Name: visualizing visual adaptation
Uploaded: 2017-04-24T13:00:00.000+00:00
Duration: 4 min 43 s
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>
	<li>Click on the &#34;standard observer&#34; option to model a standard or average observer adapting to a specific color distribution. In this case, use standard equations to convert the RGB values of the image to the cone sensitivities32.</li>
	<li>Click on &#34;individual observer&#34; option to model the spectral sensitivities of a specific observer. Because these sensitivities are wavelength-dependent, the program converts the RGB values of the image into gun spectra by using the standard or measured emission spectra for the display.</li>
	<li>Click on &#34;natural spectra&#34; option to approximate actual spectra in the world. This option converts the RGB values to spectra, for example by using standard basis functions33 or Gaussian spectra34 to approximate the corresponding spectrum for the image color.</li>
</ol>
3. Select the Adaptation Condition
<ol>
	<li>Adapt either the same observer to different environments (e.g., to the colors of a forest vs. urban landscape), or different observers to the same environment (e.g., a normal vs. color deficient observer).
	<ol>
		<li>In the former case, use the menus to select the environments. In the latter, use the menus to define the sensitivity of the observer.</li>
	</ol>
	</li>
	<li>To set the environments, select the &#34;reference&#34; and &#34;test&#34; environments from the dropdown menus. These control the two different states of adaptation by loading the mechanism responses for different environments.
	<ol>
		<li>Choose the &#34;reference&#34; menu to control the starting environment. This is the environment the subject is adapted to while viewing the original image. 
		NOTE: The choices shown have been precalculated for different environments. These were derived from measurements of the color gamuts for different collections of images. For example, one application examined how color perception might vary with changes in the seasons, by using calibrated images taken from the same location at different times27. Another study, exploring how adaptation might affect color percepts across different locations, represented the locations by sampling images of different scene categories29.</li>
		<li>Select the &#34;user defined&#34; environment to load the values for a custom environment. Observe a window to browse and select a particular file. To create these files for independent images, display each image to be included (as in step 1) and then click the &#34;save image responses&#34; button. 
		NOTE: This will display a window where one can create or append to an excel file storing the responses to each image. To create a new file, enter the filename, or browse for an existing file. For existing files, the responses to the current image are added and the responses to all images automatically averaged. These averages are input for the reference environment when the file with the &#34;user defined&#34; option is selected.</li>
		<li>Select the &#34;test&#34; menu to access a list of environments for the image to be adjusted for. Select the &#34;current image&#34; option to use the mechanism responses for the displayed image. 
		NOTE: This option assumes the subjects are adapting to the colors in the image that is currently being viewed. Otherwise select one of the precalculated environments or the &#34;user defined&#34; option to load the test environment.</li>
	</ol>
	</li>
</ol>
4. Select the Spectral Sensitivity of the Observer
NOTE: For the adaptation effects of different environments, the observer will usually remain constant, and is set to the default &#34;standard observer&#34; with average spectral sensitivity. There are 3 menus for setting an individual spectral sensitivity, which control the amount of screening pigment or the spectral sensitivities of the observer.
<ol>
	<li>Click on the &#34;lens&#34; menu to select the density of the lens pigment. The different options allow one to choose the density characteristic of different ages.</li>
	<li>Click on the &#34;macular&#34; menu to similarly select the density of the macular pigment. Observe these options in terms of the peak density of the pigment.</li>
	<li>Click on the &#34;cones&#34; menu to choose between observers with normal trichromacy or different types of anomalous trichromacy. 
	NOTE: Based on the choices the program defines the cone spectral sensitivities of the observer and a set of 26 postreceptoral channels that linearly combine the cone signals to roughly uniformly sample different color and luminance combinations.</li>
</ol>
5. Adapt the Image
<ol>
	<li>Click the &#34;adapt&#34; button. 
	NOTE: This executes the code for calculating the responses of the cones and post-receptoral mechanisms to each pixel in the image. The response is scaled so that the mean response to the adapting color distribution equals the mean responses to the reference distribution, or so that the average response is the same for an individual or reference observer. The scaling is multiplicative to simulate von Kries adaptation35. The new image is then rendered by summing the mechanism responses and converting back to RGB values for display. Details of the algorithm are given in 26,27,28,29.</li>
	<li>Observe three new images on the screen. These are labeled as 1) &#34;unadapted&#34;&#160;&#8212; how the test image should appear to someone fully adapted to the reference environment; 2) &#34;cone adaptation&#34;- this shows the image adjusted only for adaptation in the receptors; and 3) &#34;full adaptation&#34;- this shows the image predicted by complete adaptation to the change in the environment or the observer.</li>
	<li>Click the &#34;save images&#34; button to save the three calculated-images. Observe a new window on the screen to browse for the folder and select the filename.</li>
</ol>
6. Evaluate the Consequences of the Adaptation
NOTE: The original reference and adapted images simulate how the same image should appear under the two states of modeled adaptation, and importantly, differ only because of the adaptation state. The differences in the images thus provide insight into consequences of the adaptation.
<ol>
	<li>Visually look at the differences between the images. 
	NOTE: Simple inspection of the images can help show how much color vision might vary when living in different color environments, or how much adaptation might compensate for a sensitivity change in the observer.</li>
	<li>Quantify these adaptation effects by using analyses or behavioral measurements with the images to empirically evaluate the consequences of the adaptation29.
	<ol>
		<li>Measure how color appearance changes. For example, compare the colors in the two images to measure how color categories or perceptual salience shift across different environments or observers. For example, use analyses of the changes in color with adaptation to calculate how much the unique hues (e.g., pure yellow or blue) could theoretically vary because of variations in the observer&#39;s color environment29.</li>
		<li>Ask how the adaptation affects visual sensitivity or performance. For example, use the adapted images to compare whether visual search for a novel color is faster when observers are first adapted to the colors of the background. Conduct the experiment by superimposing on the images an array of targets and differently-colored distractors that were adapted along with the images, with the reaction times measured for locating the odd target29.</li>
	</ol>
	</li>
</ol>

