Name: comparing eye-tracking data of children with high-functioning asd, comorbid adhd, and of a control watching social videos
Uploaded: 2018-12-07T13:00:00.000+00:00
Duration: 5 min 32 s
Description: Watch this Scientific Journal Video about Comparing Eye-tracking Data of Children with High-functioning ASD, Comorbid ADHD, and of a Control Watching Social Videos at JoVE.com

The authors acknowledge that the wider study from which this paper is generated is financially supported by the General Research Fund under the University Grants Council of Hong Kong Special Administration Region, China (grant number: GRF 844813); and by the Research Support Scheme 2017/18 of the Department of Special Education and Counselling at the Education University of Hong Kong.

Parental and participant consent was obtained during the recruitment process in a primary school and a children service center for ASD in Hong Kong and the study was approved by the university ethical review committee of the Education University of Hong Kong.
1. Use of a Video-based Assessment
<ol>
	<li>Produce several social videos, about one minute long, that consist of daily life scenarios involving several people in a social context (Figure 1).
	<ol>
		<li>Produce several social videos, about one minute long, that consist of daily life scenarios involving several people in a social context. For the three children in our case study, each child watched the same three videos. The first video demonstrates the following social scenario. In a crowded cafeteria, a student spots an unoccupied seat that is simultaneously occupied by a lady who is talking on the phone and places her bag on the seat with no awareness of his request (Figure 1). The second video demonstrates the following social scenario. Students are playing a chess game while an unfamiliar student comes too close to watch them playing the game. The third video demonstrates the following social scenario. A boy&#8217;s painting is ruined when his friend accidentally spills water from a cup on the table.&#160;</li>
	</ol>
	</li>
	<li>Conduct expert reviews of all the videos. Select those social scenarios that are agreed on the most by the experts as containing the actors&#8217; intention, emotions, and thoughts through their expressions and gestures.</li>
</ol>
2. Recruitment of the Participants
<ol>
	<li>From the pool of participants who satisfied the research inclusion criteria, select and match participants with ASD, with ASD-ADHD, and neurotypical controls using their medical diagnostic reports and the percentile scores of Raven&#8217;s Standard Progressive Matrices16.</li>
	<li>Convert their Raven percentile scores to five percentile ranks. Select participants who perform at ranks II or III (average) and exclude those who scored above rank I (above average) or at rank IV (below average).</li>
</ol>
3. Eye-tracking Experiment
<ol>
	<li>Experimental set-up
	<ol>
		<li>On one side of the eye-tracking room, display the videos on a 23-inch color LCD monitor with a screen resolution of 1920 x 1080 pixels, using an eye tracker at a distance of approximately 60 cm from the participant. Have a research investigator operate the eye tracker from the other side of the eye-tracking room (Figure 2).</li>
		<li>Have another research investigator sit next to the participant and instruct the participant to look at the screen of the monitor. Place the monitor in front of the child on the other side of the partition and connect to the eye tracker. The choice of eye-tracking equipment, testing environment, and the set-up procedures are previously discussed17.</li>
	</ol>
	</li>
	<li>Calibration process
	<ol>
		<li>Instruct the participants to watch the calibration dots that set the viewing boundaries across the screen by capturing the eye movements using infrared corneal reflectance technology. The calibration is properly done if all the green dots or lines fall within the grey circle dots.</li>
		<li>Repeat the calibration if some of the green dots or lines do not fall within the grey circle dots.</li>
	</ol>
	</li>
	<li>Viewing of the videos
	<ol>
		<li>Instruct the participant to view the social videos one after another, and capture their eye movement data during viewing using the eye tracker.</li>
	</ol>
	</li>
</ol>
4. Data Analysis
<ol>
	<li>Define and set up the first-moment fixation within AOIs.
	<ol>
		<li>Choose context-relevant targets (face, hands, targeted objects, etc.) in their initial 500 ms of appearance in each scene of the video as AOIs (Figure 3) and label the AOIs in the information box on the left panel.</li>
		<li>Upon the completion of the addition and selection of the AOIs in the current frame, move the cursors in the timeline bar at the bottom panel to the next frame.</li>
		<li>Adjust the location and boundary of the AOIs in each frame of the video in the presentation video software of the eye tracker manually as the target areas change in each time frame of the video due to the movement of the people or objects as the story of the social video develops.</li>
		<li>Click the Select button on the top panel and add new AOIs to the new scene if necessary. If some existing AOIs are present for 500 ms in the current scene (the timestamp of the video can be checked in the bottom left panel) or if they are not relevant in the new frame in the video, right-click on these AOIs to deactivate them in the new frame.</li>
	</ol>
	</li>
	<li>Run a statistical analysis of the eye-tracking indices. Follow the steps of statistical data processing on the eye tracker as described below.
	<ol>
		<li>Choose the recordings of the children.</li>
		<li>Select the Media file for analysis.</li>
		<li>Select from the available videos.</li>
		<li>Click Analyze selected media.</li>
		<li>Choose the Descriptive statistics (e.g., Sum).</li>
		<li>Choose the dependent measures in Metrics (e.g., First fixation duration, visit count).</li>
		<li>Choose Recordings in Rows.</li>
		<li>Select AOI Media Summary in Columns.</li>
		<li>Click Update to analyze the eye-tracking patterns. The results of the eye-tracking pattern metrics are shown on the screen.</li>
	</ol>
	</li>
	<li>Create the scanpath of a scene from the eye-tracking data.
	<ol>
		<li>Choose Visualization and GazePlot in the software.</li>
		<li>Select the Media and Recordings in the left panel for visualization.</li>
		<li>In the bottom timeline, move the lower cursor to the beginning of the target scene and move the upper cursor to the end of the target scene.</li>
		<li>Make sure Accumulate is chosen for the Data field to show the accumulative scanpath.</li>
		<li>Click Export and Visualization image to save the scanpath as a separate image file.</li>
	</ol>
	</li>
</ol>

