Name: measuring engagement of spectators of social digital games
Uploaded: 2021-07-03T13:00:00.000+00:00
Duration: 14 min 2 s
Description: Watch this Scientific Journal Video about Measuring Engagement of Spectators of Social Digital Games at JoVE.com

We would like to thank MITACS in partnership with the company that created the game to have funded this research project.

The following protocol was approved by HEC Montr&#233;al&#39;s ethics committee prior to the beginning of the data collection.
1. Participant screening for the experiment
<ol>
	<li>Recruit participants 18 years and older. Ensure that participants understand the language of the experiment, can stand for 20 min, possess a smartphone dating from a maximum of 5 years, do not have skin allergies or sensitivities, do not have a pacemaker and do not suffer from epilepsy or any other diagnosed health problem.</li>
	<li>Recruit groups of people that are friends, and other groups of people that do not know each other, in order to control for familiarity. Group sizes must be determined based on the purpose of the study, the game studied, and the size of the available room.</li>
	<li>Schedule participants. Impose a date and time for group of people knowing each other and group the people who do not know each other on their most convenient dates.</li>
	<li>Ask participants to charge their smartphones and bring chargers to the data collection session.</li>
</ol>
2. Conditions and experimental design
<ol>
	<li>Prepare the randomization sheet for the interactivity condition by associating each participant number to the two conditions of interactivity for each game. Also assign numbers to players and to spectators who will wear an EDA box.</li>
</ol>
3. Preparation
NOTE: These materials are needed to perform the protocol: EDA box, the box used to record the EDA of the participant; light box, the box containing lighted digital numbers; and sync box, the box that sends signals to the EDA box and the light boxes to synchronize data. Two armbands, EDA electrodes, EDA sensors, medical tape, and antiseptic wipes are also needed.
<ol>
	<li>Plug the EDA boxes, the three light boxes, and the sync boxes into the charging station.</li>
	<li>Turn on the game in the studio (projector and 3D scanner for movement detection technology) and test the game by running it through a full game.</li>
	<li>Place the consent forms, the pre-experiment questionnaire, and jerseys on a table in the greeting area.</li>
	<li>Test the Bluetooth connection of the light boxes. Set the sync box to manual.
	<ol>
		<li>Turn on the three light boxes, the two EDA boxes, the Bluetooth on the EDA boxes, and the sync box.</li>
		<li>Push the pulse button on the sync box. The light boxes will flash the number 01.</li>
		<li>Turn off the sync box, the light boxes, and the EDA boxes.</li>
	</ol>
	</li>
	<li>Set the sync box and light boxes in place for the collection. Place the light boxes in view of each camera.
	<ol>
		<li>Put the sync box on the tripod, at a height of 6 feet.</li>
		<li>Set the sync box to auto.</li>
	</ol>
	</li>
	<li>Unplug the batteries and put them into the cameras.
	<ol>
		<li>Check that battery power can record for over an hour.</li>
	</ol>
	</li>
	<li>Place the camera in order for the framing to include all four extremities of the game&#39;s paying field and the light box. Place the two low light cameras at opposite corners of the playing field at hip level and place the go pro mid-field on a higher tripod to have an overhead shot of the playing field.</li>
	<li>Ensure that framings include the full playing field and an area of 1 m around its limits, and the light box. Ensure that sync box is not further than 20 m from where participants will stand, otherwise the pulses will not be transmitted.</li>
</ol>
4. Welcoming participants
<ol>
	<li>Greet the participants at the front door. Tell them to go sit at the table.</li>
	<li>Once all the participants have arrived and are seated, describe the tools which will be used to collect data for the present study. This description should be written in the consent form. Then, tell the two randomly chosen participants to follow the researcher to install the EDA equipment. During that time, other participants can start to fill the pre-experiment questionnaire.</li>
	<li>Ask the participants to read and sign the consent forms. Verbatim: &#34;I will ask you to read the consent form. The two copies are identical. One is for you; one is for me. Please answer all the questions and sign both copies.&#34;</li>
	<li>Go around the table to sign the consent form, verifying that all questions have been answered and put one copy of the consent form into a folder designated for this purpose and give the participant the second copy.</li>
	<li>Ask the participants to put on the jersey with their participant number.</li>
</ol>
5. Installation of the physiological device
<ol>
	<li>Ask the participants to remove any jewelry from the non-dominant hand.</li>
	<li>Use an antiseptic wipe to clean the area where the electrodes will be placed. Remove the plastic from the electrode and place them on the hands of the participant.</li>
	<li>Snap the two sensors on the two electrodes. The red wire must be placed on the thumb&#39;s side. The black wire must be placed on the other side, under the pinky finger.</li>
