Gaze-contingent displays combine a display device with an eyetracking system to rapidly update an image on the basis of the measured eye position. All such systems have a delay, the system latency, between a change in gaze location and the related change in the display. The system latency is the result of the delays contributed by the eyetracker, the display computer, and the display, and it is affected by the properties of each component, which may include variability. We present a direct, simple, and low-cost method to measure the system latency. The technique uses a device to briefly blind the eyetracker system (e.g., for video-based eyetrackers, a device with infrared light-emitting diodes (LED)), creating an eyetracker event that triggers a change to the display monitor. The time between these two events, as captured by a relatively low-cost consumer camera with high-speed video capability (1,000 Hz), is an accurate measurement of the system latency. With multiple measurements, the distribution of system latencies can be characterized. The same approach can be used to synchronize the eye position time series and a video recording of the visual stimuli that would be displayed in a particular gaze-contingent experiment. We present system latency assessments for several popular types of displays and discuss what values are acceptable for different applications, as well as how system latencies might be improved.
In gaze-contingent visual displays, a computer monitor is rapidly and continuously updated on the basis of information about where an observer is looking. In one use of gaze-contingent displays, the area of the display that corresponds to the fovea is enhanced, degraded, or masked completely. In another, areas of the display that correspond to peripheral vision are modified or masked. Since its introduction by Reder in 1973, this technique has led to major insights into the respective roles of central and peripheral vision in reading (Rayner, 1998), visual search (Geisler, Perry, & Najemnik, 2006; Loschky & McConkie, 2002), and scene perception (Henderson, McClure, Pierce, & Schrock, 1997). It has also been used in proposals to reduce the bandwidth required for video by encoding parts of the image outside fixation with a lower resolution (Duchowski, Cournia, & Murphy, 2004; Reingold, Loschky, McConkie, & Stampe, 2003). In research on vision disorders, gaze-contingent displays can simulate central or peripheral defects in the visual field, allowing normally sighted subjects to experience vision similar to that of patients with eye diseases such as macular degeneration or glaucoma (Fine & Rubin, 1999). These simulations make it possible to more efficiently test hypotheses about adaptation to these disorders, as well as strategies for rehabilitation, since this means that patients with the target condition are not required in the early stages of an investigation.
However, the effectiveness of a gaze-contingent display depends on the characteristics of the system, in particular its temporal characteristics. In the “simulated scotoma” paradigm, in which the loss of central vision is simulated by masking several degrees around the point of fixation, the position of the masking patch will lag behind the eye position by some milliseconds, depending on the characteristics of the eyetracker, software, and display device. Figure 1 illustrates a real example of this lag on a 60-Hz iMac monitor during a saccade (the creation of this figure is discussed in the Method section, under the heading Synchronizing Eye Position Data and Experimental Stimulus Recordings). Sensitivity to visual information is greatly reduced during a saccade (Burr, Morrone, & Ross, 1994; Diamond, Ross, & Morrone, 2000). However, at the end of a saccade, visual information begins to have an effect on perception within about 6 ms (McConkie & Loschky, 2002). If the simulated scotoma position is not updated on the screen within that time, the observer will have a high-resolution glimpse of the new fixation location with central vision before it is masked. We will refer to the delay between an eye movement event and the resulting change to the image on the monitor as the system latency. In the instance depicted in Fig. 1, if we define the end of the saccade by the peak of the overshoot (as recommended by Loschky & Wolverton, 2007) and define the arrival of the simulated scotoma by its alignment with the fixation location (rather than merely overlapping it), the measured system latency would be 22 ms. However, system latencies vary considerably from one saccade to another, so to characterize a particular system, it is necessary to take multiple samples in order to estimate the distribution of system latencies.
