The author would like to thank his many thesis students and research assistants for their help in piloting and administering this protocol. Data presented in this manuscript is based upon work supported by the National Science Foundation (NSF) under Grant Number (1147743) to Jason Themanson and a grant from Illinois Wesleyan University to Jason Themanson.

NOTE: The following protocol was developed in accordance with ethical standards approved by the Institutional Review Board at Illinois Wesleyan University.
1. Cyberball Stimulus Preparation
<ol>
	<li>Download the Cyberball paradigm12,18 and install it on the computer (the current protocol utilized images from Cyberball version 3.0). Alternatively, create computerized images to recreate the Cyberball paradigm to meet specific needs.</li>
	<li>Create individual images for each portion of the throws within Cyberball by using a photo-editing program. For example, break down each of the throws from player to player into the individual throw frames that are shown one after another to create the image of a ball being thrown from player to player on the computer screen (see Figure 1).</li>
	<li>Add any labels, names, or pictures to each individual throw frame in the photo-editing program, including anything to represent the human participant as the bottom player on the screen (represented by the hand at the bottom of the screen in Figure 1) to create a series of throw frames that are identical except for the movement of the ball from player to player.</li>
	<li>Note which frame in each throw sequence is the &#8220;informational frame&#8221; for that throw, or the first frame within the throw sequences that provides information to the players about the specific destination of the throw (i.e., which other player will receive the ball).</li>
	<li>Ensure that there are throw sequences creating a throw from each player to each other player on the screen (including throws from the human participant to the other players), that each throw sequence has the same number of throw frames, and the informational frame within each throw sequence has been noted.</li>
</ol>
2. Cyberball Social Interaction Programming
<ol>
	<li>Create a sequence file using stimulus presentation software to detail the exact sequence of events within the Cyberball social interaction.
	<ol>
		<li>For the sequence file, specify the specific throw frames (in order), the timing of the frames on the screen, the sequencing of the frames, the nature of the event (throw from whom to whom), the response required by the human participant (when necessary), and the overall order of events to create the desired interaction. Explicitly enter all of these specifications into the proper rows, columns, and spaces within the programming code during the creation of the sequence file.</li>
		<li>Specify all of the above-mentioned specifics within the programming code for each event within the sequence file and repeat the steps for each sequence file created (e.g., inclusion, exclusion).
		<ol>
			<li>Order each of the throw frames in the correct sequence within the sequence file so that the first ball toss is completed without error from one player to the other. Create similar ordered sequences in the file for each type of throw among the players so that each type of throw is represented in the sequence file (e.g., a three-player game consists of six different possible throws).</li>
			<li>Space the timing of each throw frame 450 msec apart. In this method, ensure that each frame appears for 450 msec before being replaced by the next frame, which provides an image of motion on the screen for the participant and creates a throw event that lasts a total of 2,700 msec.</li>
			<li>Insert an event-related marker each time an informational frame is presented in the sequence file so that the presentation of the informational frame can be marked in time in the file saving the participants&#8217; neural activity. Code this marker to represent the nature of the event by using numbers to represent the players (left player is player &#8220;1,&#8221; the bottom player is player &#8220;2,&#8221; the right player is player &#8220;3&#8221;), which would allow the code &#8220;13&#8221; to represent a throw from the player on the left to the player on the right.</li>
			<li>Copy the entire set of six different throw sequences within the file so that each throw sequence is represented at least twice within the sequence file. This will provide programming flexibility to change the order of events within each block so that they do not look pre-determined.</li>
			<li>Create &#8220;if, then&#8221; statements within the sequence file to allow the human participant to freely select which player will receive the next throw following the human participant. Give the human participant a response pad or mouse to select the next action after receiving a ball toss; potentially using the right mouse button to throw to the player on the right and using the left mouse button to throw to the player on the left in a three-player game.</li>
			<li>Ensure that the &#8220;if, then&#8221; statements lead to the appropriate next throw sequence so that the game play appears seamless (i.e., a human throw to the player on the left should be followed by a throw from the player on the left to another player).</li>
			<li>Create loops and &#8220;if, then&#8221; statements within the sequence file to represent the desired game action and allow the program to appropriately move to the next event regardless of the selections of the human participant.</li>