	<li>Plug the sensor wire to the A3 port of the EDA box. Ask the participant if they tend to have sweaty palms. If they say that they do, wrap medical tape around the electrodes without touching the metal part.</li>
	<li>Add an armband over the palm of the hand to secure the sensors and electrodes in place.</li>
	<li>Turn on the EDA device. Check that the Bluetooth switch is still on.</li>
	<li>Check that the four lights flash.</li>
	<li>Note the number of the participant and the number of the EDA box serial number associated to each participant.</li>
	<li>Place the EDA box on the belt or in the pocket of the participant. If the participant&#39;s clothes do not allow this placement, offer them a belt, and hook the EDA to the belt.</li>
	<li>Ask the participants wearing the EDA boxes to return to the table with the others and complete the pre-experimental questionnaire.</li>
</ol>
6. Record baseline
<ol>
	<li>Go around the table, starting with the participants who do not have the EDA, and check whether all the questions have been answered. If the questionnaire is completed, put it in the folder with the participant&#39;s consent form.</li>
	<li>Once all participants have completed the pre-experimental questionnaire, walk them to the game studio.</li>
	<li>Then, record baseline.
	<ol>
		<li>To do so, tell the participants to calibrate the tools and ask them to breathe calmly and to fix something in front of their eyes for 2 min.</li>
		<li>Simultaneously, turn the EDA devices off and then on.</li>
		<li>Start a timer for 2 min. After the 2 min ends, turn the EDA device off and turn on again.</li>
	</ol>
	</li>
</ol>
7. Start the experiment
<ol>
	<li>Start the recording of the three cameras and turn on the three light boxes.</li>
	<li>Verify that the light boxes and the full playing field are still within the camera frame.</li>
	<li>Verify that the sync box is on auto and turn on the sync box.</li>
	<li>After 10 s, the numbers on the light boxes will flash. 
	NOTE: This indicates that the sync box is automatically sending a pulse every 10 s to both the lights and the EDA boxes.</li>
	<li>Explain the game by informing that the game is like Ping-Pong and one will understand while playing. To win, one player needs to make 3 points. Some members of the public will use smartphones to influence the game by visiting the website URL that is projected on the playground.</li>
	<li>Using the randomization sheet with the number of participants for each condition, tell the participants who will play the game, and who will be on the sidelines as spectators. 
	NOTE: For the purpose of this study, the participants wearing the EDA boxes cannot be selected as playing participants because the spectator&#39;s engagement is being studied.</li>
	<li>Tell the participants which spectators will be using their smartphone. Ask the spectators to influence the game. Tell the participants to stay within one meter of the playing field.</li>
</ol>
8. Start the game
<ol>
	<li>Tell the game technician to start the game by turning on the projectors and the movement detection technology.</li>
	<li>Tell the players the scenario. Verbatim: &#34;Here is the context: you are walking in a public space and you see this game. You decide to participate.&#34;</li>
	<li>While the participants are playing, visually check whether the lights are flashing every 10 s.</li>
	<li>In between each game, ask the spectators (not players) to fill in the Self-Assessment Manikin (SAM) Scale8 questionnaire on their smartphone on an URL. Give them the link of the questionnaire. When the first game is over, ask all the spectators, not players, to fill out a questionnaire on the smartphone about the experience. Ensure they answer three questions using three scales. Do not evaluate the game itself but rather the feeling during the participation.</li>
</ol>
9. Removal of physiological devices
<ol>
	<li>Read this verbatim: &#34;Thank you very much for participating in the game. The last game is over. Spectators will now fill two paper questionnaires, players can leave. Please follow me to the greeting room.&#34;
	<ol>
		<li>Ask all spectators, except the ones with the EDA, to go back to the table. They will answer the UES-SF two times, one time thinking about when they had the smartphone and one time when they didn&#39;t have the smartphone, this is written in the instructions of the questionnaire. Verbatim: &#34;The participants with the physiological tool, can wait at the table. The others, can fill out the end of experiment questionnaire, please answer extensively by explaining clearly what is meant.&#39; They can ask questions if any.</li>
	</ol>
	</li>
	<li>Ask the participant to return the EDA box; turn off the device and the Bluetooth of the device.
	<ol>
		<li>Unplug the sensor from the A3 port, remove the armband, and unsnap the sensor from the electrodes.</li>
		<li>Ask the participant to remove the medical tape and electrodes on their hand. Give the participant a tissue to remove the cream from the hand.</li>
		<li>Remove the micro SD card from the EDA box and repeat steps 9.2. to 9.2.3 with the other EDA participants.</li>
	</ol>
	</li>
</ol>
10. Debrief the participants
<ol>
	<li>Bring the EDA participants to the table where the other participants are sitting.</li>
	<li>Ask the participants to fill the end of experience questionnaire. Ask the participants to answer extensively by explaining clearly what they mean. Tell them to seek help of the experimenter in case they have questions.</li>
