Another perceptual phenomenon explained by lateral inhibition is the Hermann Grid. Each intersection contains gray images in between the white “paths” and black squares. Yet when you look directly at the gray zones they vanish. Lateral inhibition can help to explain why this occurs. Signals from bipolar cells create the illusion of gray squares at each intersection. Lateral inhibition creates a slower response to the perception of gray squares and explains why perception doesn’t match the actual physical stimulus (Goldstein & Brockmole, 2017). In a sense, the brain fills in the intensity between the intensity of the two darker squares.
If you think about it, we live in a world of constantly changing light. We encounter intense light, and then we encounter less intense light in varying shades as we move throughout our day. Yet, we do not really notice this. What we see is not really the visual stimulus as it truly appears, but something processed through neural networks as the light is prepared for analysis by the brain.
Problems with the Lateral Inhibition Explanations of the Chevreul Illusion and the Hermann Grid
Of course, lateral inhibition is not the only explanation for the visual illusions that occur. Researchers have conducted studies that challenge the use of lateral inhibition as an explanation of the Chevreul illusion as well as the Hermann Gird. First, for the Chevreul illusion, researchers changed the background ramp from light on the left, dark on the right to the opposite. In so doing, one’s perception of the top and bottom changes, while lateral inhibition between the rectangles stays the same (Goldstein & Brockmole, 2017). The effect is thus impacted by the top and bottom in addition to changes in light to dark.
With regard to the Hermann Grid, problems with lateral inhibition explanations arise when the grid is made with curvy squares rather than straight. Using curvy squares should have little effect on the dark spots, but when the squares are curved, the dark spots vanish. While this calls to question the lateral inhibition theory to an extent, it does not completely discount it (Goldstein & Brockmole, 2017). Perception of changes in the stimuli in these illusions opens up the door for additional research to determine the extent of the influence of lateral inhibition, or, perhaps the change in how we see this influence.
Responding of Single Fibers in the Optic Nerve
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Receptive FieldBefore his work on the limulus, Hartline studied fiber responses by dissecting a frog’s eye. This research illuminated how light shown on the retina causes the neuron to fire, this area is labeled as the receptive field. The retina contains areas that must receive illumination in order to receive a response from an optic nerve fiber. These receptive fields overlap, which prompts retinal activation of many ganglion cell fibers by light on an overlapping receptive field. Hartline noted that the receptive field covers a greater area than a single rod or cone, which demonstrates that thousands of signals are converging (Goldstein & Brockmole, 2017).
Hubel and Wiesel’s Rationale for Studying Receptive Fields
Using stimuli from an animal, Hubel and Wiesel’s research showed how cortical neurons at higher levels of the visual system become more specialized to certain types of stimuli. Using a projector instead of direct light on the animal’s eye the researchers were able to determine which areas, excitatory or inhibitory did not respond to screen (Goldstein & Brockmole, 2017).
It is important to understand how signals travel from the retina, following Hubel and Wiesel’s approach; signals leave the eye in the optic nerve, travel to the lateral geniculate nucleus (LGN), and then to the occipital lobe (the visual receiving area). The visual receiving area is the sensory location of the cortex. Interestingly, center-surround receptive fields are present in both the optic nerve fibers and the neurons in the LGN, which calls into question the function of the LGN. It is possible that it acts to regulate neural information based on the reduction in output from the LGN in comparison to the input going into it. Another thought is that it is involved in feedback of information received from the brain (Goldstein & Brockmole, 2017).
Receptive Fields of Neurons in the Visual Cortex
Hubel and Wiesel also conducted research on receptive fields in the striate cortex. They discovered that instead of the center-surround arrangement, the receptive cells in these fields are arranged side-by-side. These side-by-side cells are called simple cortical cells. These cells respond to specific stimuli orientations, in particular these cells are sensitive to vertical orientation. This is part of the orientation turning curve, which indicates changes in cell firing based on vertical or tilted orientations. As the cells are vertically oriented, the firing response is optimum. As the bar is tilted, the cell response decreases, and begins to show the impact of inhibitory areas (Goldstein & Brockmole, 2017).
Not all cells in the striate cortex responded the same way for Hubel and Wiesel. Some cells did nothing when exposed to small spots of light. This is best defined as the measure between orientation and firing (Goldstein & Brockmole, 2017). They discovered by accident that some cells in the striate cortex respond to other stimuli.
COMPLEX CELLS
END-STOPPED CELLS
Different cells respond to different, specific features, earning the name feature detectors.
Selective Adaptation, Selective Rearing and Sensory Coding
SELECTIVE ADAPTATION
SELECTIVE REARING
SENSORY CODING
Now that we have explored how feature detectors respond to specific stimuli, it is time to see if they have anything to do with perception. One way is through selective adaptation. Selective adaptation occurs as neurons that are firing eventually become fatigued, or they adapt. Selective adaptation causes the neuron firing rate to decrease. Adaptation also causes the neuron firing frequency to decrease with the stimulus is quickly presented again. Adaptation is selective because vertical neurons adapt and non-firing neurons do not. This indicates that the adaptation selectively affects specific orientations, just like neurons selectively respond to specific orientations (Goldstein & Brockmole, 2017).
Cortical Organization
Now that we have explored receptive fields and the response properties of neurons, it is time to look how the visual system is organized.
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Spatial OrganizationWhen we look at the fallen display in the store things are organized across the visual field, including the people looking at the display on the floor, and potato chip bags scattered across the floor. Each of these stimuli represent different locations in the environment and specific locations in the visual cortex. This is called spatial organization, or the organization of stimuli in the environment as it is represented by locations in the visual cortex (Goldstein & Brockmole, 2017).
Most of what we have been talking about is based on normal function of the parts of the visual system. That, however is not always the case and you can learn as much from what happens when something does not work, as you can when it works properly. The fMRI is also used to study people who have suffered some form of damage to the visual association pathway. Visual agnosia is an inability to properly perceive a stimulus as it should be perceived (Carlson, Miller, Heth, Donahoe, & Martin, 2010). With visual agnosia, an individual is capable of sight, and can see a stimulus with visual sensory organs, but cannot identify the stimulus. Let’s look at a few types of visual agnosia.
Imagine you are on your shopping trip and your best friend is also there. Unfortunately, when you look at him you see a head with eyes, a nose, a mouth, and cheeks, but they are not where they are supposed to be and you are unable to identify him. You can recognize his voice, which gives his identify away, but your visual system is not translating the information it is processing in a way that allows you to see a complete face as it is truly put together. This is prosopagnosia.
Damage to the temporal lobe can result in prosopagnosia, or a difficulty recognizing the faces of people whose identity is known (Goldstein & Brockmole, 2017). People diagnosed with prosopagnosia understand that they are looking at a face, but cannot identify the owner of the face, even if it is a close loved one. The individual can recognize the parts of the face, but the configuration of the features does not align correctly (Carlson et al., 2010).