The quality of a team depends on its ability to deliver information through a hierarchy of team members and negotiate processes spanning different time scales. That structure and the behavior that results from it pose problems for researchers because multiply-nested interactions are not easily separated. We explored the behavior of a six-person team engaged in a Submarine Piloting and Navigation (SPAN) task using the tools of dynamical systems. The data were a single entropy time series that showed the distribution of activity across six team members, as recorded by nine-channel electroencephalography (EEG). A single team's data were analyzed for the purposes of illustrating the utility of multifractal analysis and allowing for in-depth exploratory analysis of temporal characteristics. Could the meaningful events experienced by one of these teams be captured using multifractal analysis, a dynamical systems tool that is specifically designed to extract patterns across levels of analysis? Results indicate that nested patterns of team activity can be identified from neural data streams, including both routine and novel events. The novelty of this tool is the ability to identify social patterns from the brain activity of individuals in the social interaction. Implications for application and future directions of this research are discussed.

Cross-level effects suggest that measurements could be taken at one level (e.g., neural) to assess team experience (or skill) on another level (e.g., cognitive-behavioral).

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The list of psychological processes thought to exhibit fractal behavior is growing. Although some might argue that the seeming ubiquity of fractal patterns illustrates their significance, unchecked growth of that list jeopardizes their relevance. It is important to identify when a single behavior is and is not fractal in order to make meaningful conclusions about the processes underlying those patterns. The hypothesis tested in the present experiment is that fractal patterns reflect the enactment of control. Participants performed two steering tasks: steering on a straight track and steering on a circular track. Although each task could be accomplished by holding the steering wheel at a constant angle, steering around a curve may require more constant control, at least from a psychological standpoint. Results showed that evidence for fractal behavior was strongest for the circular track; straight tracks showed evidence of two scaling regions. We argue from those results that, going forward, the goal of the fractal literature should be to bring scaling behavior under experimental control.

Rhythmic coordination with stimuli and other people's movements containing variable or unpredictable fluctuations might involve distinct processes: detecting the fluctuation structure and tuning to or matching the structure's temporal complexity. This framework predicts that global tuning and local parameter adjustments (e.g., position, velocity or phase) can operate independently during coordination (Marmelat & Delignières, 2012). Alternatively, we propose that complexity matching is a result of local phase adjustments during coordination (Delignières & Marmelat, 2014; Torre, Varlet, & Marmelat, 2013). The current study examined this relationship in a rhythmic interpersonal coordination task. Dyads coordinated swinging pendulums that differed in their uncoupled frequencies (detuning). We predicted that frequency detuning would require increased local corrections to maintain the intended phase pattern (in phase). This was expected to yield a relative phase shift accompanied by a change in period complexity and matching. Experimental data and numerical modeling of the pendulum dynamics confirmed our predictions. Increased relative phase shifts occurred simultaneously with increased dissociation between individuals' movement period complexity. This provided evidence that global complexity matching is intricately linked to local movement adjustments and is not a distinct coordination mechanism. These findings are considered with respect to dynamical and computational approaches to interpersonal coordination.