This work was supported by the Portuguese Foundation for Science and Technology under grants UIDB/00050/2020; and PTDC/PSI-GER/5845/2020. The APC was entirely funded by the Portuguese Foundation for Science and Technology under grant PTDC/PSI-GER/5845/2020 (http://doi.org/10.54499/PTDC/PSI-GER/5845/2020).

Fifty-two participants (25 diagnosed with dyslexia or signaled as potential cases and 27 controls) were recruited from the community (through social media and convenience email contacts) and a university course. Following a confirmatory neuropsychological assessment and subsequent data analysis (for more details, see Goswami10), seven participants were excluded from the study. This exclusion comprised four individuals with dyslexia who did not meet the criteria, two dyslexic participants with outlier values in the primary experimental task, and one control participant whose eye-tracking data was affected by noise. The final sample was composed of 45 participants, 19 dyslexic adults (one male), and 26 controls (five male). All participants were native Portuguese speakers, had normal or corrected-to-normal vision, and none had diagnosed hearing, neurological, or speech problems. The protocol described here was approved by the local ethics committee of the Faculty of Psychology and Educational Sciences at the University of Porto (ref. number 2021/06-07b), and all participants signed informed consent according to the Declaration of Helsinki.
1. Stimulus creation
<ol>
	<li>Define eight sequences of two time intervals (Table 1) wherein the first is shorter than the second (slow-down sequence); choose intervals that are compatible with the frame rate of the animation software (here, 30 frames/s, 33 ms/frame) using a frame-duration conversion table.</li>
	<li>For each slow-down sequence, create a speed-up analog obtained by inverting the order of the intervals (Table 1).</li>
	<li>In a spreadsheet, convert interval length to the number of frames by dividing the target interval (ms) by 33 (e.g., for 300-433 ms interval sequence, indicate 9-13 frames).</li>
	<li>Define key frames for each sequence: stimulus onset at frame 7 (after six blank frames, corresponding to 200 ms), offset of interval 1 at frame 6 + length of interval 1 (6 + 9 for the given example), same for the offset of interval 2 (6 + 9 + 13). Set two more frames at the end of interval 2 to mark the end of the stimulus (6+ 9 + 13 +2).</li>
	<li>Create flash sequences as animations.
	<ol>
		<li>Run the animation software (e.g., Adobe Animate) and create a new file with a black background.</li>
		<li>At frame 7, draw a blue circle at the screen center. Ensure that its dimensions make it occupy around 2&#176; of the visual field with the planned screen-eye distance (55 cm here), meaning that the ball diameter is 1.92 cm.</li>
		<li>Copy and paste this image into the next adjacent frame (starting on frame 7th), such that each flash lasts around 99 ms.</li>
		<li>Copy and paste this two-frame sequence into the other two key frames (onset of intervals 1 and 2).</li>
		<li>Build the remaining 15 animations by creating copies of the file and moving the interval onsets to the appropriate frames.</li>
	</ol>
	</li>
	<li>Create bouncing ball sequences as animations.
	<ol>
		<li>Open a file in the animation software with the same specifications (size, background) used in flash animations. Open the spreadsheet with key-frame specifications so that key-frames now correspond to squashed balls hitting the ground.</li>
		<li>Start with three frames with a black background (99 ms). In the 4th frame, draw a blue ball at the top center, equal to the one used for flashes.</li>
		<li>Draw a squashed ball (width larger than height) at the stimulus onset point, lasting three frames (onset of interval 1). Ensure that the ball is horizontally centered and vertically below the center of the screen.
		<ol>
			<li>Click on the button Properties of the object and then on Position and Size to position the ball at the chosen squashing height and increase width/decrease height.</li>
		</ol>
		</li>
		<li>Generate a continuous change using the tween command from the ball at the top to the squashed ball (vertical descent).</li>
		<li>Copy the three-frame sequence featuring the squashed ball into the other two key frames (onset of intervals 1 and 2).</li>
		<li>In the spreadsheet, divide the duration of each interval by 2 to define the middle points between two squashes for intervals 1 and 2, where the ball hits maximum height after ascending and before descending.</li>
		<li>Draw a non-squashed ball vertically above the lowest point of the trajectory at the middle points defined in step 1.6.6. Generate the ascendent animation between the interval onset (when the ball hits the ground) and the highest point and between the highest point and the next squash (descent).</li>
	</ol>
	</li>
	<li>Adapt the file to the other 15 time structures.</li>
	<li>Export all animations as .xvd. If the option is unavailable, export as .avi and then convert, such that it can be used in the eyelink system.</li>
</ol>
2. Experiment preparation
<ol>
	<li>Creating the experiment folder
	<ol>
		<li>Open the Experiment Builder application and choose new from the menu file.</li>
		<li>Save the project by clicking on File | Save as. Specify the name of the project and the location where it is to be saved. 
		NOTE: This will create an entire folder with subfolders for stimulus files and other materials. The experiment file will appear in the folder with the .ebd extension.</li>
		<li>Inside the project folder, click on Library and then in the folder named Video. Upload the .xvid video stimulus files to this folder. 
		NOTE: All stimuli used in the experiment must be stored in the Library.</li>
	</ol>
	</li>
	<li>Creating the basic structure for within-system and human-system interaction
	<ol>
		<li>Drag the start panel and the display screen icons to the graph editor window. Create a link between them by clicking and dragging the mouse from the first to the second.</li>
		<li>In the properties of display screen, click on Insert Multiline Text Resource button and type an instruction text explaining the calibration procedure that will follow.</li>
		<li>Select two triggers (input channels to move forward in the experiment): keyboard and el button (button box). Link the display screen to both. 
		NOTE: These triggers allow the participant or the experimenter to click on any button to proceed.</li>
		<li>Select the Camera setup icon and link both triggers to it. 
		NOTE: This will allow establishing communication with the eye-tracker so that the participant&#39;s eye(s) can be monitored for camera adjustment, calibration, and validation (see section 4).</li>
		<li>Select the icon Results file and drag it on the right side of the flow chart. 
		NOTE: This action allows to record the behavioral responses of the experiment.</li>
	</ol>
	</li>
	<li>Defining the block structure
	<ol>
		<li>Select the Sequence icon and link it (see step 2.2.1) to the Camera setup.</li>
		<li>In Properties, click on Iteration count and select 2 for number of blocks (Flashes and Balls).</li>
		<li>NOTE: This will separate the presentation of flashes from that of balls.</li>
		<li>Enter the sequence (block definition) and drag a start panel icon, a display icon, and the triggers el_button and keyboard. Link them in this order.</li>
		<li>In the display screen icon, click on the Insert Multiline Text Resource button and type an instruction text explaining the experiment.</li>
	</ol>
	</li>
	<li>Defining the trial structure
	<ol>
		<li>Inside the block sequence, drag a New sequence icon to the editor to create the trial sequence. 
		NOTE: Nesting the trial sequence inside the block sequence allows running multiple trials in each block.</li>