			<li>Initiate counters within the program to change the nature of the game so that the program does not become apparent to the human player (i.e., the same computerized player does not always make the same throw). Use these counters to switch the game action and remove patterns of play throughout the game after the repeated occurrence of a specific event or pattern of events to better give the appearance of spontaneous live play among players on the sides of the screen, not just the actual human participant represented on the bottom of the screen.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Develop different sequence files in order to study different types of social interactions. Make these interactions largely inclusive or exclusive, or even partially inclusive or exclusive, for the human participant depending on the nature of the research question by varying the proportion and order of inclusionary events and exclusionary events within each sequence file.</li>
	<li>Ensure that the event markers appear in the EEG files when collecting neural data to create event-related brain potentials (ERPs) for each event type within each of the different social interactions. These markers should appear in the EEG file as the informational frame is presented to the participant.</li>
</ol>
3. Neuroelectric Recording
<ol>
	<li>Prepare participants for electroencephalography (EEG) assessment in accordance with the guidelines of the Society for Psychophysiological Research19.</li>
	<li>Use a lycra electrode cap embedded with 64 sintered Ag-AgCl electrodes (10 mm), arranged in a 10&#8211;10 system montage20 to collect EEG data. Fit the cap on the participant&#8217;s head and prepare each electrode using conductive gel.
	<ol>
		<li>Reference the electrodes online to an electrode placed at the midpoint between Cz and CPz and use AFz as the ground electrode. 
		NOTE: Alternative online references may be needed depending upon the nature of the electrode cap used for data collection.</li>
		<li>Collect vertical and horizontal bipolar electrooculographic activity (EOG) to monitor eye movements using sintered Ag-AgCl electrodes placed above and below the right orbit and near the outer canthus of each eye.</li>
	</ol>
	</li>
	<li>Use a digital bioamplifier to continuously digitize (500 Hz sampling rate), amplify (gain depends on the specific amplifier), and filter (70 Hz low-pass filter, including a 60 Hz notch filter) the raw EEG signal in DC mode. Choose these setting from the available options in the EEG analysis software for the amplifier prior to data collection and vary depending on the specifications of the EEG hardware and software.</li>
	<li>Record EEG activity using EEG analysis software in order to further process the neural data.</li>
</ol>
4. Offline Neuroelectric Data Processing
<ol>
	<li>Correct eye blinks using a spatial filter, a multi-step procedure that generates an average eye blink, utilizes a spatial singular value decomposition based on principal component analysis (PCA) to extract the first component and covariance values, and then uses those covariance values to develop a filter that is specifically sensitive to eye blinks21.</li>
	<li>Create stimulus-locked epochs relative to the event marker that was inserted in the continuous EEG file in the EEG analysis software by selecting this function from the choices of data transformation options. Run these epochs from -900 msec to 1,800 msec relative to inserted marker, which is equivalent to the entire duration of each six-frame throw and has a timepoint of 0 msec where the event marker was inserted as displayed in Figure 1.</li>
	<li>Correct for baseline difference between the epochs by removing the average pre-stimulus baseline activity from each epoch (i.e., the 900 msec time window that runs from -900 msec to 0 msec prior to the event marker). This function can be selected or initiated from the data transformation options available in the EEG analysis software.</li>
	<li>Low-pass filter (30 Hz; 24 dB/octave) the epochs and reject any epochs with electrical artifacts that exceed +75V. Choose these setting from the available options in the EEG analysis software allowing the transformation of EEG data following data collection and vary depending on the specifications of the EEG software.</li>
	<li>Average the neural responses together for each event type within the Cyberball task blocks. 
	NOTE: This averaging process can be adapted to only average the first 20, middle 20, or even last 20 events of a similar type within each interaction to examine dynamic patterns of neural activation over the course of the social interaction13.
	<ol>
		<li>Combine the various events types to create three major event categories: throws to the participant from either other player, throws from the participant to either other player, and throws not including the participant between the two other players. For example, combine throws from the human to the left player and to the right player into one average waveform.</li>
		<li>Combine the events from the computerized players into the event types of most interest: throws to the human participant (inclusionary) and throws away from the human participant (exclusionary).</li>
	</ol>
	</li>
	<li>If applicable, quantify the N2 component as the average amplitude in the discrete latency window running from 200 - 320 msec after the event marker at FCz.</li>
	<li>If applicable, quantify the P3 component as the average amplitude in the discrete latency window running from 320 - 450 msec following the event marker at Pz.</li>
	<li>If applicable, quantify ERP components to throw frames following the informational frame to examine ongoing differences among patterns of neural activity to different event types with the social interaction.</li>
</ol>

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The author has nothing to disclose and declares no conflict of interest.