How large does the system latency need to be before this unmasked glimpse starts to have an effect on perception? Given that many gaze-contingent experiments involve the viewing of natural scenes, a number of results from the literature on scene gist perception are relevant. The typical approach is to present photographs of scenes with varying delays before a full-screen mask appears. Although a strong conceptual scene gist can be activated in 40–100 ms (see, e.g., Castelhano & Henderson, 2008), other types of high-level information about a scene are available at even shorter durations. The presence of animate objects can be detected in 27 ms (Li, Iyer, Koch, & Perona, 2007); scenes can be judged as being natural or unnatural at the 75 %-correct level in 19 ms (Greene & Oliva, 2009); photographs of animals can be distinguished from nonanimal photographs at above-chance levels in 12 ms (Bacon-Macé, Macé, Fabre-Thorpe, & Thorpe, 2005); and 12 ms was sufficient to determine at better than chance level whether photographs had been correctly matched with one of ten scene categories (Loschky et al., 2007). Given these results with high-level judgments, more schematic visual stimuli, including basic visual features, might be expected to impact perception with even briefer premask presentations. A study on the perception of grating orientation found above-chance performance at 8.4 ms (Bodelón, Fallah, & Reynolds, 2007), whereas the global blur of a natural image was detected at significantly above chance levels in only 6 ms (McConkie & Loschky, 2002). Of direct relevance to simulated scotoma experiments are the results from paradigms in which a mask appears at the location of a fixation at a fixed time delay after the end of a saccade. However, many of these studies have not involved exposures below 50 ms (Glaholt, Rayner, & Reingold, 2012), or have used only changes in gaze patterns (such as the median fixation duration) as dependent measures, rather than acquired information (van Diepen, De Graef, & d’Ydewalle, 1995; van Diepen, Ruelens, & d’Ydewalle, 1999). Critically, the majority of them either have not reported measurements of the end-to-end latency of their gaze-contingent system or have not described the method used to obtain them.
Accurate estimation of end-to-end system latency is difficult, because a number of additive factors may contribute to the delay between the measurement of eye position and display response. At the eyetracker, these factors include the sampling rate, the time to compute the gaze position from the camera image, the time for additional processing and filtering, and the time to transmit the information to the display computer. At the display computer, such factors include the time to render the updated image to send to the monitor, as well as possible delays introduced by other aspects of the experiment code or of the operating system. At the monitor, one factor is the time to wait for the next refresh cycle. In cathode ray tube (CRT), liquid-crystal display (LCD), and organic light-emitting diode (OLED) displays, the image in memory is drawn from top to bottom in horizontal scan lines, and therefore there is also the time for the vertical refresh process to reach a particular location on the screen. In digital light-processing (DLP) displays, the entire image appears (effectively) simultaneously, but in alternating primary colors; see the supplemental videos. Once a location has been signaled to update, the pixels of LCD displays take some time to reach their target brightness, which is called the pixel response time, or rise time. Finally, for digital (LCD, DLP, and OLED) displays, there is the input lag (or display lag, or response lag), which is the time added for preprocessing of the input signal. In LCD displays, this almost always includes buffering at least one frame of the input video in order to improve the pixel response time. The input lag also includes the latencies contributed by other types of digital preprocessing at the monitor, such as noise reduction or rescaling the image to match the screen resolution.
Estimating the delay added by each component of a gaze-contingent system and then summing them produces an estimate of the system latency, which is the approach that has been taken in the majority of gaze-contingent studies that have reported system latencies (Henderson et al., 1997; Loschky & McConkie, 2000; McConkie, 1981; Santini, Redner, Iovin, & Rucci, 2007). However, the manufacturer specifications for equipment can contain errors, as well omissions of key information, often including the input lag. Some of the earliest published gaze-contingent experiments were contaminated by an undocumented 25-ms delay in the eyetracker circuitry, so that the findings had to be reconfirmed in subsequent work (as was recounted in McConkie, 1997, p. 312). Although measuring a monitor’s response properties with a photodiode and custom software (Dorr & Bex, 2011; Elze & Tanner, 2012; Wiens et al., 2004) can improve the estimate of the latency added by the monitor, interactions between the monitor and eyetracker timing, as well as interactions involving the software and operating system, are still hard to anticipate. Therefore, a more reliable approach would be to measure the end-to-end system latency directly.
