The authors have nothing to disclose.

The protocol was approved by the institutional review board at the University of Wisconsin-Madison. The following steps have been written as a guide for programmers to replicate the automated process of the web application described.
1. Login Participant
<ol>
	<li>Instruct the participant to use an internet-enabled computer and navigate to the web application using an HTML5 compatible browser: <a href="http://brainandlearning.org/jove" target="_blank">http://brainandlearning.org/jove</a>. Have the participant sit in a quiet room free of distractions, with the computer at a comfortable height. 
	NOTE: Since the entire experiment is hosted online, the tasks can also be performed remotely without the presence of a research assistant. All instructions for the participant are included in the web application.</li>
	<li>Have the participant input a unique ID that will be associated with the data collected and stored in a MySQL database. Have the participant reuse this ID if the online tasks are not completed within the same session. Before logging in, obtain consent from the participant via a consent form linked on the page. 
	NOTE: A participant&#8217;s progress is saved after each task in order to allow for completion of the 2 tasks at separate times if needed. Instruct the participant to always use the same ID in order to start where one left off.</li>
</ol>
2. Screen Calibration
NOTE: The web application guides the participant through the three steps outlined in the calibration page at: <a href="http://brainandlearning.org/jove/Calibration" target="_blank">http://brainandlearning.org/jove/Calibration</a>.
<ol>
	<li>Ask the participant to input the diagonal size of the screen in inches in the labeled textbox.
	<ol>
		<li>However, if the participant does not know this information, have the participant find a CD or credit card as a calibration object (Figure 1). When one is selected, prompt the participant to place the object against the screen and align it with a representative image of the object displayed on the screen.</li>
		<li>Prompt the participant to adjust the size of the screen image to match the size of the physical object. Based on the measurements of a physical CD (diameter of 4.7&#8221;) or a credit card (width of 3.2&#8221;) in addition to the pixel size of the representative image, determine the ratio of pixels to inches for the screen.</li>
		<li>Retrieve the pixel resolution of the monitor via JavaScript&#8217;s screen.width and screen.height properties to then calculate the diagonal size of the screen in pixels. Knowing this value and the previously estimated pixel-to-inch ratio (see step 2.1.2), convert the diagonal size to inches. Have the participant confirm this value through a dialog box.</li>
	</ol>
	</li>
	<li>Prompt the participant to adjust the screen brightness settings until all 12 bands in a black-to-white gradient displayed on the screen are clearly distinguishable. Brightness setting controls vary by computer.</li>
	<li>Ask the participant to sit an arm&#8217;s length away from the monitor in a comfortable position and then set the browser window to full screen mode. The browser window must be in full screen mode to maximize the visual space used by the tasks and to remove any visual distractions, such as the browser toolbar and desktop taskbars.</li>
	<li>Knowing the resolution of the participant&#8217;s screen and the diagonal size of the monitor, use the web application to automatically calculate the pixels/degree conversion value, based on a 50 cm viewing distance. Resize the dimensions of the stimuli in the tasks using this value. All visual angle dimensions reported below are based on this assumed mean distance value from the monitor.</li>
	<li>Once calibration is complete, ask the participant to complete the two tasks described below. Choose the order of the tasks or randomly assign the order via the web application.</li>
