniculate nucleus (LGN). Information from V1 is sent to area V2, the first region of the visual association cortex. The magnocellular system is phylogenetically older, color-blind, and sensitive to movement, depth, and small differences in brightness. The parvocellular and koniocellular systems are of more recent origin. The parvocellular system receives information from “red” and “green” cones and is able to discriminate finer details. The koniocellular system provides additional information about color, received from “blue” cones.

The striate cortex (area V1) is organized into modules, each surrounding a pair of CO blobs, which are revealed by a stain for cytochrome oxidase, an enzyme found in mitochondria. Each half of a module receives information from one eye; but, because information is shared, most of the neurons respond to information from both eyes. The neurons in the CO blobs are sensitive to color and to low-frequency sine-wave gratings, whereas those between the blobs are sensitive to sine-wave gratings of higher spatial frequencies, orientation, retinal disparity, and movement.

■ THOUGHT QUESTION

Look at the scene in front of you, and try to imagine how its features are encoded by neurons in your striate cortex. Try to picture how the objects you see can be specified by an analysis of orientation, spatial frequency, and color.

Analysis of Visual Information: Role of the Visual Association Cortex

Although the striate cortex is necessary for visual perception, perception of objects and of the totality of the visual scene does not take place there. Each of the thousands of modules of the striate cortex sees only what is happening in one tiny part of the visual field. Thus, for us to perceive objects and entire visual scenes, the information from these individual modules must be combined. That combination takes place in the visual association cortex.

Two Streams of Visual Analysis

Visual information received from the striate cortex is analyzed in the visual association cortex. Neurons in the striate cortex send axons to the extrastriate cortex , the region of the visual association cortex that surrounds the striate cortex. The primate extrastriate cortex consists of several regions, each of which contains one or more independent maps of the visual field. Each region is specialized, containing neurons that respond to particular features of visual information, such as orientation, movement, spatial frequency, retinal disparity, or color. So far, investigators have identified over two dozen distinct regions and subregions of the visual cortex of the rhesus monkey. These regions are arranged hierarchically, beginning with the striate cortex (Grill-Spector and Malach, 2004 ; Wandell, Dumoulin, and Brewer, 2007 ). Most of the information passes up the hierarchy; each region receives information from regions located beneath it in the hierarchy (closer to the striate cortex), analyzes the information, and passes the results on to “higher” regions for further analysis. Some information is also transmitted in the opposite direction, but axons that descend the hierarchy are much less numerous than those that ascend it.

extrastriate cortex A region of visual association cortex; receives fibers from the striate cortex and from the superior colliculi and projects to the inferior temporal cortex.

The results of a functional-imaging study by Murray, Boyaci, and Kersten ( 2006 ) demonstrate a phenomenon that owes its existence to information that follows pathways that travel up the hierarchy, from regions of the visual association cortex back to the striate cortex. First, try the following demonstration. Stare at an object (for example, an illuminated light bulb) that has enough contrast with the background to produce an afterimage. Then look at a nearby surface, such as the back of your hand. Before the afterimage fades away, look at a more distant surface, such as the far wall of the room (assuming that you are indoors). You will see that the afterimage looks much larger when it is seen against a distant background. The investigators presented subjects with stimuli like those shown in Figure 6.31 : spheres positioned against a background in locations that made them look closer to or farther from the observer. Although the spheres were actually the same size, their location on the background made the one that was apparently farther away look larger than the other one. (See Figure 6.31 . )

Murray and his colleagues used functional MRI (fMRI) to record activation of the striate cortex while the subjects looked at the spheres. They found that looking at the sphere that appeared to be larger activated a larger area of the striate cortex. We know that perception of apparent distance in a background like that shown in Figure 6.31 cannot take place in the striate cortex but requires neural circuitry found in the visual association cortex. This fact means that computations made in higher levels of the visual system can act back on the striate cortex and modify the activity taking place there.

Figure 6.32 shows the location of the striate cortex and several regions in the extrastriate cortex of the human brain. The views of brain in Figures 6.32(a) and 6.32(b) are nearly normal in appearance. Figures 6.32(c) and 6.32(d) show “inflated” cortical surfaces, enabling us to see regions that are normally hidden in the depths of sulci and fissures. The hidden regions are shown in dark gray, while regions that are normally visible (the surfaces of gyri) are shown in light gray. Figure 6.32(e) shows an unrolling of the cortical surface caudal to the dotted red line and green lines in Figure 6.32(c) and 6.32(d) . (See Figure 6.32 . )

FIGURE 6.31 Display Used by Murray, Boyaci, and Kersten (2006)

The ball that appears to be farther away looks larger than the closer one, even though the images they cast on the retina are exactly the same size.

(From Sterzer, P., and Rees, G. Nature Neuroscience, 2006, 9, 302–304. Reprinted with permission.)

The outputs of the striate cortex (area V1) are sent to area V2, a region of the extrastriate cortex just adjacent to V1. As we saw in Figure 6.28 , a dye for cytochrome oxidase reveals blobs in V1 and three kinds of stripes in V2. Neurons in V1 blobs project to thin stripes, and neurons outside the blobs in V1 project to thick stripes and pale stripes (Sincich, Jocson, and Horton, 2010 ). Thus, neurons in the thin stripes of V2 receive information concerning color, and those in the thick stripes and pale stripes receive information about orientation, spatial frequency, movement, and retinal disparity. (See Figure 6.33 .)

The receptive fields of neurons in V2 are several times larger in diameter than those of neurons in the striate cortex, which suggests that V2 cells receive input from several V1 cells. Approximately 70 percent of orientation-sensitive neurons in V2 encode the presence of stimuli with same orientation throughout their receptive field. However, a significant minority respond to stimuli with one orientation in part of the receptive field and to those with a different orientation in the rest of the field (Anzai, Peng, and Van Essen, 2007 ). Presumably, these cells are able to recognize elements of more complex stimuli, such as their corners.

FIGURE 6.32 Striate Cortex and Regions of Extrastriate Cortex

These views of a human brain show (a) a nearly normal lateral view, (b) a nearly normal midsagittal view, (c) an “inflated” lateral view, (d) an “inflated” midsagittal view, and (e) an unrolling of the cortical surface caudal to the dotted red line and green lines shown in (c) and (d).

(From Tootell, B. H., and Hadjikhani, N. Cerebral Cortex, 2001, 11, 298–311. Reprinted with permission.)

At this point, the visual association cortex divides into two pathways. On the basis of their own research and a review of the literature, Ungerleider and Mishkin ( 1982 ) concluded that the visual association cortex contains two streams of analysis: the dorsal stream and the ventral stream . Subsequent anatomical studies have confirmed this conclusion (Baizer, Ungerleider, and Desimone, 1991 ). The streams begin to diverge after area V2. The ventral stream begins with the neurons in the pale and thin stripes of area V2, continues forward to area V4, and then projects to a variety of subareas of the inferior temporal cortex . The dorsal stream begins with the neurons in the thick stripes of area V2 and ascends into regions of the posterior parietal cortex . Some axons conveying information received from the magnocellular system bypass area V2: They project from area V1 directly to area V5 (also called area MT), a region of the dorsal stream devoted to the analysis of movement. The ventral stream recognizes what an object is and what colors it has, and the dorsal stream recognizes where the object is located and, if it is moving, its speed and direction of movement.

dorsal stream A system of interconnected regions of visual cortex involved in the perception of spatial location, beginning with the striate cortex and ending with the posterior parietal cortex.

ventral stream A system of interconnected regions of visual cortex involved in the perception of form, beginning with the striate cortex and ending with the inferior temporal cortex.

inferior temporal cortex The highest level of the ventral stream of the visual association cortex; involved in perception of objects, including people’s bodies and faces.

posterior parietal cortex The highest level of the dorsal stream of the visual association cortex; involved in perception of movement and spatial location.

FIGURE 6.33 Connections Between Areas V1 and V2

(Adapted from Sincich and Horton, Annual Review of Neuroscience, 2005, 28, 303–326.)

The dorsal and ventral streams of the visual association cortex play distinctly different roles in visual processing. The primary behavioral function of the dorsal stream is to provide visual information that guides navigation and skilled movements directed toward objects, and that of the ventral stream is to provide visual information about the size, shape, color, and texture of objects (including, as we shall see, other people). (See Figure 6.34 . )

FIGURE 6.34 Human Visual System

This figure shows the human visual system from the eye to the two streams of the visual association cortex.