One approach to a direct end-to-end measurement of latency uses an oscilloscope that receives real-time input from both the saccade detection circuit of an eyetracker and a photodiode attached to the monitor (Allison, Schumacher, Sadr, & Herpers, 2010; Triesch, Sullivan, Hayhoe, & Ballard, 2002). The distance between the spikes in the oscilloscope produced by a saccade and the consequent gaze-contingent change to the monitor, as captured by a video camera, can be an estimate of the system latency. In another approach, artificial saccades are created using LEDs, and the monitor output is measured with a photodiode, with a centralized controller comparing the timings of the two events (Bernard, Scherlen, & Castet, 2007; Bockisch & Miller, 1999). However, these techniques require a certain degree of electronics knowledge and do not give an overall sense of the gaze-contingent temporal behavior of the stimuli at the millisecond level.
The benefit of our novel approach to measuring system latency is that it is direct, intuitive, and economical enough that it can be used routinely by researchers to characterize their gaze-contingent apparatus, regardless of the scale of their involvement in eyetracking. The technique uses infrared LEDs to briefly blind the eyetracker camera, which is an eyetracker event that can be used to trigger a change to the display monitor. The time between these two events, as captured by a relatively low-cost consumer camera with high-speed capability (1000-Hz video), is an accurate measurement of the system latency. The same approach can be used to synchronize the eyetracking time series and a video recording of the monitor displaying actual experimental stimuli, such as a video superimposed with a simulated scotoma, to produce graphs such as Fig. 1. We will present the method, and then discuss the results of measuring the system latencies of seven display configurations, representing classes of display that experimenters might choose.
This method requires a high-speed camera that has some sensitivity to infrared (IR) light (as most digital cameras do). We used a Casio Exilim EX-ZR100, which has a full retail price of under $300 and advertises the ability to film at 1,000 frames per second. This sample rate was verified using the fixed timing of monitor refreshes: A 60-Hz monitor was filmed while switching between black and white with each frame (see the supplemental video), and the span of 120 screen refreshes in the footage (measured from the first appearance of white at the top of the monitor) was exactly 2,000 frames, showing that the camera was sampling once per millisecond (120 frames/60 Hz = 2,000 ms) on average. The other piece of equipment that was needed was an IR illuminator, of the type that is available inexpensively for use with home surveillance cameras, or for mounting on video cameras that have night vision capabilities. The measurements reported here were made using a simple custom circuit connecting IR LEDs to a switch, but we also tested a Polaroid IR Light Bar (PLLED36) and found it to be effective for this purpose. The eyetracker was an EyeLink 1000 (SR Research Ltd.) capturing at a rate of 1,000 samples per second, with eye position being estimated using the Centroid pupil-tracking algorithm and the corneal reflection (i.e., not “pupil-only” mode). One level of heuristic filtering was applied (specifically, the Link/Analog Filter setting was “STD”), and the viewer’s head position was fixed using a brow-and-chinrest. The software was written in MATLAB with the Psychophysics Toolbox (Brainard, 1997), which incorporates the EyeLink Toolbox (Cornelissen, Peters, & Palmer, 2002). Except in the case of the iMac (Intel Core i7, 2.93GHz), the display software ran on a Mac Pro (Quad-Core Intel Xeon 2.8GHz). The Mac Pro was connected either directly, via a digital video interface connection, or, in the case of the CRT, via a Mini-DisplayPort-to-VGA converter.
