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Neuroscience and Team Processes

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Organizational NeuroscienceNeuroscience and Team ProcessesDavid A. Waldman Danni Wang Maja Stikic Chris Berka Stephanie Korszen

Article information:To cite this document: David A. Waldman Danni Wang Maja Stikic Chris BerkaStephanie Korszen . "Neuroscience and Team Processes" In OrganizationalNeuroscience. Published online: 15 Dec 2015; 277-294.Permanent link to this document:http://dx.doi.org/10.1108/S1479-357120150000007012

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NEUROSCIENCE AND TEAM

PROCESSES

David A. Waldman, Danni Wang, Maja Stikic,

Chris Berka and Stephanie Korszen

ABSTRACT

In this chapter, we consider how neuroscience methods can enhance thestudy of team processes, as well as facilitate the development of teams.We overview exciting new neuroscience technology that can be applied tothe assessment of teams in real time. While research that has alreadyused this technology to study team engagement and workload is summar-ized, we also consider other team-based concepts to which it might beapplied, such as groupthink and shared mental models. We further sug-gest that emotional contagion and neurological mirroring concepts cancome together to help us form a better understanding of emotions andtheir effects in teams. We conclude the chapter with a consideration ofhow neurological methods can potentially help develop team processesand provide insights for both members and team leaders.

Keywords: Teams; team neurodynamics; engagement; workload;emotional contagion; neurological mirroring

Organizational Neuroscience

Monographs in Leadership and Management, Volume 7, 277�294

Copyright r 2015 by Emerald Group Publishing Limited

All rights of reproduction in any form reserved

ISSN: 1479-3571/doi:10.1108/S1479-357120150000007012

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NEUROSCIENCE AND TEAM PROCESSES

Teams comprise “collectives who exist to perform organizationally relevanttasks, share one or more common goals, interact socially, exhibit task inter-dependencies, maintain and manage boundaries, and are embedded in anorganizational context that sets boundaries, constrains the team, and influ-ences exchanges with other units in the broader entity” (Kozlowski & Bell,2013, p. 334). Teams exist in many contexts including sports, extreme con-ditions (e.g., acute care teams in medical settings), military, and industry.In addition, teams may be relatively temporary, as is the case in a teamthat forms for problem-solving purposes and then disbands, or more per-manent in nature, as is the case with production for manufacturing or ser-vice purposes. Furthermore, teams can exist at relatively low levels inorganization, as well as at the upper echelons (e.g., top management teams;see Finkelstein, Hambrick, & Cannella, 2009).

The study of teams has been quite pervasive in the organizational litera-ture (Mathieu, Maynard, Rapp, & Gilson, 2008). Existing studies of teamshave often relied on traditional methodologies, such as surveys, observa-tion, or examinations of demographic diversity (Moreland & Levine, 1992;van Knippenberg & Schippers, 2007). However, very few studies haveimplemented neuroscience to explore team processes. So what types ofresearch questions might be answered using neuroscience methods that arenot fully answered using traditional research methodologies? How couldneuroscience techniques supplement our research toolbox when it comes toexamining teams? In short, this chapter will introduce promising neu-roscience methods that can be used to capture aspects of team processes tosupplement traditional psychometric approaches.

First, we will consider the challenges of using neuroscience methods toexamine team processes. In so doing, we will overview some promising tech-nologies that can be used to assess teams of individuals in real time. Second,we will consider research that has already been initiated using this technol-ogy. Third, we will consider various issues pertaining to team processes thatmight be examined in the future. Finally, we will consider the practicalimplications of emerging neuroscience technology as applied to teams.

APPLICATIONS OF EMERGING NEUROSCIENCE

TECHNOLOGIES TO TEAMS

At first glance, it might appear that neuroscience could simply notbe applied to research designed to understand team-based phenomena.

