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 border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>BITalino (r)evolution Freestyle Kit (PLUX Wireless biosignals S.A.)&#160;</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 researchers1</td>
			<td>n/a</td>
			<td></td>
		</tr>
		<tr>
			<td>CubeHX2</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&#160;</td>
			<td>LS-8AI</td>
			<td></td>
		</tr>
		<tr>
			<td>1 plier for the light stand of the syncbox</td>
			<td>Neewer&#160;</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&#160;</td>
			<td>VideoMic Rycote</td>
			<td></td>
		</tr>
		<tr>
			<td>2 armbands</td>
			<td>Amyzor</td>
			<td>Moisture Wicking Sweatband&#160;</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: 
			1.Courtemanche, F. et al. Method of and System for Processing Signals Sensed 
			From a User. US 15/552,788 (2018). 
			2. L&#233;ger, P.M., Courtemanche, F., Fredette, M., S&#233;n&#233;cal, S. A cloud-based lab 
			management and analytics software for triangulated human-centered research. 
			In Lecture Notes in information Systems and Neuroscience. Edited by Thomas 
			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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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, ...

Moderate Prenatal Alcohol Exposure and Quantification of Social Behavior in Adult Rats

Alterations in social behavior are among the major negative consequences observed in children with Fetal Alcohol Spectrum Disorders (FASDs). Several independent laboratories have demonstrated robust alterations in the social behavior of rodents exposed to alcohol during brain development across a wide range of exposure durations, timing, doses, and ages at the time of behavioral quantification. Prior work from this laboratory has identified reliable alterations in specific forms of social interaction following moderate prenatal alcohol exposure (PAE) in the rat that persist well into adulthood, including increased wrestling and decreased investigation. These behavioral alterations have been useful in identifying neural circuits altered by moderate PAE1, and may hold importance for progressing toward a more complete understanding of the neural bases of PAE-related alterations in social behavior. This paper describes procedures for performing moderate PAE in which rat dams voluntarily consume ethanol or saccharin (control) throughout gestation, and measurement of social behaviors in adult offspring.

The goal of the protocol presented here is to describe procedures to expose rats to moderate levels of alcohol during prenatal brain development and to quantify resulting alterations in social behavior during adulthood.

Alterations in social behavior are among the major negative consequences observed in children with Fetal Alcohol Spectrum Disorders (FASDs). Several ...

The Modified Hole Board - Measuring Behavior, Cognition and Social Interaction in Mice and Rats

This protocol describes the modified hole board (mHB), which combines features from a traditional hole board and open field and is designed to measure multiple dimensions of unconditioned behavior in small laboratory mammals (e.g., mice, rats, tree shrews and small primates). This paradigm is a valuable alternative for the use of a behavioral test battery, since a broad behavioral spectrum of an animal&#8217;s behavioral profile can be investigated in one single test.
The apparatus consists of a box, representing the &#8216;protected&#8217; area, separated from a group compartment. A board, on which small cylinders are staggered in three lines, is placed in the center of the box, representing the &#8216;unprotected&#8217; area of the set-up. The cognitive abilities of the animals can be measured by baiting some cylinders on the board and measuring the working and reference memory. Other unconditioned behavior, such as activity-related-, anxiety-related- and social behavior, can be observed using this paradigm. Behavioral flexibility and the ability to habituate to a novel environment can additionally be observed by subjecting the animals to multiple trials in the mHB, revealing insight into the animals&#8217; adaptive capacities.
Due to testing order effects in a behavioral test battery, na&#239;ve animals should be used for each individual experiment. By testing multiple behavioral dimensions in a single paradigm and thereby circumventing this issue, the number of experimental animals used is reduced. Furthermore, by avoiding social isolation during testing and without the need to food deprive the animals, the mHB represents a behavioral test system, inducing if any, very low amount of stress.

This protocol describes the modified hole board, which is a behavioral test set-up that comprises the characteristics of an open field and a traditional hole board. This set-up enables the differential analysis of unconditioned behavior of small laboratory mammals as well as the analysis of cognitive abilities.

This protocol describes the modified hole board (mHB), which combines features from a traditional hole board and open field and is designed to measure ...

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: ...