</ol>
3. Multiple Object Tracking Task (MOT) &#8211; Figure 2
<ol>
	<li>Introduce and familiarize the participant with the MOT stimuli through a self-guided tutorial, seen at: <a href="http://brainandlearning.org/jove/MOT/practice.php" target="_blank">http://brainandlearning.org/jove/MOT/practice.php</a>. Ask the participant to read step-by-step instructions that demonstrate how the trials will work. Once the participant finishes reading the instructions, prompt the participant to go through the practice trials.
	<ol>
		<li>Setup the practice stimuli to consist of 8 dots at 0.8&#176; with a movement speed of 2&#176;/sec. Use the HTML5 requestAnimationFrame API to optimize browser animation at a frame rate of 60 Hz in order to control this stimulus motion.</li>
		<li>Ensure the dots move within the boundaries of a circle of 2&#176; eccentricity and a circle no larger than the height of the participant&#8217;s screen, without the instructions obscured.</li>
		<li>Set the dots to move in a random trajectory, where at each frame a dot has a 60% chance of changing direction by a maximum angle of 0.2&#176;. If a dot collides with another dot or the inner or outer radial limits, move the dot in the opposite direction.</li>
		<li>Prompt the participant to track the blue dots (varying between 1 and 2 dots per practice trial), with the yellow dots acting as distractors.</li>
		<li>After 2 sec, change the blue dots to yellow dots and continue to move them amongst the original yellow dots for another 4 sec. At the end of each trial, stop the dots and highlight one.</li>
		<li>Prompt the participant to respond via key press whether the highlighted dot was a tracked dot or a distractor dot. Next, prompt the participant to press the space bar to continue onto the next trial.</li>
		<li>After 3 consecutive correct trials, or a maximum of 6 trials, move the participant onto the full task.</li>
	</ol>
	</li>
	<li>Start the full MOT task for the participant. An example of the task can be found at: <a href="http://brainandlearning.org/jove/MOT" target="_blank">http://brainandlearning.org/jove/MOT</a>.
	<ol>
		<li>Setup the full task with 16 dots that move at 5&#176;/sec within the space between 2&#176; eccentricity and 10&#176; eccentricity. If the participant&#8217;s screen cannot fit a circle of 10&#176; eccentricity, use the maximum size the screen can contain instead.</li>
		<li>Have the participant complete a total of 45 trials: a mixture of 5 trials consisting of 1 tracked dot and 10 trials each consisting of 2 - 5 tracked dots. Match all other parameters to the practice trials (see steps 3.1.3 - 3.1.6).</li>
		<li>Record the participant&#8217;s response and response time once the dot is highlighted.</li>
		<li>For every 15 trials, suggest a break to the participant. At these breaks, display the participant&#8217;s performance (percent of correct trials) within the block on the screen.</li>
	</ol>
	</li>
</ol>
4. Moving from One Task to Another (Optional Step)
<ol>
	<li>Allow the participant to take a break between the two tasks. However, repeat steps 1 and 2 if the tasks are not completed during the same login session.</li>
</ol>
5. Useful Field of View Task (UFOV) &#8211; Figure 3
<ol>
	<li>Introduce and familiarize the participant with the UFOV stimuli through a self-guided tutorial, seen at: <a href="http://brainandlearning.org/jove/UFOV/practice.php" target="_blank">http://brainandlearning.org/jove/UFOV/practice.php</a>. Ask the participant to go through 4 stages of step-by-step instructions that demonstrate the two target stimuli that must be attended to during the task.
	<ol>
		<li>Set the central target stimulus as a 1&#176; smiley that flashes at the center of the screen with either long or short hair. Randomize the smiley&#8217;s hair length across trials.</li>