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The approaches described throughout this book, as well as the neuroscienceliterature as a whole, overwhelmingly point only toward the assessment ofindividuals using such technologies as functional magnetic resonance ima-ging (fMRI) (e.g., Bagozzi et al., 2013) or quantitative electroencephalo-gram (qEEG) (e.g., Balthazard, Waldman, Thatcher, & Hannah, 2012). Tothe extent that individuals comprise teams, and individual behavior canhave an effect on teams, perhaps the study of individuals is at least tangen-tially relevant. However, it would seem that the technology and even mind-set surrounding neuroscience research would preclude a direct, neurologicalassessment of team processes. That is, the real-time neuroscientific study ofteam processes has, until recently, been impossible due to the technical lim-itations of neuroimaging hardware and software, which had not been ableto examine multiple human brains simultaneously.

However, various authors have at least envisioned team-based, neuro-logical assessment. For example, Healey and Hodgkinson (2014,pp. 783�784) predicted that “it will soon be possible for researchers to scanthe brains of multiple actors engaged in a variety of social activities, includingthose in the workplace … Such methods could prove valuable for examiningthe neurological mediators of interpersonal phenomena in organizationsfrom consensus formation and the nature of conflict to the development ofshared cognition and emotional contagion.” Similarly, George, Haas, andPentland (2014, p. 325) described how what they termed as “big data” can beused to investigate “team behavior, using sensors … to track individuals asthey work together, … or spend time interacting.” George et al. (2014)further suggested that “big data” collection could potentially be used as analternative to more traditional techniques (e.g., observation, surveys, and soforth) to produce real-time data pertaining to teams for the purpose of form-ing a better understanding of team dynamics and outcomes.

Assessing Team Neurodynamics

But is such thinking just science fiction at this point? As the saying goes,“the future is now.” That is, very new and exciting developments haverecently been put forth by Advanced Brain Monitoring (ABM), Inc., basedin Carlsbad, California, in what they refer to as Team NeuroDynamics(Advanced Brain Monitoring, 2014). Researchers and staff at ABM haveaptly noted that traditional methods for assessing teams (e.g., surveys) areinherently subjective and are generally used during team downtime or afterteams have been disbanded. We acknowledge that it is possible to observeor videotape teams in real time. However, some observations (e.g., the

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extent to which individuals are truly engaged) can be difficult and unreli-able. But can the type of Team NeuroDynamics approach of ABM serve asa possible alternative?

ABM has designed qEEG software and hardware to study the neuralpatterns of human interactions (i.e., neural syncronicity). Their equipmentis designed to record high-quality qEEG involving lightweight, portable(i.e., wireless) devices that can be used effectively in team-based contexts(Johnson et al., 2011). Fig. 1(a) and (b) features pictures of the ABM tech-nology in teams of four or five individuals.1 It uses ABM’s wireless, qEEGacquisition system that measures the electrical activity of an individual’sbrain with sensors placed on the scalp, allowing qEEG data to be capturedreliably and unobtrusively.

Fig. 1. Pictures of Teams Engaging in a Problem-Solving Task in Real Time.

(a) Four-Member Team; (b) Five-Member Team.

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The qEEG system featured in Fig. 1 records EEG data from nine scalplocations (i.e., POz, Fz, Cz, C3, C4, F3, F4, P3, and P4 according to theinternational 10/20 naming standard), with a sampling rate of 256 samplesper second. However, ABM has recently devised a more precise, 24-channelequipment for the purpose of individual and team assessment. One poten-tial problem of real-time assessment is that artifact that is introduced bysuch naturally occurring actions as head, jaw, and eye movement canpotentially prohibit the type of equipment featured in Fig. 1(b) from beingused to accurately examine team processes. However, ABM has developedan artifact rejection algorithm by applying “wavelet transformation”(Berka et al., 2004), thus eliminating much of the artifact in real time with-out affecting the desired brain signals.

Neurological Engagement

To date, several studies have been done using the ABM technology.First, Waldman et al. showed that emergent leaders in a team problem-solving context were able to generate more (neurologically assessed)engagement on the part of other team members when they spoke duringteam meetings. Although engagement has been shown to be an importantvariable in organizational behavior at the individual level, less work hasbeen done using engagement as a team-level variable. In their research,Waldman et al. (2013) assessed emergent leadership post-task using a tra-ditional survey measure. Neurological engagement was based on a powerspectral density (PSD) measure associated with processes involving infor-mation gathering and sustained attention or alertness to both auditoryand visual stimuli (Berka et al., 2004, 2005; Westbrook et al., 2004) andto identify individual differences in susceptibility to the effects of sleepdeprivation (Berka et al., 2005). The measure uses PSD variables alongthe midline Fz�POz and Cz�POz regions of the brain that can discrimi-nate participants’ alertness and attention.