		<li>Inside the trial sequence, drag a start panel and a Prepare sequence icon, and link the second to the first. 
		NOTE: This action loads the experimental stimuli that will be presented to the participant.</li>
		<li>Drag the Drift correction icon to the interface and link it to the prepare sequence icon. 
		NOTE: The drift correction presents a single fixation target on the stimulation computer monitor and enables the comparison of the cursor gaze position to the actual stimuli position on the recording computer. The drift check and the respective correction will automatically begin after every trial to ensure the initial calibration quality persists.</li>
	</ol>
	</li>
	<li>Defining the recording structure
	<ol>
		<li>Inside the trial sequence, drag a New sequence icon to the editor to create the recording sequence. 
		NOTE: The recording sequence is responsible for eye data collection, and it is where the visual stimuli are presented.</li>
		<li>Select the option Record in the properties of this sequence. 
		NOTE: By doing this, the eye tracker starts recording when the stimulus begins and stops when the stimulus ends.</li>
		<li>In properties, click on Data Source and fill in each row the table (type or select) with the exact filename of each stimulus, the type of trial-practice or experimental, how many times each stimulus will be presented (1 here), and the expected response button. 
		NOTE: The filenames must be identical to those uploaded in the Library, file extension included (e.g. ball_sp_1.xvd).</li>
		<li>On the top panel of the interface, click on Randomization Settings, and mark the Enable trial randomization boxes to ensure that the stimuli will be randomized within each block. Click on the Ok button to return to the interface.</li>
		<li>In the recording sequence, create the start panel- display screen connection. Inside the display screen, select the Insert video resource button (camera icon) and drag it to the interface.</li>
		<li>Link the keyboard and el button triggers to the display icon (as in step 2.2.1) to allow the participant to respond.</li>
		<li>Drag the Check accuracy icon and link it to the triggers as in step 2.2.1. 
		NOTE: This action allows the software to check whether the key that was pressed matches the value of the correct response column of the Data Source.</li>
	</ol>
	</li>
	<li>Finalizing the experiment
	<ol>
		<li>On the top of the main panel, click on the Run arrow icon to run a test of the experiment.</li>
	</ol>
	</li>
</ol>
3. Apparatus setup
<ol>
	<li>Connect the stimulation computer to a 5-button button box and a keyboard.</li>
	<li>Connect the stimulation computer (with the system-dedicated presentation software) to the eye-tracker (Figure 1), placed below or in front of the monitor.</li>
	<li>Connect the eye-tracker to the recording computer.</li>
</ol>
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/65990/65990fig01.jpg" /> 
Figure 1: The eye-tracking setup. The spatial arrangement of the recording system, is composed of the stimulation computer, the recording computer, the eye-tracker, the response device (button box), and the keyboard. Participants sat 55 cm away from the stimulation screen. <a href="https://www.jove.com/files/ftp_upload/65990/65990fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
4. Preparation of data collection
<ol>
	<li>Obtain informed consent from the participants and describe the experimental format to them. Position the participant at a distance from the stimulation computer such that the stimulus circle (flash or ball) corresponds to 2&#176; of the visual field (typical distance ~ 60 cm).</li>
	<li>Choose the sampling frequency (1,000 Hz for high resolution) and eye(s) to record (dominant eye).</li>
	<li>In the visualization provided by the recording computer, ensure that the eye-tracker tracks the target (a stick placed between the participant&#39;s eyebrows) and the dominant eye in a stable manner. Move the camera up or down if necessary.</li>
	<li>Open the experiment. Run the 5-point calibration and validation procedures provided by the system from the recording computer to allow accurate and reliable recording of eye movements. Instruct the participant to gaze at a dot that will appear on the screen at (5) different places (once for calibration, twice for validation). 
	NOTE: Accept errors only below 0.5&#176;.</li>
</ol>
5. Running the experiment
<ol>
	<li>Explain the task to the participant.</li>
	<li>Present the practice trials and clarify participants&#39; doubts.</li>
	<li>Start the experiment by clicking Run.</li>
	<li>Pause the experiment between conditions and explain that the stimulus is now going to be different, but the question is the same.</li>
</ol>
6. Creating time windows for analysis
<ol>
	<li>In Dataviewer software18, go to File, then Import Data, and finally Multiple EyeLink Data files. In the dialog box, select the files of all participants.</li>
	<li>Select one trial. Select the square icon to draw an interest area. 
	NOTE: The interest area defines both a region of the screen and a time window within the trial. Here, we will always select the full screen.</li>
	<li>To create TW all (Figure 2), click on the draw icon and select the full screen. In the open dialog box, label the interest area as TW_all and define a time segment matching the full trial.
	<ol>
		<li>Click on Save the interest area set and apply this template to all trials with the same length (e.g., time structures 1 and 8 from Table 1, for both balls and flashes, for all participants).</li>
	</ol>
	</li>
	<li>Select one of the 16 time structures from Table 1. Define TW_0, TW_1, and TW_2 as in step 6.3, but following the time limits schematized in Figure 2 (time window boundaries corresponding to flash appearances and ball squashes). The length of TW0 is customizable.
	<ol>
		<li>Label each interest area and apply the template to the trials with the same time structure (balls and flashes, all participants).</li>
		<li>Repeat the process for the 15 remaining time structures.</li>
	</ol>
	</li>
</ol>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/65990/65990fig02.jpg" /> 
Figure 2: Stimulus type. Sequences of bouncing balls (left) and flashes (right) that were used in the experiment. The dashed lines indicate the time windows used for analysis: TW0 is the prestimulus period; TW1 is the stimulus&#39;s first appearance on the screen and marks the first interval-when the participant has information about stimuli characteristics and the length of the first interval, and TW2 marks the second Interval-when the participant can compare the first to the second interval to elaborate a decision (slowed-down or speeded-up). <a href="https://www.jove.com/files/ftp_upload/65990/65990fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
7. Extracting measures
<ol>
	<li>In the menu bar, click on Analysis | Report |Interest Area Report.</li>
	<li>Select the following measures to extract dwell time, number of fixations, and pupil size, and then click next. 
	NOTE: The output should contain data from 16 flash trials and 16 bouncing ball trials per participant (32 trials x n participants), specified for each of the four time windows (TW0, TW1, TW2, TW all).</li>
	<li>Export the matrix as a .xlsx file.</li>
</ol>
8. Remove trials with artifacts
<ol>
	<li>Consider dwell time measures for TW all and mark trials with more than 30% of signal loss (dwell time &#60; 70% of trial time). 
	NOTE: Take into account that each of the 32 trials has a different length.</li>
	<li>Exclude noisy (marked) trails from the matrix and save it.</li>
</ol>
9. Statistical analysis
<ol>
	<li>Perform two repeated-measures ANOVA (TW x group x stimulus) for each measure, one with TW 0 and 1, the other with TW 1 and 2.</li>
	<li>Correlate TW-related changes with behavioral results if available.</li>
</ol>

