A user's cognitive style has been found to affect how they search for information, how they analyze the information, and how they make decisions in an analytical process. In this paper, we propose an approach that uses Hidden Markov Models (HMM) to dynamically capture a user's cognitive style by automatically exploring the sequence of actions and relevant information with respect to the content of the actions. The evaluation results show that our HMM model achieves an average of 72% recall with the APEX 07 collection. We also study the link between a user's cognitive style and the various attributes relating to document content during an analytical process. The results show that the "analytic" group tends to focus on documents with significantly more specific information than the "wholist" group. The specific/general attribute of documents can help us in classifying a user's cognitive styles automatically.