It should be noted that in this research, Waldman et al. (2013) demon-strated that engagement is largely a cognitive and emotional phenomenonthat goes beyond what can actually be observed in team members’ beha-viors. In other words, neurological assessment can tell us more about amember’s true engagement, beyond that person’s overt verbal and nonver-bal behavior. In sum, this research shows the potential of neurosciencetechnology to be applied in real time to examine issues pertaining to theeffects of leaders on teams.

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Neurological Workload

Workload is another qEEG-based cognitive metric that holds greatpotential for use in assessing team phenomena (Berka et al., 2007). WhileqEEG-based engagement plays an important role during the initial infor-mation-gathering phase, qEEG workload is essential in processing gatheredinformation and comparing it to internal mental models. Furthermore,qEEG workload is associated with an increase in working memory loadduring problem solving, integration of information, and analytical reason-ing. Berka and colleagues have developed a workload measure from a data-set of qEEG recordings, during which participants performed two mentaltasks with varying levels of difficulty. The general model derived from thesedata utilizes PSD variables from differential qEEG channels (i.e., C3�C4,Cz�POz, F3�Cz, Fz�C3, and Fz�POz) to generate a continuous measureof workload. The metric has been employed in a wide range of studies,including projects focused on team neurodynamics.

In one such team neurodynamics study, Stevens, Galloway, Berka, andSprang (2009) explored the qEEG workload metric as an approach fordeveloping a deeper understanding of how teams collaborate when solvingtime-critical, complex, real-world problems. In that study, the participantswere solving substance abuse management tasks individually, and then inteams of three. The results indicated that nonrandom patterns of neurophy-siologic synchronies could be observed across teams and members of ateam when engaged in problem solving. Different patterns were discoveredearly in the collaboration when the team members were forming mentalmodels of the problem, as compared to the patterns found later in the col-laboration, when the team members were sharing their mental models andconverging to a solution. The participants expended more qEEG workloadin a teamwork situation than they did when performing the task individu-ally, which may relate to the process cost of collaboration. Furthermore,the resulting patterns found in the qEEG data differed depending on theteam’s efficiency. The efficiency of a team in completing a task (as mea-sured by time to completion) was proportional to the high levels of qEEGworkload of the team members.

In an additional study, Stevens and Galloway (2014) analyzed qEEGworkload changes during an unscripted map navigation task performed bydyads. The results of the study indicated that significant, neurodynamicreorganizations occurred in response to multiple types of perturbations tothe normal flow of teamwork � both externally through changes in theenvironment or internally as a result of the team’s decisions. The most

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significant, neurodynamic reorganizations corresponded to instances whenthe team perceived difficulties. Thus, this approach shows promise forfuture utility in monitoring the quality of teamwork and for adaptivelymodifying the team “flow” when optimal patterns are not present.

These early studies establish the potential for neurologically team-basedworkload measures. However, difficulties in defining appropriate teamworkload dimensions, as well as challenges associated with measurement inteam settings, serve as barriers to the development of a validated and com-prehensive theory characterizing team workload and its relation to indivi-dual workload (Funke, Knott, Salas, Pavlas, & Strang, 2012). A review ofteam processes by Bowers, Braun, and Morgan (1997) establishes thatgroup-based work can be divided into two main components, that teammembers must effectively balance: (1) taskwork, which broadly correspondsto individual efforts to meet task demands, including efforts traditionallyassociated with individual task performance; and (2) teamwork, whichreflects the cooperative efforts of team members required for task perfor-mance (e.g., communication, coordination, monitoring, leadership, and soforth). It follows that team workload may be expressed in terms of thedemands associated with the performance of both taskwork and teamwork.