	<li>Place the filled out post-experiment questionnaires with the pre-experiment questionnaires and consent forms in the folder.</li>
	<li>Debrief the participants. Once they finish, thank them for their participation, tell them about the compensation and walk them out.</li>
</ol>
11. Cleaning up materials
<ol>
	<li>Turn off the three light boxes.</li>
	<li>Stop the recording of the three cameras and remove the batteries and SD cards from the three cameras. Place the camera batteries in the charger.</li>
	<li>Turn off the sync box and plug the EDA boxes, light boxes, and sync box into the charging station.</li>
</ol>
12. Physiological data management
<ol>
	<li>Put the micro SD card from the EDA box in an adaptor. Transfer the data to the computer in a folder named by the number of the participant. Delete the files from the SD card.</li>
	<li>Select all the data and put it in a spreadsheet. Hide the columns that are not useful. Select approximately line 1 to line 3,000 and make a scatter plot. If all the data is between 240 and 550, the data is valid.</li>
	<li>Verify that the markers generated by the sync box are present by selecting the event column and sorting it. Press control Z to revert the sorting of the markers. 
	NOTE: All the markers that were generated will be visible. Sometimes there are markers that did not appear. This is not a problem, only one marker will provide a point of reference. From this point, the beginnings and ends of the events can be calculated using the time of the camera. There are 100 data points every second.</li>
	<li>Add an event_start_end column. Watch the footage, when there is the beginning of an event, calculate the difference between the time of the event, and the last marker. When the seconds related to the event start are found, add a marker named event1_start in the spreadsheet file. Do the same for the end of the event.</li>
	<li>Repeat step 12.4 for the baseline.</li>
	<li>When all the markers are added, export the spreadsheet in .txt format (tab delimited text). 
	NOTE: There will be two spreadsheets per participant, one with the experiment data and one with the baseline data.</li>
	<li>Import these files in the software that was developed for these EDA boxes (see the next section)19. This will generate a file ready for analysis that contains the relative time, absolute time, events, and EDA signal.</li>
	<li>Upload files to the EDA analysis software</li>
	<li>Click on Add Project. Add a title. Add a description. Enter the date of the project and the total number of participants.</li>
	<li>Click on the name of the project. Click on Experimental Design. Click on Signals and choose physiological, EDA, Bluebox recorder, Bluebox and version 3.0.</li>
	<li>Click on Events and enter the events as they were previously named in the spreadsheet (e.g., event_start_end). Choose Bluebox, version 3.0.</li>
	<li>Click on Transformations and choose GSR (galvanic skin response).</li>
	<li>Click on Unlocked to change to lock for locking the project. Click on File Import to import the files previously prepared.</li>
	<li>Click on the participant profile to give information about the participants by entering their email addresses. Click on Participant is There. Click on Ok Complete.</li>
	<li>Upload the data file which needs to be zipped in order for the software to recognize it. Click on the arrow. Click on the pies to upload the file.</li>
	<li>Go to Analysis and choose Data Exportation; select the participant and their data. Click on Export Data to create a file for statistical analysis. This can take hours if there are many participants. The file will appear under Filename at the end of the exportation. 
	&#8203;NOTE: To obtain the file ready for analysis, the software generates clean phasic data. Signal preprocessing steps were executed as follows: data was recorded at 100 Hz and resampled to 25 Hz, before applying a low-pass 2nd order Butterworth filter and a 50 Hz cut-off. Signal was then decomposed in tonic and phasic components using the convex optimization algorithm described in Greco&#39;s article20. This algorithm filters for artifacts and outlier data points.</li>
	<li>Use the file generated for physiological data analysis.</li>
</ol>
13. Analyze the data
<ol>
	<li>Subtract the EDA mean from the EDA value, then divide this value by its standard deviation (where the mean and standard deviations are based on the entire dataset)21 to standardize the EDA data.</li>
	<li>Subtract the mean of baseline EDA from each EDA standardized value, where the mean is based on the baseline data for each participant in question21 to baseline the EDA data.</li>
	<li>Calculate the means for each condition of interactivity for the SAM Scale and the post-experiment questionnaire (i.e., UES-SF).</li>
	<li>Test two mediation models, one for each type of arousal: physiological and self-reported.</li>
	<li>Test the relationship between the independent variable (interactivity) and the mediators (physiological and perceived arousal).</li>
	<li>Test the relationship between the independent (interactivity) and dependent variables (engagement assessed in the UES-SF).</li>
	<li>Assess the relationship between the combination of the independent variable and the mediators, and the dependent variable.</li>
</ol>