The authors have nothing to disclose.

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><tbody><tr><td>Computer</td><td></td><td></td><td></td></tr><tr><td>Images to adapt</td><td></td><td></td><td></td></tr><tr><td>Programming language (&lt;em&gt;e.g.,&lt;/em&gt; Visual Basic or Matlab)</td><td></td><td></td><td></td></tr><tr><td>Program for processing the images</td><td></td><td></td><td></td></tr><tr><td>Observer spectral sensitivities (for applications involving observer-specific adaptation)</td><td></td><td></td><td></td></tr><tr><td>Device emmission spectra (for device-dependent applications)</td><td></td><td></td><td></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 | Behavior | Video

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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8.9K Views.  University of Nevada, Reno. This article describes a novel method for simulating and studying adaptation in the visual system.

Video: Visualizing Visual Adaptation

State-Dependency Effects on TMS:  A Look at Motive Phosphene Behavior

Transcranial magnetic stimulation (TMS) is a non-invasive neurostimulatory and neuromodulatory technique that can transiently or lastingly modulate cortical excitability (either increasing or decreasing it) via the application of localized magnetic field pulses.1,2 Within the field of TMS, the term state dependency refers to the initial, baseline condition of the particular neural region targeted for stimulation. As can be inferred, the effects of TMS can (and do) vary according to this primary susceptibility and responsiveness of the targeted cortical area.3,4,5

In this experiment, we will examine this concept of state dependency through the elicitation and subjective experience of motive phosphenes. Phosphenes are visually perceived flashes of small lights triggered by electromagnetic pulses to the visual cortex. These small lights can assume varied characteristics depending upon which type of visual cortex is being stimulated. In this particular study, we will be targeting motive phosphenes as elicited through the stimulation of V1/V2 and the V5/MT+ complex visual regions.6

In this article, we examine the effects of visually relevant state dependency on TMS induced motive phosphenic presentations.

Transcranial magnetic stimulation (TMS) is a non-invasive neurostimulatory and neuromodulatory technique that can transiently or lastingly modulate ...

Neuroscience

A Method to Study Adaptation to Left-Right Reversed Audition

An unusual sensory space is one of the effective tools to uncover the mechanism of adaptability of humans to a novel environment. Although most of the previous studies have used special spectacles with prisms to achieve unusual spaces in the visual domain, a methodology for studying the adaptation to unusual auditory spaces has yet to be fully established. This study proposes a new protocol to set-up, validate, and use a left-right reversed stereophonic system using only wearable devices, and to study the adaptation to left-right reversed audition with the help of neuroimaging. Although individual acoustic characteristics are not yet implemented, and slight spillover of unreversed sounds is relatively uncontrollable, the constructed apparatus shows high performance in a 360&#176; sound source localization coupled with hearing characteristics with little delay. Moreover, it looks like a mobile music player and enables a participant to focus on daily life without arousing curiosity or drawing attention of other individuals. Since the effects of adaptation were successfully detected at the perceptual, behavioral, and neural levels, it is concluded that this protocol provides a promising methodology for studying adaptation to left-right reversed audition, and is an effective tool for uncovering the adaptability of humans to a novel environments in the auditory domain.