qEEG-based workload assessment holds great promise in the field ofteam workload research, as it can be assessed in real time from multipleparticipants, providing temporally sensitive assessments of workload dur-ing task performance. Innovations in qEEG-based workload assessmentmethods, including the PHYSIOPRINT platform (Popovic, Stikic, Berka,Klyde, & Rosenthal, 2013; Popovic, Stikic, Rosenthal, Klyde, & Schnell,2015), are emerging as potential new tools for organizational neuroscienceresearchers. The PHYSIOPRINT algorithm is a sensitive classifier that isbuilt around a well-defined and established theoretical workload model(Mitchell, 2000) known as the Improved Performance Research IntegrationTool (IMPRINT), which covers a large range of different workload typesthat are relevant for team processes, such as auditory, visual, and cognitiveworkload.

UNDERSTANDING TEAM CONFIGURATIONS AND

CONTAGION THROUGH NEUROSCIENCE

There are additional potential applications of neuroscience to the study ofteams. Much team theory is devoted to understanding aspects of collective

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cognition in teams in terms of configurations among members (Mathieu,Heffner, Goodwin, Salas, & Cannon-Bowers, 2000; Mohammed,Ferzandi, & Hamilton, 2010). Part of such efforts has focused on synchro-nies among individuals, or metaphorically, whether people are “on thesame wavelength” in team processes. It is interesting that at the individuallevel, several organizational neuroscience efforts can be seen focusingon connections or synchronized electrical activity between regions of thebrain within an individual (Hannah, Balthazard, Waldman, Jennings, &Thatcher, 2013; Waldman, Balthazard, & Peterson, 2011a). Is it possiblethat neuroscience methods might also help us to decipher connectionsbetween individuals at a physiological level, and thus enhance our under-standing of team processes?

We suggest that the answer to this question is yes. Below, we overviewseveral topical areas pertaining to configurations in the teams literature.Although neuroscience has not yet been applied to those areas, we take theviewpoint of recent theorists who have suggested that neuroscience canhelp provide a broader understanding of construct validity in the teams lit-erature, and it can enhance theoretical frameworks that might help accountbetter for team processes and outcomes (Powell, 2011; Senior, Lee, &Butler, 2011). We take both a static view using neuroscience to betterunderstand collective cognition and configurations, as well as a moredynamic stance in how neurological aspects of individuals may change(e.g., emotional contagion) to reflect neurological/behavioral phenomenaof others.

Configurations of Cognition and Thinking

Patterns or configurations of cognitive and behavior qualities are relevantto teams in terms of predicting such emergent states as team cohesion, aswell as team effectiveness. Neurological methods might provide insightsinto a number of constructs that are relevant to such patterns or configura-tions. As examples, we consider several concepts here including: (1) group-think, (2) shared mental model, (3) transactive memory, and (4) sharedleadership. Below, we describe these methods and current deficiencies interms of their assessment. We then turn our attention to how neurologicalassessment could be informative.

Collective or shared cognition represents an important topic that is rele-vant to, for example, decision making in teams. Shared cognition pertainsto whether team members have common understandings of team

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knowledge and ways of processing information (Mohammed & Dumville,2001). Groupthink represents a classic form of shared cognition. The termwas originally coined by Janis (1972, 1982), who observed tendencies inoverly cohesive teams to ignore alternatives and information that might becontrary to collective norms and information processing. Symptoms ofgroupthink include stereotyping outgroups in a negative manner, excessiveoptimism that creates the illusion of team invulnerability, discountinginformation that might run counter to a predominant course of action ormindset, and illusions of unanimity whereby potential dissenters are sentsignals that dissenting views will not be tolerated. While not totally con-firming the original ideas and predictions of Janis (1972, 1982), researchand practice has continued to examine the groupthink concept and itseffects (Kowert, 2002; Turner & Pratkanis, 1998).

