Name: visualizing visual adaptation
Uploaded: 2017-04-24T13:00:00
Description: Watch this Scientific Journal Video about Visualizing Visual Adaptation at JoVE.com

Supported by National Institutes of Health (NIH) grant EY-10834.

NOTE: The protocol illustrated uses a program that allows one to select images and then adapt them using options selected by different drop-down menus.
1. Select the Image to Adapt
<ol>
	<li>Click on the image and browse for the filename of the image to work with. Observe the original image in the upper left pane.</li>
</ol>
2. Specify the Stimulus and the Observer
<ol>
	<li>Click the &#34;format&#34; menu to choose how to represent the image and the observer.</li>
...

The illustrated protocol demonstrates how the effects of adaptation to a change in the environment or the observer can be portrayed in images. The form this portrayal takes will depend on the assumptions made for the model &#8212; for example, how color is encoded, and how the encoding mechanisms respond and adapt. Thus the most important step is deciding on the model for color vision &#8212; for example what the properties of the hypothesized channels are, and how they are assumed to adapt. The other important steps are...

What might the world look like to others, or to ourselves as we change? Answers to these questions are fundamentally important for understanding the nature and mechanisms of perception and the consequences of both normal and clinical variations in sensory coding. A wide variety of techniques and approaches have been developed to simulate how images might appear to individuals with different visual sensitivities. For example, these include simulations of the colors that can be discriminated by different types of color deficiencies1,2,3,4, the spatial and chromatic differences that can be resolved by infants or older observers5,6,7,8,9, how images appear in peripheral vision10, and the consequences of optical errors or disease11,12,13,14. They have also been applied to visualize the discriminations that are possible for other species15,16,17. Typically, such simulations use measurements of the sensitivity losses in different populations to filter an image and thus reduce or remove the structure they have difficulty seeing. For instance, common forms of color blindness reflect a loss of one of the two photoreceptors sensitive to medium or long wavelengths, and images filtered to remove their signals typically appear devoid of &#34;reddish-greenish&#34; hues1. Similarly, infants have poorer acuity, and thus the images processed for their reduced spatial sensitivity appear blurry5. These techniques provide invaluable illustrations of what one person can see that another may not. However, they do not &#8212; and often are not intended to &#8212; portray the actual perceptual experience of the observer, and in some cases may misrepresent the amount and types of information available to the observer.
This article describes a novel technique developed to simulate differences in visual experience which incorporates a fundamental characteristic of visual coding &#8212; adaptation18,19. All sensory and motor systems continuously adjust to the context they are exposed to. A pungent odor in a room quickly fades, while vision accommodates to how bright or dim the room is. Importantly, these adjustments occur for almost any stimulus attribute, including &#34;high-level&#34; perceptions such as the characteristics of someone&#39;s face20,21 or their voice22,23, as well as calibrating the motor commands made when moving the eyes or reaching for an object24,25. In fact, adaptation is likely an essential property of almost all neural processing. This paper illustrates how to incorporate these adaptation effects into simulations of the appearance of images, by basically &#34;adapting the image&#34; to predict how it would appear to a specific observer under a specific state of adaptation26,27,28,29. Many factors can alter the sensitivity of an observer, but adaptation can often compensate for important aspects of these changes, so that the sensitivity losses are less conspicuous than would be predicted without assuming that the system adapts. Conversely, because adaptation adjusts sensitivity according to the current stimulus context, these adjustments are also important to incorporate for predicting how much perception might vary when the environment varies.
The following protocol illustrates the technique by adapting the color content of images. Color vision has the advantage that the initial neural stages of color coding are relatively well understood, as are the patterns of adaptation30. The actual mechanisms and adjustments are complex and varied, but the main consequences of adaptation can be captured using a simple and conventional two-stage model (Figure 1a). In the first stage, color signals are initially encoded by three types of cone photoreceptors that are maximally sensitive to short, medium or long wavelengths (S, M, and L cones). In the second stage, the signals from different cones are combined within post-receptoral cells to form &#34;color-opponent&#34; channels that receive antagonistic inputs from the different cones (and thus convey &#34;color&#34; information), and &#34;non-opponent&#34; channels that sum together the cone inputs (thus coding &#34;brightness&#34; information). Adaptation occurs at both stages, and adjusts to two different aspects of the color &#8212; the mean (in the cones) and the variance (in post-receptoral channels)30,31. The goal of the simulations is to apply these adjustments to the model mechanisms and then render the image from their adapted outputs.
The process of adapting images involves six primary components. These are 1) choosing the images; 2) choosing the format for the image spectra; 3) defining the change in color of the environment; 4) defining the change in the sensitivity of the observer; 5) using the program to create the adapted images; and 6) using the images to evaluate the consequences of the adaptation. The following considers each of these steps in detail. The basic model and mechanism responses are illustrated in Figure 1, while Figures 2 - 5 show examples of images rendered with the model.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Computer</td>
		</tr>
		<tr>
			<td>Images to adapt</td>
		</tr>
		<tr>
			<td>Programming language (e.g. Visual Basic or Matlab)</td>
		</tr>
		<tr>
			<td>Program for processing the images</td>
		</tr>
		<tr>
			<td>Observer spectral sensitivities (for applications involving observer-specific adaptation)</td>
		</tr>
		<tr>
			<td>Device emmission spectra (for device-dependent applications)</td>
		</tr>
	</tbody>
</table>

visualizing visual adaptation

Many techniques have been developed to visualize how an image would appear to an individual with a different visual sensitivity: e.g., because of optical or age differences, or a color deficiency or disease. This protocol describes a technique for incorporating sensory adaptation into the simulations. The protocol is illustrated with the example of color vision, but is generally applicable to any form of visual adaptation. The protocol uses a simple model of human color vision based on standard and plausible assumptions about the retinal and cortical mechanisms encoding color and how these adjust their sensitivity to both the average color and range of color in the prevailing stimulus. The gains of the mechanisms are adapted so that their mean response under one context is equated for a different context. The simulations help reveal the theoretical limits of adaptation and generate &#34;adapted images&#34; that are optimally matched to a specific environment or observer. They also provide a common metric for exploring the effects of adaptation within different observers or different environments. Characterizing visual perception and performance with these images provides a novel tool for studying the functions and consequences of long-term adaptation in vision or other sensory systems.

Figures 2 - 4 illustrate the adaptation simulations for changes in the observer or the environment. Figure 2 compares the predicted appearance of Cezanne&#39;s Still Life with Apples for a younger and older observer who differ only in the density of the lens pigment28. The original image as seen through the younger eye (Figure 2a) appears much yellower and dimmer through the more densely pigmented lens (<s...

Watch this Scientific Journal Video about Visualizing Visual Adaptation at JoVE.com

Visualizing Visual Adaptation

This article describes a novel method for simulating and studying adaptation in the visual system.

Many techniques have been developed to visualize how an image would appear to an individual with a different visual sensitivity: e.g., because of optical ...

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