Early integrations of behavioral economics and psychology shared a common perspective: individuals vary in their approaches to decision making, especially in realistic scenarios (Simon, 1959; Tversky and Kahneman, 1974). Individuals can choose based on complex rules that involve compensatory trade-offs between decision variables or based on simplifying rules that ignore some information and emphasize other, depending on immediate task demands (Payne et al., 1992). Yet, the nature of most neuroscience experimentation discourages analysis of strategic, meta-decision processes. The fMRI signal associated with a single decision is relatively small, compared to ongoing noise, while PET and TMS studies collapse across all decisions in an entire experimental session. Thus, trial-to-trial variability is an infrequent target for analyses. Tasks in most studies are simple, with small stakes (e.g., tens to hundreds of dollars) obtained over a short duration, reducing the incentive to explore the full space of possible decision strategies. Participants are often well-practiced, especially in non-human primate single-unit studies that can involve thousands of trials; this can lead to stereotypy of behavior. Moreover, meta-decision processing can be difficult to model – in many contexts, different strategies could lead to the same expressed behavior.