As we saw, the parvocellular, koniocellular, and magnocellular systems provide different kinds of information. The magnocellular system is found in all mammals, whereas the parvocellular and koniocellular systems are found only in some primates. These systems receive information from different types of ganglion cells, which are connected to different types of bipolar cells and photoreceptors. Only the cells in the parvocellular and koniocellular system receive information about wavelength from cones; thus, these systems provide information concerning color. Cells in the parvocellular system also show high spatial resolution and low temporal resolution; that is, they are able to detect very fine details, but their response is slow and prolonged. The koniocellular system, which receives information only from “blue” cones, which are much less numerous than “red” and “green” cones, does not provide information about fine details. In contrast, neurons in the magnocellular system are color-blind. They are not able to detect fine details, but they can detect smaller contrasts between light and dark. They are also especially sensitive to movement. (See Table 6.2 . ) The dorsal stream receives mostly magnocellular input, but the ventral stream receives approximately equal input from the magnocellular and the parvocellular/koniocellular systems.

TABLE 6.2 Properties of the Magnocellular, Parvocellular, and Koniocellular Divisions of the Visual System

Property	Magnocellular Division	Parvocellular Division	Koniocellular Division
Color	No	Yes (from “red” and “green” cones)	Yes (from “blue” cones)
Sensitivity to contrast	High	Low	Low
Spatial resolution (ability to detect fine details)	Low	High	Low
Temporal resolution	Fast (transient response)	Slow (sustained response)	Slow (sustained response)

Many neurons throughout almost all regions of the visual cortex are responsive to binocular disparity, which, as we saw earlier, serves as the basis for stereoscopic depth perception (Parker, 2007 ; Roe et al., 2007 ). The disparity-sensitive neurons found in the dorsal stream, which is involved in spatial perception, respond to large, extended visual surfaces, whereas those found in the ventral stream, which is involved in object perception, respond to the contours of three-dimensional objects.

Perception of Color

As we saw earlier, neurons within the CO blobs in the striate cortex respond differentially to colors. Like the ganglion cells in the retina (and the parvocellular and koniocellular neurons in the dorsal lateral geniculate nucleus), these neurons respond in opponent fashion. This information is analyzed by the regions of the visual association cortex that constitute the ventral stream.

STUDIES WITH LABORATORY ANIMALS

In the monkey brain, color-sensitive neurons in the CO blobs of the striate cortex send color-related information to the thin stripes in area V2. Neurons in V2 send information to an adjacent region of the extrastriate cortex called V4. Zeki ( 1980 ) found that neurons in this region respond selectively to colors, but their response characteristics are much more complex than those of neurons in V1 or V2. Unlike the neurons we have encountered so far, these neurons respond to a variety of wavelengths, not just the wavelengths that correspond to red, green, yellow, and blue.

The appearance of the colors of objects remains much the same whether we observe them under artificial light, under an overcast sky, or at noon on a cloudless day. This phenomenon is known as color constancy . Our visual system does not simply respond according to the wavelength of the light reflected by objects in each part of the visual field; instead, it compensates for the source of the light. This compensation appears to be made by simultaneously comparing the color composition of each point in the visual field with the average color of the entire scene. If the scene contains a particularly high level of long-wavelength light (as it would if an object were illuminated by the light of a setting sun), then some long-wavelength light is “subtracted out” of the perception of each point in the scene. This compensation helps us to see what is actually out there.

color constancy The relatively constant appearance of the colors of objects viewed under varying lighting conditions.

Schein and Desimone ( 1990 ) performed a careful study of the response characteristics of neurons in area V4 of the monkey extrastriate cortex, which receives input from the pale and thin stripes of area V2. They found that these neurons responded to specific colors. Some also responded to colored bars of specific orientation; thus, area V4 seems to be involved in the analysis of form as well as color. The color-sensitive neurons had a rather unusual secondary receptive field: a large region surrounding the primary field. When stimuli were presented in the secondary receptive field, the neuron did not respond. However, stimuli presented there could suppress the neuron’s response to a stimulus presented in the primary field. For example, if a cell would fire when a red spot was presented in the primary field, it would fire at a slower rate (or not at all) when an additional red stimulus was presented in the surrounding secondary field. In other words, these cells responded to particular wavelengths of light but subtracted out the amount of that wavelength that was present in the background. As Schein and Desimone point out, this subtraction could serve as the basis for color constancy.

Walsh et al. ( 1993 ) confirmed this prediction; damage to area V4 does disrupt color constancy. The investigators found that, although monkeys could still discriminate between different colors after area V4 had been damaged, their performance was impaired when the color of the overall illumination was changed. But the fact that the monkeys could still perform a color discrimination task under constant illumination means that some region besides area V4 must be involved in color vision. Simulatecolor constancy on MyPsychLab to see the effects of the color of overall illumination on color perception.

A study by Heywood, Gaffan, and Cowey ( 1995 ) suggested that a portion of the inferior temporal cortex just anterior to area V4—a region of the monkey brain that is usually referred to as area TEO—plays a critical role in visual discrimination. The investigators destroyed area TEO, leaving area V4 intact, and observed severe impairment in color discrimination. The monkeys had no difficulty in discriminating shades of gray, so the deficit was restricted to impaired color perception.

Conway, Moeller, and Tsao ( 2007 ) performed a detailed analysis of the responsiveness of neurons in a large region of the visual association cortex in monkeys, including areas V4 and TEO. Using fMRI, the investigators identified color “hot spots”—small scattered regions that were strongly activated by changes in the color of visual stimuli. Next, they recorded the response characteristics of neurons inside and outside these spots, which they called globs. (I’m sure the similarity between the terms “blobs” and “globs” was intentional.) They found that glob neurons were indeed responsive to colors but also had some weak sensitivity to shapes. In contrast, interglob neurons (those located outside globs) did not respond to colors but were strongly selective to shape. Thus, within a large region of visual association cortex, patches of neurons were strongly sensitive to colors or to shape but not to both. The fact that color-sensitive globs are spread across a wide area of visual association cortex probably explains why only rather large brain lesions cause severe disruptions in perception of color.

STUDIES WITH HUMANS

Lesions of a restricted region of the human extrastriate cortex can cause loss of color vision without disruption of visual acuity. The patients describe their vision as resembling a black-and-white film. In addition, they cannot even imagine colors or remember the colors of objects they saw before their brain damage occurred (Damasio et al., 1980 ; Heywood and Kentridge, 2003 ). The condition is known as cerebral achromatopsia (“vision without color”). If the brain damage is unilateral, people will lose color vision in only half of the visual field.

cerebral achromatopsia ( ay krohm a top see a ) Inability to discriminate among different hues; caused by damage to area V8 of the visual association cortex.

As we just saw, Heywood, Gaffan, and Cowey ( 1995 ) found a region of the inferior temporal cortex of the monkey brain whose damage disrupted the ability to make color discriminations. The analogous region appears to play a critical role in color perception in humans. An fMRI study by Hadjikhani et al. ( 1998 ) found a color-sensitive region that included the lingual and fusiform gyri, in a location corresponding to area TEO in the monkey’s cortex, which they called area V8. An analysis of ninety-two cases of achromatopsia by Bouvier and Engel ( 2006 ) confirmed that damage to this region (which is adjacent to and partly overlaps the fusiform face area, discussed later in this chapter) disrupts color vision. (Refer back to Figure 6.32 . )

The function of our ability to perceive different colors is to help us perceive different objects in our environment. Thus, to perceive and understand what is in front of us, we must have information about color combined with other forms of information. Some people with brain damage lose the ability to perceive shapes but can still perceive colors. For example, Zeki et al. ( 1999 ) described a patient who could identify colors but was otherwise blind. Patient P. B. received an electrical shock that caused both cardiac and respiratory arrest. He was revived, but the period of anoxia caused extensive damage to his extrastriate cortex. As a result, he lost all form perception. However, even though he could not recognize objects presented on a video monitor, he could still identify their colors.