In this article, a protocol allowing for the measurement of ongoing event-related neural and behavioral data during social interactions was presented. This procedure creates opportunities to look at multiple different event types (inclusionary, exclusionary) within and across varied social interactions. Specifically, the procedure can quantify moment-to-moment event-related neural activity in response to any event that occurs during a computerized social interaction. This neural activity is independent of any particular movement or action initiated by the participant and the nature of the social interactions can be customized to address a multitude of research questions beyond social exclusion research.
Critical to this protocol is the use of event markers for the collection of neural activity during ongoing social interactions. The insertion of these event markers allows for time-locked analyses of event-related activity within any type of social interaction (inclusionary, exclusionary). This capability is not possible in fMRI methodologies3,11 due to the limited temporal resolution of hemodynamic measures. An additional critical element is the creation of a series of images reflecting an ongoing and dynamic social interaction among players. This differs from other ERP protocols that present a single image showing the human participant that he/she was the recipient of the throw15,16,22. In the present protocol, the participants can experience the development of a social event &#8211; just like in real-life interactions &#8211; and researchers have the capability to examine each frame throughout each throw sequence. This allows for the parsing of neural responses into a component structure, rather than seeing social events as some aggregate occurrence. 
Further, because of the open-ended capability to explicitly program the behavior of the computer-generated players within the Cyberball interaction, the researcher has the ultimate flexibility to generate any social interaction for the human participant. This easily allows investigators to examine issues beyond social exclusion research. For example, this protocol could be adapted to address questions pertaining to a number of topics in psychology, across multiple sub-disciplines in the field by varying the nature of the &#8220;back-story&#8221; surrounding the protocol and the conditions of play within the protocol itself. There is no limit on the number of players or the structure to the game. Accordingly, a variety of conditions or controls could be introduced to assess conformity, social ethics, attentional control, information processing, aggression, cooperation and competition, racial or gender bias, and a host of additional constructs and variables of import to researchers examining human behavior. Additionally, researcher could adapt the present protocol to utilize ERP methodologies in chat room research paradigms23 or other &#8220;online&#8221; social interactions24. These paradigms may have more external validity in comparison to Cyberball and the ability to examine ERPs during these chat room interactions, by creating event markers during the presentation of each chat room entry, would allow for the ongoing examination of dynamic neural activity during these more realistic social exchanges while still maintaining a significant level of control over the experimental protocol. This would add to the breadth of understanding related to the process of social exclusion and using these chat room paradigms would allow for much more flexible applications of these ERP techniques to social exclusion research.
Although this protocol has been shown to be effective in obtaining neural and behavioral data from individuals during ongoing social interactions, it is important to mention the limitations of the procedure. Notably, the nature of ERP data requires the averaging of many event trials together in order to improve the signal-to-noise ratio within the data25. Because of this requirement inherent within ERP research, the social interactions in the current protocol can become long and monotonous for the participants as each task block needs to be programmed to include a large number of trials for each type of event. Further, depending on the software program used to implement the presentation of the protocol, the protocol may be dependent upon the human participant&#8217;s response within a certain time-window after receiving the ball. If the participant does not response within the programmed timeframe, the software closes the sequence file, which means that the task block has to be restarted. This can lead to differences in how the participants in the same task condition complete the protocol as some participants will be exposed to a longer protocol due to the restart. Finally, neural data by itself is not sufficient for a complete examination of social exclusion processes. The ERP components described in this protocol could be active in response to some other aspect of the social interaction, or other stimuli entirely, depending on the nature of the social exchange and the setting where the participant is completing the research. Accordingly, the neural data need to be linked with self-report questionnaire data to ensure the functional significance of the neural data by showing that the changes in the ERP components are related to social exclusion and not some other processes or stimuli.
The current protocol offers new empirical insights into the dynamic nature of social interactions by exploring the event-related patterns of neural activation present during ongoing social interactions. Future research using this protocol would be well-served to utilize a variety of software platforms and manipulations of the social interactions to more completely assess the patterns of neural activity present during social interactions. Additionally, future investigations can build upon this protocol by adding layers of complexity to the study designs to investigate individual differences, social influences, and contextual effects to uncover important variables and characteristics that may moderate the exclusion-related effects discovered using the present methodology.