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The authors have nothing to disclose.

Please note that the steps were performed in the studio of the creators of the game but could be replicated in a laboratory setting or another environment that has enough space to fit the game. It is important to note that the sync box can only transmit a pulse to the lights and EDA boxes that are within 20 meters. Therefore, the game room or playing field must not be larger.
Existing laboratory methods have used software to simultaneously begin both the recording of the videogame screen and p...

Studying the experience of game spectators helps to better understand the positive and negative aspects of the game, and in turn, can help to improve its design1. Recent innovations in the gaming industry have allowed new types of experiences that move forward from traditional console-based gaming2. With digital games that use motion tracking systems that are not confined within a screen, audiences do not have to be positioned in a fixed spot anymore. This new reality creates challenges in the assessment of spectators&#39; experience. The experiment was performed in the studio of the creators of the game but could be replicated in a laboratory setting or another environment that has enough space to fit the game.
The purpose of this methodology is to measure spectator engagement during a social digital game. More precisely, arousal, which leads to engagement, will be measured when the spectator has access to a web application that influences the gameplay. This method combines physiological and self-reported data. As this game is social and involves a group of people that move, the experiment is filmed. With the use of cameras and portable physiological devices, we were able to synchronize physiological data with events in the game. The portable devices (EDA boxes) are 3D printed boxes that are connected to electrodes that record physiological activity. The boxes have an ON/OFF switch, visual indicators, a microSD card slot and charging slots. The visual indicators help in case of troubleshooting. For example, these indicate whether the microSD is functional, show the state of the Bluetooth and Wi-Fi connections and signal whether physiological data are being recorded.
The use of physiological measures is a common and validated approach for measuring game engagement3. Physiological valence has been measured in the context of video games4. It has also been used in other research domains such as education5. Because emotional engagement is not observable and self-report can be biased, Charland et al. have used physiological arousal to assess emotional engagement in learners that were solving problems5. They used electrodermal activity (EDA) to measure physiological arousal, which is a widely used method6. EDA is the measurement of skin conductivity, which varies according to the differences in sweat gland activity3. This measurement is an important correlation to real-time emotional variations. EDA is associated with many constructs such as stress, excitement, frustration, and engagement7. Complementing EDA data with self-report responses are therefore recommended to associate the data with the right construct3. The Self-Assessment Manikin (SAM) is a self-reported pictographic scale that assesses three dimensions of emotion: valence, arousal, and dominance8. The current work used the arousal dimension, assessed using a visual 9-point Likert scale, ranging from calm to excited. Perceived arousal has been used in combination with physiological arousal7.
In traditional video games contexts, spectators are seated in a chair and stay more or less in the same position for the duration of the experiment. They are expected to look at a screen where the actions take place. This setting has been seen in previous games studies using physiological data9. In this case, it is simple to start the recording of the game at the same time as the recording of the physiological data10.
In the context of new digital games that are played outside of the screen, and in which participants stand and are free to move, traditional EDA recording might not be appropriate. The game used in this study is akin to a life-size Pong11. This game is composed of a ball and two paddles, each on an extremity of the playing field. Players move their paddle in order to push the ball from one end of the field to the other. In the version used for this research, the game is projected on the ground and players use their bodies as controllers for the paddles. Movement detection technology allows the paddle to follow the two players who are situated at opposite sides of the playground. An example of how the players prevent the ball from hitting the virtual wall behind them is presented in Figure 1. The game also involves spectators standing on the sides of the playground, who can use their smartphones to influence the gameplay. Using a mobile web application, spectators can vote for certain power-ups or obstacles that can either help or harm the players (e.g., less walls versus more balls, or modulating the speed of the ball). The option with the most votes wins.
In this study, we investigate the influence of interactivity on spectators. The conditions of interactivity are with or without smartphone. We compared the engagement of the spectators in these two conditions. A within-subject design was used for the interactivity condition, in order to assess the difference in arousal, and therefore in engagement. In the current study, groups of 12 people were ideal to promote ecological validity of the game12. two people as players and 10 as spectators. Only two EDA boxes were available for our study, so we had a total of eight groups which totalized 16 EDA data sets (two participants with EDA recording per group of 12). Each member of the public was randomly assigned to two games with access to their smartphone to influence the gameplay and one game without access to their smartphone. Game engagement literature suggests that giving many interactive options can lead to higher engagement13. Research in education has found that physiological arousal is a correlate of emotional engagement5. Building on game engagement literature and research in education, we hypothesized that giving the spectators access to interactivity will increase arousal which will in turn increase their engagement.
Contrary to studies about player experience, studies about spectators of a digital game rarely use psychophysiological measures. They are mostly done with questionnaires14, observation15, and interviews16. One difficulty of using psychophysiological measures with spectators is that they are often a group and their movements are less predictable than those of the players. This methodology uses multiple cameras to capture the participants and light boxes, enabling linking of participants video and physiological data.
As we used a within-subject design for the smartphone condition, each subject participated in two games with the interactivity condition, using their smartphone, and one game in the control condition, without the use of their smartphone. Synchronization of EDA data with the starts and ends of each game was therefore crucial to enable the assessment of the differences in each condition of interactivity. It would be impossible to start the recording of all the three cameras at the same time as the recording of the EDA on the spectators due to the dimensions of the room. To overcome that issue, we have used a new synchronization technique called wireless synchronization protocol for the acquisition of multimodal user data17. Bluetooth Low Energy (BLE) signals are sent from a sync box simultaneously to the EDA boxes and to light boxes (see Figure 2). The sync box is a 3D printed box with ON/OFF and auto/manual switches and a button. The manual function is used for testing the signals using the button. The signals are incrementing numbers that start at one and that are shown on the 3D printed light boxes. There numbers are shown to the cameras, and the same numbers are also logged on the EDA data file (see Figure 3). This allows synchronization of events happening in the game to variations in the EDA recordings. In our case, the events identified were the starts and ends of the three games. Then we could link the game to the condition and to the participant number. In this way, we identified which dataset corresponded to each condition.
The following section describes the protocol that allows the use of the technique developed by Courtemanche et al.17. We adapted the technique to answer our research question. This protocol received an ethical certificate from our institution&#39;s ethics committee. In this protocol, we use physiological devices18, mounted into a 3D-printed casing. We will refer to the device as the EDA boxes (boxes used to record the EDA of the participant), the light box (the box with a digital light), and the sync box (box that sends signals to the EDA boxes and the light boxes to synchronize data). The synchronization software enabling the wireless synchronisation protocol for the acquisition of multimodal user data17 was embedded onto the boxes.