Perception of Form

The analysis of visual information that leads to the perception of form begins with neurons in the striate cortex that are sensitive to orientation and spatial frequency. These neurons send information to area V2 and then on to the subregions of the visual association cortex that constitute the ventral stream.

STUDIES WITH LABORATORY ANIMALS

In primates the recognition of visual patterns and identification of particular objects take place in the inferior temporal cortex, located on the ventral part of the temporal lobe. This region of visual association cortex is located at the end of the ventral stream. It is here that analyses of form and color are put together, and perceptions of three-dimensional objects and backgrounds are achieved. The inferior temporal cortex consists of two major regions: a posterior area (TEO) and an anterior area (TE). Damage to these regions causes severe deficits in visual discrimination (Mishkin, 1966 ; Gross, 1973 ; Dean, 1976 ).

As we saw earlier, the analysis of visual information is hierarchical: Area V1 is concerned with the analysis of elementary aspects of information in very small regions of the visual field, and successive regions analyze more complex characteristics. The size of the receptive fields also grows as the hierarchy is ascended. The receptive fields of neurons in area TEO are larger than those in area V4, and the receptive fields of neurons in area TE are the largest of all, often encompassing the entire contralateral half of the visual field (Boussaoud, Desimone, and Ungerleider, 1991 ). In general, these neurons respond best to three-dimensional objects (or photographs of them). They respond poorly to simple stimuli such as spots, lines, or sine-wave gratings. Most of them continue to respond even when complex stimuli are moved to different locations, are changed in size, are placed against a different background, or are partially occluded by other objects (Rolls and Baylis, 1986 ; Kovács, Vogels, and Orban, 1995 ). Thus, they appear to participate in the recognition of objects rather than the analysis of specific features.

The fact that neurons in the primate inferior temporal cortex respond to very specific complex shapes suggests that the development of the circuits responsible for detecting them must involve learning. Indeed, that seems to be the case. For example, several studies have found neurons in the inferior temporal cortex that respond specifically to objects that the monkeys have already seen many times but not to unfamiliar objects (Kobatake, Tanaka, and Tamori, 1992 ; Logothetis, Pauls, and Poggio, 1995 ; Baker, Behrman, and Olson, 2002 ). The role of the inferior temporal cortex in learning will be discussed in more detail in Chapter 13 .

STUDIES WITH HUMANS

Study of people who have sustained brain damage to the visual association cortex has told us much about the organization of the human visual system. In recent years our knowledge has been greatly expanded by functional-imaging studies.

Visual Agnosia.

Damage to the human visual association cortex can cause a category of deficits known as visual agnosia . Agnosia (“failure to know”) refers to an inability to perceive or identify a stimulus by means of a particular sensory modality, even though its details can be detected by means of that modality and the person retains relatively normal intellectual capacity.

visual agnosia ( ag no zha ) Deficits in visual perception in the absence of blindness; caused by brain damage.

Mrs. R., whose case was described in the opening of this chapter, had visual agnosia caused by damage to the ventral stream of her visual association cortex. As we saw, she could not identify common objects by sight, even though she had relatively normal visual acuity. However, she could still read, even small print, which indicates that reading involves different brain regions than object perception does. ( Chapter 14 discusses research that has identified brain regions involved in visual recognition of letters and words.) When she was permitted to hold an object that she could not recognize visually, she could immediately recognize it by touch and say what it is, which proves that she had not lost her memory for the object or simply forgotten how to say its name.

Analysis of Specific Categories of Visual Stimuli.

Visual agnosia is caused by damage to the parts of the visual association cortex that contribute to the ventral stream. In fact, damage to specific regions of the ventral stream can impair the ability to recognize specific categories of visual stimuli. Of course, even if specific regions of the visual association cortex are involved in analyzing specific categories of stimuli, the boundaries of brain lesions will seldom coincide the boundaries of brain regions with particular functions.

With the advent of functional imaging, investigators have studied the responses of the normal human brain and have discovered several regions of the ventral stream that are activated by the sight of particular categories of visual stimuli. For example, researchers have identified regions of the inferior temporal and lateral occipital cortex that are specifically activated by categories such as animals, tools, cars, flowers, letters and letter strings, faces, bodies, and scenes. (See Tootell, Tsao, and Vanduffel, 2003 , and Grill-Spector and Malach, 2004 , for a review.) However, not all of these findings have been replicated, and, of course, general-purpose regions contain circuits that can learn to recognize shapes that do not fall into these categories. A relatively large region of the ventral stream of the visual association cortex, the lateral occipital complex (LOC) , appears to respond to a wide variety of objects and shapes.

lateral occipital complex (LOC) A region of the extrastriate cortex, involved in perception of objects other than people’s bodies and faces.

A functional-imaging study by Downing et al. (2006) suggests that there are few regions of the visual association cortex devoted to the analysis of specific categories of stimuli. The investigators presented images of objects from nineteen different categories to normal subjects and found only three regions that showed the greatest activation to the sight of specific categories: faces, bodies, and scenes. Bell et al. ( 2009 ) found that in both the human and the monkey brain, regions that responded to faces and body parts were adjacent to each other, as were those that responded to objects and scenes of places.

FIGURE 6.35 Responses to Categories of Visual Stimuli

These functional MRI scans show the regions of the human visual cortex that respond to six categories of visual stimuli. Neural activity is shown on “inflated” ventral views of the cerebral cortex. The fusiform face area is shown as a black outline, derived from the responses to faces shown in the upper left scan.

(From Grill-Spector, K., Knouf, N., and Kanwisher, N. Nature Neuroscience, 2004, 7, 555–561. Reprinted with permission.)

The distinction between the behavioral functions of the dorsal and ventral streams is vividly illustrated by a case report by Karnath et al. ( 2009 ). Patient J. S. sustained a stroke that damaged the medial occipitotemporal cortex, including the fusiform and lingual gyrus, bilaterally. The ventral stream was seriously damaged, but the dorsal stream was intact. The patient was unable to recognize objects or faces and could no longer read. He could not recognize shapes or orientations of visual stimuli. His ability to reach for and pick up objects was preserved, and if he knew in advance what they were, he could handle them appropriately. For example, if he knew where his clothes were, he could pick them up and get dressed. He could shake hands when someone else extended his hand to him. He could walk around his neighborhood, enter a store, and give a written list to the clerk.

A common symptom of visual agnosia is prosopagnosia , inability to recognize particular faces (prosoponis Greek for “face”). That is, patients with this disorder can recognize that they are looking at a face, but they cannot say whose face it is—even if it belongs to a relative or close friend. They see eyes, ears, a nose, and a mouth, but they cannot recognize the particular configuration of these features that identifies an individual face. They still remember who these people are and will usually recognize them when they hear the person’s voice. As one patient said, “I have trouble recognizing people from just faces alone. I look at their hair color, listen to their voices . . . I use clothing, voice, and hair. I try to associate something with a person one way or another . . . what they wear, how their hair is worn” (Buxbaum, Glosser, and Coslett, 1999 , p. 43).

prosopagnosia ( prah soh pag no zha ) Failure to recognize particular people by the sight of their faces.

Studies with brain-damaged people and functional-imaging studies suggest that these special face-recognizing circuits are found in the fusiform face area (FFA) , located in the fusiform gyrus on the base of the temporal lobe. For example, Grill-Spector, Knouf, and Kanwisher ( 2004 ) obtained fMRI scans of the brains of people who looked at pictures of faces and several other categories of objects. Figure 6.35 shows the results, projected on an “inflated” ventral view of the cerebral cortex. The black outlines show the regions of the fusiform cortex that were activated by viewing faces, drawn on all images of the brain for comparison with the activation produced by other categories of objects. As you can see, images of faces activated the regions indicated by these outlines better than other categories of visual stimuli. (See Figure 6.35 . )

fusiform face area (FFA) A region of the visual association cortex located in the inferior temporal; involved in perception of faces and other complex objects that require expertise to recognize.

Perhaps the strangest piece of evidence for a special face-recognition region comes from a report by Moscovitch, Winocur, and Behrmann ( 1997 ), who studied a man with a visual agnosia for objects but not for faces. For example, he recognized the face shown in Figure 6.36 but not the flowers, fruits, and vegetables that compose it. (See Figure 6.36 . ) Presumably, some regions of his visual association cortex were damaged, but the fusiform face region was not.