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The authors have nothing to disclose.

The first-moment fixation duration was shorter for the ASD-ADHD and ASD children than for the neurotypical child. The total fixation duration was shorter for the ASD-ADHD child than for the neurotypical child, demonstrating a general reduction in visual attention to social stimuli. This showed that the ASD-ADHD child showed a delay in attending to the entry of social stimuli in a social scene. This delay might cause the child to skip registering important momentary social information, which may lead to the misinterpretation of social information and subsequent social cognitive processing.
The total number of fixation counts was lower for the ASD-ADHD child than for the neurotypical child, while the total number of fixation counts within localized AOIs was the highest for the ASD child. This seems to support past ASD findings under the framework of enhanced perceptual functioning (EPF), which suggests that children with ASD employ featural processing; hence, they visually attend to more details of the AOIs then neurotypical controls do.
When the results of the three children are compared, it shows that the ASD child performed the fewest scans across multiple AOIs of social stimuli. This might be explained by the difficulty experienced by the ASD child in pulling together the relationship between relevant social stimuli. This can be accounted for by the weak central coherence theory (CWW), which states that ASD shows deficits in sensory perceptual processing which demands simultaneous attending to and scanning between multiple AOIs.
For scanpath analysis, several limitations are noted. Even though the same scanpath picture is used, it actually contains different scenes within a temporal period (in this study, it was predefined as a video length of 10 seconds). Therefore, there might be spatial errors of gaze spots on the scanpath plot that do not necessarily represent the actual locations of what the participant is focusing on the plot. Investigators need to be cautious of these potential eyeballing errors during data analysis and interpretation.
Since the AOIs have to be marked manually on the eye tracker, there might be a latency of visual fixation from the markers themselves. Since the AOIs were manually plotted against the moving social stimuli, there might be slight errors in the duration of how long each AOI lasts across all AOIs. For example, for a predefined 500 ms, an AOI may have been marked for 498 ms or 510 ms. This may make the comparison of performances across different videos, in contrast to that in the same video, difficult as the performance baselines differ from one video to another. Nonetheless, this artifact will have the same impact on all three participants, and therefore, this may not create a bias for a particular type of participant.

