Participation shifts explain degree distributions in a human communications network.

Human interpersonal communications drive political, technological, and economic systems, placing importance on network link prediction as a fundamental problem of the sciences. These systems are often described at the network-level by degree counts -the number of communication links associated with individuals in the network-that often follow approximate Pareto distributions, a divergence from Poisson-distributed counts associated with random chance. A defining challenge is to understand the inter-personal dynamics that give rise to such heavy-tailed degree distributions at the network-level; primarily, these distributions are explained by preferential attachment, which, under certain conditions, can create power law distributions; preferential attachment's prediction of these distributions breaks down, however, in conditions with no network growth. Analysis of an organization's email network suggests that these degree distributions may be caused by the existence of individual participation-shift dynamics that are necessary for coherent communication between humans. We find that the email network's degree distribution is best explained by turn-taking and turn-continuing norms present in most social network communication. We thus describe a mechanism to explain a long-tailed degree distribution in conditions with no network growth.

Cyber Teaming and Role Specialization in a Cyber Security Defense Competition.

A critical requirement for developing a cyber capable workforce is to understand how to challenge, assess, and rapidly develop human cyber skill-sets in realistic cyber operational environments. Fortunately, cyber team competitions make use of simulated operational environments with scoring criteria of task performance that objectively define overall team effectiveness, thus providing the means and context for observation and analysis of cyber teaming. Such competitions allow researchers to address the key determinants that make a cyber defense team more or less effective in responding to and mitigating cyber attacks. For this purpose, we analyzed data collected at the 12th annual Mid-Atlantic Collegiate Cyber Defense Competition (MACCDC, http://www.maccdc.org), where eight teams were evaluated along four independent scoring dimensions: maintaining services, incident response, scenario injects, and thwarting adversarial activities. Data collected from the 13-point OAT (Observational Assessment of Teamwork) instrument by embedded observers and a cyber teamwork survey completed by all participants were used to assess teamwork and leadership behaviors and team composition and work processes, respectively. The scores from the competition were used as an outcome measure in our analysis to extract key features of team process, structure, leadership, and skill-sets in relation to effective cyber defense. We used Bayesian regression to relate scored performance during the competition to team skill composition, team experience level, and an observational construct of team collaboration. Our results indicate that effective collaboration, experience, and functional role-specialization within the teams are important factors that determine the success of these teams in the competition and are important observational predictors of the timely detection and effective mitigation of ongoing cyber attacks. These results support theories of team maturation and the development of functional team cognition applied to mastering cybersecurity.

SUAG/on across the adult lifespan.

One of the central concepts within the literature on cognitive aging is the notion of dedifferentiation-the idea that increasing age is associated with an increase in the interrelatedness of different cognitive abilities. Despite the centrality of this dedifferentiation hypothesis, there is a great deal of evidence that both supports and does not support dedifferentiation. We hypothesized that these inconsistent findings were due to (a) the use of different cognitive abilities (i.e., memory vs. speed of processing) that were correlated; and (b) the differing age groups that were used across studies. By using data from 11 well-validated cognitive test batteries (K = 2,355, range of the mean ages of correlations 18-85+), we found evidence for linear dedifferentiation when a test assessing speed of processing was included in the correlation with test of other cognitive abilities. We speculate that previous findings of nonlinear dedifferentiation are likely a result of undiagnosed or unrecognized pathology in a subsample of participants. (PsycINFO Database Record