Investigating Oculomotor Responses in Visual Duration Perception Tasks

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The authors have no conflicts of interest to disclose.

The current protocol contains a novel component that might be critical to tackling current obstacles to incorporating eye-tracking in visual duration perception tasks. The critical step here is the definition of time windows based on cognitive processes that putatively take place in each of these time windows. In the system we used, time windows can only be defined as Areas of Interest (a space-related concept that is coupled with time in these systems), but in other systems, it is possible to do this by exporting different segments of the trial. In addition to this temporal segmentation of the trial, it is important to focus on analyzing changes across time windows rather than the parameters per time window.
Concerning the modifications to the protocol that had to be made, they were mostly related to the dimensions of the area of interest. We made a first attempt using dynamic AOIs - defining a spatial selection around the stimulus that followed it, rather than the whole screen. However, we soon realized that we could be missing relevant events outside that area. Given that our measures were unrelated to focus on the stimulus (pupil size was expected to change according to cognitive load and not according to attention to the flash or ball; the number of fixations was expected to reflect spatial search), we chose to use the full screen as the region of interest.
The current protocol is an embryonic proposal that is still subject to many refinements. We will only highlight two of these, even though there is much more room for improvement. The first concerns the differences in the length of the three time windows, which preclude us from interpreting time window effects on the number of fixations (e.g., a longer time window entails more fixations, hence the decrease from TW0 to TW1, see Figure 3). One way of addressing this problem would be to consider the number of fixations per time unit.
The second relates to the correspondence between time windows and putative ongoing processes, which includes various issues. One is that TW1 does not represent just stimulus appearance but probably also an explicit form of interval estimation (first interval) subsidiary to interval comparison and likely absent in TW0. In a similar fashion, changes across time windows may also reflect changes in general processes such as sustained attention and working memory18, even though some of these changes could be expected in an interval comparison task (working memory load is expected to increase from TW1 over TW2). One way to attenuate these potential confounds would be to introduce control tasks related to pure duration estimation, sustained attention and working memory, and then base the eye-tracking data analysis on the comparison between experimental (interval comparison) and control tasks. Another issue is that the duration of TW0 was irrelevant to the task, and it is known that task-irrelevant durations may be deleterious to performance19. Future work could focus on improving this, namely by creating a difference of 300 ms between TW0 (irrelevant interval) and TW1 to better delimit visual processing responses, since a short event can be biased to be perceived earlier or later than its presentation by simply adding another event in near temporal proximity20,21.
Finally, spontaneous eye blinks can affect time perception by distorting it (dilating time if an eye blink precedes the interval, contracting if it occurs simultaneously), potentially introducing variability in intra-individual timing performance22. One way of minimizing this problem would be to apply an eye-blink-based correction factor in participants&#39; behavioral judgments (e.g., assign a reliability rate to each judgment depending on the presence of blinks before or during the stimuli. Additionally, incorporating the statistical approach of treating trials as random variables may also aid in addressing this problem.
Regarding future research, an important topic to address would be the association between spontaneous eye blink rate (EBR) and time perception. EBR has been known to be a non-invasive indirect marker of central dopamine function (DA)23, and, more recently, high ERB was associated with poorer temporal perception. The study suggests an implication of dopamine in interval timing and points to the use of ERB as a proxy of dopamine measure24. Another important topic is the functional meaning of the (change-related) measures we analyzed, which is yet to be determined in the context of our paradigm. In the original study, as well as in the current simplified dataset, increases in pupil size from TW0 to TW1 were consistent with the idea of increased cognitive load, but the absence of group effects on this measure precludes further considerations. One pattern that seems to present is that smaller changes across time windows correlated with better behavioral performance (Flashes better than Balls, and d-prime in dyslexics related to smaller changes), but further research is needed.
Despite its limitations, the current protocol is, to our knowledge, the first to show parallel results in eye-tracking and behavioral data (same profile of effects), as well as some evidence of the correlation between the two.