Persons with ASD are known to be characterized by behavioral deficits in social communication, based on conventional behavioral evidence from structured observational assessments and parent interviews. In addition, sensory processing abnormalities have been recently incorporated into the DSM-5 diagnostic criteria of ASD1. Social information processing involves the lower level sensory-perceptual processing and higher level social cognitive processing of social information. Sensory-perceptual processing refers to the ability to attend to social stimuli and encode them in a short-term memory bank for instant retrieval and response-planning, while social cognitive processing refers to the interpretation of social information by social reasoning and problem-solving2,3. As such, social information-processing deficits often lead to other psychobehavioral characteristics, such as social anxiety and inattentiveness. This can be illustrated by the high comorbid prevalence rate of ASD with attention deficit-hyperactive disorder (ADHD). The range of comorbidity for ADHD in ASD has been estimated at 30% to 80%, whereas the presence of comorbid ASD in ADHD has been estimated at 20% to 50%4.
Two major hypotheses have been put forward to account for the deficits in social information processing&#8212;namely, enhanced perceptual functioning (EPF) and weak central coherence (WCC). EPF refers to the overattentiveness to or preoccupation with specific parts by individuals with ASD, whereas WCC refers to their weakness to derive the essence of wholes by pulling together the interelement relationships of the parts5. Both theoretical frameworks attest to their failure to globally configure or process the multiple stimuli concurrently presented in a confined social context6,7. In an earlier face emotion recognition study using static face expression photos8, it was found that the ASD group tended to show localized processing of facial features (such as the shape of the mouth) using EPF, but seem to be weaker in configural processing, which demands pulling together the more abstract perceptual concepts as postulated by WCC, such as the spatial relationships between multiple facial components (e.g., the distance between the eyebrows and the intensity of the eye gaze)9,10.
Since daily social episodes consist of dynamic moment-to-moment subtle changes in social information, any failure to attend or engage in the sensory-perceptual processing of subtle human nonverbal cues, such as facial expression, postures, and gestures, and to make sense of the relationships of the different social stimuli might lead to inappropriate social cognitive processing. Eye-tracking experiments have been increasingly used to supplement clinical observation in social information processing studies. Eye-tracking data, in the form of scanpath patterns, visual fixation counts, and visual duration, have been major biomarkers to investigate social information processing in ASD11,12,13,14,15.
In this study, we illustrate the use of the eye-tracking technique to investigate whether the two participants with ASD and with ASD-ADHD process the first moments of social video scenes differently than the neurotypical child. The eye tracker equipment captures four major indices during viewing: the number of visual fixations, the first fixation duration, the total fixation duration, and the scanpath patterns in the form of spatial arrangement and sequence of fixation points. In this way, how fast each participant attends to the audio-visual stimuli predefined by AOIs as they first appear into the social scenes, for how long they continue to look at those AOIs, and their gaze shifts between multiple AOIs appearing concurrently in the same social scene can be captured. Any delay to fixate AOIs during the first moments (i.e., 500 ms) and the trajectory of the scanpaths provide important evidence for data analysis. Representative findings from the qualitative analysis of this single-case comparative study using this paradigm are reported.

<table><tbody><tr><td>Tobii Pro TX300</td><td>Tobii</td><td>N/A</td><td>Screen based eye-tracker (300Hz refreshing rate)</td></tr><tr><td>Tobii Pro Studio</td><td>Tobii</td><td>N/A</td><td>Software for analyzing eyetracking data</td></tr></tbody></table>

comparing eye-tracking data of children with high-functioning asd, comorbid adhd, and of a control watching social videos

