Communication Analysis for Enhanced Team Performance in the AOC

Improving team communication in distributed C2 environments must start with addressing underlying components of communication problems. First, it is important to understand what defines “good” communications (e.g., domain-specific frequencies, types, shifts from explicit to implicit communication) and what communications, or lack thereof, contribute to breakdowns. By neglecting the examination of these influences on team communication, data interpretations remain ambiguous, possibly misrepresentative. Next, it is important to know when communication failures occur to monitor and measure team performance. Finally, communication analyses must consider why errors occur (i.e., information is not exchanged, received, correct, or comprehended). The measurement of these cognitions will be described via state-of-the-art EEG and eye tracking metrics to assess the reasons for communication failure. To address these issues, this effort proposes to develop a Multi-axis APproach to Measuring and Interpreting Team Communications (MAP IT-C), a conceptual model which describes: 1) the process to develop a Predictive Framework driven by task analysis and social networks analysis, and 2) the specifications for Real-time Communication and Performance data collection and 3) an Online Diagnostic Engine used to diagnose team performance real-time (i.e., mapping a comprehensive suite of metrics [communication, physiological, and behavioral metrics] against expectation data generated from the Predictive Framework).