The scientific examination of social interactions has undergone a renaissance in recent years, with an explosion of new theoretical explanations, models, and paradigms aimed at understanding and exploring the effects of being the target or source of social exclusion and how those interactions lead to the many consequences of exclusion1-6. Though the literature had made tremendous strides in developing a better understanding of the consequences of social exclusion at behavioral, emotional, cognitive, and neural levels, a great deal remains unknown in relation to the dynamics involved in social exclusion. One notable gap in the literature relates to the measurement of various dynamic social exclusion processes during social interactions. For instance, multiple theoretical models3,5-8 suggest that the monitoring and assessment of instances of social exclusion is an initial step in a larger self-regulatory system aimed at coping with social exclusion and maintaining healthy and acceptable levels of belonging and social inclusion. These models, and much of the existing literature on exclusion, provide tremendous insights into the consequences of social exclusion and the harmful effects exclusion causes on neural, behavioral, cognitive, and emotional levels. However, the specific processes ongoing in targets of exclusion during social interactions, which lead to both the perception of exclusion and the subsequent emotional and cognitive reactions to exclusion, remain undefined. Researchers have adapted methodologies to obtain self-reported feeling states during social interactions9, but these data did not examine the ongoing neural processes that may motivate any self-reported effects.
Accordingly, examinations of exclusion during social interactions were initiated using functional magnetic resonance imaging (fMRI) to &#8220;see&#8221; what is happening while individuals are being excluded3,4,10,11. These studies revealed different patterns of neural activation during exclusion compared to inclusion. Though tremendously important in enhancing the understanding of ongoing neural processes present during exclusion and their relations with the self-reported consequences of being excluded, these studies are limited in how they can represent the dynamic nature of social interactions. Specifically, these fMRI methodologies aggregated neural activity across entire social interactions and were unable to examine exclusion on a moment-to-moment basis. This limitation prohibits a complete understanding of the dynamic nature of exclusion-related emotional and cognitive processing that is taking place during social interactions as researchers are unable to determine which moments or events during an exchange are meaningful in relation to the development of one&#8217;s perceptions of exclusion and the associated emotional response.
To address these limitations, recent research has implemented the measurement of one class of neural activity, known as event-related brain potentials (ERPs), during the execution of the Cyberball paradigm12 to examine the moment-to-moment patterns of neural activation present during social exclusion13. ERPs refer to neuroelectric activity measured on the scalp that is time-locked to discrete events and represents brain activity in response to or in preparation for a stimulus or response14. Further, ERPs possess a superior temporal resolution when compared to fMRI, which provides valuable insights into the dynamic responses to social exclusion. As such, neural indices obtained through the event-related examination of brain activity in response to instances of social inclusion and exclusion, which can be implemented and controlled through the Cyberball paradigm and are described in the present protocol, are necessary to evaluate the models and predictions present in current social exclusion theory.
The goal of the current methodology is to measure ongoing neural responses to social events (inclusionary events, exclusionary events) during computerized social interactions in a human participant. In this methodology, researchers have the ability to quantify neural activity in response to each event within the interaction. Further, the current protocol allows for the ongoing examination of each social event as each event is made up of multiple throw images. This allows researchers to look at changes in neural activity as the events unfold. This level of analysis is not available in other methodologies that examine ERPs during social interactions15,16 as these methodologies only capture neural activity in relation to one image for each event without allowing for the examination of the unfolding event as it occurs. Additionally, the human participant is led to believe that he or she is playing an online game with other people, but is actually playing within a pre-programmed game with a computer. Because the interaction is pre-programmed within the computer, with the flexibility to interact with the decisions made by the human participant, the nature of the social interactions can be pre-determined and programmed to vary depending on the nature of the research question13,17. For example, the behavior of the computer-generated players during the protocol can be tailored to create instances of social inclusion or social exclusion of any specified duration by altering the pre-programmed schedule of throws (e.g., which player throws the ball to which other player, when those throws occur, the number of throws, and the timing of throws). Accordingly, this allows researchers to measure neural activity in response to events that may or may not match the overall context of the interaction. For example, researchers can quantify a participant&#8217;s neural response to an exclusionary social event within an interaction that is largely inclusionary for that participant and potentially compare it to that participant&#8217;s neural response to an exclusionary event within a largely exclusionary interaction. These research opportunities are not readily available using fMRI technology given the temporal limitations of fMRI. With this programming flexibility, the current protocol allows researchers from various neuroscientific and psychological backgrounds to address research questions in new ways and obtain dynamic neural and behavioral activity during social interactions.