The present study proposes a protocol to investigate the adaptation to left-right reversed audition achieved only by wearable devices, using neuroimaging, which can be an effective tool for uncovering the adaptability of humans to a novel environment in the auditory domain.

An unusual sensory space is one of the effective tools to uncover the mechanism of adaptability of humans to a novel environment. Although most of the ...

A Two-interval Forced-choice Task for Multisensory Comparisons

We provide a procedure for a psychophysics experiment in humans based on a previously described paradigm aimed to characterize the perceptual duration of intervals within the range of milliseconds of visual, acoustic, and audiovisual aperiodic trains of six pulses. In this task, each of the trials consists of two consecutive intramodal intervals where the participants press the upward arrow key to report that the second stimulus lasted longer than the reference, or the downward arrow key to indicate otherwise. The analysis of the behavior results in psychometric functions of the probability of estimating the comparison stimulus to be longer than the reference, as a function of the comparison intervals. In conclusion, we advance a way of implementing standard programming software to create visual, acoustic, and audiovisual stimuli, and to generate a two-interval forced-choice (2IFC) task by delivering stimuli through noise-blocking headphones and a computer&#39;s monitor.

Psychophysics is essential for studying perception phenomena through sensory information. Here we present a protocol to perform a two-interval forced-choice task as implemented in a previous report on human psychophysics where participants estimated the duration of visual, auditory, or audiovisual intervals of aperiodic trains of pulses.

We provide a procedure for a psychophysics experiment in humans based on a previously described paradigm aimed to characterize the perceptual duration of ...

Investigating the Deployment of Visual Attention Before Accurate and Averaging Saccades via Eye Tracking and Assessment of Visual Sensitivity

This experimental protocol was designed to investigate whether visual attention is obligatorily deployed at the endpoint of saccades. To this end, we recorded the eye position of human participants engaged in a saccade task via eye tracking and assessed visual orientation discrimination performance at various locations during saccade preparation. Importantly, instead of using a single saccade target paradigm for which the saccade endpoint typically coincides roughly with the target, this protocol comprised the presentation of two nearby saccade targets, leading to a distinct spatial dissociation between target locations and saccade endpoint on a substantial number of trials. The paradigm allowed us to compare presaccadic visual discrimination performance at the endpoint of accurate saccades (landing at one of the saccade targets) and of averaging saccades (landing at an intermediate location in between the two targets). We observed a selective enhancement of visual sensitivity at the endpoint of accurate saccades but not at the endpoint of averaging saccades. Rather, before the execution of averaging saccades, visual sensitivity was equally enhanced at both targets, suggesting that saccade averaging follows from unresolved attentional selection among the saccade targets. These results argue against a mandatory coupling between visual attention and saccade programming based on a direct measure of presaccadic visual sensitivity rather than saccadic reaction times, which have been used in other protocols to draw similar conclusions. While our protocol provides a useful framework to investigate the relationship between visual attention and saccadic eye movements at the behavioral level, it can also be combined with electrophysiological measures to extend insights at the neuronal level.

This experimental protocol combined eye tracking and the assessment of presaccadic visual sensitivity in a dual task paradigm, consisting of a free choice saccade task and a visual discrimination task, to investigate the deployment of visual spatial attention before both, accurate and averaging saccades.

This experimental protocol was designed to investigate whether visual attention is obligatorily deployed at the endpoint of saccades. To this end, we ...

Integrating Visual Psychophysical Assays within a Y-Maze to Isolate the Role that Visual Features Play in Navigational Decisions

Collective animal behavior arises from individual motivations and social interactions that are critical for individual fitness. Fish have long inspired investigations into collective motion, specifically, their ability to integrate environmental and social information across ecological contexts. This demonstration illustrates techniques used for quantifying behavioral responses of fish, in this case, Golden Shiner (Notemigonus crysoleucas), to visual stimuli using computer visualization and digital image analysis. Recent advancements in computer visualization allow for empirical testing in the lab where visual features can be controlled and finely manipulated to isolate the mechanisms of social interactions. The purpose of this method is to isolate visual features that can influence the directional decisions of the individual, whether solitary or with groups. This protocol provides specifics on the physical Y-maze domain, recording equipment, settings and calibrations of the projector and animation, experimental steps and data analyses. These techniques demonstrate that computer animation can elicit biologically-meaningful responses. Moreover, the techniques are easily adaptable to test alternative hypotheses, domains, and species for a broad range of experimental applications. The use of virtual stimuli allows for the reduction and replacement of the number of live animals required, and consequently reduces laboratory overhead.
This demonstration tests the hypothesis that small relative differences in the movement speeds (2 body lengths per second) of virtual conspecifics will improve the speed and accuracy with which shiners follow the directional cues provided by the virtual silhouettes. Results show that shiners directional decisions are significantly affected by increases in the speed of the visual cues, even in the presence of background noise (67% image coherency). In the absence of any motion cues, subjects chose their directions at random. The relationship between decision speed and cue speed was variable and increases in cue speed had a modestly disproportionate influence on directional accuracy.

