Using Hidden Markov Models to Determine Cognitive States of Visual Analytic Users

Many visual analytics tools exist to assist users in examining large amounts of information at once via coordinated views that include graphs, network connections and maps. However, the cognitive processes that those users undergo while using such tools remain a mystery. Many psychological studies suggest that individuals may undergo some planning stage followed by analysis before finally making conclusions when examining large amounts of analytical data with the goal of reaching a decision. While the general order of these cognitive states has been theorized, the exact states of individuals at specific points during their interaction with visual analytic systems remain unclear. In this work, we developed models to determine the cognitive states of users based solely on their interactions with visual analytics systems via Hidden Markov Models. Hidden Markov Models allow for the classification of observations through hidden states (cognitive states in our case) as well as the prediction of future cognitive states. We generate these models through unsupervised learning and use established metrics such as AIC and BIC metrics to evaluate our models. Our solutions are designed to help improve visual analytics tools by providing a better understanding of cognitive thought processes of users during data intensive analysis tasks.