Children with autism spectrum disorders (ASD) are known to have sensory-perceptual processing deficits that weaken their abilities to attend and perceive social stimuli in daily living contexts. Since daily social episodes consist of subtle dynamic changes in social information, any failure to attend to or process subtle human nonverbal cues, such as facial expression, postures, and gestures, might lead to inappropriate social interaction. Traditional behavioral rating scales or assessment tools based on static social scenes have limitations in capturing the moment-to-moment changes in social scenarios. An eye-tracking assessment, which can be administered in a video-based mode, is therefore preferred, to augment clinical observation. In this study, using the single-case comparison design, the eye-tracking data of three participants, a child with autism spectrum disorder (ASD), another with comorbid attention deficit-hyperactive disorder (ADHD), and a neurotypical control, are captured while they view a video of social scenarios. The eye-tracking experiment has helped answer the research question: How does social attention differ between the three participants? By predefining areas of interest (AOIs), their visual attention on relevant or irrelevant social stimuli, how fast each participant attends to the first social stimuli appearing in the videos, for how long each participant continues to attend to those stimuli within the AOIs, and the gaze shifts between multiple social stimuli appearing concurrently in the same social scene are captured, compared, and analyzed in a video-based eye-tracking experiment.

The eye-tracking data of the three Cantonese-speaking children (with ASD, with ASD-ADHD, and a control) aged between the ages of 7 and 9 viewing three social videos using the aforementioned paradigm is presented here (Table 1).
The first fixation duration (per 500 ms target AOI) was longer for the neurotypical child (150 ms) than for the ASD and ASD-ADHD children (both 110 ms). The total fixation duration (per 500 ms target AOI) was shorter for the ASD-ADHD child (120 ms) than for both the neurotypical child (170 ms) and the ASD child (180 ms). The total number of fixation counts (per 500 ms target AOI) was the largest for the ASD child (4.62), second for the neurotypical child (4.09), and the shortest for the ASD-ADHD child (3.19).
A scanpath plot captures the visual scanning of multiple AOIs in a social scene. An example of the scanpaths of the three children for one 10 s episode in the first video&#160;is shown in Figure 4 and Videos 1&#160;- 3.
Video 1: Scanpaths of the control. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58694/Video8.mp4" target="_blank">Please click here to view this video. (Right-click to download.)</a>
Video 2: Scanpath of the child with ASD. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58694/Video9.mp4" target="_blank">Please click here to view this video. (Right-click to download.)</a>
Video 3: Scanpath of the child with ASD-ADHD. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58694/Video10.mp4" target="_blank">Please click here to view this video. (Right-click to download.)</a>
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/58694/58694fig1.jpg" /> 
Figure 1: An example of essential social scenes in Video 1. In the first scene, the boy is waiting to get his meal from the cafeteria staff. In the second scene, he is looking for a seat near the lady who is talking on the phone. In the third scene, he asks the lady whether he can sit on the empty chair next to her. In the last scene, the lady does not notice his request and puts a bag on the unoccupied chair. The boy is disappointed because he could not find a place to sit. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58694/58694fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/58694/58694fig2.jpg" /> 
Figure 2: Eye-tracking experimental set-up. A research investigator gave instructions to the child about viewing the videos in front of the monitor on one side of the eye-tracking experiment room. The display of the videos was controlled by another investigator using another computer on the other side of the same room separated by a partition. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58694/58694fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/58694/58694fig3.jpg" /> 
Figure 3: An example of the target AOIs in Video 1. The colored ovals are the AOIs (i.e., face, eyes, mouth, hands, mobile phone, and the bag of the lady) that show the first moments in one of the scenes in Video 1. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58694/58694fig3large.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/58694/58694fig4.jpg" /> 
Figure 4: Scanpaths of the control (top), the child with ASD (middle), and the child with ASD-ADHD (bottom). Taking a social scene in Video 1 as an example, the blue dots trace the scanpaths for the neurotypical control child, the green dots for the ASD child, and the red dots for the ASD-ADHD child. The dots in the figure indicate the locations of the visual fixations. The bigger the dots are, the longer the child attend to that particular spot on the visual stimulus. The numbers in the dots represent the sequence of visual fixations within 500 ms of the video scene. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58694/58694fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Participant groups</td>
			<td>Raven Score</td>
			<td>Grade</td>
			<td>First fixation duration (ms)</td>
			<td>Total fixation duration (ms)</td>
			<td>Fixation counts</td>
		</tr>
		<tr>
			<td>Control</td>
			<td>120</td>
			<td>3</td>
			<td>150</td>
			<td>170</td>
			<td>4.09</td>
		</tr>
		<tr>
			<td>ASD</td>
			<td>129</td>
			<td>1</td>
			<td>110</td>
			<td>180</td>
			<td>4.62</td>
		</tr>
		<tr>
			<td>ASD-ADHD</td>
			<td>115</td>
			<td>3</td>
			<td>110</td>
			<td>120</td>
			<td>3.19</td>
		</tr>
	</tbody>
</table>
Table 1:&#160;Descriptive statistics of the eye-tracker measurements of the three children.