		<li>Set the peripheral target stimulus as a 1&#176; star that flashes at 4&#176; eccentricity at one of 8 locations around the circle (0&#176;, 45&#176;, 90&#176;, 135&#176;, 180&#176;, 225&#176;, 270&#176;, and 315&#176;). Randomize the location of the star across trials.</li>
		<li>Control stimulus duration via number of frames used for presentation time. Optimize frame refresh at about 17 msec per frame by using the HTML5 requestAnimationFrame API.</li>
		<li>To check if the expected presentation time was achieved, use JavaScript&#8217;s getTime() method to obtain the stimulus duration&#8217;s start time and end time based on the participant&#8217;s system clock. Calculate the measured presentation time from these two values and use this value for data analysis.</li>
		<li>For each practice trial, wait 500 msec before displaying the stimuli for approximately 200 msec (about 12 frames).</li>
		<li>Follow stimulus presentation with a noise mask composed of a randomly generated grayscale dot array for 320 msec (about 19 frames).</li>
		<li>For stage 1, only display the central target and then prompt the participant to respond via key press which hair length was displayed.</li>
		<li>For stage 2, only display the peripheral target and then prompt the participant to click on one of 8 radial lines, representing the 8 possible target locations, to indicate where the star appeared.</li>
		<li>For stage 3, display both central and peripheral target stimuli and then prompt the participant to provide responses to both the type of smiley and the location of the star. 
		NOTE: Participants can freely choose the order of these two responses.</li>
		<li>For stage 4, display both target stimuli in addition to peripheral distractors, and then prompt the participant to respond to both target stimuli. For the distractors, display 1&#176; squares presented at the remaining 7 locations at 4&#176; eccentricity, in addition to 8 more squares at 2&#176; eccentricity.</li>
		<li>After the participant&#8217;s response, show the participant feedback (a green checkmark for a correct answer or a red cross for an incorrect answer) for each target response after each trial.</li>
		<li>Move the participant onto the next practice stage after getting 3 consecutive correct trials. After stage 4, move the participant onto the full task.</li>
	</ol>
	</li>
	<li>Prompt the participant to start the full UFOV task. An example of the task can be found at: <a href="http://brainandlearning.org/jove/UFOV" target="_blank">http://brainandlearning.org/jove/UFOV</a>.
	<ol>
		<li>Present the same central stimulus as in the practice session (see step 5.1.1). Display the peripheral target at 7&#176; eccentricity at one of the previously mentioned 8 locations (see step 5.1.2). 24 distractor squares are also displayed at 3&#176; eccentricity, 5&#176; eccentricity, and the remaining 7&#176; eccentricity locations.</li>
		<li>Use a 3-down, 1-up staircase procedure to determine the presentation time of the stimuli: decrease the duration of stimuli after 3 consecutive correct trials and increase after each error trial.</li>
		<li>Before the first 3 reversals in the staircase, use a step size of 2 frames (approximately every 33 msec). After 3 reversals, use a step size of 1 frame. Vary the delay before the stimulus onset between 1 frame and 99 frames per trial, and keep the noise mask duration at 320 msec (about 19 frames). 
		NOTE: Reversals are the points at which the duration changes either from increasing to decreasing, or decreasing to increasing.</li>
		<li>End the task when one of three conditions is met: the staircase procedure reaches 8 reversals; the participant completes 10 consecutive trials at either the ceiling duration (99 frames) or the floor duration (1 frame); or the participant reaches a maximum of 72 trials.</li>
		<li>Record the participant&#8217;s response and response time for both the central stimulus and the peripheral stimulus.</li>
	</ol>
	</li>
</ol>