Quantifying Social Motivation in Mice Using Operant Conditioning

In this protocol, social motivation is measured in mice through a pair of operant conditioning paradigms. To conduct the experiments, two-chambered shuttle boxes were equipped with two operant levers (left and right) and a food receptacle in one chamber, which was then divided from the second chamber by an automated guillotine door covered by a wire grid. Different stimulus mice, rotated across testing days, served as a social stimulus behind the wire grid, and were only visible following the opening of the guillotine door. Test mice were trained to lever press in order to open the door and gain access to the stimulus partner for 15 sec. The number of lever presses required to obtain the social reward progressively increased on a fixed schedule of 3. Testing sessions ended after test mice stopped lever pressing for 5 consecutive minutes. The last reinforced ratio or breakpoint can be used as a quantitative measure of social motivation. For the second paradigm, test mice were trained to discriminate between left and right lever presses in order to obtain either a food reward or the social reward. Mice were rewarded for every 3 presses of each respective lever. The number of food and social rewards can be compared as a measurement of the value placed upon each reward. The ratio of each reward type can also be compared between mouse strains and the change in this ratio can be monitored within testing sessions to measure satiation with a given reward type. Both of these operant conditioning paradigms are highly useful for the quantification of social motivation in mouse models of autism and other disorders of social behavior.

This article describes a new protocol for the quantitative measurement of social motivation in mice using operant conditioning for a social reward. The protocol is useful for the measurement of social motivation in mouse models of autism and other disorders of social behavior.

In this protocol, social motivation is measured in mice through a pair of operant conditioning paradigms. To conduct the experiments, two-chambered ...

The Social Dimension of Stress: Experimental Manipulations of Social Support and Social Identity in the Trier Social Stress Test

In many situations humans are influenced by the behavior of other people and their relationships with them. For example, in stressful situations supportive behavior of other people as well as positive social relationships can act as powerful resources to cope with stress. In order to study the interplay between these variables, this protocol describes two effective experimental manipulations of social relationships and supportive behavior in the laboratory. In the present article, these two manipulations are implemented in the Trier Social Stress Test (TSST)&#8212;a standard stress induction paradigm in which participants are subjected to a simulated job interview. More precisely, we propose (a) a manipulation of the relationship between different protagonists in the TSST by making a shared social identity salient and (b) a manipulation of the behavior of the TSST-selection committee, which acts either supportively or unsupportively. These two experimental manipulations are designed in a modular fashion and can be applied independently of each other but can also be combined. Moreover, these two manipulations can also be integrated into other stress protocols and into other standardized social interactions such as trust games, negotiation tasks, or other group tasks.

Previous research on the social dimension of stress has focused on two important variables: social identity and social support. This protocol introduces an effective experimental manipulation of these two social variables and describes their implementation in a standard stress induction paradigm (Trier Social Stress Test).

In many situations humans are influenced by the behavior of other people and their relationships with them. For example, in stressful situations ...

Experimental Research Examining How People Can Cope with Uncertainty Through Soft Haptic Sensations

Human beings are constantly surrounded by uncertainty and change. The question arises how people cope with such uncertainty. To date, most research has focused on the cognitive strategies people adopt to deal with uncertainty. However, especially when uncertainty is due to unpredictable societal events (e.g., economical crises, political revolutions, terrorism threats) of which one is unable to judge the impact on one&#8217;s future live, cognitive strategies (like seeking additional information) is likely to fail to combat uncertainty. Instead, the current paper discusses a method demonstrating that people might deal with uncertainty experientially through soft haptic sensations. More specifically, because touching something soft creates a feeling of comfort and security, people prefer objects with softer as compared to harder properties when feeling uncertain. Seeking for softness is a highly efficient and effective tool to deal with uncertainty as our hands are available at all times. This protocol describes a set of methods demonstrating 1) how environmental (un)certainty can be situationally activated with an experiential priming procedure, 2) that the quality of the softness experience (what type of softness and how it is experienced) matters and 3) how uncertainty can be reduced using different methods.

To date research has focused on cognitive strategies people adopt to cope with uncertainty. This research examines instead an experiential way of dealing with uncertainty and introduces a set of experimental methods showing how the experience of haptic softness can serve as a tool to deal with uncertainty.

Human beings are constantly surrounded by uncertainty and change. The question arises how people cope with such uncertainty. To date, most research has ...

