This book reports recent landmark developments and the state of the art in NDS science in psychological theory and research.

Although its roots can be traced to the 19th century, progress in the study of nonlinear dynamical systems has taken off in the last 30 years. While pertinent source material exists, it is strewn about the literature in mathematics, physics, biology, economics, and psychology at varying levels of accessibility. A compendium research methods reflecting the expertise of major contributors to NDS psychology, Nonlinear Dynamical Systems Analysis for the Behavioral Sciences Using Real Data examines the techniques proven to be the most (...) useful in the behavioral sciences. The editors have brought together constructive work on new practical examples of methods and application built on nonlinear dynamics. They cover dynamics such as attractors, bifurcations, chaos, fractals, catastrophes, self-organization, and related issues in time series analysis, stationarity, modeling and hypothesis testing, probability, and experimental design. The analytic techniques discussed include several variants of the fractal dimension, several types of entropy, phase-space and state-space diagrams, recurrence analysis, spatial fractal analysis, oscillation functions, polynomial and Marquardt nonlinear regression, Markov chains, and symbolic dynamics. The book outlines the analytic requirements faced by social scientists and how they differ from those of mathematicians and natural scientists. It includes chapters centered on theory and procedural explanations for running the analyses with pertinent examples and others that illustrate applications where a particular form of analysis is seen in the context of a research problem. This combination of approaches conveys theoretical and practical knowledge that helps you develop skill and expertise in framing hypotheses dynamically and building viable analytic models to test them. (shrink)

Behavioral and physiological synchronization have important implications for work teams with regard to workload management, coordinated behavior and overall functioning. This study extended previous work on the nonlinear statistical structure of GSR series in dyads to larger teams and included subjective ratings of workload and contributions to problem solving. Eleven teams of 3 or 4 people played a series of six emergency response (ER) games against a single opponent. Seven of the groups worked under a time pressure instruction at the (...) beginning of the first game. The other four groups were not given that instruction until the beginning of the fourth game. The optimal lag length for the teams, which appeared to be phase-locked, was substantially shorter than that obtained previously for loosely-coupled dyads. There was a complex nonlinear effect from the time pressure manipulation on the autocorrelation over time that reflected workload and fatigue dynamics that were operating. The R2 values for linear and nonlinear statistical models differed by less than.01. The average amount of influence from one ER team member to another was 4.5-4.7% of the variance in GSR readings. ER team members were classified as drivers and empaths, based on the autocorrelations and transfer influences to and from other players in the GSR time series. Empaths were rated by their peers as making more types of positive contributions to the problem solving discussions than others, and drivers received the lowest ratings. Larger Lyapunov exponents that were calculated from the GSR time series were positively correlated with individuals’ ratings of subjective workload and were negatively correlated with leadership indicators. Several directions for further research are outlined. (shrink)

Synchronization is a special case of self-organization in which one can observe close mimicry in behavior of the system components. Synchrony in body movements, autonomic arousal, and EEG activity among human individuals has attracted considerable attention for their possible roles in social interaction. This article is specifically concerned with autonomic synchrony and finding the best model for the dyadic relationships, with regard to both theoretical and empirical accuracy, that could be extrapolated to synchrony levels for groups and teams of three (...) or more people. The four models that are compared in this study have different theoretical origins: the two-variable linear regression function, a three-parameter nonlinear regression function, the logistic map function stated in polynomial form, and the logistic map function stated as an exponential regression structure. The data for this study were electrodermal responses collected from a team of four people engaged in an emergency response simulation that produced 12 dyadic time series. Results shows strong levels of fit between the data and all four models, although there were significant differences among them. Further research directions point toward finding conditions that favor one model over another and exploring other possible nonlinear structures. (shrink)

Synchronization of autonomic arousal levels within dyads and larger teams has been associated with several types of social-behavioral outcome. One previous study reported greater physiological influence of leaders on followers than of followers on leaders; influence was measured pairwise within triadic problem solving groups. The present study explored synchronized autonomic arousal with leadership outcomes in two experiments with group sizes of three to eight members. Drivers, who had the greatest physiological impact on other team members were consistently less like the (...) leader of the group. Empaths, who were the most receptive to autonomic signals from others, were not consistently associated with leadership roles, although they did show sensitivity to team dynamics in their ratings of cognitive and social sources of workload. The tentative conclusion, subject to future research, is that successful leadership requires a balance between the driver and empath orientations. (shrink)

To further the understanding of how to build or reduce synchrony in a work team, we examined two principles for defining the optimal condition to produce or limit synchrony: the empath-driver ratio, and the balance between autocorrelated autonomic arousal and the degree of influence that transfers from each group member to other group members. In study 1, we employed a series of computational simulations designed to manipulate the four variables. The results indicated that there is a four-way balance between driver (...) strength, empath strength, autocorrelational and transfer effects among team members. The relationship between the synchronization coefficient and the empath-driver ratio was moderated by whether the group adopted a network structure for group problem solving or command-and-control. In study 2 we analyzed autonomic arousal in four teams of five participants playing a first-person shooter computer game. The correlation between the synchronization coefficient and the empath-driver ratio was 0.280 based on 64 pairs of observations. The relationship was moderated by both the network structure and the statistical model that one adopted to analyze dyadic relationships within the group. The implications of these relationships for a growing theory of team synchrony are discussed. (shrink)

This study investigated the stationarity of electrodermal time series collected in situations where turn taking in human interactions are involved. In this context, the stationarity of the time series is the extent to which a simple model can be used to fit the entire time series. The experiment involved seven participants in an emergency response simulation against one opponent. They generated 48 time series across six simulations, which were split and re-spliced to separate the team’s turns and the opponent’s turns. (...) Significant differences in R2 coefficients were found for both linear and nonlinear statistical models between experimental conditions, but the difference only amounted to 3% of the accuracy of those models relative to the original data. It was thus concluded that the impact of turn taking on stationarity was a small effect at most. A comparison of synchronization coefficients for the team data, which rely on the collective accuracy of the individual time series models, indicated stronger synchronization during periods when the team was watching the opponent’s actions compared to when they took their own turns. It was thus concluded, furthermore, that the common focus of attention prevailed against any non-stationarity that was introduced by turn taking. (shrink)