Time perception abilities have attracted increasing research attention over the last years, in part due to accumulating evidence that these may be linked to reading skills or pathological conditions1,2,3,4,5. Visual duration perception-the ability to estimate, discriminate, or compare time intervals defined by visual events-is one subfield of interest6,7in which eye-tracking methods could make a contribution. However, results remain focused on poststimulus behavioral decisions like pressing a button to indicate how much time has passed (estimate), whether time intervals are the same or different (discrimination), or which of a series of time intervals is the longest or shortest. A few studies have attempted to correlate behavioral results with eye-tracking data8,9 but they failed to find correlations between the two, suggesting that a direct relation is absent.
In the current paper, we present a protocol for registering and analyzing oculomotor responses during stimulus presentation in a visual duration perception task. Specifically, the description refers to an interval comparison task where participants saw sequences of three events that defined two time intervals and were asked to judge whether they speeded up (first interval longer than second) or slowed down (first shorter than second). The time intervals used in the study spanned from 133&#160;to 733&#160;ms, adhering to the principles of the Temporal Sampling Framework (TSF)10. TSF suggests that the brain&#39;s oscillatory activity, particularly in frequency bands such as delta oscillations (1-4 Hz), synchronizes with incoming speech units such as sequences of stress accents. This synchronization enhances the encoding of speech, improves attention to speech units, and helps extract sequential regularities that may be relevant in understanding conditions like dyslexia, which exhibit atypical low-frequency oscillations. The goal of the study in which we developed the method presented here was to determine whether dyslexics&#39; difficulties in visual duration perception (group effects on the interval comparison task) reflect problems in processing the visual object itself, namely movement and luminance contrasts11. If this was the case, we expected that dyslexics&#39; disadvantage towards controls would be larger for stimuli with movement and low luminance contrasts (interaction between group and stimulus type).
The main result of the original study was driven by poststimulus behavioral judgments. Eye-tracking data - pupil size and number of fixations - recorded during stimulus presentation were used to explore processes preceding the behavioral decisions. We believe, however, that the current protocol may be used independently from behavioral data collection, provided that the goals are set accordingly. It may also be possible to adjust it for interval discrimination tasks. Using it in time estimation tasks is not so immediate, but we would not rule out that possibility. We used pupil size because it reflects cognitive load12,13,14, among other states, and may thus provide information on participants&#39; skills (higher load meaning fewer skills). Regarding the number of fixations, more fixations may reflect the participants&#39; stronger engagement with the task15,16. The original study used five stimulus types. For simplification, we only used two in the current protocol (Ball vs. Flash, representing a movement-related contrast).
The main challenge we tried to address was disentangling responses to the visual stimulus itself from those related to interval comparison since it is known that oculomotor responses change according to characteristics such as movement or luminance contrasts17. Based on the premise that the visual stimulus is processed as soon as it appears on screen (first interval), and interval comparison is only made possible once the second time interval begins, we defined three time windows: prestimulus window, first interval, second interval (behavioral response not included). By analyzing changes from the prestimulus window over the first interval, we would get indices of participants&#39; responses to the stimulus itself. Comparing the first to the second interval would tap into possible oculomotor signatures of interval comparison-the task participants were asked to perform.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Adobe Animate</td>
			<td>Adobe</td>
			<td></td>
			<td>It is a tool for designing flash animation films, GIFs, and cartoons.</td>
		</tr>
		<tr>
			<td>EyeLink Data Viewer</td>
			<td></td>
			<td></td>
			<td>It is robust software that provides a comprehensive solution for visualizing and analyzing gaze data captured by EyeLink eye trackers. It is accessible on Windows, macOS, and Linux platforms. Equipped with advanced capabilities, Data Viewer enables effortless visualization, grouping, processing, and reporting of EyeLink gaze data.</td>
		</tr>
		<tr>
			<td>Eye-tracking system</td>
			<td>SR Research</td>
			<td>EyeLink 1000 Portable Duo</td>
			<td>It has a portable duo camera, a Laptop PC Host, and a response device. The EyeLink integrates with SR Research Experiment Builder, Data Viewer, and WebLink as well as many third-party stimulus presentation software and tools.</td>
		</tr>
		<tr>
			<td>Monitor</td>
			<td>Samsung Syncmaster</td>
			<td>&#160;957DF</td>
			<td>It is a 19&#34; flat monitor&#160;</td>
		</tr>
		<tr>
			<td>SR Research Experiment Builder</td>
			<td>SR Research</td>
			<td></td>
			<td>It is an advanced and user-friendly drag-and-drop graphical programming platform designed for developing computer-based experiments in psychology and neuroscience. Utilizing Python as its foundation, this platform is compatible with both Windows and macOS, facilitating the creation of experiments that involve both EyeLink eye-tracking and non-eye-tracking functionalities.</td>
		</tr>
	</tbody>
</table>