A functional-imaging study by Cox, Meyers, and Sinha ( 2004 ) found that visual cues correlated with faces can activate the fusiform face area. They found that photographs that implied the presence of a face (a blurry gray shape above a photograph of a man’s torso) activated the FFA even though no facial features were present. This finding suggests not simply that the FFA is prewired to recognize facial features, but also that the activity of this region can be affected by previously learned information. (See Figure 6.37 . )

Some people suffer from congenital prosopagnosia—the inability to recognize faces without obvious damage to the FFA. Such people often report that their inability to recognize people they have met several times is perceived by the other people as an insult. Our ability to recognize other people’s faces is so automatic that it is difficult for us to understand that someone we have met many times can fail to recognize us, so we conclude that the recognition failure is really a snub. Behrman et al. ( 2007 ) found that the anterior fusiform gyrus is smaller in people with congenital prosopagnosia, and a diffusion tensor imaging study by Thomas et al. ( 2009 ) found evidence that people with congenital prosopagnosia show decreased connectivity within the occipitotemporal cortex.

FIGURE 6.36 Visual Object Agnosia Without Prosopagnosia

A patient could recognize the face in this painting but not the flowers, fruits, and vegetables that compose it

(Giuseppe Arcimboldo. 1527–1593. Vertumnus. Erich Lessing/Art Resource, New York.)

FIGURE 6.37 Implied Faces

The fusiform face area was activated by actual faces (e) and by a blurry gray shape in the appropriate position that implied the presence of a face (a).

FIGURE 6.38 Perception of Faces and Bodies

The fusiform face area (FFA) and extrastriate body area (EBA) were activated by images of faces, headless bodies, body parts, and assorted objects.

(Adapted from Schwarzlose, R. F., Baker, C. I., and Kanwisher, N. Journal of Neuroscience, 2005, 23, 11055–11059.)

Another interesting region of the ventral stream is the extrastriate body area (EBA) , which is just posterior to the FFA and partly overlaps it. Downing et al. ( 2001 ) found that this region was specifically activated by photographs, silhouettes, or stick drawings of human bodies or body parts and not by control stimuli such as photographs or drawings of tools, scrambled silhouettes, or scrambled stick drawings of human bodies. Figure 6.38 shows the magnitude of the fMRI response in the nonoverlapping regions of the FFA and EBA to several categories of stimuli (Schwarzlose, Baker, and Kanwisher ( 2005 ). As you can see, the FFA responded to faces more than any of the other categories, and the EBA showed the greatest response to headless bodies and body parts. (See Figure 6.38 . )

extrastriate body area (EBA) A region of the visual association cortex located in the lateral occipitotemporal cortex; involved in perception of the human body and body parts other than faces.

Urgesi, Berlucchi, and Aglioti ( 2004 ) used transcranial magnetic stimulation to temporarily disrupt the normal neural activity of the EBA. (As we saw in Chapter 5 , the TMS procedure applies a strong localized magnetic field to the brain by passing an electrical current through a coil of wire placed on the scalp.) The investigators found that the disruption temporarily impaired people’s ability to recognize photographs of body parts, but not parts of faces or motorcycles.

As we will see in Chapter 13 , the hippocampus and nearby regions of the medial temporal cortex are involved in spatial perception and memory. Several studies have identified a parahippocampal place area (PPA) , located in a region of limbic cortex bordering the ventromedial temporal lobe, that is activated by the sight of scenes and backgrounds. For example, Steeves et al. ( 2004 ) studied Patient D. F., a 47-year-old woman who had sustained brain damage caused by accidental carbon monoxide poisoning fourteen years earlier. Bilateral damage to her lateral occipital cortex (an important part of the ventral stream) caused a profound visual agnosia for objects. However, she was able to recognize both natural and human-made scenes (beaches, forests, deserts, cities, markets, and rooms). Functional imaging showed activation of her intact PPA. These results suggest that scene recognition does not depend on recognition of particular objects found within the scene, because D. F. was incapable of recognizing these objects. Figure 6.39 shows the activation in her brain and that of a control subject. (See Figure 6.39 . )

parahippocampal place area (PPA) A region of limbic cortex on the medial temporal lobe; involved in perception of particular places (“scenes”).

FIGURE 6.39 The Parahippocampal Place Area

The scans show activation of the parahippocampal cortex in Patient D. F., a woman with a profound visual agnosia for objects, in response to viewing scenes (a) and similar responses in a control subject (b).

(From Steeves, J. K. E., Humphrey, G. K., Culham, J. C., et al. Journal of Cognitive Neuroscience, 2004, 16, 955–965. Reprinted by permission.)

Are Faces Special?

As we just saw, the ability to recognize faces by sight depends on a specific region of the fusiform gyrus. But must we conclude that the development of this region is a result of natural selection and that the FFA comes prewired with circuits devoted to the analysis of faces? Several kinds of evidence suggest that the answer is no—that the face-recognition circuits develop as a result of the experience we have of seeing people’s faces. Because of the extensive experience we have of looking at faces, we are all experts at recognizing them.

What about people who have become experts at recognizing other types of objects? It appears that recognition of specific complex stimuli by experts, too, is disrupted by lesions that cause prosopagnosia: inability of a farmer to recognize his cows, inability of a bird expert to recognize different species of birds, and inability of a driver to recognize his own car except by reading its license plate (Bornstein, Stroka, and Munitz, 1969 ; Damasio, Damasio, and Van Hoesen, 1982 ). Two functional-imaging studies (Gauthier et al., 2000 ; Xu, 2005 ) found that when bird or car experts (but not nonexperts) viewed pictures of birds or cars, the fusiform face area was activated. Another study (Gauthier et al., 1999 ) found that when people had spent a long time becoming familiar with computer-generated objects they called “greebles,” viewing the greebles activated the fusiform face area. (See Figure 6.40 . ) Tarr and Gauthier ( 2000 ) suggested we should relabel the FFA as the flexible fusiform area.

FIGURE 6.40 Greebles

“Greebles” are computer-created objects from the study by Gauthier and Tarr (1997). Greebles were categorized by family and gender, and different individuals each had their own particular shapes. Two greebles of the same gender and family would resemble each other more closely than any other two greebles.

A functional-imaging study (Golby et al., 2001 ) found higher activation of the fusiform face area when people viewed pictures of faces of members of their own race (African Americans or European Americans). Indeed, the subjects in this study were able to recognize faces of people of their own race more accurately than faces of people of the other race. Presumably, this difference reflected the fact that people have more experience of seeing other members of their own race, which indicates that expertise does appear to play a role in face recognition.

There is no doubt that a region of the fusiform gyrus plays an essential role in the analysis of particular faces. In fact, a face-responsive area exists in a similar location in the monkey brain, and this area contains neurons that respond to the faces of both monkeys and humans (Tsao et al., 2006 ). Two issues are still disputed by investigators interested in the FFA. First, is analysis of faces the sole function of this region, or is it really a “flexible fusiform area” involved in visual analysis of categories of very similar stimuli that can be discriminated only by experts? The activation of the FFA by greebles in the brains of greeble experts suggests that the FFA is an expertise area rather than an exclusively face area. However, according to Kanwisher and Yovel ( 2006 ), “Since Greebles resemble faces (and/or bodies), they are a poor choice of stimulus to distinguish between the face-specificity and expertise hypotheses” (p. 2113). Perhaps a more important issue is the relative roles of genetic programming and experience in development of a brain region critically involved in face perception.

