Some Promising Results of Communication-Based Automatic Measures of Team Cognition

Abstract

Some have argued that the most appropriate measure of team cognition is a holistic measure directed at the entire team. In particular, communication data are useful for measuring team cognition because of the holistic nature of the data, and because of the connection between communication and declarative cognition. In order to circumvent the logistic difficulties of communication data, the present paper proposes several relatively automatic methods of analysis. Four data types are identified, with low-level physical data vs. content data being one dimension, and sequential vs. static data being the other. Methods addressing all four of these data types are proposed, with the exception of static physical data. Latent Semantic Analysis is an automatic method used to assess content, either statically or sequentially. PRONET is useful to address either physical or content-based sequential data, and we propose CHUMS to address sequential physical data. The usefulness of each method to predict team performance data is assessed.