However, Esser (1998) questioned whether existing methodologies weresufficient to fully understand groupthink and its effects. For example, Choiand Kim (1999, p. 305) measured two different behavioral symptoms ofgroupthink: (1) concurrence seeking and (2) group identity during a crisisevent. A sample survey item for concurrence seeking is “members criticizedothers who raised questions concerning the selected solution.” A samplesurvey item for group identity is “all members completely agreed to theselected solution.” It may not be appropriate to measure groupthink after agroup process because retrospective sensemaking can create perceptual bias(Henningsen, Henningsen, Eden, & Cruz, 2006).

Another, more recently developed example of shared cognition is theshared mental model concept. It refers to team members’ organized, com-mon understanding of team knowledge (Klimoski & Mohammed, 1994).The measurement of shared mental models is a challenge because theremay be multiple forms of shared mental models, and those models can bedynamic or changing over time (Klimoski & Mohammed, 1994). For exam-ple, members can share equipment functioning knowledge, as well as task-related knowledge such as task procedures and strategies (Cannon-Bowers,Salas, & Converse, 1993). In addition, members can share teamwork men-tal models (Lim & Klein, 2006). Some researchers have tried to measureshared mental models by asking team members to rate their own attributes(Mathieu et al., 2000). For example, Mathieu et al. (2000) used networkanalysis to examine the extent to which members’ own ratings of attributesconverge. Other researchers have developed alternative indices to measurevarious types of shared mental models. For example, Lim and Klein (2006)considered shared mental model in terms of similarities among team mem-bers’ ratings. Those ratings reflect the degree to which each statement

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relates to team task procedures and interaction processes (Lim & Klein,2006). Nevertheless, such self-ratings may be limited in terms of theirvalidity.

Yet another form of collective thinking is transactive memory system.While shared mental model describes team members’ shared understandingof knowledge (Klimoski & Mohammed, 1994), a transactive memory sys-tem is an interdependent cognitive system, whereby each team member isaware of who knows what within the team (Wegner, 1987). Lewis (2003)proposed three dimensions of transactive memory system: (1) specializa-tion, (2) credibility, and (3) coordination. That is, to form a transactivememory system, members need to have specialized knowledge, trust eachother’s knowledge, and collaborate well together (Lewis, 2003). She devel-oped a 15-item scale to measure members’ own perceptions of the transac-tive memory system. For example, a sample item for knowledgespecialization is “I have knowledge about an aspect of the project that noother team member has” (Lewis, 2003, p. 604). Similarly, Austin (2003)proposed a four-dimension conceptualization of transactive memory sys-tem, including group knowledge stock (combination of individual knowl-edge), consensus about knowledge sources (agreement), specialization ofexpertise, and accuracy of knowledge identification.

As noted above, transactive memory system differs from shared mentalmodel in that the former emphasizes specialization of expertise for eachteam member, while the latter focuses on the sharing of overlapping knowl-edge (Mohammed et al., 2010). Lewis (2003) suggested that it is importantfor teams to have both overlapped and differentiated knowledge. However,how to achieve the balance is not altogether clear. While some studies havetried to integrate these two ways of collective thinking using survey-basedresearch approaches (Nandkeolyar, 2008; Pearsall, Ellis, & Bell, 2010), thesurvey approach may not be fully capable of capturing how a transactivememory system and shared mental models coexist, evolve, and codevelopover time.

A somewhat different configuration example can be seen in the forma-tion of informal leadership and its distribution in teams. Informal leader-ship is an emergent team property and is different from traditionalhierarchical leadership in that informal leadership is displayed by groupmembers rather than the designated leader (Morgeson, DeRue, & Karam,2010). If leadership is distributed among team members, the team de factohas shared leadership (Carson, Tesluk, & Marrone, 2007). Typically, teammembers’ ratings are used to assess both informal/emergent and shared lea-dership (Zhang, Waldman, & Wang, 2012). Regarding the latter, the sheer

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amount of informal leadership shown is often a proxy for sharedleadership.

There are two common ways to access the sheer amount of informal lea-dership in teams. First, researchers can use team members as a whole as thereferent or aggregate each peer’s individual leadership score (Wang,Waldman, & Zhang, 2014). However, it is also possible for researchers toexamine configurations (Wang et al., 2014). For example, some individualswithin teams might stress a visionary role in terms of leadership, whileothers might attempt to provide supportive or relation-oriented leadership.In addition, it is important to address the change of sharedness of leader-ship in team and the development of leadership configurations over time(Wang et al., 2014).