A functional-imaging study indicates that although the relative size of the LOC, which responds to objects other than faces and bodies, is the same in children and adults, the left FFA does not reach its eventual size until adulthood, and the ability to recognize faces is directly related to the expansion of the FFA (Golarai et al., 2007 ). These findings are consistent with the suggestion that the ability to recognize faces is a learned skill that grows with experience. Figure 6.41 shows the regions on the left and right fusiform cortex of an 8-year-old child and an adult. You can see the age-related size difference and also the difference between the size of this region in the left and right hemispheres. (See Figure 6.41 . )

Evidence indicates that newborn babies prefer to look at stimuli that resemble faces, which suggests the presence of prewired circuits in the human brain that dispose babies to look at faces and hence learn to recognize them. Farroni et al. ( 2005 ) presented newborn babies (between 13 and 168 hours old) with pairs of stimuli and found that they preferred to look at the ones that bore the closest resemblance to faces viewed in their normal, upright orientation, with the lighting coming from above, as it normally does. Figure 6.42 illustrates the stimuli that Farroni and her colleagues used. An asterisk above a stimulus indicates that the babies spent more time looking at it than at the other member of the pair. If neither stimulus is marked with an asterisk, that means that the baby indicated no preference—and as you can see, these pairs of stimuli bore the least resemblance to a face illuminated from above. (See Figure 6.42 . )

FIGURE 6.41 Fusiform Gyrus Responses to Faces

This “inflated” ventral view of the brain of an 8-year-old child and an adult from the study by Golarai et al. ( 2007 ) shows the regions of the fusiform gyrus that responded to the sight of faces. The FFA is much larger in adults.

(Courtesy of Golijeh Golarai, Department of Psychology, Stanford University.)

FIGURE 6.42 Preference of Newborn Babies for Viewing Stimuli That Resemble Faces

An asterisk above a stimulus indicates that the babies spent more time looking at it than the other member of the pair. If neither stimulus is marked with an asterisk, the baby indicated no preference.

(Adapted from Farroni, T., Johnson, M. H., Menon, E., Zulian, L., Faraguna, D., and Csibra, G. Proceedings of the National Academy of Sciences, USA, 2005, 102, 17245–17250.)

A review of the literature by Johnson ( 2005 ) suggests that a baby’s preference for faces is controlled by a fast, low-spatial-frequency, subcortical pathway that is present in newborn infants. This circuit survives in many adults with prosopagnosia caused by cortical damage, who can realize that they are looking at a face even though they cannot recognize it and can even recognize facial expressions such as happiness, fear, or anger. (This phenomenon is discussed in more detail in Chapter 11 , which deals with emotion.) The subcortical pathway guarantees that babies will look at faces, which increases social bonding with other humans as well as facilitating the development of face-sensitive circuits in the cerebral cortex.

A study by Le Grand et al. ( 2001 ) discovered that the experience of seeing faces very early in life plays a critical role in the development of the skills necessary for recognizing them later in life. The investigators tested the ability of people (aged 9–21 years) who had been born with congenital cataracts to recognize subtle differences between pairs of faces. These people had been unable to see more than light and dark until they received eye surgery at 62–187 days of age that made normal vision possible. The early visual deprivation resulted in a severe deficit, compared with the performance of control subjects, in recognizing the facial differences.

A follow-up study by Le Grand et al. ( 2003 ) tested people who were born with cataracts in only one eye. Because of the immaturity of the newborn brain, visual information received by one eye is transmitted only to the contralateral visual cortex. (You may recall that I said earlier in this chapter that it is not correct to say that each hemisphere receives visual information solely from the contralateral eye. However, my admonition does not apply to newborn babies.) This means that the right hemisphere of a person born with a cataract in the left eye does not receive patterned visual information until the cataract is removed. Le Grand and his colleagues predicted that because the right fusiform gyrus is critical for facial recognition, people born with cataracts in their left eye would show a deficit in recognizing faces but that people born with cataracts in the right eye would show normal discrimination—and that is exactly what they found.

By the way, there are three basic ways in which we can recognize individual faces: differences in features (for example, the size and shape of the eyes, nose, and mouth), differences in contour (the overall shape of the face), and differences in configuration of features (for example, the spacing of the eyes, nose, and mouth). Figure 6.43 illustrates these differences in a series of composite faces from the study by Le Grand et al. ( 2003 ). (You can see that the face on the far left is the same in each of the rows.) The top row of faces contain different features: eyes and mouths from photos of different people. (The noses are all the same.) The middle row of faces are all of the same person, but the contours of the faces have different shapes. The bottom row contains different configurations of features from one individual. In these faces, the spacing between the eyes and between the eyes and the mouth has been altered. Differences in configuration are the most difficult to detect, and the people with early visual deprivation showed a deficit in configural recognition. (See Figure 6.43 . )

FIGURE 6.43 Composite Faces

The faces in the top row contain different features: eyes and mouths from photos of different people. The middle row of faces are all of the same person, but the contours of the faces have different shapes. The bottom row contains different configurations of features from one individual: The spacing between the eyes and between the eyes and the mouth have been altered.

(From Le Grand, R., Mondloch, C. J., Maurer, D., and Brent, H. P. Nature Neuroscience, 2003, 6, 1108–1112. Reprinted with permission.)

As we will see in Chapter 17 , people with autistic disorder fail to develop normal social relations with other people. Indeed, in severe cases they give no signs that they recognize that other people exist. Grelotti, Gauthier, and Schultz ( 2002 ) found that people with autistic disorder showed a deficit in the ability to recognize faces and that looking at faces failed to activate the fusiform gyrus. The authors speculate that the lack of interest in other people, caused by the brain abnormalities responsible for autism, resulted in a lack of motivation that normally promotes the acquisition of expertise in recognizing faces as a child grows up. Chapter 17 discusses autistic disorder in more detail.

Williams syndrome is a genetic condition caused by a mutation on Chromosome 7. People with this disorder usually show intellectual deficits, but they often show an intense interest in music. They are generally very sociable, charming, and kind. They show great interest in other people and spend more time looking closely at their faces. They are generally better at recognizing faces than people without the syndrome. A functional imaging study by Golarai et al. ( 2010 ) found (not surprisingly) that the fusiform face area was enlarged in people with Williams syndrome and that the size of the FFA was positively correlated with a person’s ability to recognize faces.

Perception of Movement

We need to know not only what things are, but also where they are and where they are going. Without the ability to perceive the direction and velocity of movement of objects, we would have no way to predict where they will be. We would be unable to catch the objects (or avoid letting them catch us). This section examines the perception of movement; the final section examines the perception of location.

STUDIES WITH LABORATORY ANIMALS

One of the regions of the extrastriate cortex—area V5, also known as area MT, for medial temporal—contains neurons that respond to movement. Damage to this region severely disrupts a monkey’s ability to perceive moving stimuli (Siegel and Andersen, 1986 ). Area V5 receives input directly from the striate cortex and from several regions of the extrastriate cortex. It also receives input from the superior colliculus, which is involved in visual reflexes, including reflexive control of eye movements.

Accurately determining the velocity and direction of movement of an object is an important ability. That moving object could be a prey animal trying to run away, a predator trying to catch you, or a projectile you are trying to catch (or keep from hitting you). If we are to accurately track moving objects, the information received by V5 must be up to date. In fact, the axons that transmit information from the magnocellular system are thick and heavily myelinated, which increases the rate at which they conduct action potentials. Petersen, Miezin, and Allman ( 1988 ) recorded the responses of neurons in areas V4 and V5. As you can see in Figure 6.44 , visual information reached the V5 neurons sooner than it reached those in area V4, whose neurons are involved in the analysis of form and color. (See Figure 6.44 . )

The input from the superior colliculus contributes in some way to the movement sensitivity of neurons in area V5. Rodman, Gross, and Albright ( 1989 , 1990 ) found that destruction of the striate cortex or the superior colliculus alone does not eliminate the movement sensitivity of V5 neurons, but destruction of both areas does. The roles played by these two sources of input are not yet known. Clearly, both inputs provide useful information; Seagraves et al. ( 1987 ) found that monkeys still could detect movement after lesions of the striate cortex but had difficulty estimating its rate.

FIGURE 6.44 Responses of Neurons in Areas V4 and V5

Note that neurons in the motion-sensitive area V5 responded sooner to stimuli presented in their receptive field, and their firing ceased sooner, than neurons in the form- and color-sensitive area V4. The faster, briefer response is what one would expect of neurons involved in perceiving a moving object’s velocity and direction of movement.

(Adapted from Petersen, S., Miezin, F., and Allman, J. Transient and sustained responses in four extrastriate visual areas of the owl monkey. Experimental Brain Research, 1988, 70, 55–60.)