Watch this Scientific Journal Video about Comparing Eye-tracking Data of Children with High-functioning ASD, Comorbid ADHD, and of a Control Watching Social Videos at JoVE.com

Comparing Eye-tracking Data of Children with High-functioning ASD, Comorbid ADHD, and of a Control Watching Social Videos

This is a qualitative comparative case study analysis of eye-tracking data on the first moments of social video scenes as viewed by three participants: one with autism spectrum disorder, one with comorbid attention deficit-hyperactive disorder, and one neurotypical control.

Children with autism spectrum disorders (ASD) are known to have sensory-perceptual processing deficits that weaken their abilities to attend and perceive ...

comparing-eye-tracking-data-children-with-high-functioning-asd

Nanyang Technological University

Research

JoVE Journal

Behavior

8.8K Views.  Education University of Hong Kong. This is a qualitative comparative case study analysis of eye-tracking data on the first moments of social video scenes as viewed by three participants: one with autism spectrum disorder, one with comorbid attention deficit-hyperactive disorder, and one neurotypical control.

Video: Comparing Eye-tracking Data of Children with High-functioning ASD, Comorbid ADHD, and of a Control Watching Social Videos

Testing Sensory and Multisensory Function in Children with Autism Spectrum Disorder

In addition to impairments in social communication and the presence of restricted interests and repetitive behaviors, deficits in sensory processing are now recognized as a core symptom in autism spectrum disorder (ASD). Our ability to perceive and interact with the external world is rooted in sensory processing. For example, listening to a conversation entails processing the auditory cues coming from the speaker (speech content, prosody, syntax) as well as the associated visual information (facial expressions, gestures). Collectively, the &#8220;integration&#8221; of these multisensory (i.e., combined audiovisual) pieces of information results in better comprehension. Such multisensory integration has been shown to be strongly dependent upon the temporal relationship of the paired stimuli. Thus, stimuli that occur in close temporal proximity are highly likely to result in behavioral and perceptual benefits &#8211; gains believed to be reflective of the perceptual system&#39;s judgment of the likelihood that these two stimuli came from the same source. Changes in this temporal integration are expected to strongly alter perceptual processes, and are likely to diminish the ability to accurately perceive and interact with our world. Here, a battery of tasks designed to characterize various aspects of sensory and multisensory temporal processing in children with ASD is described. In addition to its utility in autism, this battery has great potential for characterizing changes in sensory function in other clinical populations, as well as being used to examine changes in these processes across the lifespan.

We describe how to implement a battery of behavioral tasks to examine the processing and integration of sensory stimuli in children with ASD. The goal is to characterize individual differences in temporal processing of simple auditory and visual stimuli and relate these to higher order perceptual skills like speech perception.

In addition to impairments in social communication and the presence of restricted interests and repetitive behaviors, deficits in sensory processing are ...