Here, we present a protocol to demonstrate a behavioral assay that quantifies how alternative visual features, such as motion cues, influence directional decisions in fish. Representative data are presented on the speed and accuracy where Golden Shiner (Notemigonus crysoleucas) follow virtual fish movements.

Collective animal behavior arises from individual motivations and social interactions that are critical for individual fitness. Fish have long inspired ...

The Optokinetic Response as a Quantitative Measure of Visual Acuity in Zebrafish

Zebrafish are a proven model for vision research, however many of the earlier methods generally focused on larval fish or demonstrated a simple response. More recently adult visual behavior in zebrafish has become of interest, but methods to measure specific responses are new coming. To address this gap, we set out to develop a methodology to repeatedly and accurately utilize the optokinetic response (OKR) to measure visual acuity in adult zebrafish. Here we show that the adult zebrafish's visual acuity can be measured, including both binocular and monocular acuities. Because the fish is not harmed during the procedure, the visual acuity can be measured and compared over short or long periods of time. The visual acuity measurements described here can also be done quickly allowing for high throughput and for additional visual procedures if desired. This type of analysis is conducive to drug intervention studies or investigations of disease progression.

Zebrafish have been a valuable tool for many research areas. Here we demonstrate a method to elicit a visual response and calculate a functional visual acuity in the adult zebrafish.

Zebrafish are a proven model for vision research, however many of the  earlier methods generally focused on larval fish or demonstrated a simple  ...

Long-term Sensory Conflict in Freely Behaving Mice

Long-term sensory conflict protocols are a valuable means of studying motor learning. The presented protocol produces a persistent sensory conflict for experiments aimed at studying long-term learning in mice. By permanently wearing a device fixed on their heads, mice are continuously exposed to a sensory mismatch between visual and vestibular inputs while freely moving in home cages. Therefore, this protocol readily enables the study of the visual system and multisensory interactions over an extended timeframe that would not be accessible otherwise. In addition to lowering the experimental costs of long-term sensory learning in naturally behaving mice, this approach accommodates the combination of in vivo and in vitro experiments. In the reported example, video-oculography is performed to quantify the vestibulo-ocular reflex (VOR) and optokinetic reflex (OKR) before and after learning. Mice exposed to this long-term sensory conflict between visual and vestibular inputs presented a strong VOR gain decrease but exhibited few OKR changes. Detailed steps of device assembly, animal care, and reflex measurements are hereby reported.

The presented protocol produces a persistent sensory conflict for experiments aimed at studying long-term learning. By permanently wearing a fixed device on their heads, mice are continuously exposed to a sensory mismatch between visual and vestibular inputs while freely moving in home cages.

Long-term sensory conflict protocols are a valuable means of studying motor learning. The presented protocol produces a persistent sensory conflict for ...

Quantification of Oculomotor Responses and Accommodation Through Instrumentation and Analysis Toolboxes

Through the purposeful stimulation and recording of eye movements, the fundamental characteristics of the underlying neural mechanisms of eye movements can be observed. VisualEyes2020 (VE2020) was developed based on the lack of customizable software-based visual stimulation available for researchers that does not rely on motors or actuators within a traditional haploscope. This new instrument and methodology have been developed for a novel haploscope configuration utilizing both eye tracking and autorefractor systems. Analysis software that enables the synchronized analysis of eye movement and accommodative responses provides vision researchers and clinicians with a reproducible environment and shareable tool. The Vision and Neural Engineering Laboratory&#39;s (VNEL) Eye Movement Analysis Program (VEMAP) was established to process recordings produced by VE2020&#39;s eye trackers, while the Accommodative Movement Analysis Program (AMAP) was created to process the recording outputs from the corresponding autorefractor system. The VNEL studies three primary stimuli: accommodation (blur-driven changes in the convexity of the intraocular lens), vergence (inward, convergent rotation and outward, divergent rotation of the eyes), and saccades (conjugate eye movements). The VEMAP and AMAP utilize similar data flow processes, manual operator interactions, and interventions where necessary; however, these analysis platforms advance the establishment of an objective software suite that minimizes operator reliance. The utility of a graphical interface and its corresponding algorithms allow for a broad range of visual experiments to be conducted with minimal required prior coding experience from its operator(s).

