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Real-time search techniques have been used extensively in the areas of task planning and decision making. In order to be effective, however, these techniques require task-specific domain knowledge in the form of heuristic or utility functions. These functions can either be embedded by the programmer, or learned by the system over time. Unfortunately, many of the reinforcement learning techniques that might be used to acquire this knowledge generally demand static feature vector representations defined a priori. Current neurobiological research offers key insights into how the cognitive processing of experience may be used to alleviate dependence on preprogrammed heuristic functions, as well as on static feature representations. Research also suggests that internal appraisals are influenced by such processing and that these appraisals integrate with the cognitive decision-making process, providing a range of useful and adaptive control signals that focus, inform, and mediate deliberation. This paper describes a neuromorphically inspired approach for cognitively processing experience in order to: 1) abstract state information; 2) learn utility functions over this state abstraction; and 3) learn to tradeoff between performance and deliberation time.