Eye Tracking Young Children with Autism

The rise of accessible commercial eye-tracking systems has fueled a rapid increase in their use in psychological and psychiatric research. By providing a direct, detailed and objective measure of gaze behavior, eye-tracking has become a valuable tool for examining abnormal perceptual strategies in clinical populations and has been used to identify disorder-specific characteristics1, promote early identification2, and inform treatment3. In particular, investigators of autism spectrum disorders (ASD) have benefited from integrating eye-tracking into their research paradigms4-7. Eye-tracking has largely been used in these studies to reveal mechanisms underlying impaired task performance8 and abnormal brain functioning9, particularly during the processing of social information1,10-11. While older children and adults with ASD comprise the preponderance of research in this area, eye-tracking may be especially useful for studying young children with the disorder as it offers a non-invasive tool for assessing and quantifying early-emerging developmental abnormalities2,12-13. Implementing eye-tracking with young children with ASD, however, is associated with a number of unique challenges, including issues with compliant behavior resulting from specific task demands and disorder-related psychosocial considerations. In this protocol, we detail methodological considerations for optimizing research design, data acquisition and psychometric analysis while eye-tracking young children with ASD. The provided recommendations are also designed to be more broadly applicable for eye-tracking children with other developmental disabilities. By offering guidelines for best practices in these areas based upon lessons derived from our own work, we hope to help other investigators make sound research design and analysis choices while avoiding common pitfalls that can compromise data acquisition while eye-tracking young children with ASD or other developmental difficulties.

Eye tracking has long been used to study gaze patterns in typically-developing individuals, but recent technological advancements have made its use with clinical populations, including autism, more feasible. While eye-tracking young children with autism can offer insight into early symptom manifestations, it involves methodological challenges. Suggestions for best practices are provided.

The rise of accessible commercial eye-tracking systems has fueled a rapid increase in their use in psychological and psychiatric research. By providing a ...

Medicine

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 ...

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 ...

An Electrophysiology Protocol to Measure Reward Anticipation and Processing in Children

We present a protocol designed to measure the neural correlates of reward in children. The protocol allows researchers to measure both reward anticipation and processing. Its purpose is to create a reward task that is appropriate for young children with and without autism while controlling reward properties between two conditions: social and nonsocial. The current protocol allows for comparisons of brain activity between social and nonsocial reward conditions while keeping the reward itself identical between conditions. Using this protocol, we found evidence that neurotypical children demonstrate enhanced anticipatory brain activity during the social condition. Furthermore, we found that neurotypical children anticipate social reward more robustly than children with autism diagnoses. As the task uses snacks as a reward, it is most appropriate for young children. However, the protocol may be adapted for use with adolescent or adult populations if snacks are replaced by monetary incentives. The protocol is designed to measure electrophysiological events (event-related potentials), but it may be customized for use with eye-tracking or fMRI.

This protocol is designed to measure reward anticipation and processing in young children with and without autism. Specifically, the protocol is designed to study the neural correlates of reward during social and nonsocial conditions while controlling for reward between conditions.

We present a protocol designed to measure the neural correlates of reward in children. The protocol allows researchers to measure both reward anticipation ...

Using the Visual World Paradigm to Study Sentence Comprehension in Mandarin-Speaking Children with Autism

Sentence comprehension relies on the ability to rapidly integrate different types of linguistic and non-linguistic information. However, there is currently a paucity of research exploring how preschool children with autism understand sentences using different types of cues. The mechanisms underlying sentence comprehension remains largely unclear. The present study presents a protocol to examine the sentence comprehension abilities of preschool children with autism. More specifically, a visual world paradigm of eye-tracking is used to explore the moment-to-moment sentence comprehension in the children. The paradigm has multiple advantages. First, it is sensitive to the time course of sentence comprehension and thus can provide rich information about how sentence comprehension unfolds over time. Second, it requires minimal task and communication demands, so it is ideal for testing children with autism. To further minimize the computational burden of children, the present study measures eye movements that arise as automatic responses to linguistic input rather than measuring eye movements that accompany conscious responses to spoken instructions.

We present a protocol to examine the use of morphological cues during real-time sentence comprehension by children with autism.

Sentence comprehension relies on the ability to rapidly integrate different types of linguistic and non-linguistic information. However, there is ...

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 ...