VisualEyes2020 (VE2020) is a custom scripting language that presents, records, and synchronizes visual eye movement stimuli. VE2020 provides stimuli for conjugate eye movements (saccades and smooth pursuit), disconjugate eye movements (vergence), accommodation, and combinations of each. Two analysis programs unify the data processing from the eye tracking and accommodation recording systems.

Through the purposeful stimulation and recording of eye movements, the fundamental characteristics of the underlying neural mechanisms of eye movements ...

Bioengineering

Evaluating Optokinetic Reflex Responses in Mice: A New High-Precision Protocol

Quantification of Visual Feature Selectivity of the Optokinetic Reflex in Mice

The optokinetic reflex (OKR) is an essential innate eye movement that is triggered by the global motion of the visual environment and serves to stabilize retinal images. Due to its importance and robustness, the OKR has been used to study visual-motor learning and to evaluate the visual functions of mice with different genetic backgrounds, ages, and drug treatments. Here, we introduce a procedure for evaluating OKR responses of head-fixed mice with high accuracy. Head fixation can rule out the contribution of vestibular stimulation on eye movements, making it possible to measure eye movements triggered only by visual motion. The OKR is elicited by a virtual drum system, in which a vertical grating presented on three computer monitors drifts horizontally in an oscillatory manner or unidirectionally at a constant velocity. With this virtual reality system, we can systematically change visual parameters like spatial frequency, temporal/oscillation frequency, contrast, luminance, and the direction of gratings, and quantify tuning curves of visual feature selectivity. High-speed infrared video-oculography ensures accurate measurement of the trajectory of eye movements. The eyes of individual mice are calibrated to provide opportunities to compare the OKRs between animals of different ages, genders, and genetic backgrounds. The quantitative power of this technique allows it to detect changes in the OKR when this behavior plastically adapts due to aging, sensory experience, or motor learning; thus, it makes this technique a valuable addition to the repertoire of tools used to investigate the plasticity of ocular behaviors.

Author Spotlight: An Accurate and Quantitative Approach to Study Visual Feature Selectivity of the Optokinetic Reflex in Mice 

Here, we describe a standard protocol for quantifying the optokinetic reflex. It combines virtual drum stimulation and video-oculography, and thus allows precise evaluation of the feature selectivity of the behavior and its adaptive plasticity.

The optokinetic reflex (OKR) is an essential innate eye movement that is triggered by the global motion of the visual environment and serves to stabilize ...

Assessing Vision via Shape Recognition in Random Dot Kinematograms

Motion-Acuity Test for Visual Field Acuity Measurement with Motion-Defined Shapes

The standard visual acuity measurements rely on stationary stimuli, either letters (Snellen charts), vertical lines (vernier acuity) or grating charts, processed by those regions of the visual system most sensitive to the stationary stimulation, receiving visual input from the central part of the visual field. Here, an acuity measurement is proposed based on discrimination of simple shapes, that are defined by motion of the dots in the random dot kinematograms (RDK) processed by visual regions sensitive to motion stimulation and receiving input also from the peripheral visual field. In the motion-acuity test, participants are asked to distinguish between a circle and an ellipse, with matching surfaces, built from RDKs, and separated from the background RDK either by coherence, direction, or velocity of dots. The acuity measurement is based on ellipse detection, which with every correct response becomes more circular until reaching the acuity threshold. The motion-acuity test can be presented in negative contrast (black dots on white background) or in positive contrast (white dots on black background). The motion defined shapes are located centrally within 8 visual degrees and are surrounded by RDK background. To test the influence of visual peripheries on centrally measured acuity, a mechanical narrowing of the visual field to 10 degrees is proposed, using opaque goggles with centrally located holes. This easy and replicable narrowing system is suitable for MRI protocols, allowing further investigations of the functions of the peripheral visual input. Here, a simple measurement of shape and motion perception simultaneously is proposed. This straightforward test assesses vision impairments depending on the central and peripheral visual field inputs. The proposed&#160;motion-acuity test advances the capability of standard tests to reveal spare or even strengthened vision functions in patients with injured visual system, that until now remained undetected.

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing

A novel motion-based acuity test that allows the assessment of central and peripheral visual processing in low-vision and healthy individuals, along with goggles limiting peripheral vision compatible with MRI protocols, are described here. This method offers a comprehensive vision assessment for functional impairments and dysfunctions of the visual system.

The standard visual acuity measurements rely on stationary stimuli, either letters (Snellen charts), vertical lines (vernier acuity) or grating charts, ...

Visualizing Visual Adaptation

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