Researchers have developed several theoretical models for identifying and categorizing data analysis tasks for visualization systems. However, these models focus primarily on abstraction or generalizing specific tasks into higher-level concepts, resulting in broad guidelines that are not always straightforward to implement within visualization systems. Few models flow in the opposite direction to enable instantiation or a precise approach to applying high-level task concepts to specific analysis scenarios or user interaction logs. This paper presents a synthesis of existing task theory into a new instantiation-focused model and Pyxis, a specification language for applying this model to existing evaluation methods. Specifically, Pyxis enables researchers to dissect theoretical and study-driven analysis sessions to identify instances of tasks that users have performed. Further, it formalizes the relationship between tasks, insights, and objectives implied in prior work. We present three use cases that apply Pyxis to a wide range of analysis scenarios from the literature to demonstrate its utility. Finally, we discuss the model's implications and opportunities for future work.
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