<table><tbody><tr><td>Cyberball (Williams et al., 2000) computerized social interaction program</td><td><a href="https://cyberball.wikispaces.com" target="_blank"><img src="/img/new_window.png" /> https://cyberball.wikispaces.com</a></td><td></td><td>An Alternate set of computerized images can be used or created by the researcher</td></tr><tr><td>Neuroscan SynAmps2 64-Channel Amplifier with SCAN 4.3.1 Acquisition and Analysis Software</td><td><a href="http://www.neuroscan.com/supplies.cfm" target="_blank"><img src="/img/new_window.png" /> Compumedics Neuroscan Neuromedical Supplies</a></td><td>9032-0010-01</td><td>Alternate amplifiers and EEG acquisition equipment and sofware can be used</td></tr><tr><td>STIM2 Complete Version 2.1 Stimulus Presentation Software</td><td><a href="http://www.neuroscan.com/supplies.cfm" target="_blank"><img src="/img/new_window.png" /> Compumedics Neuroscan Neuromedical Supplies</a></td><td>666M</td><td>Alternate stimulus presenation software can be used</td></tr><tr><td>SynAmps2 Quik-Cap Sintered Ag/AgCl 64 Ch./Medium</td><td><a href="http://www.neuroscan.com/supplies.cfm" target="_blank"><img src="/img/new_window.png" /> Compumedics Neuroscan Neuromedical Supplies</a></td><td>96050255</td><td>Alternate EEG caps can be used</td></tr><tr><td>Quik-Gel Conductive Gel</td><td><a href="http://www.neuroscan.com/supplies.cfm" target="_blank"><img src="/img/new_window.png" /> Compumedics Neuroscan Neuromedical Supplies</a></td><td>92000016</td><td>Alternate EEG conductive electrode gel can be used</td></tr><tr><td>NuPrep</td><td><a href="http://www.neuroscan.com/supplies.cfm" target="_blank"><img src="/img/new_window.png" /> Compumedics Neuroscan Neuromedical Supplies</a></td><td>92100025</td><td>Alternate skin preparation exfoliants can be used</td></tr><tr><td>Blunt needle and syringe kit</td><td><a href="http://www.neuroscan.com/supplies.cfm" target="_blank"><img src="/img/new_window.png" /> Compumedics Neuroscan Neuromedical Supplies</a></td><td>104207</td><td>Needle and syringe kit is used to apply conductive gel to electrode embedded in the EEG cap</td></tr></tbody></table>

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.

This protocol has been used in previously published research examining the influence of social exclusion on ongoing neural and behavioral activity13. Twenty-two college-aged participants (15 females, 7 males) completed three sessions of the Cyberball task under conditions described above. After providing informed consent, participants were told that they would be playing a computerized ball-tossing game with other undergraduate participants. However, the other participants were not real, they were represented by the computerized players detailed in this protocol. Every human participant completed the same three blocks of the protocol (inclusion, exclusion, re-inclusion). Each block consisted of 80 total throws. In the inclusion and re-inclusion blocks, all players had an equal chance of receiving the ball on each ball toss. In the exclusion block, the human participant had the same equal chance of receiving the ball until receiving 10 throws from the other players. After this initial phase, the human participant was completed excluded for the remainder of the task block.
Representative results from this protocol can include examinations of multiple ERP components for each type of event within a social interaction as well as an examination of ERP components across different types of interactions. Analyses of the N2 component indicate an effect for the type of event, but no effect for the type of social interaction, with larger N2 amplitudes for exclusionary throws regardless of the larger context of the social interaction. Representative findings for the P3 component reveal a similar pattern with an effect for the type of event within the interaction, but not for the type of interaction itself, with larger P3 amplitude for inclusionary events and no overall effects for the nature of the social interaction. Figure 2 provides ERP waveforms by Cyberball block and throw type, highlighting the observed differences in N2 and P3 amplitudes.
Additionally, by utilizing ERPs, this protocol allows for the examination of potential alterations in neural activation over the course of social interactions. Representative analyses can be conducted to examine changes in neural activation to exclusionary events over the course of the entire exclusion process. In examinations of early exclusionary trials compared to later exclusionary trials, analyses of both the N2 and P3 have indicated larger amplitudes for both ERP components during the first 20 exclusionary events following the initial inclusion phase compared to the second 20 exclusionary events following the initial inclusionary phase of the exclusion block (see Figure 3).
<img alt="Figure 1" fo:content-width="6in" src="/files/ftp_upload/52060/52060fig1highres.jpg" width="600" /> 
Figure 1. Cyberball throw sequence examples with event marker placement. Examples of throw frames along with the placement of ERP markers during different throw sequences in the ongoing Cyberball game. Event markers are inserted as the first informational frame providing information about the nature of each throw is presented to the participant. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/52060/52060fig1highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" src="/files/ftp_upload/52060/52060fig2highres.jpg" /> 
Figure 2. Representative ERP waveforms by throw type and block type. This protocol is capable of providing ERP waveforms for each type of social event within each task block of Cyberball. The different patterns of neural activity to each event type can be represented by different waveforms within the same figure, with separate lines for each type of throw (inclusionary, exclusionary) for each block of Cyberball (inclusion, exclusion, re-inclusion). The time point 0 msec represents the timing of the ERP event marker within each throw sequence, with the top graph displaying waveforms at FCz and the bottom graph displaying waveforms at Pz. This figure has been modified from Themanson et al.13 with permission.
<img alt="Figure 3" src="/files/ftp_upload/52060/52060fig3highres.jpg" /> 
Figure 3. Representative ERP waveforms displaying component differences over the course of social exclusion. ERP waveforms derived from this protocol parsing the first 20 and second 20 exclusionary events following the initial inclusionary phase of the exclusion block. This capability to show the alterations in neural activity during the course of the social interaction can be applied to different ERP components and electrode sites, as shown by the waveforms for FCz (top) and Pz (bottom). This figure has been modified from Themanson et al.13 with permission.