As suggested above, traditional methodologies may not be sufficient tofully examine shared cognition and configurations within teams. We see atleast two possibilities for applying neuroscience technology to the study ofthese issues in teams. First, it may also be possible to use the technologyproduced by ABM described earlier to examine team members simulta-neously in real time, that is, during a team process without interruption.For example, as already described above, researchers have successfullyused this technology to yield neurological indices of team engagement(Waldman et al., 2013). Similarly, researchers might utilize the same tech-nology to examine team members’ groupthink during a team decision-making process to reduce the retrospective bias associated with groupthink.Moreover, this technology could directly assess team members’ brain activ-ity patterns (Waldman et al., 2013), which is more ecologically valid, ascompared to individuals’ self-rating of their own attributes. Therefore, it ispossible for us to examine directly whether team members have similarbrain patterns (e.g., shared mental model) and different brain activities(e.g., transactive memory system) in the process of their interactionswithout interrupting a team process.

In addition, it may be possible to compare configurations of brain activ-ity patterns across teams to see which configuration might be associatedwith more cohesion, including the excessive cohesion that accompaniesgroupthink, as well as conflict in teams. Perhaps, researchers could evenexplore and compare brain pattern configurations within teams when teammembers reach consensus, as well as when their cognition diverges, sincewe can now pinpoint the brain activities of each team member on a second-by-second basis (Waldman et al., 2013). Thus, researchers can now have abetter understanding of how shared mental model and transactive memorysystems evolve and complement each other over time.

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Second, researchers might apply the type of individualized methodsdescribed by Waldman, Balthazard, and Peterson (2011b). For example,Balthazard et al. (2012) used qEEG technology to examine the brains ofindividual, formal leaders in a resting state. They were able to delineate thebrains of transformational versus nontransformational leaders. As anexample, perhaps the same methodology could be used to assess individualteam members in terms of the extent of their potential as informal leadersor to aggregate such information to produce shared leadership potential asa whole in a respective team.

Emotional Contagion and Neurological Mirroring

Emotional contagion occurs when individuals “catch” the emotions ofothers, largely on the basis of observation (Barsade, 2002; Pugh, 2001).Emotions could be either negative, as in the case of “catching” fear or anxi-ety on the part of others, or they can be more positive, as in the case of“catching” hope, optimism, or excitement (e.g., excitement pertaining tovision). Besides helping us to understand team cognition, neurotechniquesmay also provide insights into the dynamic emotional processes in teams,such as emotional contagion. Specifically, we propose that neurologicalmirroring concepts may come into play.

Mirroring occurs when one imitates (i.e., mirrors) another person’s emo-tions through empathy (Iacoboni, 2009). The brain’s mirror system consistsof neurons that get activated when he or she observes another personexperiencing an emotion (e.g., fear or excitement), whereby we subse-quently imitate that person’s emotion (Rizzolatti & Craighero, 2004).Iacoboni (2009) suggested that mirror neurons get activated effortlesslyand automatically. Accordingly, it would seem obvious that mirror neuronsshould be associated with emotional contagion processes in teams, forexample, how members “catch” the emotions of leaders or other members(Galvin, Balkundi, & Waldman, 2010; Pastor, Meindl, & Mayo, 2002). Asan example, engagement has a strong affective component (Kahn, 1990;Rich, Lepine, & Crawford, 2010). Affectively engaged individuals displayhigh levels of enthusiasm, excitement, and other positive emotions thatothers can observe both consciously and unconsciously. We thus proposethat team members will “catch” emotional engagement from one anotherthrough the mirror neuron activity (Rizzolatti & Craighero, 2004).