<table><tbody><tr><td>BITalino (r)evolution Freestyle Kit (PLUX Wireless biosignals S.A.)&amp;nbsp;</td><td>BITalino</td><td>810121006</td><td></td></tr><tr><td>Devices (1 syncbox, 3 light boxes, 2 EDA boxes)</td><td>Developed by Tech3Lab researchers&lt;sup&gt;1&lt;/sup&gt;</td><td>n/a</td><td></td></tr><tr><td>CubeHX&lt;sup&gt;2&lt;/sup&gt;</td><td>n/a</td><td>n/a</td><td></td></tr><tr><td>Charging station</td><td>Prime 60W 12A 6-Port Desktop Charger</td><td>RP-PC028</td><td></td></tr><tr><td>6 USB3 wires for charging</td><td>Insignia 3m (10 ft.) Charge-and-Play USB A/ Micro USB Cable</td><td>NS-GPS4CC101-C2</td><td></td></tr><tr><td>3D scanner</td><td>Velodyne LiDAR</td><td>VLP-16</td><td></td></tr><tr><td>Projectors</td><td>Barco</td><td>F90-W13</td><td></td></tr><tr><td>Jerseys* (fabric, tape, string)</td><td>Any</td><td>Any</td><td></td></tr><tr><td>2 low light cameras</td><td>Sony</td><td>A7S</td><td></td></tr><tr><td>2 tripods for the A7S</td><td>Manfrotto</td><td>MVK500190XV</td><td></td></tr><tr><td>2 light stands for the go pro and the syncbox</td><td>Impact&amp;nbsp;</td><td>LS-8AI</td><td></td></tr><tr><td>1 plier for the light stand of the syncbox</td><td>Neewer&amp;nbsp;</td><td>Super Clamp Plier Clip</td><td></td></tr><tr><td>1 magic arm for the light box of the go pro</td><td>Magic Arm</td><td>143A</td><td></td></tr><tr><td>1 Go Pro</td><td>Go Pro</td><td>5</td><td></td></tr><tr><td>1 Microphone</td><td>Rode&amp;nbsp;</td><td>VideoMic Rycote</td><td></td></tr><tr><td>2 armbands</td><td>Amyzor</td><td>Moisture Wicking Sweatband&amp;nbsp;</td><td></td></tr><tr><td>*Make them yourself by taping the number on the fabric and perforating two holes to enter the string</td><td></td><td></td><td></td></tr><tr><td>Sources:&lt;br/&gt; 1.Courtemanche, F. et al. Method of and System for Processing Signals Sensed&lt;br/&gt; From a User. US 15/552,788 (2018).&lt;br/&gt; 2. L&amp;eacute;ger, P.M., Courtemanche, F., Fredette, M., S&amp;eacute;n&amp;eacute;cal, S. A cloud-based lab&lt;br/&gt; management and analytics software for triangulated human-centered research.&lt;br/&gt; In Lecture Notes in information Systems and Neuroscience. Edited by Thomas&lt;br/&gt; Fischer, 93-99, Springer. Cham (2019).</td><td></td><td></td><td></td></tr></tbody></table>

measuring engagement of spectators of social digital games

The goal of this methodology is to assess explicit and implicit measures of engagement of spectators during social digital games in a group of participants with motion tracking systems. In the context of games that are not confined within a screen, measuring the different dimensions of engagement such as physiological arousal can be challenging. The focus of the study is made on the spectators of the game and the differences in their engagement according to interactivity. Engagement is measured with physiological and self-reported arousal, as well as an engagement questionnaire at the end of the experiment. Physiological arousal is measured with electrodermal activity (EDA) sensors that record the data on a portable device (EDA box). Portability was essential because of the nature of the game, which is akin to a life-size pong and includes many participants that move. To have an overview of the events of the game, three cameras are used to film three angles of the playing field. To synchronize the EDA data with events happening in the game, boxes with digital numbers are used and put in the frames of cameras. Signals are sent from a sync box simultaneously to the EDA boxes and to light boxes. The light boxes show the synchronization numbers to the cameras, and the same numbers are also logged on the EDA data file. That way, it is possible to record EDA of many people that move freely in a large space and synchronize this data with events in the game. In our particular study, we were able to assess the differences in arousal for the different conditions of interactivity. One of the limitations of this method is that the signals cannot be sent farther than 20 meters away. This method is, therefore, appropriate for recording physiological data in games with an unlimited number of players but is restricted to a limited space.

This section describes the representative results of this study. We recruited participants using social media and our institution&#39;s panel of participants. Of the 78 participants, 40 were women. The mean age was 22 years old. None of the participants had previously played the game. Other exclusion criteria can be found in step 1 of the protocol.
The descriptive statistics, which can be seen in Table 1, contain the mean per condition, for each measure. The mean of the arousa...

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Measuring Engagement of Spectators of Social Digital Games

We propose a methodology that enables measuring engagement of spectators in a social digital game combining physiological and self-reported data. As this digital game involves a group of freely moving people, the experience is filmed using a synchronizing technique that links physiological data with events in the game.

The goal of this methodology is to assess explicit and implicit measures of engagement of spectators during social digital games in a group of ...

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Research

JoVE Journal

Behavior

3.4K Views.  HEC Montréal. We propose a methodology that enables measuring engagement of spectators in a social digital game combining physiological and self-reported data. As this digital game involves a group of freely moving people, the experience is filmed using a synchronizing technique that links physiological data with events in the game.