A region adjacent to area V5, area MST, or medial superior temporal, receives information about movement from V5 and performs a further analysis. MST neurons respond to complex patterns of movement, including radial, circular, and spiral motion (see Vaina, 1998 , for a review). One important function of this region—in particular, the dorsolateral MST, or MSTd—appears to be analysis of optic flow . As we move around in our environment or as objects in our environment move in relation to us, the sizes, shapes, and locations of environmental features on our retinas change. Imagine the image seen by a video camera as you walk along a street, pointing the lens of the camera straight in front of you. Suppose your path will pass just to the right of a mailbox. The image of the mailbox will slowly get larger. Finally, as you pass the mailbox, its image will veer to the left and disappear. Points on the sidewalk will move downward, and branches of trees that you pass under will move upward. Analysis of the relative movement of the visual elements of your environment—the optic flow—will tell you where you are heading, how fast you are approaching different items in front of you, and whether you will pass to the left or right (or under or over) these items. The point toward which we are moving does not move, but all other points in the visual scene move away from it. Therefore, this point is called the center of expansion.If we keep moving in the same direction, we will eventually bump into an object that lies at the center of expansion. We can also use optic flow to determine whether an object approaching us will hit us or pass us by.

optic flow The complex motion of points in the visual field caused by relative movement between the observer and environment; provides information about the relative distance of objects from the observer and of the relative direction of movement.

Bradley et al. ( 1996 ) recorded from single units in MSTd of monkeys and found that particular neurons responded selectively to expansion foci located in particular regions of the visual field. These neurons compensated for eye movements, which means that their activity identified the location in the environment toward which an animal was moving. (The ability of the visual system to compensate for eye movements is discussed in the next subsection of this chapter.) Britten and van Wezel ( 1998 ) found that electrical stimulation of MSTd disrupted monkeys’ ability to perceive the apparent direction in which they were heading; thus, these neurons do indeed seem to play an essential role in heading estimation derived from optic flow.

STUDIES WITH HUMANS

Perception of Motion.

Functional-imaging studies suggest that a motion-sensitive area V5 (usually called MT/MST) is found within the inferior temporal sulcus of the human brain (Dukelow et al., 2001 ). However, a more recent study suggests that this region is located in the lateral occipital cortex, between the lateral and inferior occipital sulci (Annese, Gazzaniga, and Toga, 2005 ). Annese and his colleagues examined sections of the brains of deceased subjects that had been stained for the presence of myelin. As we just saw, area V5 receives a dense projection of thick, heavily myelinated axons, and the location of this region was revealed by the myelin stain. (See Figure 6.45 . )

Bilateral damage to the human brain that includes area V5 produces an inability to perceive movement— akinetopsia . For example, Zihl et al. ( 1991 ) reported the case of a woman with bilateral lesions of the lateral occipital cortex and area MT/MST.

akinetopsia Inability to perceive movement, caused by damage to area V5 (also called MST) of the visual association cortex.

Patient L. M. had an almost total loss of movement perception. She was unable to cross a street without traffic lights, because she could not judge the speed at which cars were moving. Although she could perceive movements, she found moving objects very unpleasant to look at. For example, while talking with another person, she avoided looking at the person’s mouth because she found its movements very disturbing. When the investigators asked her to try to detect movements of a visual target in the laboratory, she said, “First the target is completely at rest. Then it suddenly jumps upwards and downwards” (Zihl et al., 1991 , p. 2244). She was able to see that the target was constantly changing its position, but she was unaware of any sensation of movement.

FIGURE 6.45 The Location of Visual Area V5

The location of this area in the human brain (also called MT/MST or MST+), was identified by a stain that showed the presence of a dense projection of thick, heavily myelinated axons. (LOS = lateral occipital sulcus, IOS = inferior occipital sulcus.)

(From Annese, J., Gazzaniga, M. S., and Toga, A. W. Cerebral Cortex, 2005, 15, 1043–1044. Reprinted with permission.)

Walsh et al. ( 1998 ) used transcranial magnetic stimulation (TMS) to temporarily inactivate area MT/MST in normal human subjects. The investigators found that during the stimulation people were unable to detect which of several objects displayed on a computer screen was moving. When the current was off, the subjects had no trouble detecting the motion. The current had no effect on the subjects’ ability to detect stimuli that varied in their form. ( Simulate motion aftereffects on MyPsychLab to see an interesting movement-related phenomenon.)

Optic Flow.

As we saw in the previous subsection, neurons in area MSTd of the monkey brain respond to optic flow, an important source of information about the direction in which the animal is heading. A functional-imaging study by Peuskens et al. ( 2001 ) found that area MT/MST became active when people judged their heading while viewing a display showing optic flow. Vaina and her colleagues (Jornales et al., 1997 ; Vaina, 1998 ) found that people with lesions that included this region were able to perceive motion but could not perceive heading from optic flow.

Form from Motion.

Perception of movement can even help us to perceive three-dimensional forms—a phenomenon known as form from motion. Johansson ( 1973 ) demonstrated just how much information we can derive from movement. He dressed actors in black and attached small lights to several points on their bodies, such as their wrists, elbows, shoulders, hips, knees, and feet. He made movies of the actors in a darkened room while they were performing various behaviors, such as walking, running, jumping, limping, doing push-ups, and dancing with a partner who was also equipped with lights. Even though observers who watched the films could see only a pattern of moving lights against a dark background, they could readily perceive the pattern as belonging to a moving human and could identify the behavior the actor was performing. Subsequent studies (Kozlowski and Cutting, 1977 ; Barclay, Cutting, and Kozlowski, 1978 ) showed that people could even tell, with reasonable accuracy, the sex of the actor wearing the lights. The cues appeared to be supplied by the relative amounts of movement of the shoulders and hips as the person walked. ( Simulate form from motion on MyPsychLab to see a demonstration of this phenomenon.)

McCleod et al. ( 1996 ) suggest that the ability to perceive form from motion does not involve area V5. They reported that patient L. M. (studied by Zihl et al., 1991 ) could recognize people depicted solely by moving points of light even though she could not perceive the movements themselves. Vaina and her colleagues (reported by Vaina, 1998 ) found a patient with a lesion in the medial right occipital lobe who showed just the opposite deficits: Patient R. A. could perceive movement—even complex radial and circular optic flow—but could not perceive form from motion. Thus, perception of motion and perception of form from motion involve different regions of the visual association cortex.

A functional-imaging study by Grossman et al. ( 2000 ) found that when people viewed a video that showed form from motion, a small region on the ventral bank of the posterior end of the superior temporal sulcus became active. More activity was seen in the right hemisphere, whether the images were presented to the left or right visual field. Grossman and Blake ( 2001 ) found that this region became active even when people imagined that they were watching points of light representing form from motion. (See Figure 6.46 . ) Grossman, Battelli, and Pascual-Leone ( 2005 ) found that inactivation of this area with transcranial magnetic stimulation disrupted perception of form from motion.

Perception of form from motion might not seem like a phenomenon that has any importance outside the laboratory. However, this phenomenon does occur under natural circumstances, and it appears to involve brain mechanisms different from those involved in normal object perception. For example, as we saw in the prologue to this chapter, people with visual agnosia can often still perceive actions (such as someone pretending to stir something in a bowl or deal out some playing cards) even though they cannot recognize objects by sight. They may be able to recognize friends by the way the friends walk, even though they cannot recognize the friends’ faces.

FIGURE 6.46 Responses to Viewing Form from Motion

This figure shows horizontal and lateral views of neural activity that occurred while the subject was viewing videos of biological motion such as those shown in the simulation Form from Motion on MyPsychLab. Maximum activity is seen in a small region on the ventral bank of the posterior end of the superior temporal sulcus, primarily in the right hemisphere.

(From Grossman, E. D., and Blake, R. Vision Research, 2001, 41, 1475–1482. Reprinted with permission.)

Lî et al. ( 2002 ) reported the case of patient S. B., a 30-year-old man whose ventral stream was damaged extensively bilaterally by encephalitis when he was 3 years old. As a result, he was unable to recognize objects, faces, textures, or colors. However, he could perceive movement and could even catch a ball that was thrown to him. Furthermore, he could recognize other people’s arm and hand movements that mimed common activities such as cutting something with a knife or brushing one’s teeth, and he could recognize people he knew by their gait.

Biological Motion.