eye movements in visual duration perception: disentangling stimulus from time in predecisional processes

Eye-tracking methods may allow the online monitoring of cognitive processing during visual duration perception tasks, where participants are asked to estimate, discriminate, or compare time intervals defined by visual events like flashing circles. However, and to our knowledge, attempts to validate this possibility have remained inconclusive so far, and results remain focused on offline behavioral decisions made after stimulus appearance. This paper presents an eye-tracking protocol for exploring the cognitive processes preceding behavioral responses in an interval comparison task, where participants viewed two consecutive intervals and had to decide whether it speeded up (first interval longer than second) or slowed down (second interval longer). 
Our main concern was disentangling oculomotor responses to the visual stimulus itself from correlates of duration related to judgments. To achieve this, we defined three consecutive time windows based on critical events: baseline onset, the onset of the first interval, the onset of the second interval, and the end of the stimulus. We then extracted traditional oculomotor measures for each (number of fixations, pupil size) and focused on time-window-related changes to separate the responses to the visual stimulus from those related to interval comparison per se. As we show in the illustrative results, eye-tracking data showed significant differences that were consistent with behavioral results, raising hypotheses on the mechanisms engaged. This protocol is embryonic and will require many improvements, but it represents an important step forward in the current state of art.

To better understand TW-related changes, our analysis focused on the interaction of time windows (TW0 vs. TW1, TW1 vs.TW2) with stimulus type and group. As depicted in Figure 3, both TW-related comparisons (TW01 and TW12) showed different levels of change according to Stimulus (TW x Stimulus interaction), with Balls eliciting more TW-related changes in oculomotor responses than flashes in both groups (no TW x stimulus x group interaction). This occurred for both the pupil size and the number of fixations. Regarding group influences, we found a TW x group interaction on the change in the number of fixations from TW0 to TW1 (response to stimulus onset): dyslexics showed decreased change, mainly due to lower prestimulus values. Interactions between TW, stimulus, and group were absent. This shows that group influences were similar for both balls and flashes.
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/65990/65990fig03.jpg" /> 
Figure 3: Results. Time-window-related changes in pupil size and number of fixations as a function of group (control vs. dyslexic, TW x Group) and stimulus type (Balls, B, vs. Flashes, F, TW x Stimulus). TW 0-1 addresses the contrast between no stimulus and stimulus visibility; TW 1-2 compares the first and second intervals to address interval comparison. The 95% confidence intervals are represented by vertical bars. Balls elicited more changes than flashes from TW0 over TW1 (more decrease) and from TW1 over TW2 (more increase) in both eye-tracking measures and both groups (TW x stimulus, no TW x stimulus x group). Changes in the number of fixations across TW 0-1 were smaller in dyslexics than controls regardless of stimulus type (TW x group, no TW x stimulus x group). <a href="https://www.jove.com/files/ftp_upload/65990/65990fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/65990/65990fig04.jpg" /> 
Figure 4: Behavioral results. (A) Discrimination between speed-up and slow-down sequences (d-prime) per group and stimulus type. (B) Significant correlations between behavioral performance (d-prime) and time-window-related changes in eye movements, both stimulus-averaged. <a href="https://www.jove.com/files/ftp_upload/65990/65990fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Critically, these values paralleled behavioral findings (Figure 4A), in line with the main study: behavioral findings pointed to stimulus effects (less accuracy for Balls than for Flashes) and group effects (worse performance in dyslexics), without group x stimulus interactions. Moreover, in the original study with five different stimuli, we correlated behavioral with eye-tracking data (number of fixations) averaged for all stimulus types and found a correlation in the dyslexic group: smaller changes from TW0 over TW1 coexisted with improved performance. Altogether, the results seemed consistent with the hypothesis that these (adult) dyslexics may be resorting to compensatory strategies for deliberate control of attention to the stimulus itself in the prestimulus period (fewer fixations on the empty screen would favor focusing on the stimulus by the time it appeared). We found no such correlation in controls, suggesting that they might not need to resort to strategies to keep focus. The restricted dataset used here for illustration (two stimuli only, Balls and Flashes) showed the same pattern (Figure 4B): dyslexics, but not controls, showed significant correlations between d-prime (behavioral discrimination index) and TW01-related changes.