Measurement of system latency
In our method, to measure the latency of the system, the experimenter seats a subject in front of the eyetracker and begins a regular eyetracking session, including calibration and verification controlled by a MATLAB Psychophysics Toolbox program. Subsequently, the program monitors the eyetracker input and changes the screen from black to white when tracking is lost. Then the experimenter holds the IR illuminator close to the eyetracking camera lens (Fig. 2). The illuminator is turned on, and the glare from the infrared LEDs causes an instantaneous loss of tracking (the LEDs that we used had a rise time of 0.8 ms). The high-speed camera is positioned so that it captures both the face of the illuminator and the screen. The entire screen need not be captured, but at least a part of the top must be within the frame for the most accurate measurements. In this configuration, the camera records both events with high temporal precision: the moment of illumination of the LEDs and the moment when the monitor begins to change from black to white. The LEDs are turned off and then on again for each sample that is desired. In the resulting 1,000-Hz video, the number of frames between the LED illumination and the first appearance of white at the top of the screen is one latency measurement. This count can be determined easily and unambiguously using a program, such as QuickTime Player 7 or FinalCut Pro, that allows frame-by-frame inspection of movie files.
The mean of the values obtained in this way is an estimate of the average time between any eyetracking event, such as the beginning or end of a saccade, and the beginning of the refresh reflecting that event. In a real gaze-contingent system, the latency for eye movement events of interest will not be a constant, as is demonstrated in Fig. 3. A large proportion of this variance is due to the monitor’s refresh cycle, especially if the eyetracking sample rate is much higher than the refresh rate. The timing of eye movement events is unlikely to be phase-locked with the refresh cycle. For example, if the end of a saccade occurs just before the new update is about to begin (including enough time to render the new frame), the latency in that instance will be less than if the end of a saccade occurs well before the update. The range of latencies contributed is a function of the monitor’s current refresh rate and should be uniformly distributed between 0 and T refr, the time between screen refreshes.
The estimates discussed so far represent the latency for a location at the top of the display area, so for objects that are not at the top of the display, additional time needs to be added, depending on the vertical distance. The maximum time added will be T refr for objects at the bottom of the screen. Therefore, for a particular apparatus using a 60-Hz monitor, for which T refr = 16.7 ms, the time of drawing initiation and the position on the screen could contribute a combined latency between 0 and 33 ms. With a 120-Hz monitor, this range decreases to 0–17 ms, and with a 200-Hz monitor, to 0–10 ms.
Synchronizing eye position data and experimental stimulus recordings
Besides helping to obtain individual estimates of the latency, an IR illuminator combined with a high-speed camera can also be used to create a diagram such as Fig. 1, showing the relative position of the gaze location and the simulated scotoma during each millisecond of a particular saccade. (We show the simulated scotoma position being updated to the most recently sampled eye position with each monitor refresh, but this may produce a distracting “jittering” motion, especially during fixations, so in practice it may be desirable to only relocate scotomas at the beginning of fixation periods, or to employ other motion-smoothing approaches.) The technique allows for the retrospective alignment of two streams of data, one being the measured eye position, and one the position of the scotoma (or other gaze-contingent image modification) on the screen. To closely model the system’s behavior during the gaze-contingent experiment, the stimuli from the experiment should be used, with the addition of large numerals near the top of the screen corresponding to the horizontal eye position that was used to draw the overlaid simulated scotoma (Fig. 3). The vertical eye position can be displayed as well, if tracking a more complex scanpath than a single horizontal saccade is desired. The time series of the position of the scotoma, as captured by the video camera, is aligned with the eye position time series by activating the IR illuminator at the beginning or end of the recording, temporarily disrupting tracking. In this case, the frame of the video recording in which the LEDs are seen to turn on represents the same point in time as the first missing sample of eyetracking data, providing synchronization to within 1–2 ms (the variance is due to the eyetracker processing time and the LED rise time). Thereafter, the two time series can be compared.
Using the equipment described in the Method section and the first technique, we measured the system latency 50 times for a selection of displays and obtained summary statistics (Table 1). For each system, the time to render the frames was well below the time between two screen refreshes minus the time to retrieve an eye position sample, ensuring that the screen refresh deadline was not missed (which would have increased the latency by the time of one refresh cycle).