<ol><li>Henrich, J., Heine, S. J., Norenzayan, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+weirdest+people+in+the+world%5BTitle%5D)+AND+%22The+Behavioral+And+Brain+Sciences%22%5BJournal%5D)">The weirdest people in the world.</a> The Behavioral And Brain Sciences. 33, 61-135 (2010).</li>
<li>Buhrmester, M., Kwang, T., Gosling, S. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Amazon%27s+Mechanical+Turk%3A+A+new+source+of+inexpensive%2C+yet+high-quality%2C+data%5BTitle%5D)+AND+%22Perspectives+on+Psychological+Science%22%5BJournal%5D)">Amazon's Mechanical Turk: A new source of inexpensive, yet high-quality, data.</a> Perspectives on Psychological Science. 6 (1), 3-5 (2011).</li>
<li>Goodman, J. K., Cryder, C. E., Cheema, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Data+collection+in+a+flat+world%3A+the+strengths+and+weaknesses+of+mechanical+turk+samples%5BTitle%5D)+AND+%22Journal+of+Behavioral+Decision+Making%22%5BJournal%5D)">Data collection in a flat world: the strengths and weaknesses of mechanical turk samples.</a> Journal of Behavioral Decision Making. 26 (3), 213-224 (2013).</li>
<li>Mason, W., Suri, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Conducting+behavioral+research+on+Amazon%27s+Mechanical+Turk%5BTitle%5D)+AND+%22Behavior+Research+Methods%22%5BJournal%5D)">Conducting behavioral research on Amazon's Mechanical Turk.</a> Behavior Research Methods. 44 (1), 1-23 (2012).</li>
<li>Crump, M. J., McDonnell, J. V., Gureckis, T. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Evaluating+Amazon%27s+Mechanical+Turk+as+a+tool+for+experimental+behavioral+research%5BTitle%5D)+AND+%22PLoS+One%22%5BJournal%5D)">Evaluating Amazon's Mechanical Turk as a tool for experimental behavioral research.</a> PLoS One. 8, e57410(1371).</li>
<li>Lupyan, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+difficulties+of+executing+simple+algorithms%3A+why+brains+make+mistakes+computers+don%27t%5BTitle%5D)+AND+%22Cognition%22%5BJournal%5D)">The difficulties of executing simple algorithms: why brains make mistakes computers don't.</a> Cognition. 129, 615-636 (2013).</li>
<li>Ball, K., Owsley, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+useful+field+of+view+test%3A+a+new+technique+for+evaluating+age-related+declines+in+visual+function%5BTitle%5D)+AND+%22J+Am+Optom+Assoc%22%5BJournal%5D)">The useful field of view test: a new technique for evaluating age-related declines in visual function.</a> J Am Optom Assoc. 64 (1), 71-79 (1993).</li>
<li>Pylyshyn, Z. W., Storm, R. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Tracking+multiple+independent+targets%3A+Evidence+for+a+parallel+tracking+mechanism%5BTitle%5D)+AND+%22Spatial+Vision%22%5BJournal%5D)">Tracking multiple independent targets: Evidence for a parallel tracking mechanism.</a> Spatial Vision. 3 (3), 179-197 (1988).</li>
<li>Dye, M. W. G., Bavelier, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Differential+development+of+visual+attention+skills+in+school-age+children%5BTitle%5D)+AND+%22Vision+Research%22%5BJournal%5D)">Differential development of visual attention skills in school-age children.</a> Vision Research. 50 (4), 452-459 (2010).</li>
<li>Green, C. S., Bavelier, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Enumeration+versus+multiple+object+tracking%3A+The+case+of+action+video+game+players%5BTitle%5D)+AND+%22Cognition%22%5BJournal%5D)">Enumeration versus multiple object tracking: The case of action video game players.</a> Cognition. 101 (1), 217-245 (2006).</li>
<li>Chapter 1. Kodak Company. Ergonomics Design Philosophy. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=%22Kodak%E2%80%99s+Ergonomic+Design+for+People+at+Work%22">Kodak’s Ergonomic Design for People at Work</a>. Chengalur, S. N., Rodgers, S. H., Bernard, T. E. , 2nd Edition, John Wiley & Sons, Inc. Hoboken, NJ. (2004).</li>
<li>Elze, T., Bex, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((P1-7%3A+Modern+display+technology+in+vision+science%3A+Assessment+of+OLED+and+LCD+monitors+for+visual+experiments%5BTitle%5D)+AND+%22i-Perception%22%5BJournal%5D)">P1-7: Modern display technology in vision science: Assessment of OLED and LCD monitors for visual experiments.</a> i-Perception. 3 (9), 621(2012).</li>
<li>Elze, T., Tanner, T. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Temporal+Properties+of+Liquid+Crystal+Displays%3A+Implications+for+Vision+Science+Experiments%5BTitle%5D)+AND+%22PLoS+One%22%5BJournal%5D)">Temporal Properties of Liquid Crystal Displays: Implications for Vision Science Experiments.</a> PLoS One. 7 (9), e44048(2012).</li>
</ol>