In sum, the eye-tracking results addressing participants&#39; responses to both stimulus onset (TW 0-1) and interval comparison (TW 1-2) replicated the behavioral evidence that balls versus flashes elicit different responses in individuals with and without dyslexia (TW x stimulus on eye-tracking measures, stimulus effects on d-prime). One part of the eye-tracking results also paralleled the group effects on d-prime, in that changes in the number of fixations at stimulus onset (TW 0-1) were smaller in dyslexics. Moreover, interactions between stimulus and group (different levels of deviance in dyslexics for balls vs. flashes) were null for behavioral and eye-tracking data. Finally, the correlation between behavioral performance and the oculomotor response was significant in the dyslexic group.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Sequence</td>
			<td>Type</td>
			<td>Interval 1</td>
			<td>Interval 2</td>
			<td>Difference</td>
		</tr>
		<tr>
			<td>1</td>
			<td>Speed up</td>
			<td>433</td>
			<td>300</td>
			<td>133</td>
		</tr>
		<tr>
			<td>2</td>
			<td>Speed up</td>
			<td>300</td>
			<td>167</td>
			<td>133</td>
		</tr>
		<tr>
			<td>3</td>
			<td>Speed up</td>
			<td>467</td>
			<td>433</td>
			<td>34</td>
		</tr>
		<tr>
			<td>4</td>
			<td>Speed up</td>
			<td>733</td>
			<td>167</td>
			<td>566</td>
		</tr>
		<tr>
			<td>5</td>
			<td>Speed up</td>
			<td>467</td>
			<td>300</td>
			<td>167</td>
		</tr>
		<tr>
			<td>6</td>
			<td>Speed up</td>
			<td>433</td>
			<td>134</td>
			<td>299</td>
		</tr>
		<tr>
			<td>7</td>
			<td>Speed up</td>
			<td>534</td>
			<td>233</td>
			<td>301</td>
		</tr>
		<tr>
			<td>8</td>
			<td>Speed up</td>
			<td>500</td>
			<td>433</td>
			<td>67</td>
		</tr>
		<tr>
			<td>9</td>
			<td>Slow down</td>
			<td>300</td>
			<td>433</td>
			<td>-133</td>
		</tr>
		<tr>
			<td>10</td>
			<td>Slow down</td>
			<td>167</td>
			<td>300</td>
			<td>-133</td>
		</tr>
		<tr>
			<td>11</td>
			<td>Slow down</td>
			<td>433</td>
			<td>467</td>
			<td>-34</td>
		</tr>
		<tr>
			<td>12</td>
			<td>Slow down</td>
			<td>167</td>
			<td>733</td>
			<td>-566</td>
		</tr>
		<tr>
			<td>13</td>
			<td>Slow down</td>
			<td>300</td>
			<td>467</td>
			<td>-167</td>
		</tr>
		<tr>
			<td>14</td>
			<td>Slow down</td>
			<td>133</td>
			<td>434</td>
			<td>-301</td>
		</tr>
		<tr>
			<td>15</td>
			<td>Slow down</td>
			<td>233</td>
			<td>534</td>
			<td>-301</td>
		</tr>
		<tr>
			<td>16</td>
			<td>Slow down</td>
			<td>433</td>
			<td>500</td>
			<td>-67</td>
		</tr>
		<tr>
			<td colspan="2">Average interval</td>
			<td></td>
			<td></td>
			<td>377.1</td>
		</tr>
		<tr>
			<td colspan="2">Average difference</td>
			<td></td>
			<td></td>
			<td>212.6</td>
		</tr>
		<tr>
			<td colspan="2">Average difference/interval</td>
			<td></td>
			<td></td>
			<td>294.8</td>
		</tr>
	</tbody>
</table>
Table 1: Interval duration. Stimulus sequences for speed-up and slow-down sequences in milliseconds.

Watch this Scientific Journal Video about Author Spotlight: Exploring the Link Between Time Perception of Visual Stimuli and Reading Skills at JoVE.com

Author Spotlight: Exploring the Link Between Time Perception of Visual Stimuli and Reading Skills

We present a protocol that employs eye tracking to monitor eye movements during an interval comparison (duration perception) task based on visual events. The aim is to provide a preliminary guide to separate oculomotor responses to duration perception tasks (comparison or discrimination of time intervals) from responses to the stimulus itself.

Eye Movements in Visual Duration Perception: Disentangling Stimulus from Time in Predecisional Processes

Eye-tracking methods may allow the online monitoring of cognitive processing during visual duration perception tasks, where participants are asked to ...

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1.1K Views.  University of Porto. We present a protocol that employs eye tracking to monitor eye movements during an interval comparison (duration perception) task based on visual events. The aim is to provide a preliminary guide to separate oculomotor responses to duration perception tasks (comparison or discrimination of time intervals) from responses to the stimulus itself. 

Video: Author Spotlight: Exploring the Link Between Time Perception of Visual Stimuli and Reading Skills

Using Eye Movements to Evaluate the Cognitive Processes Involved in Text Comprehension

The present article describes how to use eye tracking methodologies to study the cognitive processes involved in text comprehension. Measuring eye movements during reading is one of the most precise methods for measuring moment-by-moment (online) processing demands during text comprehension. Cognitive processing demands are reflected by several aspects of eye movement behavior, such as fixation duration, number of fixations, and number of regressions (returning to prior parts of a text). Important properties of eye tracking equipment that researchers need to consider are described, including how frequently the eye position is measured (sampling rate), accuracy of determining eye position, how much head movement is allowed, and ease of use. Also described are properties of stimuli that influence eye movements that need to be controlled in studies of text comprehension, such as the position, frequency, and length of target words. Procedural recommendations related to preparing the participant, setting up and calibrating the equipment, and running a study are given. Representative results are presented to illustrate how data can be evaluated. Although the methodology is described in terms of reading comprehension, much of the information presented can be applied to any study in which participants read verbal stimuli.