We observed that a higher frame rate does not necessarily result in lower latency. The average system latency at the top of the iMac monitor running at 60 Hz was significantly lower than the average system latency at the top of the Acer monitor running at 120 Hz, according to a Welch’s t test, t(91.9) = 3.05, p < .005. However, this advantage applied only to the top of the monitor: The time for the refresh process to update items at the midpoint was slightly faster on the Acer monitor (34.6 vs. 35.9 ms), and notably faster for items near the bottom of the monitor (38.7 vs. 44.3 ms). A different 120-Hz LCD, the Samsung, had a much lower average system latency at all screen positions. The differences among the displays that had the same refresh rate must be primarily due to differences in input lag, since the software and eyetracker were constant. Therefore, as expected, the best latency (12 ms) was observed using the fast CRT, which has no input lag. In fact, our initial estimate overstated the latency, since with the Mac Pro the video signal had to be converted from Mini DisplayPort to VGA before reaching the monitor. Therefore, we tested the latency again, using a Windows XP computer that had built-in VGA output. We inferred from the lower latency values that video conversion added approximately 3 ms to the average latency. The DLP display that we tested had a latency comparable to that of the best LCD monitor, showing evidence of requiring one frame to be buffered, but no more than one frame.
We have described a method for obtaining measurements of end-to-end system latency in gaze-contingent systems. We showed that large discrepancies exist between the latencies contributed by different displays. The choice of monitor could mean the difference between an average latency at the center of the screen of 16 ms (the CRT) or 35 ms (the iMac).
A limitation of system latency measurements obtained in this way is that they only partially account for pixel response time—that is, the time for the screen elements in an LCD monitor to reach the target brightness level after receiving the signal to change. The monitor onset times that are recorded are based on the first change to monitor brightness that can be unambiguously detected in the high-speed video camera footage, rather than one of the standard criteria used to assess pixel response time, such as the time to transition between 10 % and 90 % of maximum luminance. If the precise level of luminance is critical for a gaze-contingent application, our LED method would underestimate the system latency. However, since the pixel response times of modern displays are usually below 10 ms (Elze & Tanner, 2012), if we assume that the refreshed pixels respond strongly enough to be detected in the recording at half brightness, the latency would be underestimated by no more than 5 ms.
This method requires more of a time investment to collect samples than do the methods using data acquisition devices in combination with photodiodes and artificial saccades (Bernard et al., 2007; Bockisch & Miller, 1999), since it is necessary to locate the frames in the video where the key events happen. Therefore, the photodiode-based methods are viable alternatives to measuring system latency for labs with access to the required technical expertise. However, the ability to synchronize high-speed footage of the stimuli with the eyetracking data is an advantage of the present method, facilitating a good understanding of the stimulus as presented to the eye, particularly as it is affected by the monitor properties (see the supplemental high-speed videos of monitor performance).
What is an acceptable level of system latency?
The maximum acceptable system latency depends on the properties of the visual system, but also on the application. For example, for a multiresolution display, in which areas outside of central vision are represented at lower resolution, latencies up to 60 ms are not typically detected (Loschky & Wolverton, 2007). Latencies in this type of application are less critical, because only the effects on conscious awareness are relevant, not any effects outside of conscious awareness. Another factor is whether central vision or the periphery is being masked. When the periphery is masked, the area that is hidden postsaccade will largely overlap with the area that was hidden presaccade, so longer latencies do not result in the viewer obtaining much extra information that could affect the results. However, the delay in unmasking the target location should be taken into account when computing reaction times. On the other hand, when central vision is masked, the postsaccade target will not already be masked (unless it is close to the original fixation location), so there is the danger of a central-vision glimpse of the target when latencies are long. Other experimental paradigms that depend on low system latencies include gaze-contingent investigations into perception during saccades. Most saccadic suppression experiments have used eyetracking to retroactively discard trials in which the stimulus was not presented at the correct time during the saccade (e.g., Watson & Krekelberg, 2011). However, when the system latency can be characterized well, more precise and efficient experiments are possible, with the presentation of the stimulus being contingent on the time course of the saccade.