This work was supported by the Swiss National Foundation (100014_140676), the National Science Foundation (1227168), and the National Eye Institute of the National Institutes of Health (P30EY001319).

Online data collection has a number of advantages over standard laboratory-based data collection. These include the potential to sample far more representative populations than the typical college undergraduate pool utilized in the field, and the ability to obtain far greater sample sizes in less time than it takes to obtain sample sizes that are an order of magnitude smaller in the lab1-6 (e.g., the data points collected from 1,700+ participants in the current paper were obtained in less than one wee...

Over the past five years there has been a surge of interest in the use of online behavioral data collection methods. While the vast majority of publications in the domain of psychology have utilized potentially non-representative subject populations1 (i.e., primarily college undergraduates) and often reasonably small sample sizes as well (i.e., typically in the range of tens of subjects), online methods offer the promise of far more diverse and larger samples. For instance, Amazon&#8217;s Mechanical Turk service has been the subject of a number of recent studies, both describing the characteristics of the &#8220;worker&#8221; population and the use of this population in behavioral research2-6.
However, one significant concern related to such methods is the relative lack of control over critical stimulus variables. For example, in most visual psychophysics tasks, stimuli are described in terms of visual angle. The calculation of visual angles requires precise measurements of viewing distance, screen size, and screen resolution. While these parameters are trivial to measure and control in a lab setting (where there is a known monitor and participants view stimuli while in a chin rest placed a known distance from the monitor), the same is not true of online data collection. In an online environment, not only will participants inevitably use a wide variety of monitors of different sizes with different software settings, they also may not have easy access to rulers/tape measures that would allow them to determine their monitor size or have the knowledge necessary to determine their software and hardware settings (e.g., refresh rate, resolution).
Here we describe a set of methods to collect data on two well-known tests of visual attention &#8211; the Useful Field of View (UFOV) paradigm7 and the multiple object tracking (MOT) task8 &#8211; while avoiding as much as possible the sources of variability that are inherent in online measurements. These tasks can be run by any participant with an internet connection and an HTML5 compatible browser. Participants who do not know their screen size are walked through a measurement process utilizing commonly available items of standard size (i.e., credit card/CD &#8211; see Figure 1).
Data on these two tasks were collected from over 1,700 participants in a Massive Online Open Course. Average performance of this online sample was highly consistent with results obtained in tightly controlled laboratory-based measures of the exact same tasks9,10. Our results are thus consistent with the growing body of literature demonstrating the efficacy of online data collection methods, even in tasks that require specific control over viewing conditions.

<table><tbody><tr><td>Computer/tablet</td><td></td><td></td><td>It must have an internet connection and an HTML5 compatible browser</td></tr><tr><td>CD or credit card</td><td></td><td></td><td>May not be needed if participant already knows the monitor size</td></tr></tbody></table>

methods to test visual attention online

Online data collection methods have particular appeal to behavioral scientists because they offer the promise of much larger and much more representative data samples than can typically be collected on college campuses. However, before such methods can be widely adopted, a number of technological challenges must be overcome &#8211; in particular in experiments where tight control over stimulus properties is necessary. Here we present methods for collecting performance data on two tests of visual attention. Both tests require control over the visual angle of the stimuli (which in turn requires knowledge of the viewing distance, monitor size, screen resolution, etc.) and the timing of the stimuli (as the tests involve either briefly flashed stimuli or stimuli that move at specific rates). Data collected on these tests from over 1,700 online participants were consistent with data collected in laboratory-based versions of the exact same tests. These results suggest that with proper care, timing/stimulus size dependent tasks can be deployed in web-based settings.

Outlier Removal
A total of 1,779 participants completed the UFOV task. Of those, 32 participants had UFOV thresholds that were greater than 3 standard deviations from the mean, suggesting that they were unable to perform the task as instructed. As such, the UFOV data from these participants were removed from the final analysis, leaving a total of 1,747 participants.
Data were obtained from 1,746 participants for the MOT task. Two participants had mean accuracy scores t...

Watch this Scientific Journal Video about Methods to Test Visual Attention Online at JoVE.com

Methods to Test Visual Attention Online

To replicate laboratory settings, online data collection methods for visual tasks require tight control over stimulus presentation. We outline methods for the use of a web application to collect performance data on two tests of visual attention.

Online data collection methods have particular appeal to behavioral scientists because they offer the promise of much larger and much more representative ...

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11.8K Views.  University of Rochester. To replicate laboratory settings, online data collection methods for visual tasks require tight control over stimulus presentation. We outline methods for the use of a web application to collect performance data on two tests of visual attention.

Video: Methods to Test Visual Attention Online

A Dual Task Procedure Combined with Rapid Serial Visual Presentation to Test Attentional Blink for Nontargets