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

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, ...

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13.8K Views.  Illinois Wesleyan University. 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. 

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

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 ...

Measurement of Neurophysiological Signals of Ignoring and Attending Processes in Attention Control

Attention control is the ability to selectively attend to some sensory signals while ignoring others. This ability is thought to involve two processes: enhancement of sensory signals that are to be attended and the attenuation of sensory signals that are to be ignored. The overall strength of attentional modulation is often measured by comparing the amplitude of a sensory neural response to an external input when attended versus when ignored. This method is robust for detecting attentional modulation, but precludes the ability to assess the separate dynamics of attending and ignoring processes. Here, we describe methodology to measure independently the neurophysiological signals of attending and ignoring using the intermodal attention task (IMAT). This task, when combined with electroencephalography, isolates neurophysiological sensory responses in auditory and visual modalities, when either attending or ignoring, with respect to a passive control. As a result, independent dynamics of attending and of a ignoring can be assessed in either modality. Our results using this task indicate that the timing and cortical sources of attending and ignoring effects differ, as do their contributions to the attention modulation effect, pointing to unique neural trajectories and demonstrating sample utility of measuring them separately.

Attention control comprises enhancement of target signals and attenuation of distractor signals. We describe an approach to measure separately but concurrently, the neurophysiology of attending and ignoring in sustained intermodal attention, utilizing a passive control condition during which neither process is continuously engaged.

Attention control is the ability to selectively attend to some sensory signals while ignoring others. This ability is thought to involve two processes: ...

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Understanding typical and atypical development remains one of the fundamental questions in developmental human neuroscience. Traditionally, experimental paradigms and analysis tools have been limited to constrained laboratory tasks and contexts due to technical limitations imposed by the available set of measuring and analysis techniques and the age of the subjects. These limitations severely limit the study of developmental neural dynamics and associated neural networks engaged in cognition, perception and action in infants performing &#8220;in action and in context&#8221;. This protocol presents a novel approach to study infants and young children as they freely organize their own behavior, and its consequences in a complex, partly unpredictable and highly dynamic environment. The proposed methodology integrates synchronized high-density active scalp electroencephalography (EEG), inertial measurement units (IMUs), video recording and behavioral analysis to capture brain activity and movement non-invasively in freely-behaving infants. This setup allows for the study of neural network dynamics in the developing brain, in action and context, as these networks are recruited during goal-oriented, exploration and social interaction tasks.

This protocol presents a novel methodology for the neural decoding of intent from freely-behaving infants during unscripted social interaction with an actor. Neural activity is acquired using non-invasive high-density active scalp electroencephalography (EEG). Kinematic data is collected with inertial measurement units and supplemented with synchronized video recording.

Understanding typical and atypical development remains one of the fundamental questions in developmental human neuroscience. Traditionally, experimental ...

A Within-subjects Experimental Protocol to Assess the Effects of Social Input on Infant EEG