Broadly speaking, if the goal is to mask central vision, then given the results on rapid perception of scene gist and the experiments suggesting that visual suppression lessens within 5–25 ms of the end of a saccade (McConkie & Loschky, 2002; Ross, Morrone, Goldberg, & Burr, 2001; Shioiri, 1993; Volkmann, Riggs, White, & Moore, 1978), experimenters should aim to achieve an average system latency at screen midpoint of less than 25 ms. Of the displays that we tested, the Samsung LCD, the CRT, and the DLP display offered this latency level in combination with the other hardware and software that we used.
Reducing system latency
The choice of monitor is critical for controlling system latency. The monitor should be capable of refreshing at least 120 times per second and should have a small or nonexistent input lag, as with the CRT and the Samsung 2233RZ, the latter of which has been shown to have other desirable temporal properties (Wang & Nikolic, 2011). The presence of a video converter can add to the latency, as we discovered from the extra 3 ms contributed by conversion from a Mini DisplayPort signal to a VGA signal for use with the CRT monitor. The smallest latency that we measured, a 16-ms average at the center of the monitor, was obtained with a CRT connected to a PC with a video card that had native VGA output. No currently manufactured Apple computer has native VGA output, and VGA is being phased out of the PCs shipped by major manufacturers (Shah, 2012), so just as CRTs are becoming more difficult to obtain, so are computers that can drive analog displays without the delay introduced by conversion from a digital video signal.
What other steps can be taken? Many displays have features that, when active, perform additional processing on the input, increasing the input lag, and therefore the system latency. For example, the Adaptive Contrast Management mode on the Acer GD235HZ increases the average system latency by an average of 3 ms, whereas the Dynamic Contrast mode on the Samsung 2233RZ increases the system latency by an average of 8 ms (both based on ten measurements). In addition to the added latency of these modes, inspection of the high-speed footage shows that they can cause unexpected time-varying changes to the contrast and brightness. Researchers should consult their display documentation to find the settings that correspond to the least amount of input processing possible, which may mean deactivating features with names like Adaptive Contrast Management or Dynamic Contrast, or activating features with names like Video Game Mode that are intended to minimize input lag.
The settings of the eyetracker also affect the system latency, although to a lesser degree than the display does. The EyeLink 1000 can sample at 250 Hz, in which case the sampling stage alone would add an average of 2 ms to the latency (4 ms worst case, 0 ms best case). However, beyond 1000 Hz the returns appear to diminish; the change from 1000 to 2000 Hz only reduces the mean latency by 0.4 ms (SR Research, 2013). At those frequencies, the processing of the eye images is likely the bottleneck at the eyetracker, not the sample rate. EyeLink eyetrackers provide two levels of heuristic filtering (Stampe, 1993), which reduce noise in the eye position data in real time. However, each level of filtering increases the mean latency by 1 ms (1000-Hz sampling rate, fixed head, SR Research, 2013). Since the measurements reported here used one level of filtering, they overestimate the latency by 1 ms relative to what would be possible with no filtering (Link/Analog Filter set to “OFF”). The “Remote Option,” which enables tracking of the participant’s head so that a head-fixing chinrest is not required, also contributes 1.2 ms to the latency when in use (EyeLink 1000 user manual, version 1.5.2, p. 9). Therefore, to achieve the minimum possible latency contributed by the EyeLink 1000, experimenters should use at least a 1000-Hz sampling rate, no heuristic filtering, and fixed-head mode (with the viewer using a brow-and-chinrest). With these settings, this model of eyetracker should be responsible for an average of 1.8 ms of the system latency (EyeLink 1000 user manual, version 1.5.2, p. 9). However, heuristic filtering may be desirable in some cases, since it can use previously buffered samples to achieve more accurate estimation of eye position, at the cost of only a few milliseconds of added latency. For other models of eyetracker, equivalent latency measurements and information about settings that will affect latency should be obtained from the vendor.