When viewers search for targets in a rapid serial visual presentation (RSVP) stream, if two targets are presented within about 500 msec of each other, the first target may be easy to spot but the second is likely to be missed. This phenomenon of attentional blink (AB) has been widely studied to probe the temporal capacity of attention for detecting visual targets. However, with the typical procedure of AB experiments, it is not possible to examine how the processing of non-target items in RSVP may be affected by attention. This paper describes a novel dual task procedure combined with RSVP to test effects of AB for nontargets at varied stimulus onset asynchronies (SOAs). In an exemplar experiment, a target category was first displayed, followed by a sequence of 8 nouns. If one of the nouns belonged to the target category, participants would respond &#8216;yes&#8217; at the end of the sequence, otherwise participants would respond &#8216;no&#8217;. Two 2-alternative forced choice memory tasks followed the response to determine if participants remembered the words immediately before or after the target, as well as a random word from another part of the sequence. In a second exemplar experiment, the same design was used, except that 1) the memory task was counterbalanced into two groups with SOAs of either 120 or 240 msec and 2) three memory tasks followed the sequence and tested remembrance for nontarget nouns in the sequence that could be anywhere from 3 items prior the target noun position to 3 items following the target noun position. Representative results from a previously published study demonstrate that our procedure can be used to examine divergent effects of attention that not only enhance targets but also suppress nontargets. Here we show results from a representative participant that replicated the previous finding.&#160;

This paper describes a novel dual task procedure combined with Rapid Serial Visual Presentation to look at attentional blink at varied stimulus onset asynchronies. This experimental procedure differed from others in that it tested attentional blink for nontargets.

When viewers search for targets in a rapid serial visual presentation (RSVP) stream, if two targets are presented within about 500 msec of each other, the ...

Measurement of Neurophysiological Signals of Ignoring and Attending Processes in Attention Control

Attention control is the ability to selectively attend to some sensory signals while ignoring others. This ability is thought to involve two processes: enhancement of sensory signals that are to be attended and the attenuation of sensory signals that are to be ignored. The overall strength of attentional modulation is often measured by comparing the amplitude of a sensory neural response to an external input when attended versus when ignored. This method is robust for detecting attentional modulation, but precludes the ability to assess the separate dynamics of attending and ignoring processes. Here, we describe methodology to measure independently the neurophysiological signals of attending and ignoring using the intermodal attention task (IMAT). This task, when combined with electroencephalography, isolates neurophysiological sensory responses in auditory and visual modalities, when either attending or ignoring, with respect to a passive control. As a result, independent dynamics of attending and of a ignoring can be assessed in either modality. Our results using this task indicate that the timing and cortical sources of attending and ignoring effects differ, as do their contributions to the attention modulation effect, pointing to unique neural trajectories and demonstrating sample utility of measuring them separately.

Attention control comprises enhancement of target signals and attenuation of distractor signals. We describe an approach to measure separately but concurrently, the neurophysiology of attending and ignoring in sustained intermodal attention, utilizing a passive control condition during which neither process is continuously engaged.

Attention control is the ability to selectively attend to some sensory signals while ignoring others. This ability is thought to involve two processes: ...

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

This protocol describes how to conduct temporal-order experiments to measure visual processing speed and the attentional resource distribution. The proposed method is based on a new and synergistic combination of three components: the temporal-order judgments (TOJ) paradigm, Bundesen&#39;s Theory of Visual Attention (TVA), and a hierarchical Bayesian estimation framework. The method provides readily interpretable parameters, which are supported by the theoretical and neurophysiological underpinnings of TVA. Using TOJs, TVA-based estimates can be obtained for a broad range of stimuli, whereas traditional paradigms used with TVA are mainly limited to letters and digits. Finally, the meaningful parameters of the proposed model allow for the establishment of a hierarchical Bayesian model. Such a statistical model allows assessing results in one coherent analysis both on the subject and the group level.
To demonstrate the feasibility and versatility of this new approach, three experiments are reported with attention manipulations in synthetic pop-out displays, natural images, and a cued letter-report paradigm.

Temporal-order judgments can be used to estimate processing speed parameters and attentional weights and thereby to infer the mechanisms of attentional processing. This methodology can be applied to a wide range of visual stimuli and works with many attention manipulations.

This protocol describes how to conduct temporal-order experiments to measure visual processing speed and the attentional resource distribution. The ...