Video: Measuring Engagement of Spectators of Social Digital Games

Measuring Neural and Behavioral Activity During Ongoing Computerized Social Interactions: An Examination of Event-Related Brain Potentials

Social exclusion is a complex social phenomenon with powerful negative consequences. Given the impact of social exclusion on mental and emotional health, an understanding of how perceptions of social exclusion develop over the course of a social interaction is important for advancing treatments aimed at lessening the harmful costs of being excluded. To date, most scientific examinations of social exclusion have looked at exclusion after a social interaction has been completed. While this has been very helpful in developing an understanding of what happens to a person following exclusion, it has not helped to clarify the moment-to-moment dynamics of the process of social exclusion. Accordingly, the current protocol was developed to obtain an improved understanding of social exclusion by examining the patterns of event-related brain activation that are present during social interactions. This protocol allows greater precision and sensitivity in detailing the social processes that lead people to feel as though they have been excluded from a social interaction. Importantly, the current protocol can be adapted to include research projects that vary the nature of exclusionary social interactions by altering how frequently participants are included, how long the periods of exclusion will last in each interaction, and when exclusion will take place during the social interactions. Further, the current protocol can be used to examine variables and constructs beyond those related to social exclusion. This capability to address a variety of applications across psychology by obtaining both neural and behavioral data during ongoing social interactions suggests the present protocol could be at the core of a developing area of scientific inquiry related to social interactions.

Research on social exclusion has grown tremendously in recent years. As the field expands, it is imperative to develop sophisticated methodologies allowing for the simultaneous measurement of neural and behavioral outcomes during social exclusion. This protocol utilizes event-related brain potentials to record ongoing neural activity during computerized social interactions.

Social exclusion is a complex social phenomenon with powerful negative consequences. Given the impact of social exclusion on mental and emotional health, ...

Combining Computer Game-Based Behavioural Experiments With High-Density EEG and Infrared Gaze Tracking

Experimental paradigms are valuable insofar as the timing and other parameters of their stimuli are well specified and controlled, and insofar as they yield data relevant to the cognitive processing that occurs under ecologically valid conditions. These two goals often are at odds, since well controlled stimuli often are too repetitive to sustain subjects' motivation. Studies employing electroencephalography (EEG) are often especially sensitive to this dilemma between ecological validity and experimental control: attaining sufficient signal-to-noise in physiological averages demands large numbers of repeated trials within lengthy recording sessions, limiting the subject pool to individuals with the ability and patience to perform a set task over and over again. This constraint severely limits researchers' ability to investigate younger populations as well as clinical populations associated with heightened anxiety or attentional abnormalities. Even adult, non-clinical subjects may not be able to achieve their typical levels of performance or cognitive engagement: an unmotivated subject for whom an experimental task is little more than a chore is not the same, behaviourally, cognitively, or neurally, as a subject who is intrinsically motivated and engaged with the task. A growing body of literature demonstrates that embedding experiments within video games may provide a way between the horns of this dilemma between experimental control and ecological validity. The narrative of a game provides a more realistic context in which tasks occur, enhancing their ecological validity (Chaytor &amp; Schmitter-Edgecombe, 2003). Moreover, this context provides motivation to complete tasks. In our game, subjects perform various missions to collect resources, fend off pirates, intercept communications or facilitate diplomatic relations. In so doing, they also perform an array of cognitive tasks, including a Posner attention-shifting paradigm (Posner, 1980), a go/no-go test of motor inhibition, a psychophysical motion coherence threshold task, the Embedded Figures Test (Witkin, 1950, 1954) and a theory-of-mind (Wimmer &amp; Perner, 1983) task. The game software automatically registers game stimuli and subjects' actions and responses in a log file, and sends event codes to synchronise with physiological data recorders. Thus the game can be combined with physiological measures such as EEG or fMRI, and with moment-to-moment tracking of gaze. Gaze tracking can verify subjects' compliance with behavioural tasks (e.g. fixation) and overt attention to experimental stimuli, and also physiological arousal as reflected in pupil dilation (Bradley et al., 2008). At great enough sampling frequencies, gaze tracking may also help assess covert attention as reflected in microsaccades - eye movements that are too small to foveate a new object, but are as rapid in onset and have the same relationship between angular distance and peak velocity as do saccades that traverse greater distances. The distribution of directions of microsaccades correlates with the (otherwise) covert direction of attention (Hafed &amp; Clark, 2002).

Procedures for recording high-density EEG and gaze data during computer game-based cognitive tasks are described. Using a video game to present cognitive tasks enhances ecological validity without sacrificing experimental control.

Experimental paradigms are valuable insofar as the timing and other parameters of their stimuli are well specified and controlled, and insofar as they ...

Neuroscience

Brain Imaging Investigation of the Neural Correlates of Observing Virtual Social Interactions

The ability to gauge social interactions is crucial in the assessment of others&#x2019; intentions. Factors such as facial expressions and body language affect our decisions in personal and professional life alike 1. These "friend or foe" judgements are often based on first impressions, which in turn may affect our decisions to "approach or avoid". Previous studies investigating the neural correlates of social cognition tended to use static facial stimuli 2. Here, we illustrate an experimental design in which whole-body animated characters were used in conjunction with functional magnetic resonance imaging (fMRI) recordings. Fifteen participants were presented with short movie-clips of guest-host interactions in a business setting, while fMRI data were recorded; at the end of each movie, participants also provided ratings of the host behaviour. This design mimics more closely real-life situations, and hence may contribute to better understanding of the neural mechanisms of social interactions in healthy behaviour, and to gaining insight into possible causes of deficits in social behaviour in such clinical conditions as social anxiety and autism 3.