Exploring Infant Sensitivity to Visual Language using Eye Tracking and the Preferential Looking Paradigm

We discuss the use of the preferential looking paradigm in eye tracking studies in order to study how infants develop, understand, and attend to the world around them. Eye tracking is a safe and non-invasive way to collect gaze data from infants, and the preferential looking paradigm is simple to design and only requires the infant to be attending to the screen. By simultaneously showing two visual stimuli that differ in one dimension, we can assess whether infants show different looking behavior for either stimulus, thus demonstrating sensitivity to that difference. The challenges in such experimental approaches are that experiments must be kept brief (no more than 10 min) and be carefully controlled such that the two stimuli differ in only one way. The interpretation of null results must also be carefully considered. In this paper, we illustrate a successful example of an infant eye tracking study with a preferential looking paradigm to discover that 6-month-olds are sensitive to linguistic cues in a signed language despite having no prior exposure to signed language, suggesting that infants possess intrinsic or innate sensitivities to these cues.

Eye tracking studies using a preferential looking paradigm can be used to study infants&#39;&#160;emerging understanding of, and attention to, their external visual world.

We discuss the use of the preferential looking paradigm in eye tracking studies in order to study how infants develop, understand, and attend to the world ...

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Behavioral analysis of adults engaged in learning tasks is a major challenge in the field of adult education. Nowadays, in a world of continuous technological changes and scientific advances, there is a need for life-long learning and education within both formal and non-formal educational environments. In response to this challenge, the use of eye-tracking technology and data-mining techniques, respectively, for supervised (mainly prediction) and unsupervised (specifically cluster analysis) learning, provide methods for the detection of forms of learning among users and/or the classification of their learning styles. In this study, a protocol is proposed for the study of learning styles among adults with and without previous knowledge at different ages (18 to 69-year-old) and at different points throughout the learning process (start and end). Statistical analysis-of-variance techniques mean that differences may be detected between the participants by type of learner and previous knowledge of the task. Likewise, the use of&#160;unsupervised learning clustering techniques throws light on similar forms of learning among the participants across different groups. All these data will facilitate personalized proposals from the teacher for the presentation of each task at different points in the chain of information processing. It will likewise be easier for the teacher to adapt teaching materials to the learning needs of each student or group of students with similar characteristics.

We present a protocol for a behavioral analysis of adults (ages 18 to 70-year-old) engaged in learning processes, undertaking tasks designed for Self-Regulated Learning (SRL). The participants, university teachers and students, and adults from the University of Experience, were monitored with eye-tracking devices and the data were analyzed with data-mining techniques.

Behavioral analysis of adults engaged in learning tasks is a major challenge in the field of adult education. Nowadays, in a world of continuous ...

A Familiarization Protocol Facilitates the Participation of Children with ASD in Electrophysiological Research

This paper includes a detailed description of a familiarization protocol, which is used as an integral component of a larger research protocol to collect electroencephalography (EEG) data and Event-Related Potentials (ERPs). At present, the systems available for the collection of high-quality EEG/ERP data make significant demands on children with developmental disabilities, such as those with an Autism Spectrum Disorder (ASD). Children with ASD may have difficulty adapting to novel situations, tolerating uncomfortable sensory stimuli, and sitting quietly. This familiarization protocol uses Evidence-Based Practices (EBPs) to increase research participants&#39; knowledge and understanding of the specific activities and steps of the research protocol. The tools in this familiarization protocol are a social narrative, a visual schedule, the Premack principle, role-playing, and modeling. The goal of this familiarization protocol is to increase understanding and agency and to potentially reduce anxiety for child participants, resulting in a greater likelihood of the successful completion of the research protocol for the collection of EEG/ERP data.

Electrophysiological research is an important tool for identifying biomarkers of developmental disorders, including Autism Spectrum Disorders (ASD), but data collection in these populations remains challenging. This work presents a familiarization protocol to accompany research that includes electroencephalography (EEG) to improve the likelihood of collecting EEG data from children with ASD.

This paper includes a detailed description of a familiarization protocol, which is used as an integral component of a larger research protocol to collect ...