The present article describes how to use eye tracking methodologies to study the cognitive processes involved in text comprehension. Descriptions of eye tracking equipment, how to develop experimental stimuli, and procedural recommendations are included. The information presented can be applied to most any study using verbal stimuli.

The present article describes how to use eye  tracking methodologies to study the cognitive processes involved in text  comprehension. Measuring eye ...

Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior

Kinesthetic awareness is important to successfully navigate the environment. When we interact with our daily surroundings, some aspects of movement are deliberately planned, while others spontaneously occur below conscious awareness. The deliberate component of this dichotomy has been studied extensively in several contexts, while the spontaneous component remains largely under-explored. Moreover, how perceptual processes modulate these movement classes is still unclear. In particular, a currently debated issue is whether the visuomotor system is governed by the spatial percept produced by a visual illusion or whether it is not affected by the illusion and is governed instead by the veridical percept. Bistable percepts such as 3D depth inversion illusions (DIIs) provide an excellent context to study such interactions and balance, particularly when used in combination with reach-to-grasp movements. In this study, a methodology is developed that uses a DII to clarify the role of top-down processes on motor action, particularly exploring how reaches toward a target on a DII are affected in both deliberate and spontaneous movement domains.

It is unclear how top-down signals from the ventral visual stream affect movement. We developed a paradigm to test motor behavior towards a target on a 3D depth inversion illusion. Significant differences are reported in both deliberate, goal-directed movements and automatic actions under illusory and veridical viewing conditions.

Kinesthetic awareness is important to successfully navigate the environment. When we interact with our daily surroundings, some aspects of movement are ...

A Method to Quantify Visual Information Processing in Children Using Eye Tracking

Visual problems that occur early in life can have major impact on a child's development. Without verbal communication and only based on observational methods, it is difficult to make a quantitative assessment of a child's visual problems. This limits accurate diagnostics in children under the age of 4 years and in children with intellectual disabilities. Here we describe a quantitative method that overcomes these problems. The method uses a remote eye tracker and a four choice preferential looking paradigm to measure eye movement responses to different visual stimuli. The child sits without head support in front of a monitor with integrated infrared cameras. In one of four monitor quadrants a visual stimulus is presented. Each stimulus has a specific visual modality with respect to the background, e.g., form, motion, contrast or color. From the reflexive eye movement responses to these specific visual modalities, output parameters such as reaction times, fixation accuracy and fixation duration are calculated to quantify a child's viewing behavior. With this approach, the quality of visual information processing can be assessed without the use of communication. By comparing results with reference values obtained in typically developing children from 0-12 years, the method provides a characterization of visual information processing in visually impaired children. The quantitative information provided by this method can be advantageous for the field of clinical visual assessment and rehabilitation in multiple ways. The parameter values provide a good basis to: (i) characterize early visual capacities and consequently to enable early interventions; (ii) compare risk groups and follow visual development over time; and (iii), construct an individual visual profile for each child.

A method is described to quantify the quality of visual information processing based on reflexive eye movements in response to specific visual modalities. Reaction times and fixation output parameters are used to characterize visual performance in children with and without visual impairments from 6 months of age.

Visual problems that occur early in life can have major impact on a child's development. Without verbal communication and only based on observational ...

Using Electroencephalography Measurements and High-quality Video Recording for Analyzing Visual Perception of Media Content

This article explores a method to detect differences in visual perception in humans. The method used is based on the psychological (or "cognitive") function of eyeblinks. Participants' eyeblinks are detected and acquired while watching videos specifically created for the investigation. The detection and acquisition of eyeblinks are carried out with the help of a 20-channel electroencephalographic (EEG) wireless device. The international 10-20 system for electrode placement is followed. A high-definition (HD) video camera is used to record participants' facial expressions, for contrast purposes. Instead of using pre-existing media content, purpose-made video content has been created following specific criteria of interest for this investigation, with stimuli enabling researchers to manage the precise parameters of interest. Otherwise, results could be contaminated with uncontrolled variables. The synchronization of the presentation of video stimuli with EEG recordings needs to be done in milliseconds. Analysis of collected data is performed with robust software for working with big matrices. Statistically significant differences in eyeblink rate related to media professionalization and editing style are found with the reported experimental procedures.

We present detection, acquisition, and analysis of eyeblink rates while watching media content.

This article explores a method to detect differences in visual perception in humans. The method used is based on the psychological (or "cognitive") ...

Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects

The present work is a description and an assessment of a methodology designed to quantify different aspects of the interaction between language processing and the perception of the visual world. The recording of eye-gaze patterns has provided good evidence for the contribution of both the visual context and linguistic/world knowledge to language comprehension. Initial research assessed object-context effects to test&#160;theories of modularity in language processing. In the introduction, we describe how subsequent investigations have taken the role of the wider visual context in language processing as a research topic in its own right, asking questions such as how our visual perception of events and of speakers contributes to&#160;comprehension informed by&#160;comprehenders&#39; experience. Among the examined aspects of the visual context are actions, events, a speaker&#39;s gaze, and emotional facial expressions, as well as spatial object configurations. Following an overview of the eye-tracking method and its different applications, we list the key steps of the methodology in the protocol, illustrating how to successfully use it to study visually-situated language comprehension. A final section presents three sets of representative results and illustrates the benefits and limitations of eye tracking for investigating the interplay between the perception of the visual world and language comprehension.

