The standard fusion model includes active and passive user interaction in level 5 - “User Refinement”. User refinement is more than just details of passive automation partitioning - it is the active management of information. While a fusion system can explore many operational conditions over myopic changes, the user has the ability to reason about the hyperopic “big picture.” Blasch and Plano developed cognitive-fusion models that address user constraints including: intent, attention, trust, workload, and throughput to facilitate hyperopic analysis. To enhance user-fusion performance modeling (i.e. confidence, timeliness, and accuracy); we seek to explore the nature of context. Context, the interrelated conditions of which something exists, can be modeled in many ways including geographic, sensor, object, and environmental conditioning. This paper highlights user refinement actions based on context to constrain the fusion analysis for accurately representing the trade space in the real world. As an example, we explore a target identification task in which contextual information from the user’s cognitive model is imparted to a fusion belief filter.