This article demonstrates an experimental design in which whole-body animated characters are used in conjunction with functional magnetic resonance imaging (fMRI) to investigate the neural correlates of observing virtual social interactions.

The ability to gauge social interactions is crucial in the assessment of others&#x2019; intentions. Factors such as facial expressions and body language ...

Methods to Test Visual Attention Online

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

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

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

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

In a recent theoretical synthesis on the concept of engagement, Fredricks, Blumenfeld and Paris1 defined engagement by its multiple dimensions: behavioral, emotional and cognitive. They observed that individual types of engagement had not been studied in conjunction, and little information was available about interactions or synergy between the dimensions; consequently, more studies would contribute to creating finely tuned teaching interventions. Benefiting from the recent technological advances in neurosciences, this paper presents a recently developed methodology to gather and synchronize data on multidimensional engagement during learning tasks. The technique involves the collection of (a) electroencephalography, (b) electrodermal, (c) eye-tracking, and (d) facial emotion recognition data on four different computers. This led to synchronization issues for data collected from multiple sources. Post synchronization in specialized integration software gives researchers a better understanding of the dynamics between the multiple dimensions of engagement. For curriculum developers, these data could provide informed guidelines for achieving better instruction/learning efficiency. This technique also opens up possibilities in the field of brain-computer interactions, where adaptive learning or assessment environments could be developed.

This paper aims to describe the techniques involved in the collection and synchronization of the multiple dimensions (behavioral, affective and cognitive) of learners&#8217; engagement during a task.

In a recent theoretical synthesis on the concept of engagement, Fredricks, Blumenfeld and Paris1 defined engagement by its multiple dimensions: ...

Peering into the Dynamics of Social Interactions: Measuring Play Fighting in Rats

Play fighting in the rat involves attack and defense of the nape of the neck, which if contacted, is gently nuzzled with the snout. Because the movements of one animal are countered by the actions of its partner, play fighting is a complex, dynamic interaction. This dynamic complexity raises methodological problems about what to score for experimental studies. We present a scoring schema that is sensitive to the correlated nature of the actions performed. The frequency of play fighting can be measured by counting the number of playful nape attacks occurring per unit time. However, playful defense, as it can only occur in response to attack, is necessarily a contingent measure that is best measured as a percentage (#attacks defended/total # attacks X 100%). How a particular attack is defended against can involve one of several tactics, and these are contingent on defense having taken place; consequently, the type of defense is also best expressed contingently as a percentage. Two experiments illustrate how these measurements can be used to detect the effect of brain damage on play fighting even when there is no effect on overall playfulness. That is, the schema presented here is designed to detect and evaluate changes in the content of play following an experimental treatment.

Play fighting in the rat involves attack and defense of the nape of the neck, which if contacted, is gently nuzzled with the snout. Because the movements of one animal are countered by the actions of its partner, play fighting is a complex, dynamic interaction. This dynamic complexity raises methodological problems about what to score for experimental studies. We present a scoring schema that is sensitive to the correlated nature of the actions performed. Two experiments illustrate how these measurements can be used to detect the effect of brain damage on play fighting even when there is no effect on overall playfulness. That is, the schema presented here is designed to detect and evaluate changes in the content of play following an experimental treatment.

Play fighting in the rat involves attack and defense of the nape of the neck, which if contacted, is gently nuzzled with the snout. Because the movements ...

Transcranial Direct Current Stimulation for Online Gamers

Transcranial direct current stimulation (tDCS) is a noninvasive brain stimulation technique that applies a weak electric current to the scalp to modulate neuronal membrane potentials. Compared to other brain stimulation methods, tDCS is relatively safe, simple, and inexpensive to administer.
Since excessive online gaming can negatively affect mental health and daily functioning, developing treatment options for gamers is necessary. Although tDCS over the dorsolateral prefrontal cortex (DLPFC) has demonstrated promising results for various addictions, it has not been tested in gamers. This paper describes a protocol and a feasibility study for applying repeated tDCS over the DLPFC and neuroimaging to examine the underlying neural correlates in gamers.
At baseline, individuals who play online games report average weekly hours spent on games, complete questionnaires on addiction symptoms and self-control, and undergo brain 18F-&#64258;uoro-2-deoxyglucose positron emission tomography (FDG-PET). The tDCS protocol consists of 12 sessions over the DLPFC for 4 weeks (anode F3/cathode F4, 2 mA for 30 min per session). Then, a follow-up is conducted using the same protocol as the baseline. Individuals who do not play online games receive only baseline FDG-PET scans without tDCS. Changes of clinical characteristics and asymmetry of regional cerebral metabolic rate of glucose (rCMRglu) in the DLPFC are examined in gamers. In addition, asymmetry of rCMRglu is compared between gamers and non-gamers at baseline.
In our experiment, 15 gamers received tDCS sessions and completed baseline and follow-up scans. Ten non-gamers underwent FDG-PET scans at the baseline. The tDCS reduced addiction symptoms, time spent on games, and increased self-control. Moreover, abnormal asymmetry of rCMRglu in the DLPFC at baseline was alleviated after tDCS.
The current protocol may be useful for assessing treatment efficacy of tDCS and its underlying brain changes in gamers. Further randomized sham-controlled studies are warranted. Moreover, the protocol can be applied to other neurological and psychiatric disorders.