As we saw earlier in this chapter, neurons in the extrastriate body area (EBA) are activated by the sight of human body parts. A functional-imaging study by Pelphrey et al. ( 2005 ) showed subjects a computer-generated image of a person who made hand, eye, and mouth movements. (Note that the subjects were perceiving motion made by a human being, not form from the motion of individual points of light as described in the previous subsection.) The investigators found that movements of different body parts activated different locations just anterior to the EBA.

Compensation for Eye Movements.

So far, this discussion has been confined to movement of objects in the visual field. But if a person moves his or her eyes, head, or whole body, the image on the retina will move even if everything within the person’s visual field remains stable. Often, of course, both kinds of movements will occur at the same time. The problem for the visual system is to determine which of these images are produced by movements of objects in the environment and which are produced by the person’s own eye, head, and body movements.

To illustrate this problem, think about how the page of this book looks as you read it. If we could make a videotape of one of your retinas, we would see that the image of the page projected there is in constant movement as your eyes make several saccades along a line and then snap back to the beginning of the next line. Yet the page seems perfectly still to you. On the other hand, if you look at a single point on the page (say, a period at the end of a sentence) and then move the page around while following the period with your eyes, you perceive the book as moving, even though the image on your retina remains relatively stable. (Try it.) Then think about the images on your retina while you are driving in busy traffic, constantly moving your eyes around to keep track of your own location and that of other cars moving in different directions at different speeds. You are perceiving not only the simple movement of objects, but optic flow as well, which helps you keep track of the trajectories of the objects relative to each other and to yourself.

Haarmeier et al. ( 1997 ) reported the case of a patient with bilateral damage to the extrastriate cortex who could not compensate for image movement caused by head and eye movements. When the patient moved his eyes, it looked to him as if the world was moving in the opposite direction. Without the ability to compensate for head and eye movements, any movement of a retinal image was perceived as movement of the environment. On the basis of evidence from EEG and MEG (magnetoencephalography) studies in human subjects and single-unit recordings in monkeys, Thier et al. ( 2001 ) suggest that this compensation involves extrastriate cortex located at the junction of the temporal and parietal lobes near a region involved in the analysis of signals from the vestibular system. Indeed, the investigators note that when patients with damage to this region move their eyes, the lack of compensation for these movements makes them feel very dizzy.

Perception of Spatial Location

The parietal lobe is involved in spatial and somatosensory perception, and it receives visual, auditory, somatosensory, and vestibular information to perform these tasks. Damage to the parietal lobes disrupts performance on a variety of tasks that require perceiving and remembering the locations of objects and controlling movements of the eyes and the limbs. The dorsal stream of the visual association cortex terminates in the posterior parietal cortex.

FIGURE 6.47 The Posterior Parietal Cortex

An “inflated” dorsal view of the left hemisphere of a human brain shows the anatomy of the posterior parietal cortex.

(Adapted from Astafiev, S. V., Shulman, G. L., Stanley, C. M., et al. Journal of Neuroscience, 2003, 23, 4689–4699.)

The anatomy of the posterior parietal cortex is shown in Figure 6.47 . We see an “inflated” dorsal view of the left hemisphere of a human brain. Five regions within the intraparietal sulcus (IPS) are of particular interest: AIP, LIP, VIP, CIP, and MIP (anterior, lateral, ventral, caudal, and medial IPS) are indicated. (See Figure 6.47 . )

intraparietal sulcus (IPS) The end of the dorsal stream of the visual association cortex; involved in perception of location, visual attention, and control of eye and hand movements.

Single-unit studies with monkeys and functional-imaging studies with humans indicate that neurons in the IPS are involved in visual attention and control of saccadic eye movements (LIP and VIP), visual control of reaching and pointing (VIP and MIP), visual control of grasping and manipulating hand movements (AIP), and perception of depth from stereopsis (CIP) (Snyder, Batista, and Andersen, 2000 ; Culham and Kanwisher, 2001 ; Astafiev et al., 2003 ; Tsao et al., 2003 ; Frey et al., 2005 ).

Goodale and his colleagues (Goodale and Milner, 1992 ; Goodale et al., 1994 ; Goodale and Westwood, 2004 ) suggested that the primary function of the dorsal stream of the visual cortex is to guide actions rather than simply to perceive spatial locations. As Ungerleider and Mishkin ( 1982 ) originally put it, the ventral and dorsal streams tell us “what” and “where.” Goodale and his colleagues suggested that the better terms are “what” and “how.” First, they noted that the visual cortex of the posterior parietal lobe is extensively connected to regions of the frontal lobe involved in controlling eye movements, reaching movements of the limbs, and grasping movements of the hands and fingers. Second, they noted that damage to the dorsal stream can produce deficits in visually guided movements. ( Chapter 8 discusses in more detail the role of the posterior parietal cortex in control of movements.) They cited the case of a woman with damage to the dorsal stream who had no difficulty recognizing line drawings (that is, her ventral stream was intact) but who had trouble picking up objects (Jakobson et al., 1991 ). The patient could easily perceive the difference in size of wooden blocks that were set out before her, but she failed to adjust the distance between her thumb and forefinger to the size of the block she was about to pick up. In contrast, a patient with profound visual agnosia caused by damage to the ventral stream could not distinguish between wooden blocks of different sizes but could adjust the distance between her thumb and forefinger when she picked them up. She made this adjustment by means of vision, before she actually touched them (Milner et al., 1991 ; Goodale et al., 1994 ). A functional-imaging study of this patient (James et al., 2003 ) showed normal activity in the dorsal stream while she was picking up objects—especially in the anterior intraparietal sulcus (AIP), which is involved in manipulating and grasping.

The suggestion by Goodale and his colleagues seems a reasonable one. Certainly, the dorsal stream is involved in perception of the location of object’s space—but then, if its primary role is to direct movements, it must be involved in location of these objects, or else how could it direct movements toward them? In addition, it must contain information about the size and shape of objects, or else how could it control the distance between thumb and forefinger?

Two functional-imaging studies provide further evidence that the dorsal stream is involved in visual control of movement. Valyear et al. ( 2006 ) presented photographs of pairs of elongated stimuli, one after the other, and noted which regions of the brain responded to the difference between the two stimuli. They found that a region of the ventral stream responded differentially to pairs of stimuli that differed in their form (for example, a fork versus a clarinet) but did not distinguish between the same object shown in different orientations (for example, one tipped 45 degrees to the right of vertical and the other tipped 45 degrees to the left). In contrast, a region of the dorsal stream distinguished between different orientations but ignored changes in the identity of the two objects. A follow-up study published the next year (Rice et al., 2007 ) showed subjects photographs of two different types of objects: graspable ones, such as forks and hammers, and nongraspable ones, such as tractors and pieces of furniture. The investigators found that, as before, the region of the dorsal stream ignored changes in the identity of the objects but distinguished between orientations. However, the region distinguished between the orientations only of stimuli that a person could grasp. This region did not distinguish between the orientations of photos of stimuli that could not be picked up, such as tractors and pieces of furniture.

FIGURE 6.48 Components of the Ventral and Dorsal Streams of the Visual Cortex

The figure shows some major components of the ventral and dorsal streams. The view is similar to that seen in Figure 6.32(e) .

(Adapted from Tootell, R. B. H., Tsao, D., and Vanduffel, W. Journal of Neuroscience, 2003, 23, 3981–3989.)

A fascinating (and delightful) study with young children demonstrates the importance of communication between the dorsal and ventral streams of the visual system (DeLoache, Uttal, and Rosengren, 2004 ). The experimenters let children play with large toys: an indoor slide that they could climb and slide down, a chair that they could sit on, and a toy car that they could enter. After the children played in and on the large toys, the children were taken out of the room, the large toys were replaced with identical miniature versions, and the children were then brought back into the room. When the children played with the miniature toys, they acted as if they were the large versions: They tried to climb onto the slide, climb into the car, and sit on the chair. Simulate dissociation of perception and action on MyPsychLab to see a video of a 2-year-old boy trying to climb into the toy car. He says “In!” several times and turns to his mother, apparently asking her to help him. The authors suggest that this child’s behavior reflects incomplete maturation of connections between the dorsal and ventral streams. The ventral stream recognizes the identity of the objects, and the dorsal stream recognizes their size, but the information is not adequately shared between these two systems.

