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This chapter describes a neurodynamic modeling approach which may be useful for dynamically assessing teamwork in healthcare and military situations. It begins with a description of electroencephalographic (EEG) signal acquisition and the transformation of the physical units of EEG signals into quantities of information. This transformation provides quantitative, dynamic, and generalizable neurodynamic models that are directly comparable across teams, tasks, training protocols, and team experience levels using the same measurement scale, bits of information. These bits of information can be further used to dynamically guide team performance or to provide after-action feedback that is linked to task events and team actions.

These ideas are instantiated and expanded in the second section of the chapter by showing how these data abstractions, compressions, and transformations take advantage of the natural information redundancy in biologic signals to substantially reduce the number of data dimensions, making the incorporation of neurodynamic feedback into Intelligent Tutoring Systems (ITSs) achievable.

In this chapter we highlight a neurodynamic approach that is showing promise as a quantitative measure of team performance.

During teamwork the rapid electroencephalographic (EEG) oscillations that emerge on the scalp were transformed into symbolic data streams which provided historical details at a second-by-second resolution of how the team perceived the evolving task and how they adjusted their dynamics to compensate for, and anticipate new task challenges. Key to this approach are the different strategies that can be used to reduce the data dimensionality, including compression, abstraction and taking advantage of the natural redundancy in biologic signals.

The framework emerging is that teams continually enter and leave organizational neurodynamic partnerships with each other, so-called metastable states, depending on the evolving task, with higher level dynamics arising from mechanisms that naturally integrate over faster microscopic dynamics.

The development of quantitative measures of the momentary dynamics of teams is anticipated to significantly influence how teams are assembled, trained, and supported. The availability of such measures will enable objective comparisons to be made across teams, training protocols, and training sites. They will lead to better understandings of how expertise is developed and how training can be modified to accelerate the path toward expertise.

The innovation of this study is the potential it raises for developing globally applicable quantitative models of team dynamics that will allow comparisons to be made across teams, tasks, and training protocols.

This chapter introduces electroencephalography (EEG), a measure of neurophysiological activity, as a critical method for investigating individual and team decision-making and cognition. EEG is a useful tool for expanding the theoretical and research horizons in organizational cognitive neuroscience, with a lower financial cost and higher portability than other neuroimaging methods (e.g., functional magnetic resonance imaging). This chapter briefly reviews past work that has applied cognitive neuroscience methods to investigate cognitive processes and outcomes. The focus is on describing contemporary EEG measures that reflect individual cognition and compare them to complementary measures in the field of psychology and management. The authors discuss how neurobiological measures of cognition relate to and may predict both individual cognitive performance and team cognitive performance (decision-making). This chapter aims to assist scholars in the field of managerial and organizational cognition in understanding the complementarity between psychological and neurophysiological methods, and how they may be combined to develop new hypotheses in the intersection of these research fields.

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

Through a review of historically famous cases and a chronicle of neurotechnology development, this chapter discusses brain structure and brain function as two distinct yet interrelated paths to understand the relative contributions of anatomical and physiological mechanisms to the human brain–behavior relationship. From an organizational neuroscience perspective, the chapter describes over a dozen neuroimaging technologies that are classified under four groupings: morphologic, invasive metabolic, noninvasive metabolic, and electromagnetic. We then discuss neuroimaging variables that may be useful in social science investigations, and we underscore electroencephalography as a particularly useful modality for the study of individuals and groups in organizational settings. The chapter concludes by considering emerging science and novel brain technologies for the organizational researcher as we look to the future.

Teams focus on a common and valued goal, and effective teams are able to alter their behaviors in pursuit of this goal. When teams are viewed in the context of a dynamic environment, they must adapt to challenges in the environment in order to maintain team effectiveness. In this light, we describe various sources of team variation and how they combine with individual-level, team-level, and dynamical mechanisms for maintaining team effectiveness in a dynamic environment. The combination of these elements produces a systems view of team effectiveness. Our goals are to begin to define, both in words and in operational terms, team effectiveness from this perspective and to evaluate this definition in the context of team training using intelligent tutoring systems (team ITS). In addressing these goals, we present an example of real-time analysis of team effectiveness and some challenges for team ITS training based on a dynamical systems view of team effectiveness.

While reiterating the benefits of applications of neuroscience to both research and practice, we also acknowledge in this concluding chapter the potential issues that will continually need to be addressed. Specifically, we overview ontological and epistemological concerns, such as the potential for excessive reductionism. We also address ethical issues that could come into play for both researchers and practitioners. Finally, we conclude with a look forward to the future by suggesting that the “approach,” rather than the “avoidance,” of organizational neuroscience is likely to grow over time. One exciting possibility is how an examination of the human brain in work and organizational settings is likely to be a prime example of the “big data” trends that the future will bring.

The purpose of this paper is to consider the leader-following formation control problem for nonholonomic vehicles based on a novel biologically inspired neurodynamics approach.

The interactions among the networked multi-vehicle system is modeled by an undirected graph. First, a distributed estimation law is proposed for each follower vehicle to estimate the state including the position, orientation and linear velocity of the leader. Then, a distributed formation tracking control law is designed based on the estimated state of the leader, where a bio-inspired neural dynamic is introduced to solve the impractical velocity jumps problem. Explicit stability and convergence analyses are presented using Lyapunov tools.

The effectiveness and efficiency of the proposed control law are demonstrated by numerical simulations and physical vehicle experiments. Consequently, the proposed protocol can successfully achieve the desired formation under connected topologies while tracking the trajectory generated by the leader.

This paper proposes a neurodynamics-based leader–follower formation tracking algorithm for multiple nonholonomic vehicles.