Despite the importance of social interactions for infant brain development, little research has assessed functional neural activation while infants socially interact. Electroencephalography (EEG) power is an advantageous technique to assess infant functional neural activation. However, many studies record infant EEG only during one baseline condition. This protocol describes a paradigm that is designed to comprehensively assess infant EEG activity in both social and nonsocial contexts as well as tease apart how different types of social inputs differentially relate to infant EEG. The within-subjects paradigm includes four controlled conditions. In the nonsocial condition, infants view objects on computer screens. The joint attention condition involves an experimenter directing the infant's attention to pictures. The joint attention condition includes three types of social input: language, face-to-face interaction, and the presence of joint attention. Differences in infant EEG between the nonsocial and joint attention conditions could be due to any of these three types of input. Therefore, two additional conditions (one with language input while the experimenter is hidden behind a screen and one with face-to-face interaction) were included to assess the driving contextual factors in patterns of infant neural activation. Representative results demonstrate that infant EEG power varied by condition, both overall and differentially by brain region, supporting the functional nature of infant EEG power. This technique is advantageous in that it includes conditions that are clearly social or nonsocial and allows for examination of how specific types of social input relate to EEG power. This paradigm can be used to assess how individual differences in age, affect, socioeconomic status, and parent-infant interaction quality relate to the development of the social brain. Based on the demonstrated functional nature of infant EEG power, future studies should consider the role of EEG recording context and design conditions that are clearly social or nonsocial.

This novel protocol is designed to assess the neural bases of social interaction in infants. The paradigm is designed to tease apart how various social inputs such as language, joint attention, and face-to-face interaction relate to infant neural activation. Infant EEG power is recorded during both social and nonsocial conditions.

Despite the importance of social interactions for infant brain development, little research has assessed functional neural activation while infants ...

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

When considering human neuroimaging data, an appreciation of signal variability represents a fundamental innovation in the way we think about brain signal. Typically, researchers represent the brain's response as the mean across repeated experimental trials and disregard signal fluctuations over time as "noise". However, it is becoming clear that brain signal variability conveys meaningful functional information about neural network dynamics. This article describes the novel method of multiscale entropy (MSE) for quantifying brain signal variability. MSE may be particularly informative of neural network dynamics because it shows timescale dependence and sensitivity to linear and nonlinear dynamics in the data.

Neuroimaging researchers typically consider the brain's response as the mean activity across repeated experimental trials and disregard signal variability over time as "noise". However, it is becoming clear that there is signal in that noise. This article describes the novel method of multiscale entropy for quantifying brain signal variability in the time domain.

When considering human neuroimaging data, an appreciation of signal variability represents a fundamental innovation in the way we think about brain ...

How to Find Effects of Stimulus Processing on Event Related Brain Potentials of Close Others when Hyperscanning Partners

The partners of each pair must be able to pass the McGill Friendship Questionnaire without communicating. Each partner is then seated in front of a screen in one of two adjacent rooms. These rooms are separated by a glass window through which participants communicate to maintain feelings of togetherness while being fitted with the EEG cap. After checking for adequate EEG signals, the glass is covered by a curtain to prevent visual communication. Then, partners must be silent but are instructed to try to feel in the presence of their partner during the entire experiment. Just before it starts, participants are told that each of them will be presented with one image at a time and that these images will occur at the same time for both of them on their own screen. They are also instructed that, for each trial, the simultaneous images will always be different. However, unbeknownst to them, trials are randomized: only half of them are consistent with this instruction and actually include two different images. These trials form the DSC, that is, the different-stimuli condition. The other half of the trials are inconsistent with the instruction. They include two identical images and form the ISC (identical-stimuli condition). After the experiment, participants are sorted into two groups: those who reported that they felt in the presence of their partner during the majority of the trials and those who reported they did not. The impact of the stimulus processing of the partner is found by subtracting the mean voltages of the ERPs of the ISC (inconsistent with the instructions) from the ERPs of the DSC (consistent with the instructions) in at least two time windows (TWs): firstly, in the 75 to 150 ms TW, where the absolute values of these subtractions are greater, especially at right frontal sites, in those who felt in the presence of their partner than in those who did not; secondly, in the LPP time window (i.e., from 650 to 950 ms post onset), where ERPs are significantly less positive in the DSC than in the ISC in those in whom the raw results of the early (75-150ms) subtractions are negative.

This protocol describes key steps involved in assessing the sensitivity of the brain of one person to the stimulus processing of a close other by selecting pairs of partners, recording their electroencephalogram (EEG) simultaneously and computing their event-related brain potentials (ERPs).

The partners of each pair must be able to pass the McGill Friendship Questionnaire without communicating. Each partner is then seated in front of a screen ...