Previous studies have attempted to measure the emotional contagionprocess using surveys and video coding (Barsade, 2002). For example,

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video coders can record participants’ facial expressions and gestures asindicators of mood (Barsade, 2002). While those methods are prevalent inemotions research, they are potentially flawed in that participants may hidetheir true emotions in their verbal and nonverbal behavior, as well as whenanswering survey questions. However, individuals cannot fake brain activ-ity. Neuroscience techniques can thus be potentially more accurate in cap-turing emotions and emotional changes of individuals by studying differentnetworks of brain activities (Lindquist, Wager, Kober, Bliss-Moreau, &Barrett, 2012). For example, by using the ABM technology described ear-lier, we can examine the brain activity of team members on a second-by-second basis during a team process to see whether mirroring activities areoccurring. Researchers might even attempt to use confederates who portraya certain emotion at designated times to see the extent of neurological mir-roring activity. In short, neurological assessment might be used to helpform a better understanding of how individuals transfer emotions in teams.

Putting Neuroscience Techniques in Perspective

On the basis of our discussion thus far in this chapter, it might be con-cluded that we are suggesting that neuroscience methods represent a pana-cea for studying teams and that we should now “throw out the baby withthe bathwater,” so to speak with regard to traditional methods. Nothingcould be further from the truth or our intention. Instead, we are simplysuggesting that neuroscience methods might be joined with more traditionaltechniques (e.g., observation and survey) to form a more complete and eco-logically valid approach toward studying teams. In the study of individuals,we have already witnessed several studies in recent times that have com-bined psychometric and neurometric approaches to, for example, get a bet-ter handle on leadership processes and their effects (Balthazard et al., 2012;Hannah et al., 2013). We suggest that similar, synergistic approaches mightbe taken in the study of teams.

Implications for Teams and Managers

The technology and research reviewed above can provide valuable insightsinto why some teams succeed while others do not. It highlights neurologi-cally based methods that might be used to revolutionize the way that scho-lars might conceptualize and execute research on teams. Moreover, this

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technology may open up new avenues for practical applications. Teamfacilitators or coaches could potentially access neurological metrics thatmight be used to identify team weaknesses or track team improvementsover time.

For example, formal leaders are interested in employees coming togetheras a team and engaging in problem solving. Without team-level engagement,there may be uneven involvement in problem-solving processes, as well as lesssubsequent commitment to implementing team solutions (Metiu &Rothband, 2012; Tims, Bakker, Derks, & van Rhenen, 2013). Through theuse of videotaping and the type of neurological data collection describedabove, facilitators may be able to clearly and definitively point to instances ofmaximum team engagement in team meetings and development exercises.Such information could eventually be quite valuable as feedback to team lea-ders who have the goal of maximum engagement on the part of team mem-bers. Feedback of this nature could be used to help leaders to betterunderstand the types of verbal and nonverbal communication that engagesteam members. More specifically, by examining team processes in a post-eventreview, leaders might be able to see the types of communication that couldcause team members to truly engage in the team process, versus disengage.

Other possible applications can be envisioned. For example, unproduc-tive stress and conflict in teams might be identified in more ecologicallyvalid ways, as compared to the traditional techniques of observation or sur-veying of team members. That is, through future research, neurological sig-natures for stress and conflict could be identified, which in turn, could beused to alert facilitators and leaders to problems that might be developingin respective teams. Beyond, organizationally oriented applications, it isalso foreseeable that the technology described in this chapter could be usedin the domain of marketing. For example, key aspects of advertising mes-sages that would engage, versus disengage, consumers could be pinpointed.

CONCLUSIONS

Neuroscience and its applications have been conceived traditionally interms of their potential to analyze and better the lives of individuals. Buteven with a focus on the brains of individuals, as suggested elsewhere inthis book, the greater teams and organizations in which individuals existcould also realize important effects. Until very recently, we could not evenenvision how neuroscience or neurosensing technologies could be applied

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to a team as a whole in real time. In this chapter, we have shown that thesynchronized, neurological assessment of a team as a whole is now a rea-lity. While the future is never clear, we suggest that the type of neurosciencetechnologies and applications outlined in this chapter will continue todevelop and expand.

NOTE

1. Note that all participants signed waivers agreeing to the use of such pictures inreports of this project, such as the current chapter. In addition, as part of our IRBprocedures, all participants were assured of the confidentiality of all data (i.e., bothpsychometric and neurometric).

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