The Department of Defense has growing needs to identify and understand methods for determining information value as a basis for future decision support systems. Understanding how humans process information, determine salience, and combine seemingly unrelated information is essential to automated processing of large amounts of information that is partially relevant or of unknown relevance. Recent research on human perception and cognition, along with research in information science regarding context-based modeling, provides us with a theoretical basis for developing automated systems for managing large amounts of information and enhancing the human recognition of salient information and facilitating its use by human planners and decision makers. A formalized representation of human perception and cognition is a first step toward the goal of bridging from cognitive science to computer science in this multi-disciplinary research topic. Once this is accomplished, it should be tractable to apply information contextual models and other techniques leading to development of generalized models and subsequent automation of human cognition-like processes. This STTR Phase I effort will focus on the development of an empirical and then a mathematical framework for representing human perception and cognition, and the correlation of empirical and mathematical approaches using a representative data set.