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

Measuring neural activity and connectivity associated with cognitive functions at high spatial and temporal resolutions is an important goal in cognitive neuroscience. Intracranial electroencephalography (EEG) can directly record electrical neural activity and has the unique potential to accomplish this goal. Traditionally, averaging analysis has been applied to analyze intracranial EEG data; however, several new techniques are available for depicting neural activity and intra- and inter-regional connectivity. Here, we introduce two analytical protocols we recently applied to analyze intracranial EEG data using the Statistical Parametric Mapping (SPM) software: time-frequency SPM analysis for neural activity and dynamic causal modeling of induced responses for intra- and inter-regional connectivity. We report our analysis of intracranial EEG data during the observation of faces as representative results. The results revealed that the inferior occipital gyrus (IOG) showed gamma-band activity at very early stages (110 ms) in response to faces, and both the IOG and amygdala showed rapid intra- and inter-regional connectivity using various types of oscillations. These analytical protocols have the potential to identify the neural mechanisms underlying cognitive functions with high spatial and temporal profiles.

We present two analytical protocols that can be used to analyze intracranial electroencephalography data using the Statistical Parametric Mapping (SPM) software: time-frequency statistical parametric mapping analysis for neural activity, and dynamic causal modeling of induced responses for intra- and inter-regional connectivity.

Measuring neural activity and connectivity associated with cognitive functions at high spatial and temporal resolutions is an important goal in cognitive ...

Event Related Potentials (ERPs) and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder (ADHD)

Neuropsychiatric diagnoses like ADHD are based on subjective methods like interviews, rating scales and observations. There is a need for more brain-based supplements. Stimulant medication is the most common treatment for ADHD. Clinically useful predictors of response have so far not been reported. The aim of this paper is to describe the EEG based methods we apply to extract potential biomarkers for brain dysfunction. Examples relate to biomarkers for pediatric ADHD, and prediction of medication response. The main emphasis is on Event Related Potentials (ERPs).
A nineteen channel EEG is recorded during a 3 min eyes-opened task, a 3 min eyes-closed task, and a 20 min cued visual GO/NOGO task (VCPT). ERPs are recorded during this task. The goal of the ERP protocol is to extract biomarkers of assumed brain dysfunctions that significantly differentiate between a patient group and healthy controls. The protocol includes recording during standard conditions and artifact correction. ERP waves can be used or transformed into latent components. The components of the patient group are compared with controls, empathizing components that, when compared, show relatively high effect sizes. Sub-groups of the patients are selected on the basis of the cluster analysis in the space of the components. Treatment procedure (such as medication, tDCS or neurofeedback protocol) can be applied and the changes in components related to treatment in the subgroups are observed, forming the basis for clinical recommendations.
The methods described were applied in a study of 87 pediatric ADHD patients. The index of medication response discriminated significantly between responders and non-responders with a large, and clinically meaningful effect size (d = 1.84). In an ongoing study comparing ADHD children with matched controls, several variables discriminate significantly between patients and controls. The global index will exceed d = .8. The EEG based methods described here could be clinically meaningful.

Event Related Potentials ERPs and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder ADHD

EEG-methods are applied for extracting biomarkers of brain dysfunctions. The focus is on multi-channel event-related potentials (ERPs) recorded in a cued GO/NOGO task. Non-brain artifacts are corrected and ERPs are compared with the normative data. Examples relate to biomarkers for ADHD diagnosis and prediction of medication response.

Neuropsychiatric diagnoses like ADHD are based on subjective methods like interviews, rating scales and observations. There is a need for more brain-based ...

Real-Life EEG and ECG Synchronization in Infant-Caregiver Interactions

Home-Based EEG Hyperscanning for Infant-Caregiver Social Interactions

Prior hyperscanning studies that record the brain activities of caregivers and children concurrently have primarily been conducted within the confines of the laboratory, thus limiting the generalizability of results to real-life settings. Here, a comprehensive protocol for capturing synchronized electroencephalography (EEG), electrocardiography (ECG), and behavioral recordings from infant-caregiver dyads during various interactive tasks at home is proposed. This protocol demonstrates how to synchronize the different data streams and report EEG data retention rates and quality checks. Additionally, critical issues and possible solutions with respect to the experimental setup, tasks, and data collection in home settings are discussed. The protocol is not limited to infant-caregiver dyads but can be applied to various dyadic constellations. Overall, we demonstrate the flexibility of EEG hyperscanning setups, which allow experiments to be conducted outside of the laboratory to capture participants' brain activities in more ecologically valid environmental settings. Yet, movement and other types of artifacts still constrain the experimental tasks that can be performed in the home setting.

Author Spotlight: Capturing Infant-Caregiver Interactions Through Synchronized Multimodal Data Collection

This protocol describes how synchronized electroencephalography, electrocardiography, and behavioral recordings were captured from infant-caregiver dyads in a home setting.

Prior hyperscanning studies that record the brain activities of caregivers and children concurrently have primarily been conducted within the confines of ...

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

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