Examining Recall Memory in Infancy and Early Childhood Using the Elicited Imitation Paradigm

The ability to recall the past allows us to report on details of previous experiences, from the everyday to the significant. Because recall memory is commonly assessed using verbal report paradigms in adults, studying the development of this ability in preverbal infants and children proved challenging. Over the past 30 years, researchers have developed a non-verbal means of assessing recall memory known as the elicited or deferred imitation paradigm. In one variant of the procedure, participants are presented with novel three-dimensional stimuli for a brief baseline period before a researcher demonstrates a series of actions that culminate in an end- or goal-state. The participant is allowed to imitate the demonstrated actions immediately, after a delay, or both. Recall performance is then compared to baseline or to performance on novel control sequences presented at the same session; memory can be assessed for the individual target actions and the order in which they were completed. This procedure is an accepted analogue to the verbal report techniques used with adults, and it has served to establish a solid foundation of the nature of recall memory in infancy and early childhood. In addition, the elicited or deferred imitation procedure has been modified and adapted to answer questions relevant to other aspects of cognitive functioning. The broad utility and application of imitation paradigms is discussed, along with limitations of the approach and directions for future research.

The elicited imitation procedure was established to examine the development of recall memory in infancy and early childhood. This procedure has been widely used to establish a solid foundation of the nature of recall memory in infancy and early childhood.

The ability to recall the past allows us to report on details of previous experiences, from the everyday to the significant. Because recall memory is ...

Assessment of Social Transmission of Food Preferences Behaviors

Olfactory recognition deficits are suggested to be able to serve as clinical marker to differentiate Alzheimer&#39;s disease (AD) subjects from healthy aging groups. For example, olfactory dysfunction in AD can present as impairment in olfactory recognition, emerging during early stages of the disease and worsening while the disease progresses. The social transmission of food preferences (STFP) task is based on a rudimentary form of communication between rodents concerning distant foods dependent on the transmission of olfactory cues. Healthy wild-type mice would prefer to eat a novel, flavored food that was previously cued by a conspecific, and this food preference would be hampered in transgenic AD mice, such as the APP/PS1 model. Indeed, a strong preference for the cued food in C57Bl6/J mice of 3 months of age was found, and this was reduced in 3 months old transgenic APP/PS1 mice. In summary, STFP task could be a powerful measure to be integrated in present subclinical detection assays of AD.

This paper presents a protocol for the investigation of social transmission of food preference in mice. The advantages and possible applications for this procedure, for instance, in detecting early changes in AD mouse models, are highlighted. To conclude, interpretation of the results in light of critical details are discussed.

Olfactory recognition deficits are suggested to be able to serve as clinical marker to differentiate Alzheimer&#39;s disease (AD) subjects from healthy ...

A Visual Guide for Studying Behavioral Defenses to Pathogen Attacks in Leaf-Cutting Ants

The complex lifestyle, evolutionary history of advanced cooperation, and disease defenses of leaf-cutting ants are well studied. Although numerous studies have described the behaviors connected with disease defense, and the associated use of chemicals and antimicrobials, no common visual reference has been made. The main aim of this study was to record short clips of behaviors involved in disease defense, both prophylactically and directly targeted towards an antagonist of the colony following infection. To do so we used an infection experiment, with sub-colonies of the leaf-cutting ant species Acromyrmex echinatior, and the most significant known pathogenic threat to the ants&#39; fungal crop (Leucoagaricus gongylophorus), a specialized pathogenic fungus in the genus Escovopsis. We filmed and compared infected and uninfected colonies, at both early and more advanced stages of infection. We quantified key defensive behaviors across treatments and show that the behavioral response to pathogen attack likely varies between different castes of worker ants, and between early and late detection of a threat. Based on these recordings we have made a library of behavioral clips, accompanied by definitions of the main individual defensive behaviors. We anticipate that such a guide can provide a common frame of reference for other researchers working in this field, to recognize and study these behaviors, and also provide greater scope for comparing different studies to ultimately help better understand the role these behaviors play in disease defense.

We present a visual guide to disease defense behaviors in leaf-cutting ants, with individual clips and accompanying definitions, illustrated in an experimental infection scenario. Our main aim is to help other researchers recognize key defensive behaviors and to provide a common understanding for future research in this field.

The complex lifestyle, evolutionary history of advanced cooperation, and disease defenses of leaf-cutting ants are well studied. Although numerous studies ...