How to Build a Dichoptic Presentation System That Includes an Eye Tracker

The presentation of different stimuli to the two eyes, dichoptic presentation, is essential for studies involving 3D vision and interocular suppression. There is a growing literature on the unique experimental value of pupillary and oculomotor measures, especially for research on interocular suppression. Although obtaining eye-tracking measures would thus benefit studies that use dichoptic presentation, the hardware essential for dichoptic presentation (e.g. mirrors) often interferes with high-quality eye tracking, especially when using a video-based eye tracker. We recently described an experimental setup that combines a standard dichoptic presentation system with an infrared eye tracker by using infrared-transparent mirrors1. The setup is compatible with standard monitors and eye trackers, easy to implement, and affordable (on the order of US$1,000). Relative to existing methods it has the benefits of not requiring special equipment and posing few limits on the nature and quality of the visual stimulus. Here we provide a visual guide to the construction and use of our setup.

We recently proposed a method that allows dichoptic visual stimulus presentation and binocular eye tracking simultaneously1. The key is the combination of an infrared eye tracker and the corresponding infrared-transparent mirrors. This manuscript provides an in depth protocol for initial setup and everyday operation.

The presentation of different stimuli to the two eyes, dichoptic presentation, is essential for studies involving 3D vision and interocular suppression. ...

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 ...

An Automated Assessment of Visual Acuity in Infants and Toddlers

An Automated Method for Assessing Visual Acuity in Infants and Toddlers Using an Eye-Tracking System

Visual acuity measurement is an important visual function test to perform in infancy and childhood. However, accurate visual acuity measurement in infants is difficult because of deficiencies in their communication ability. This paper presents a novel automated method to assess visual acuity in children (5-36 months old). This method, the automated acuity card procedure (AACP), uses a webcam for eye tracking and recognizes children's watching behaviors automatically. A two-choice preferential-looking test is performed when the tested child watches the visual stimuli shown on a high-resolution digital display screen. When the tested child watches the stimuli, their facial pictures are recorded by the webcam. These pictures are used by the set computer program to analyze their watching behavior. With this procedure, the child's eye movement responses to different stimuli are measured, and their visual acuity is assessed without communication. By comparing the results with grating acuity obtained by Teller Acuity Cards (TACs), AACP performance is deemed comparable to that of TACs.

Author Spotlight: An Automated Method for Assessing Visual Acuity in Infants and Toddlers Using an Eye-Tracking System  

In this paper, we present a novel automated method for assessing visual acuity in infants and toddlers by using an eye-tracking system.

Visual acuity measurement is an important visual function test to perform in infancy and childhood. However, accurate visual acuity measurement in infants ...

Gaze-Contingent Framework for Studying and Training in Simulated Vision Loss

A Gaze-Contingent Display Framework for Perceptual Learning Research with Simulated Central Vision Loss

Macular degeneration (MD) is one of the leading causes of vision impairment in the Western world. Patients with MD tend to develop spontaneous eye movement strategies to compensate for their vision loss, including adopting a preferred retinal locus, or PRL, a spared peripheral region that they use more frequently to replace the damaged fovea. However, not all patients are successful in developing a PRL, and even when they do, it might take them months. Currently, no gold standard rehabilitative therapy exists, and MD research is further hindered by issues of recruitment, compliance, and comorbidity. To help address these issues, a growing body of research has used eye tracking-guided, gaze-contingent displays in a simulated central vision loss paradigm in individuals with intact vision. While simulated vision loss is qualitatively different than pathological central vision loss, our framework provides for a highly controlled model through which to study compensatory eye movements and test possible rehabilitation interventions in low vision. By developing a comprehensive framework, rather than relying on isolated and disconnected tasks, we create a cohesive environment where we can test larger-scale hypotheses, allowing us to examine interactions between tasks, assess training effects across multiple measures, and establish a consistent methodology for future research. Furthermore, participants in simulated central vision loss studies show similarities in their oculomotor compensatory behaviors compared to patients with MD. Here, we present a framework for conducting gaze-contingent studies related to simulated central vision loss. We emphasize the utilization of the framework to test behavioral and oculomotor performance of healthy individuals on a wide range of perceptual tasks encompassing different levels of visual processing. We also discuss how this framework can be adapted for training MD patients.

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

We present development of a gaze-contingent display framework designed for perceptual and oculomotor research simulating central vision loss. This framework is particularly adaptable for studying compensatory behavioral and oculomotor strategies in individuals experiencing both simulated and pathological central vision loss.