The present article reviews an eye-tracking methodology for studies on language comprehension. To obtain reliable data, key steps of the protocol must be followed. Among these are the correct set-up of the eye tracker (e.g., ensuring good quality of the eye and head images) and accurate calibration.

The present work is a description and an assessment of a methodology designed to quantify different aspects of the interaction between language processing ...

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

In a typical eye tracking study using the visual world paradigm, participants' eye movements to objects or pictures in the visual workspace are recorded via an eye tracker as the participant produces or comprehends a spoken language describing the concurrent visual world. This paradigm has high versatility, as it can be used in a wide range of populations, including those who cannot read and/or who cannot overtly give their behavioral responses, such as preliterate children, elderly adults, and patients. More importantly, the paradigm is extremely sensitive to fine grained manipulations of the speech signal, and it can be used to study the online processing of most topics in language comprehension at multiple levels, such as the fine grained acoustic phonetic features, the properties of words, and the linguistic structures. The protocol described in this article illustrates how a typical visual world eye tracking study is conducted, with an example showing how the online processing of some semantically complex statements can be explored with the visual world paradigm.

The visual world paradigm monitors participants' eye movements in the visual workspace as they are listening to or speaking a spoken language. This paradigm can be used to investigate the online processing of a wide range of psycholinguistic questions, including semantically complex statements, such as disjunctive statements.

In a typical eye tracking study using the visual world paradigm, participants' eye movements to objects or pictures in the visual workspace are recorded ...

Eye-tracking to Distinguish Comprehension-based and Oculomotor-based Regressive Eye Movements During Reading

Regressive eye movements are eye movements that move backwards through the text and comprise approximately 10-25% of eye movements during reading. As such, understanding the causes and mechanisms of regressions plays an important role in understanding eye movement behavior. Inhibition of return (IOR) is an oculomotor effect that results in increased latency to return attention to a previously attended target versus a target that was not previously attended. Thus, IOR may affect regressions. This paper describes how to design materials to distinguish between regressions caused by comprehension-related and oculomotor processes; the latter is subject to IOR. The method allows researchers to identify IOR and control the causes of regressions. While the method requires tightly controlled materials and large numbers of participants and materials, it allows researchers to distinguish and control the types of regressions that occur in their reading studies.

The method was designed to investigate the role of inhibition of return (IOR) in regressive eye movements during reading. The focus is on differentiating between regressions triggered as a result of comprehension difficulty versus those triggered from oculomotor error, including the role of IOR in the two types of regressions.

Regressive eye movements are eye movements that move backwards through the text and comprise approximately 10-25% of eye movements during reading. As ...

Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior

Young children's visual environments are dynamic, changing moment-by-moment as children physically and visually explore spaces and objects and interact with people around them. Head-mounted eye tracking offers a unique opportunity to capture children's dynamic egocentric views and how they allocate visual attention within those views. This protocol provides guiding principles and practical recommendations for researchers using head-mounted eye trackers in both laboratory and more naturalistic settings. Head-mounted eye tracking complements other experimental methods by enhancing opportunities for data collection in more ecologically valid contexts through increased portability and freedom of head and body movements compared to screen-based eye tracking. This protocol can also be integrated with other technologies, such as motion tracking and heart-rate monitoring, to provide a high-density multimodal dataset for examining natural behavior, learning, and development than previously possible. This paper illustrates the types of data generated from head-mounted eye tracking in a study designed to investigate visual attention in one natural context for toddlers: free-flowing toy play with a parent. Successful use of this protocol will allow researchers to collect data that can be used to answer questions not only about visual attention, but also about a broad range of other perceptual, cognitive, and social skills and their development.

Young children do not passively observe the world, but rather actively explore and engage with their environment. This protocol provides guiding principles and practical recommendations for using head-mounted eye trackers to record infants' and toddlers' dynamic visual environments and visual attention in the context of natural behavior.

Young children's visual environments are dynamic, changing moment-by-moment as children physically and visually explore spaces and objects and interact ...

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 ...

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

With the advent of new technological advances, it is possible to study social interactions at a microlevel with unprecedented accuracy. High frequency sensors, such as eye-trackers, electrodermal activity wristbands, EEG bands, and motion sensors provide observations at the millisecond level. This level of precision allows researchers to collect large datasets on social interactions. In this paper, I discuss how multiple eye-trackers can capture a fundamental construct in social interactions, joint visual attention (JVA). JVA has been studied by developmental psychologists to understand how children acquire language, learning scientists to understand how small groups of learners work together, and social scientists to understand interactions in small teams. This paper describes a methodology for capturing JVA in colocated settings using mobile eye-trackers. It presents some empirical results and discusses implications of capturing microobservations to understand social interactions.

Using multimodal sensors is a promising way to understand the role of social interactions in educational settings. This paper describes a methodology for capturing joint visual attention from colocated dyads using mobile eye-trackers.

With the advent of new technological advances, it is possible to study social interactions at a microlevel with unprecedented accuracy. High frequency ...