Prioritizing tasks appropriately is particularly critical when performing multiple tasks concurrently. Although necessary to achieve one's goals or avoid serious consequences, prioritization has not received much attention in the research literature, especially with respect to modeling human performance computationally. A conceptual framework that integrates several motivational theories, empirical studies, and neuroscience research is proposed to guide future studies of dynamic prioritization in multiple-goal contexts. Rooted in control theory, the proposed framework illustrates self-regulation processes in prioritizing tasks and explicitly shows important factors affecting the prioritization process so that empirical results can be integrated into the framework and future studies can be inferred. By illustrating information flow in the self-regulation processes and the brain structures associated with prioritization, the framework should help facilitate development of robust computational models of task prioritization.