Macular degeneration (MD) is one of the leading causes of vision impairment in the Western world. Patients with MD tend to develop spontaneous eye ...

Neuroscience

Visual Search for Features and Conjunctions

Source: Laboratory of Jonathan Flombaum&#8212;Johns Hopkins University

How do people find objects in cluttered visual scenes? Think, for example, of looking for keys on a messy desk, finding the ripest-looking fruit at the grocery store, locating your car when you can&#8217;t quite remember where you parked it, or finding an old friend at an airport exit gate. Clearly, an understanding of visual perception is going to play a role in any answers, and more specifically, an understanding of visual attention will be crucial.
Visual attention refers to the ability to focus in on just part of an image, mustering one&#8217;s processing resources selectively to determine whether the thing being looked for&#8212;the target, in the standard experimental jargon&#8212;is present. To study search and attention, experimental psychologists have developed a widely used paradigm known (unsurprisingly) as visual search.
Psychologists have also motivated a great deal of research by the intuition that any good theory of search is going to have, to explain why some things are easy to find and others are hard to find. So in the context of the visual search paradigm, perceptual psychologists have often focused on contrasting easy searches with more difficult ones. The most influential contrast is between what researchers call a feature search and a conjunction search.

Investigating Visual Attention by Feature and Conjunction Search

Source: Laboratory of Jonathan Flombaum&#8212;Johns Hopkins University

How do people find objects in cluttered visual scenes? Think, for example, of ...

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Psychology

Cognitive Psychology

Inattentional Blindness

Source: Laboratory of Jonathan Flombaum&#8212;Johns Hopkins University
We generally think that we see things pretty well if they are close by and right in front of us. But do we? We know that visual attention is a property of the human brain that controls what parts of the visual world we process, and how effectively. Limited attention means that we can't process everything at once, it turns out, even things that might be right in front of us. 
In the 1960s, the renowned cognitive psychologist Ulrich Neisser began to demonstrate experimentally that people can be blind to objects that are right in front of them, literally, if attention is otherwise distracted. In the 1980s and 1990s, Arien Mack and Irvin Rock followed up on Neisser's work, developing a simple paradigm for examining how, when, and why distracted attention can make people fail to see the whole object. Their experiments, and Neisser's, did not involve people with brain damage, disease, or anything of the sort, just regular people who failed to see objects that were right in front of them. This phenomenon has been called inattentional blindness. This video will demonstrate basic procedures for investigating inattentional blindness using the methods of Mack and Rock.1

Investigating Visual Awareness and Inattentional Blindness

Source: Laboratory of Jonathan Flombaum&#8212;Johns Hopkins University
We generally think that we see things pretty well if they are close by and right in ...

Sensation and Perception

The Attentional Blink

Source: Laboratory of Jonathan Flombaum&#8212;Johns Hopkins University
In order for recognition of a certain stimulus to take place, visual attention needs to be directed towards said stimulus. To the earliest parts of the visual system, objects are not objects, they are collections of visual features-lines, corners, changes in texture, color, and light. Attention is the resource that is necessary for later processing in order to recognize what a given bundle of features adds up to. This makes attention a central focus of research. One especially important set of questions concerns how people sustain attention, that is, the extent to which they can continuously maintain a focus of attention from moment-to-moment. It is now known that sustained attention takes great effort. When attention needs to be focused very rapidly on something that is moving or changing very quickly, the effort involved causes a momentary lapse in attention once it is disengaged. This kind of lapse in attention is called an attentional blink. It is like the brain blinks for a moment, shutting down attention for a rest. Stimuli that appear during an attentional blink will not be perceived. 
In 1992, a group of researchers devised a paradigm to study the attentional blink, and the paradigm has come to be known by the same name.1 It demonstrates some of the challenges to maintaining focused attention. This video demonstrates how to implement the attentional blink paradigm in order to study sustained visual attention.

Sustained Visual Attention and the Attentional Blink Paradigm

Source: Laboratory of Jonathan Flombaum&#8212;Johns Hopkins University
In order for recognition of a certain stimulus to take place, visual attention ...

Methods to Test Visual Attention Online

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