Another approach to reducing latency is to change the scheduling of the eyetracker sampling and subsequent rendering. The measurements reported in our results used an algorithm that samples the eye position as soon as possible after the previous refresh, and then immediately renders the new image updated by the gaze location. After that, program execution is blocked until the current screen refresh finishes and the next one is ready to begin. During this time, many fresher eye position samples may become available, but are ignored. An alternative to this sample-then-wait scheme is described in Aguilar and Castet (2011): After the monitor refresh is triggered, rather than sampling the eyetracker again immediately, the program waits to sample until there is just enough time left to render the updated image before the refresh. The advantage to a wait-and-then-sample scheme is that the next monitor refresh is based on a more recent sample of the eye position. How much reduction in latency can be achieved? For the pixels at the top of the monitor, if the sample-and-then-wait scheme is used, the delay for the sample to reach the screen will always be at least T refr ms (in which T refr is the time between screen refreshes), whereas if the wait-and-then-sample scheme is used, the delay for the sample to reach the screen can be as little as the rendering time, as in Fig. 3A). On an iMac computer, which has a T refr = 16.7 ms, rendering an alpha-blended simulated scotoma at a particular location takes approximately 5 ms. Thus, the system latency would be reduced by over 10 ms. However, the potential benefit of waiting to sample later in the refresh cycle will be considerably less when the refresh rate is higher—for example, only around 2 ms on the Samsung monitor at 120 Hz. Also, the time to wait must be based on an estimate of the rendering time, which should be conservative, in order to avoid missing the monitor refresh deadline. If this occurred, it would add T refr to the system latency for that frame.
Decreasing the rendering time—for example, by preloading frames of a movie into memory—can be a strategy to reduce latency (O’Sullivan, Dingliana, & Howlett, 2002). This applies particularly when the wait-and-then-sample algorithm is used, in which render time is often the limiting factor. In the sample-and-then-wait algorithm, on the other hand, the duration of rendering does not make a difference, provided that it stays within the available time, which is equal to T refr minus the time to retrieve a sample from the eyetracker. Only if this time were regularly exceeded would the rendering time need to be optimized.
Latency can also be reduced slightly by forcing the monitor to begin using the newly generated image immediately, regardless of where it is in the refresh cycle (Loschky & McConkie, 2000; McConkie, Wolverton, & Zola, 1984). Items near the bottom of the screen would be updated more quickly on average using this option, with an average benefit of 0.5 * T refr ms, but the drawback is that the new image could be applied partway through drawing an object of interest. If the object is moving it might appear distorted—for example, by a “shearing” effect. These types of artifacts may be acceptable, depending on the application.
Finally, in a simulated-scotoma paradigm, the effective average latency will decrease as the scotoma size increases, since for shorter saccades the next fixation point may already be covered by the simulated scotoma at its previous location. This applies when only the point of fixation is important to obscure, and a match between the center of the visual field modification and the point of fixation is not necessary. The expected reduction in latency depends on the distribution of saccade distances relative to the scotoma size.
Gaze-contingent experiments require control of the latency between eye movements and the corresponding changes to the onscreen stimuli. We have described an efficient and inexpensive way to measure the end-to-end latency of a gaze-contingent system directly, and such measurements should be reported in accounts of gaze-contingent research, in place of estimations based on manufacturers’ reported device parameters. Given the past research that some information about natural scenes and other complex stimuli can be perceived from very brief presentations, latencies for experiments simulating visual disorders, in particular, should be as small as possible. High-speed CRTs, running at a high refresh rate, should be preferred, or at least high-performance LCDs. But for other purposes, such as the development of multiple-resolution displays, displays with poorer latencies can be used. However, the latency should still be measured, and the present method is accessible to most researchers.
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This research was supported by National Eye Institute Grant No. R01EY019100, awarded to R.L.W.
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Saunders, D.R., Woods, R.L. Direct measurement of the system latency of gaze-contingent displays. Behav Res 46, 439–447 (2014). https://doi.org/10.3758/s13428-013-0375-5