I realize that I have presented a large amount of information in this section—and I’m sure you do, too. The importance of the visual system is attested to by the fact that approximately 25 percent of our cerebral cortex is devoted to this sense modality and by the many discoveries being made in the laboratories that are busy discovering interesting things about vision. Figure 6.48 . shows the location of the regions that make up the ventral stream and some of the dorsal stream. (The rest of the dorsal stream lies in the intraparietal sulcus, which is illustrated in Figure 6.47 .) Table 6.3 lists these regions and summarizes their major functions.

TABLE 6.3 Regions of the Human Visual Cortex and Their Functions

Region of Human Visual Cortex	Name of Region (If Different)	Function
V1	Striate cortex	Small modules that analyze orientation, movement, spatial frequency, retinal disparity, and color
V2		Further analysis of information from V1
Ventral Stream
V3+VP		Further analysis of information from V2
V3A		Processing of visual information across entire visual field of contralateral eye
V4d/V4v	V4 dorsal/ventral	Analysis of form Processing of color constancy V4d = lower visual field, V4v = upper visual field
V8		Color perception
LO	Lateral occipital complex	Object recognition
FFA	Fusiform face area	Face recognition, object recognition by experts (“flexible fusiform area”)
PPA	Parahippocampal place area	Recognition of particular places
EBA	Extrastriate body area	Perception of body parts other than face
Dorsal Stream
V7		Visual attention Control of eye movements
MT/MST	Medial temporal/medial superior temporal (named for locations in monkey brain)	Perception of motion Perception of biological motion and optic flow in specific subregions
LIP	Lateral intraparietal area	Visual attention Control of saccadic eye movements
VIP	Ventral intraparietal area	Control of visual attention to particular locations Control of eye movements Visual control of pointing
AIP	Anterior intraparietal area	Visual control of hand movements: grasping, manipulation
MIP	Middle intraparietal area Parietal reach region (monkeys)	Visual control of reaching
CIP	Caudal intraparietal area Caudal parietal disparity region	Perception of depth from stereopsis

SECTION SUMMARY: Analysis of Visual Information: Role of the Visual Association Cortex

The visual cortex consists of area V1 (striate cortex), area V2, and two streams of visual association cortex. The ventral stream, which ends with the inferior temporal cortex, is involved with perception of objects. Lesions of this region disrupt visual object perception. Also, single neurons in the inferior temporal cortex respond best to complex stimuli and continue to do so even if the object is moved to a different location, changed in size, placed against a different background, or partially hidden. The dorsal stream, which ends with the posterior parietal cortex, is involved with perception of movement, location, visual attention, and control of eye and hand movements. There are at least two dozen different subregions of the visual cortex, arranged in a hierarchical fashion. Each region analyzes a particular characteristic of visual information and passes the results of this analysis to other regions in the hierarchy. However, some information from the association cortex is sent back to the striate cortex. Neurons in the thin stripes of V2 receive information concerning color from the blobs in the striate cortex (V1), and those in the thick stripes and pale stripes receive information about orientation, spatial frequency, movement, and retinal disparity from the interblob regions of V1. Neurons in the pale and thin stripes project to area V4, the start of the ventral stream.

Damage to area V4 abolishes color constancy (accurate perception of color under different lighting conditions), and damage to area V8 causes cerebral achromatopsia, a loss of color vision but not of form perception. A condition opposite to achromatopsia can also be seen: A patient with extensive damage to the extrastriate cortex was functionally blind but could still recognize colors. His brain damage apparently destroyed regions of the visual association cortex that are responsible for form perception but not those for color perception.

Functional-imaging studies indicate that specific regions of the cortex are involved in perception of form, movement, and color, and these studies are enabling us to discover the correspondences between the anatomy of the human visual system and that of laboratory animals. Humans who have sustained damage to the ventral stream of visual association cortex have difficulty recognizing objects by sight, even though fine details can often be detected—a disorder known as visual agnosia. Prosopagnosia—failure to recognize faces—is caused by damage to the fusiform face area (FFA), a region on the base of the right temporal lobe. Congenital prosopagnosia appears to be associated with an FFA that is smaller than normal, and people with Williams syndrome have a special interest in people and their faces, recognize faces well, and have a FFA that is larger than normal. The development of this region may be a result of extensive experience looking at faces; expertise with other complex stimuli such as artificial creatures (greebles) causes the development of circuits devoted to the perception of these stimuli as well.

The extrastriate body area (EBA), a region adjacent to the FFA, contains neurons that respond to the sight of bodies or body parts, and the parahippocampal place area (PPA) responds to scenes that depict particular places. Newborn babies prefer to look at facelike stimuli, a preference that may involve subcortical mechanisms. Babies deprived of visual input for the first few months of life because of congenital cataracts demonstrate impaired discrimination of faces later in life. The fusiform face area fails to develop in people with autism, presumably because of insufficient motivation to become expert in recognizing other people’s faces.

Damage to area V5 (also called area MT) disrupts an animal’s ability to perceive movement, and damage to the posterior parietal cortex disrupts perception of the spatial location of objects. Damage to the human visual association cortex corresponding to area V5 disrupts perception of movement, producing a disorder known as akinetopsia. In addition, transcranial magnetic stimulation of V5 causes a temporary disruption, and functional-imaging studies show that perception of moving stimuli activate this region. In both monkeys and humans, area MSTd, a region of extrastriate cortex that is adjacent to area V5, appears to be specialized for perceiving optic flow, one of the cues we use to perceive the direction in which we are heading.

The ability to perceive form from motion—recognition of complex movements of people indicated by lights attached to parts of their body—is probably related to the ability to recognize people by the way they walk. This ability apparently depends on a region of cerebral cortex on the ventral bank of the posterior end of the superior temporal sulcus. The visual association cortex receives information about eye movements from the motor system and information about movement of retinal images from the visual cortex and determines which movements are caused by head and eye movements and which are caused by movements in the environment. A patient with extrastriate damage was unable to compensate for eye movements; when he moved his eyes, he perceived movement in the environment. The location of the region responsible for this compensation appears to be in the extrastriate cortex at the junction of the temporal and parietal lobes.

Some people with visual agnosia caused by damage to the ventral stream can still perceive the meanings of mimed actions or recognize friends by the way the friends walk, which indicates that the dorsal stream of these people’s visual cortex is largely intact. Most of the visual association cortex at the end of the dorsal stream is located in the intraparietal sulcus: LIP and VIP are involved in visual attention and control of saccadic eye movements, VIP and MIP are involved in visual control of reaching and pointing, AIP is involved in visual control of grasping and manipulating, and CIP is involved in perception of depth from stereopsis.

Goodale and his colleagues suggest that the primary function dorsal stream of visual association cortex is better characterized as “how” rather than “where”; the role of the posterior parietal cortex in control of reaching, grasping, and manipulation requires visually derived information of movement, depth, and location.

■ THOUGHT QUESTION

Some psychologists are interested in “top-down” processes in visual perception—that is, the effects of context on perceiving ambiguous stimuli. For example, if you are in a dimly lighted kitchen and see a shape that could be either a loaf of bread or a country mailbox, you will be more likely to perceive the object as a loaf of bread. Where in the brain might contextual information affect perception?

Review Questions

Study and Review on MyPsychLab

Describe the characteristics of light and color, outline the anatomy of the eye and its connections with the brain, and describe the transduction of visual information.

Describe the coding of visual information by photoreceptors and ganglion cells in the retina.

Discuss the striate cortex and how its neurons respond to orientation and movement, spatial frequency, and texture.

Discuss how neurons in the striate cortex respond to retinal disparity and color and describe the modular organization of striate cortex.

Describe the anatomy of the visual association cortex and discuss the location and functions of the two streams of visual analysis that take place there.

Discuss the perception of color by neurons in the ventral stream.

Describe the role of the ventral stream in the perception of faces, bodies, objects, and scenes of places.

Describe how neurons in the dorsal stream respond to movement and location and discuss the effects of brain damage on perception of these features.

Explore the Virtual Brain in MyPsychLab

■ VISUAL SYSTEM

The human brain devotes more cortex to the visual system than any other sensory modality. Qualitatively different aspects of vision are carried and analyzed by distinct neural circuits. The Visual System module of the virtual brain shows brain regions involved in the detection, analysis, and perception of visual stimuli.