We present a protocol and a feasibility study for applying transcranial direct current stimulation (tDCS) and neuroimaging assessment in online gamers.

Transcranial direct current stimulation (tDCS) is a noninvasive brain stimulation technique that applies a weak electric current to the scalp to modulate ...

Loneliness Assuaged: Eye-Tracking an Audience Watching Barrage Videos

Researchers usually theorize media exposure based on assumptions of legacy media. However, a new interactive video viewing format, in this case barrage video where viewers&#8217; comments are overlaid over visual content, challenges past perspectives. This study proposes an activation and match satisfaction model to study viewing behaviors of lonely people and to challenge previous claims. It presents a protocol to examine the mechanism of how loners use barrage videos by combining eye tracking and self-report measures. Eye tracking documents the audience&#39;s conscious and subconscious watching behaviors in real time and allows for inference of the amount of allocated cognitive resources in response to rational and emotional content. The self-report gauges the amount of satisfaction obtained. Overall, results from the measures supported an activation and match satisfaction model regarding loners and their barrage video viewing behaviors. Implications are discussed.

The study proposes an activation-match model to study how loneliness is mitigated when a lonely audience watches barrage videos of rational and emotional appeals. The protocol uses eye tracking to document duration and fixation, accounting for the degree of satisfaction when emotional needs are appeased by content and barrage.

Researchers usually theorize media exposure based on assumptions of legacy media. However, a new interactive video viewing format, in this case barrage ...

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm

The need to understand trust in groups holistically has led to a surge in new approaches to measuring collective trust. However, this construct is often not fully captured in its emergent qualities by the available research methods. In this paper, the Collective Trust Game (CTG) is presented, a computer-based, multi-agent trust game based on the HoneyComb paradigm, which enables researchers to assess the emergence of collective trust. The CTG builds on previous research on interpersonal trust and adapts the widely known Trust Game to a group setting in the HoneyComb paradigm. Participants take on the role of either an investor or trustee; both roles can be played by groups. Initially, investors and trustees are endowed with a sum of money. Then, the investors need to decide how much, if any, of their endowment they want to send to the trustees. They communicate their tendencies as well as their final decision by moving back and forth on a playfield displaying possible investment amounts. At the end of their decision time, the amount the investors have agreed upon is multiplied and sent to the trustees. The trustees have to communicate how much of that investment, if any, they want to return to the investors. Again, they do so by moving on the playfield. This procedure is repeated for multiple rounds so that collective trust can emerge as a shared construct through repeated interactions. With this procedure, the CTG provides the opportunity to follow the emergence of collective trust in real time through the recording of movement data. The CTG is highly customizable to specific research questions and can be run as an online experiment with little, low cost equipment. This paper shows that the CTG combines the richness of group interaction data with the high internal validity and time-effectiveness of economic games.

The Collective Trust Game is a computer-based, multi-agent trust game based on the HoneyComb paradigm, which enables researchers to assess the emergence of collective trust and related constructs, such as fairness, reciprocity, or forward-signaling. The game allows detailed observations of group processes through movement behavior in the game.

The need to understand trust in groups holistically has led to a surge in new approaches to measuring collective trust. However, this construct is often ...

Enhancing Classroom Engagement: AI-Based Analysis of Student Attention Levels

Artificial Intelligence-Based System for Detecting Attention Levels in Students

The attention level of students in a classroom can be improved through the use of Artificial Intelligence (AI) techniques. By automatically identifying the attention level, teachers can employ strategies to regain students' focus. This can be achieved through various sources of information.
One source is to analyze the emotions reflected on students' faces. AI can detect emotions, such as neutral, disgust, surprise, sadness, fear, happiness, and anger. Additionally, the direction of the students' gaze can also potentially indicate their level of attention. Another source is to observe the students' body posture. By using cameras and deep learning techniques, posture can be analyzed to determine the level of attention. For example, students who are slouching or resting their heads on their desks may have a lower level of attention. Smartwatches distributed to the students can provide biometric and other data, including heart rate and inertial measurements, which can also be used as indicators of attention. By combining these sources of information, an AI system can be trained to identify the level of attention in the classroom. However, integrating the different types of data poses a challenge that requires creating a labeled dataset. Expert input and existing studies are consulted for accurate labeling. In this paper, we propose the integration of such measurements and the creation of a dataset and a potential attention classifier. To provide feedback to the teacher, we explore various methods, such as smartwatches or direct computers. Once the teacher becomes aware of attention issues, they can adjust their teaching approach to re-engage and motivate the students. In summary, AI techniques can automatically identify the students' attention level by analyzing their emotions, gaze direction, body posture, and biometric data. This information can assist teachers in optimizing the teaching-learning process.

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

This paper proposes an artificial intelligence-based system to automatically detect whether students are paying attention to the class or are distracted. This system is designed to help teachers maintain students' attention, optimize their lessons, and dynamically introduce modifications in order for them to be more engaging.

The attention level of students in a classroom can be improved through the use of Artificial Intelligence (AI) techniques. By automatically identifying ...